A multiscale decomposition approach to detect abnormal vasculature in the optic disc.

PubMed

Agurto, Carla; Yu, Honggang; Murray, Victor; Pattichis, Marios S; Nemeth, Sheila; Barriga, Simon; Soliz, Peter

2015-07-01

This paper presents a multiscale method to detect neovascularization in the optic disc (NVD) using fundus images. Our method is applied to a manually selected region of interest (ROI) containing the optic disc. All the vessels in the ROI are segmented by adaptively combining contrast enhancement methods with a vessel segmentation technique. Textural features extracted using multiscale amplitude-modulation frequency-modulation, morphological granulometry, and fractal dimension are used. A linear SVM is used to perform the classification, which is tested by means of 10-fold cross-validation. The performance is evaluated using 300 images achieving an AUC of 0.93 with maximum accuracy of 88%. Copyright © 2015 Elsevier Ltd. All rights reserved.

Dynamic gadolinium-enhanced magnetic resonance imaging allows accurate assessment of the synovial inflammatory activity in rheumatoid arthritis knee joints: a comparison with synovial histology.

PubMed

Axelsen, M B; Stoltenberg, M; Poggenborg, R P; Kubassova, O; Boesen, M; Bliddal, H; Hørslev-Petersen, K; Hanson, L G; Østergaard, M

2012-03-01

To determine whether dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) evaluated using semi-automatic image processing software can accurately assess synovial inflammation in rheumatoid arthritis (RA) knee joints. In 17 RA patients undergoing knee surgery, the average grade of histological synovial inflammation was determined from four biopsies obtained during surgery. A preoperative series of T(1)-weighted dynamic fast low-angle shot (FLASH) MR images was obtained. Parameters characterizing contrast uptake dynamics, including the initial rate of enhancement (IRE), were generated by the software in three different areas: (I) the entire slice (Whole slice); (II) a manually outlined region of interest (ROI) drawn quickly around the joint, omitting large artefacts such as blood vessels (Quick ROI); and (III) a manually outlined ROI following the synovial capsule of the knee joint (Precise ROI). Intra- and inter-reader agreement was assessed using the intra-class correlation coefficient (ICC). The IRE from the Quick ROI and the Precise ROI revealed high correlations to the grade of histological inflammation (Spearman's correlation coefficient (rho) = 0.70, p = 0.001 and rho = 0.74, p = 0.001, respectively). Intra- and inter-reader ICCs were very high (0.93-1.00). No Whole slice parameters were correlated to histology. DCE-MRI provides fast and accurate assessment of synovial inflammation in RA patients. Manual outlining of the joint to omit large artefacts is necessary.

SU-E-J-168: Automated Pancreas Segmentation Based On Dynamic MRI

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gou, S; Rapacchi, S; Hu, P

2014-06-01

Purpose: MRI guided radiotherapy is particularly attractive for abdominal targets with low CT contrast. To fully utilize this modality for pancreas tracking, automated segmentation tools are needed. A hybrid gradient, region growth and shape constraint (hGReS) method to segment 2D upper abdominal dynamic MRI is developed for this purpose. Methods: 2D coronal dynamic MR images of 2 healthy volunteers were acquired with a frame rate of 5 f/second. The regions of interest (ROIs) included the liver, pancreas and stomach. The first frame was used as the source where the centers of the ROIs were annotated. These center locations were propagatedmore » to the next dynamic MRI frame. 4-neighborhood region transfer growth was performed from these initial seeds for rough segmentation. To improve the results, gradient, edge and shape constraints were applied to the ROIs before final refinement using morphological operations. Results from hGReS and 3 other automated segmentation methods using edge detection, region growth and level set were compared to manual contouring. Results: For the first patient, hGReS resulted in the organ segmentation accuracy as measure by the Dices index (0.77) for the pancreas. The accuracy was slightly superior to the level set method (0.72), and both are significantly more accurate than the edge detection (0.53) and region growth methods (0.42). For the second healthy volunteer, hGReS reliably segmented the pancreatic region, achieving a Dices index of 0.82, 0.92 and 0.93 for the pancreas, stomach and liver, respectively, comparing to manual segmentation. Motion trajectories derived from the hGReS, level set and manual segmentation methods showed high correlation to respiratory motion calculated using a lung blood vessel as the reference while the other two methods showed substantial motion tracking errors. hGReS was 10 times faster than level set. Conclusion: We have shown the feasibility of automated segmentation of the pancreas anatomy based on dynamic MRI.« less

A comparative study of automatic image segmentation algorithms for target tracking in MR-IGRT.

PubMed

Feng, Yuan; Kawrakow, Iwan; Olsen, Jeff; Parikh, Parag J; Noel, Camille; Wooten, Omar; Du, Dongsu; Mutic, Sasa; Hu, Yanle

2016-03-01

On-board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real-time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image-guided radiotherapy (MR-IGRT) system. Manual contours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k-means (FKM), k-harmonic means (KHM), and reaction-diffusion level set evolution (RD-LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR-TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR-TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD-LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP-TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high-contrast images (i.e., kidney), the thresholding method provided the best speed (<1 ms) with a satisfying accuracy (Dice=0.95). When the image contrast was low, the VR-TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different methods for optimal segmentation with the on-board MR-IGRT system. PACS number(s): 87.57.nm, 87.57.N-, 87.61.Tg. © 2016 The Authors.

A comparative study of automatic image segmentation algorithms for target tracking in MR‐IGRT

PubMed Central

Feng, Yuan; Kawrakow, Iwan; Olsen, Jeff; Parikh, Parag J.; Noel, Camille; Wooten, Omar; Du, Dongsu; Mutic, Sasa

2016-01-01

On‐board magnetic resonance (MR) image guidance during radiation therapy offers the potential for more accurate treatment delivery. To utilize the real‐time image information, a crucial prerequisite is the ability to successfully segment and track regions of interest (ROI). The purpose of this work is to evaluate the performance of different segmentation algorithms using motion images (4 frames per second) acquired using a MR image‐guided radiotherapy (MR‐IGRT) system. Manual contours of the kidney, bladder, duodenum, and a liver tumor by an experienced radiation oncologist were used as the ground truth for performance evaluation. Besides the manual segmentation, images were automatically segmented using thresholding, fuzzy k‐means (FKM), k‐harmonic means (KHM), and reaction‐diffusion level set evolution (RD‐LSE) algorithms, as well as the tissue tracking algorithm provided by the ViewRay treatment planning and delivery system (VR‐TPDS). The performance of the five algorithms was evaluated quantitatively by comparing with the manual segmentation using the Dice coefficient and target registration error (TRE) measured as the distance between the centroid of the manual ROI and the centroid of the automatically segmented ROI. All methods were able to successfully segment the bladder and the kidney, but only FKM, KHM, and VR‐TPDS were able to segment the liver tumor and the duodenum. The performance of the thresholding, FKM, KHM, and RD‐LSE algorithms degraded as the local image contrast decreased, whereas the performance of the VP‐TPDS method was nearly independent of local image contrast due to the reference registration algorithm. For segmenting high‐contrast images (i.e., kidney), the thresholding method provided the best speed (<1 ms) with a satisfying accuracy (Dice=0.95). When the image contrast was low, the VR‐TPDS method had the best automatic contour. Results suggest an image quality determination procedure before segmentation and a combination of different methods for optimal segmentation with the on‐board MR‐IGRT system. PACS number(s): 87.57.nm, 87.57.N‐, 87.61.Tg

Alzheimer disease: Quantitative analysis of I-123-iodoamphetamine SPECT brain imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hellman, R.S.; Tikofsky, R.S.; Collier, B.D.

1989-07-01

To enable a more quantitative diagnosis of senile dementia of the Alzheimer type (SDAT), the authors developed and tested a semiautomated method to define regions of interest (ROIs) to be used in quantitating results from single photon emission computed tomography (SPECT) of regional cerebral blood flow performed with N-isopropyl iodine-123-iodoamphetamine. SPECT/IMP imaging was performed in ten patients with probable SDAT and seven healthy subjects. Multiple ROIs were manually and semiautomatically generated, and uptake was quantitated for each ROI. Mean cortical activity was estimated as the average of the mean activity in 24 semiautomatically generated ROIs; mean cerebellar activity was determinedmore » from the mean activity in separate ROIs. A ratio of parietal to cerebellar activity less than 0.60 and a ratio of parietal to mean cortical activity less than 0.90 allowed correct categorization of nine of ten and eight of ten patients, respectively, with SDAT and all control subjects. The degree of diminished mental status observed in patients with SDAT correlated with both global and regional changes in IMP uptake.« less

Computer Aided Solution for Automatic Segmenting and Measurements of Blood Leucocytes Using Static Microscope Images.

PubMed

Abdulhay, Enas; Mohammed, Mazin Abed; Ibrahim, Dheyaa Ahmed; Arunkumar, N; Venkatraman, V

2018-02-17

Blood leucocytes segmentation in medical images is viewed as difficult process due to the variability of blood cells concerning their shape and size and the difficulty towards determining location of Blood Leucocytes. Physical analysis of blood tests to recognize leukocytes is tedious, time-consuming and liable to error because of the various morphological components of the cells. Segmentation of medical imagery has been considered as a difficult task because of complexity of images, and also due to the non-availability of leucocytes models which entirely captures the probable shapes in each structures and also incorporate cell overlapping, the expansive variety of the blood cells concerning their shape and size, various elements influencing the outer appearance of the blood leucocytes, and low Static Microscope Image disparity from extra issues outcoming about because of noise. We suggest a strategy towards segmentation of blood leucocytes using static microscope images which is a resultant of three prevailing systems of computer vision fiction: enhancing the image, Support vector machine for segmenting the image, and filtering out non ROI (region of interest) on the basis of Local binary patterns and texture features. Every one of these strategies are modified for blood leucocytes division issue, in this manner the subsequent techniques are very vigorous when compared with its individual segments. Eventually, we assess framework based by compare the outcome and manual division. The findings outcome from this study have shown a new approach that automatically segments the blood leucocytes and identify it from a static microscope images. Initially, the method uses a trainable segmentation procedure and trained support vector machine classifier to accurately identify the position of the ROI. After that, filtering out non ROI have proposed based on histogram analysis to avoid the non ROI and chose the right object. Finally, identify the blood leucocytes type using the texture feature. The performance of the foreseen approach has been tried in appearing differently in relation to the system against manual examination by a gynaecologist utilizing diverse scales. A total of 100 microscope images were used for the comparison, and the results showed that the proposed solution is a viable alternative to the manual segmentation method for accurately determining the ROI. We have evaluated the blood leucocytes identification using the ROI texture (LBP Feature). The identification accuracy in the technique used is about 95.3%., with 100 sensitivity and 91.66% specificity.

Optimization of Region of Interest Drawing for Quantitative Analysis: Differentiation Between Benign and Malignant Breast Lesions on Contrast-Enhanced Sonography.

PubMed

Nakata, Norio; Ohta, Tomoyuki; Nishioka, Makiko; Takeyama, Hiroshi; Toriumi, Yasuo; Kato, Kumiko; Nogi, Hiroko; Kamio, Makiko; Fukuda, Kunihiko

2015-11-01

This study was performed to evaluate the diagnostic utility of quantitative analysis of benign and malignant breast lesions using contrast-enhanced sonography. Contrast-enhanced sonography using the perflubutane-based contrast agent Sonazoid (Daiichi Sankyo, Tokyo, Japan) was performed in 94 pathologically proven palpable breast mass lesions, which could be depicted with B-mode sonography. Quantitative analyses using the time-intensity curve on contrast-enhanced sonography were performed in 5 region of interest (ROI) types (manually traced ROI and circular ROIs of 5, 10, 15, and 20 mm in diameter). The peak signal intensity, initial slope, time to peak, positive enhancement integral, and wash-out ratio were investigated in each ROI. There were significant differences between benign and malignant lesions in the time to peak (P < .05), initial slope (P < .001), and positive enhancement integral (P < .05) for the manual ROI. Significant differences were found between benign and malignant lesions in the time to peak (P < .05) for the 5-mm ROI; the time to peak (P < .05) and initial slope (P< .05) for the 10-mm ROI; absolute values of the peak signal intensity (P< .05), time to peak (P< .01), and initial slope (P< .005) for the 15-mm ROI; and the time to peak (P < .05) and initial slope (P < .05) for the 20-mm ROI. There were no statistically significant differences in any wash-out ratio values for the 5 ROI types. Kinetic analysis using contrast-enhanced sonography is useful for differentiation between benign and malignant breast lesions. © 2015 by the American Institute of Ultrasound in Medicine.

Fully automatic region of interest selection in glomerular filtration rate estimation from 99mTc-DTPA renogram.

PubMed

Lin, Kun-Ju; Huang, Jia-Yann; Chen, Yung-Sheng

2011-12-01

Glomerular filtration rate (GFR) is a common accepted standard estimation of renal function. Gamma camera-based methods for estimating renal uptake of (99m)Tc-diethylenetriaminepentaacetic acid (DTPA) without blood or urine sampling have been widely used. Of these, the method introduced by Gates has been the most common method. Currently, most of gamma cameras are equipped with a commercial program for GFR determination, a semi-quantitative analysis by manually drawing region of interest (ROI) over each kidney. Then, the GFR value can be computed from the scintigraphic determination of (99m)Tc-DTPA uptake within the kidney automatically. Delineating the kidney area is difficult when applying a fixed threshold value. Moreover, hand-drawn ROIs are tedious, time consuming, and dependent highly on operator skill. Thus, we developed a fully automatic renal ROI estimation system based on the temporal changes in intensity counts, intensity-pair distribution image contrast enhancement method, adaptive thresholding, and morphological operations that can locate the kidney area and obtain the GFR value from a (99m)Tc-DTPA renogram. To evaluate the performance of the proposed approach, 30 clinical dynamic renograms were introduced. The fully automatic approach failed in one patient with very poor renal function. Four patients had a unilateral kidney, and the others had bilateral kidneys. The automatic contours from the remaining 54 kidneys were compared with the contours of manual drawing. The 54 kidneys were included for area error and boundary error analyses. There was high correlation between two physicians' manual contours and the contours obtained by our approach. For area error analysis, the mean true positive area overlap is 91%, the mean false negative is 13.4%, and the mean false positive is 9.3%. The boundary error is 1.6 pixels. The GFR calculated using this automatic computer-aided approach is reproducible and may be applied to help nuclear medicine physicians in clinical practice.

Content-based image retrieval applied to bone age assessment

NASA Astrophysics Data System (ADS)

Fischer, Benedikt; Brosig, André; Welter, Petra; Grouls, Christoph; Günther, Rolf W.; Deserno, Thomas M.

2010-03-01

Radiological bone age assessment is based on local image regions of interest (ROI), such as the epiphysis or the area of carpal bones. These are compared to a standardized reference and scores determining the skeletal maturity are calculated. For computer-aided diagnosis, automatic ROI extraction and analysis is done so far mainly by heuristic approaches. Due to high variations in the imaged biological material and differences in age, gender and ethnic origin, automatic analysis is difficult and frequently requires manual interactions. On the contrary, epiphyseal regions (eROIs) can be compared to previous cases with known age by content-based image retrieval (CBIR). This requires a sufficient number of cases with reliable positioning of the eROI centers. In this first approach to bone age assessment by CBIR, we conduct leaving-oneout experiments on 1,102 left hand radiographs and 15,428 metacarpal and phalangeal eROIs from the USC hand atlas. The similarity of the eROIs is assessed by cross-correlation of 16x16 scaled eROIs. The effects of the number of eROIs, two age computation methods as well as the number of considered CBIR references are analyzed. The best results yield an error rate of 1.16 years and a standard deviation of 0.85 years. As the appearance of the hand varies naturally by up to two years, these results clearly demonstrate the applicability of the CBIR approach for bone age estimation.

Anatomy guided automated SPECT renal seed point estimation

NASA Astrophysics Data System (ADS)

Dwivedi, Shekhar; Kumar, Sailendra

2010-04-01

Quantification of SPECT(Single Photon Emission Computed Tomography) images can be more accurate if correct segmentation of region of interest (ROI) is achieved. Segmenting ROI from SPECT images is challenging due to poor image resolution. SPECT is utilized to study the kidney function, though the challenge involved is to accurately locate the kidneys and bladder for analysis. This paper presents an automated method for generating seed point location of both kidneys using anatomical location of kidneys and bladder. The motivation for this work is based on the premise that the anatomical location of the bladder relative to the kidneys will not differ much. A model is generated based on manual segmentation of the bladder and both the kidneys on 10 patient datasets (including sum and max images). Centroid is estimated for manually segmented bladder and kidneys. Relatively easier bladder segmentation is followed by feeding bladder centroid coordinates into the model to generate seed point for kidneys. Percentage error observed in centroid coordinates of organs from ground truth to estimated values from our approach are acceptable. Percentage error of approximately 1%, 6% and 2% is observed in X coordinates and approximately 2%, 5% and 8% is observed in Y coordinates of bladder, left kidney and right kidney respectively. Using a regression model and the location of the bladder, the ROI generation for kidneys is facilitated. The model based seed point estimation will enhance the robustness of kidney ROI estimation for noisy cases.

QR-on-a-chip: a computer-recognizable micro-pattern engraved microfluidic device for high-throughput image acquisition.

PubMed

Yun, Kyungwon; Lee, Hyunjae; Bang, Hyunwoo; Jeon, Noo Li

2016-02-21

This study proposes a novel way to achieve high-throughput image acquisition based on a computer-recognizable micro-pattern implemented on a microfluidic device. We integrated the QR code, a two-dimensional barcode system, onto the microfluidic device to simplify imaging of multiple ROIs (regions of interest). A standard QR code pattern was modified to arrays of cylindrical structures of polydimethylsiloxane (PDMS). Utilizing the recognition of the micro-pattern, the proposed system enables: (1) device identification, which allows referencing additional information of the device, such as device imaging sequences or the ROIs and (2) composing a coordinate system for an arbitrarily located microfluidic device with respect to the stage. Based on these functionalities, the proposed method performs one-step high-throughput imaging for data acquisition in microfluidic devices without further manual exploration and locating of the desired ROIs. In our experience, the proposed method significantly reduced the time for the preparation of an acquisition. We expect that the method will innovatively improve the prototype device data acquisition and analysis.

Evaluation of Deformable Image Coregistration in Adaptive Dose Painting by Numbers for Head-and-Neck Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Olteanu, Luiza A.M., E-mail: AnaMariaLuiza.Olteanu@uzgent.be; Madani, Indira; De Neve, Wilfried

Purpose: To assess the accuracy of contour deformation and feasibility of dose summation applying deformable image coregistration in adaptive dose painting by numbers (DPBN) for head and neck cancer. Methods and Materials: Data of 12 head-and-neck-cancer patients treated within a Phase I trial on adaptive {sup 18}F-FDG positron emission tomography (PET)-guided DPBN were used. Each patient had two DPBN treatment plans: the initial plan was based on a pretreatment PET/CT scan; the second adapted plan was based on a PET/CT scan acquired after 8 fractions. The median prescription dose to the dose-painted volume was 30 Gy for both DPBN plans.more » To obtain deformed contours and dose distributions, pretreatment CT was deformed to per-treatment CT using deformable image coregistration. Deformed contours of regions of interest (ROI{sub def}) were visually inspected and, if necessary, adjusted (ROI{sub def{sub ad}}) and both compared with manually redrawn ROIs (ROI{sub m}) using Jaccard (JI) and overlap indices (OI). Dose summation was done on the ROI{sub m}, ROI{sub def{sub ad}}, or their unions with the ROI{sub def}. Results: Almost all deformed ROIs were adjusted. The largest adjustment was made in patients with substantially regressing tumors: ROI{sub def} = 11.8 {+-} 10.9 cm{sup 3} vs. ROI{sub def{sub ad}} = 5.9 {+-} 7.8 cm{sup 3} vs. ROI{sub m} = 7.7 {+-} 7.2 cm{sup 3} (p = 0.57). The swallowing structures were the most frequently adjusted ROIs with the lowest indices for the upper esophageal sphincter: JI = 0.3 (ROI{sub def}) and 0.4 (ROI{sub def{sub ad}}); OI = 0.5 (both ROIs). The mandible needed the least adjustment with the highest indices: JI = 0.8 (both ROIs), OI = 0.9 (ROI{sub def}), and 1.0 (ROI{sub def{sub ad}}). Summed doses differed non-significantly. There was a trend of higher doses in the targets and lower doses in the spinal cord when doses were summed on unions. Conclusion: Visual inspection and adjustment were necessary for most ROIs. Fast automatic ROI propagation followed by user-driven adjustment appears to be more efficient than labor-intensive de novo drawing. Dose summation using deformable image coregistration was feasible. Biological uncertainties of dose summation strategies warrant further investigation.« less

Evaluation of intrinsic respiratory signal determination methods for 4D CBCT adapted for mice

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martin, Rachael; Pan, Tinsu, E-mail: tpan@mdanderson.org; Rubinstein, Ashley

Purpose: 4D CT imaging in mice is important in a variety of areas including studies of lung function and tumor motion. A necessary step in 4D imaging is obtaining a respiratory signal, which can be done through an external system or intrinsically through the projection images. A number of methods have been developed that can successfully determine the respiratory signal from cone-beam projection images of humans, however only a few have been utilized in a preclinical setting and most of these rely on step-and-shoot style imaging. The purpose of this work is to assess and make adaptions of several successfulmore » methods developed for humans for an image-guided preclinical radiation therapy system. Methods: Respiratory signals were determined from the projection images of free-breathing mice scanned on the X-RAD system using four methods: the so-called Amsterdam shroud method, a method based on the phase of the Fourier transform, a pixel intensity method, and a center of mass method. The Amsterdam shroud method was modified so the sharp inspiration peaks associated with anesthetized mouse breathing could be detected. Respiratory signals were used to sort projections into phase bins and 4D images were reconstructed. Error and standard deviation in the assignment of phase bins for the four methods compared to a manual method considered to be ground truth were calculated for a range of region of interest (ROI) sizes. Qualitative comparisons were additionally made between the 4D images obtained using each of the methods and the manual method. Results: 4D images were successfully created for all mice with each of the respiratory signal extraction methods. Only minimal qualitative differences were noted between each of the methods and the manual method. The average error (and standard deviation) in phase bin assignment was 0.24 ± 0.08 (0.49 ± 0.11) phase bins for the Fourier transform method, 0.09 ± 0.03 (0.31 ± 0.08) phase bins for the modified Amsterdam shroud method, 0.09 ± 0.02 (0.33 ± 0.07) phase bins for the intensity method, and 0.37 ± 0.10 (0.57 ± 0.08) phase bins for the center of mass method. Little dependence on ROI size was noted for the modified Amsterdam shroud and intensity methods while the Fourier transform and center of mass methods showed a noticeable dependence on the ROI size. Conclusions: The modified Amsterdam shroud, Fourier transform, and intensity respiratory signal methods are sufficiently accurate to be used for 4D imaging on the X-RAD system and show improvement over the existing center of mass method. The intensity and modified Amsterdam shroud methods are recommended due to their high accuracy and low dependence on ROI size.« less

2D image classification for 3D anatomy localization: employing deep convolutional neural networks

NASA Astrophysics Data System (ADS)

de Vos, Bob D.; Wolterink, Jelmer M.; de Jong, Pim A.; Viergever, Max A.; Išgum, Ivana

2016-03-01

Localization of anatomical regions of interest (ROIs) is a preprocessing step in many medical image analysis tasks. While trivial for humans, it is complex for automatic methods. Classic machine learning approaches require the challenge of hand crafting features to describe differences between ROIs and background. Deep convolutional neural networks (CNNs) alleviate this by automatically finding hierarchical feature representations from raw images. We employ this trait to detect anatomical ROIs in 2D image slices in order to localize them in 3D. In 100 low-dose non-contrast enhanced non-ECG synchronized screening chest CT scans, a reference standard was defined by manually delineating rectangular bounding boxes around three anatomical ROIs -- heart, aortic arch, and descending aorta. Every anatomical ROI was automatically identified using a combination of three CNNs, each analyzing one orthogonal image plane. While single CNNs predicted presence or absence of a specific ROI in the given plane, the combination of their results provided a 3D bounding box around it. Classification performance of each CNN, expressed in area under the receiver operating characteristic curve, was >=0.988. Additionally, the performance of ROI localization was evaluated. Median Dice scores for automatically determined bounding boxes around the heart, aortic arch, and descending aorta were 0.89, 0.70, and 0.85 respectively. The results demonstrate that accurate automatic 3D localization of anatomical structures by CNN-based 2D image classification is feasible.

SamuROI, a Python-Based Software Tool for Visualization and Analysis of Dynamic Time Series Imaging at Multiple Spatial Scales.

PubMed

Rueckl, Martin; Lenzi, Stephen C; Moreno-Velasquez, Laura; Parthier, Daniel; Schmitz, Dietmar; Ruediger, Sten; Johenning, Friedrich W

2017-01-01

The measurement of activity in vivo and in vitro has shifted from electrical to optical methods. While the indicators for imaging activity have improved significantly over the last decade, tools for analysing optical data have not kept pace. Most available analysis tools are limited in their flexibility and applicability to datasets obtained at different spatial scales. Here, we present SamuROI (Structured analysis of multiple user-defined ROIs), an open source Python-based analysis environment for imaging data. SamuROI simplifies exploratory analysis and visualization of image series of fluorescence changes in complex structures over time and is readily applicable at different spatial scales. In this paper, we show the utility of SamuROI in Ca 2+ -imaging based applications at three spatial scales: the micro-scale (i.e., sub-cellular compartments including cell bodies, dendrites and spines); the meso-scale, (i.e., whole cell and population imaging with single-cell resolution); and the macro-scale (i.e., imaging of changes in bulk fluorescence in large brain areas, without cellular resolution). The software described here provides a graphical user interface for intuitive data exploration and region of interest (ROI) management that can be used interactively within Jupyter Notebook: a publicly available interactive Python platform that allows simple integration of our software with existing tools for automated ROI generation and post-processing, as well as custom analysis pipelines. SamuROI software, source code and installation instructions are publicly available on GitHub and documentation is available online. SamuROI reduces the energy barrier for manual exploration and semi-automated analysis of spatially complex Ca 2+ imaging datasets, particularly when these have been acquired at different spatial scales.

SamuROI, a Python-Based Software Tool for Visualization and Analysis of Dynamic Time Series Imaging at Multiple Spatial Scales

PubMed Central

Rueckl, Martin; Lenzi, Stephen C.; Moreno-Velasquez, Laura; Parthier, Daniel; Schmitz, Dietmar; Ruediger, Sten; Johenning, Friedrich W.

2017-01-01

The measurement of activity in vivo and in vitro has shifted from electrical to optical methods. While the indicators for imaging activity have improved significantly over the last decade, tools for analysing optical data have not kept pace. Most available analysis tools are limited in their flexibility and applicability to datasets obtained at different spatial scales. Here, we present SamuROI (Structured analysis of multiple user-defined ROIs), an open source Python-based analysis environment for imaging data. SamuROI simplifies exploratory analysis and visualization of image series of fluorescence changes in complex structures over time and is readily applicable at different spatial scales. In this paper, we show the utility of SamuROI in Ca2+-imaging based applications at three spatial scales: the micro-scale (i.e., sub-cellular compartments including cell bodies, dendrites and spines); the meso-scale, (i.e., whole cell and population imaging with single-cell resolution); and the macro-scale (i.e., imaging of changes in bulk fluorescence in large brain areas, without cellular resolution). The software described here provides a graphical user interface for intuitive data exploration and region of interest (ROI) management that can be used interactively within Jupyter Notebook: a publicly available interactive Python platform that allows simple integration of our software with existing tools for automated ROI generation and post-processing, as well as custom analysis pipelines. SamuROI software, source code and installation instructions are publicly available on GitHub and documentation is available online. SamuROI reduces the energy barrier for manual exploration and semi-automated analysis of spatially complex Ca2+ imaging datasets, particularly when these have been acquired at different spatial scales. PMID:28706482

Automated retinal vessel type classification in color fundus images

NASA Astrophysics Data System (ADS)

Yu, H.; Barriga, S.; Agurto, C.; Nemeth, S.; Bauman, W.; Soliz, P.

2013-02-01

Automated retinal vessel type classification is an essential first step toward machine-based quantitative measurement of various vessel topological parameters and identifying vessel abnormalities and alternations in cardiovascular disease risk analysis. This paper presents a new and accurate automatic artery and vein classification method developed for arteriolar-to-venular width ratio (AVR) and artery and vein tortuosity measurements in regions of interest (ROI) of 1.5 and 2.5 optic disc diameters from the disc center, respectively. This method includes illumination normalization, automatic optic disc detection and retinal vessel segmentation, feature extraction, and a partial least squares (PLS) classification. Normalized multi-color information, color variation, and multi-scale morphological features are extracted on each vessel segment. We trained the algorithm on a set of 51 color fundus images using manually marked arteries and veins. We tested the proposed method in a previously unseen test data set consisting of 42 images. We obtained an area under the ROC curve (AUC) of 93.7% in the ROI of AVR measurement and 91.5% of AUC in the ROI of tortuosity measurement. The proposed AV classification method has the potential to assist automatic cardiovascular disease early detection and risk analysis.

Dosimetry and prescription in liver radioembolization with 90Y microspheres: 3D calculation of tumor-to-liver ratio from global 99mTc-MAA SPECT information

NASA Astrophysics Data System (ADS)

Mañeru, Fernando; Abós, Dolores; Bragado, Laura; Fuentemilla, Naiara; Caudepón, Fernando; Pellejero, Santiago; Miquelez, Santiago; Rubio, Anastasio; Goñi, Elena; Hernández-Vitoria, Araceli

2017-12-01

Dosimetry in liver radioembolization with 90Y microspheres is a fundamental tool, both for the optimization of each treatment and for improving knowledge of the treatment effects in the tissues. Different options are available for estimating the administered activity and the tumor/organ dose, among them the so-called partition method. The key factor in the partition method is the tumor/normal tissue activity uptake ratio (T/N), which is obtained by a single-photon emission computed tomography (SPECT) scan during a pre-treatment simulation. The less clear the distinction between healthy and tumor parenchyma within the liver, the more difficult it becomes to estimate the T/N ratio; therefore the use of the method is limited. This study presents a methodology to calculate the T/N ratio using global information from the SPECT. The T/N ratio is estimated by establishing uptake thresholds consistent with previously performed volumetry. This dose calculation method was validated against 3D voxel dosimetry, and was also compared with the standard partition method based on freehand regions of interest (ROI) outlining on SPECT slices. Both comparisons were done on a sample of 20 actual cases of hepatocellular carcinoma treated with resin microspheres. The proposed method and the voxel dosimetry method yield similar results, while the ROI-based method tends to over-estimate the dose to normal tissues. In addition, the variability associated with the ROI-based method is more extreme than the other methods. The proposed method is simpler than either the ROI or voxel dosimetry approaches and avoids the subjectivity associated with the manual selection of regions.

Factors affecting volume calculation with single photon emission tomography (SPECT) method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, T.H.; Lee, K.H.; Chen, D.C.P.

1985-05-01

Several factors may influence the calculation of absolute volumes (VL) from SPECT images. The effect of these factors must be established to optimize the technique. The authors investigated the following on the VL calculations: % of background (BG) subtraction, reconstruction filters, sample activity, angular sampling and edge detection methods. Transaxial images of a liver-trunk phantom filled with Tc-99m from 1 to 3 ..mu..Ci/cc were obtained in 64x64 matrix with a Siemens Rota Camera and MDS computer. Different reconstruction filters including Hanning 20,32, 64 and Butterworth 20, 32 were used. Angular samplings were performed in 3 and 6 degree increments. ROI'smore » were drawn manually and with an automatic edge detection program around the image after BG subtraction. VL's were calculated by multiplying the number of pixels within the ROI by the slice thickness and the x- and y- calibrations of each pixel. One or 2 pixel per slice thickness was applied in the calculation. An inverse correlation was found between the calculated VL and the % of BG subtraction (r=0.99 for 1,2,3 ..mu..Ci/cc activity). Based on the authors' linear regression analysis, the correct liver VL was measured with about 53% BG subtraction. The reconstruction filters, slice thickness and angular sampling had only minor effects on the calculated phantom volumes. Detection of the ROI automatically by the computer was not as accurate as the manual method. The authors conclude that the % of BG subtraction appears to be the most important factor affecting the VL calculation. With good quality control and appropriate reconstruction factors, correct VL calculations can be achieved with SPECT.« less

Noninvasive imaging of human foveal capillary network using dual-conjugate adaptive optics.

PubMed

Popovic, Zoran; Knutsson, Per; Thaung, Jörgen; Owner-Petersen, Mette; Sjöstrand, Johan

2011-04-22

To demonstrate noninvasive imaging of human foveal capillary networks with a high-resolution, wide-field, dual-conjugate adaptive optics (DCAO) imaging instrument. The foveal capillary networks of five healthy subjects with no previous history of ocular or neurologic disease or surgery were imaged with a novel high-resolution, wide-field DCAO instrument. The foveal avascular zone (FAZ) in each image was defined using a manual procedure. An automated algorithm based on publicly available and custom-written software was used to identify vessels and extract morphologic FAZ and vessel parameters. Capillary densities were calculated in two annular regions of interest (ROIs) outside the FAZ (500 μm and 750 μm outer radius from the foveal center) and in the superior, inferior, temporal, and nasal quadrants within the two ROIs. Mean FAZ area was 0.302 ± 0.100 mm(2), and mean capillary density (length/area) in the inner ROI was 38.0 ± 4.0 mm(-1) and 36.4 ± 4.0 mm(-1) in the outer ROI. The difference in ROI capillary density was not significant. There was no significant difference in quadrant capillary density within the two ROIs or between quadrants irrespective of ROI. The authors have demonstrated a technique for noninvasive imaging and semiautomated detection and analysis of foveal capillaries. In comparison with other studies, their method yielded lower capillary densities than histology but similar results to the current clinical gold standard, fluorescein angiography. The increased field of view of the DCAO instrument opens up new possibilities for high-resolution noninvasive clinical imaging of foveal capillaries.

SU-F-I-45: An Automated Technique to Measure Image Contrast in Clinical CT Images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sanders, J; Abadi, E; Meng, B

Purpose: To develop and validate an automated technique for measuring image contrast in chest computed tomography (CT) exams. Methods: An automated computer algorithm was developed to measure the distribution of Hounsfield units (HUs) inside four major organs: the lungs, liver, aorta, and bones. These organs were first segmented or identified using computer vision and image processing techniques. Regions of interest (ROIs) were automatically placed inside the lungs, liver, and aorta and histograms of the HUs inside the ROIs were constructed. The mean and standard deviation of each histogram were computed for each CT dataset. Comparison of the mean and standardmore » deviation of the HUs in the different organs provides different contrast values. The ROI for the bones is simply the segmentation mask of the bones. Since the histogram for bones does not follow a Gaussian distribution, the 25th and 75th percentile were computed instead of the mean. The sensitivity and accuracy of the algorithm was investigated by comparing the automated measurements with manual measurements. Fifteen contrast enhanced and fifteen non-contrast enhanced chest CT clinical datasets were examined in the validation procedure. Results: The algorithm successfully measured the histograms of the four organs in both contrast and non-contrast enhanced chest CT exams. The automated measurements were in agreement with manual measurements. The algorithm has sufficient sensitivity as indicated by the near unity slope of the automated versus manual measurement plots. Furthermore, the algorithm has sufficient accuracy as indicated by the high coefficient of determination, R2, values ranging from 0.879 to 0.998. Conclusion: Patient-specific image contrast can be measured from clinical datasets. The algorithm can be run on both contrast enhanced and non-enhanced clinical datasets. The method can be applied to automatically assess the contrast characteristics of clinical chest CT images and quantify dependencies that may not be captured in phantom data.« less

TU-G-BRD-02: Automated Systematic Quality Assurance Program for Radiation Oncology Information System Upgrades

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, B; Yi, B; Eley, J

Purpose: To: (1) describe an independent, automated, systematic software-based protocol for verifying clinical data accuracy/integrity for mitigation of data corruption/loss risks following radiation oncology information system (ROIS) upgrades; and (2) report on application of this approach in an academic/community practice environment. Methods: We propose a robust approach to perform quality assurance on the ROIS after an upgrade, targeting four data sources: (1) ROIS relational database; (2) ROIS DICOM interface; (3) ROIS treatment machine data configuration; and (4) ROIS-generated clinical reports. We investigated the database schema for differences between pre-/post-upgrade states. Paired DICOM data streams for the same object (such asmore » RT-Plan/Treatment Record) were compared between pre-/post-upgrade states for data corruption. We examined machine configuration and related commissioning data files for changes and corruption. ROIS-generated treatment appointment and treatment parameter reports were compared to ensure patient encounter and treatment plan accuracy. This protocol was supplemented by an end-to-end clinical workflow test to verify essential ROI functionality and integrity of components interfaced during patient care chain of activities. We describe the implementation of this protocol during a Varian ARIA system upgrade at our clinic. Results: We verified 1,638 data tables with 2.4 billion data records. For 222 under-treatment patients, 605 DICOM RT plans and 13,480 DICOM treatment records retrieved from the ROIS DICOM interface were compared, with no differences in fractions, doses delivered, or treatment parameters. We identified 82 new data tables and 78 amended/deleted tables consistent with the upgrade. Reports for 5,073 patient encounters over a 2-week horizon were compared and were identical to those before the upgrade. Content in 12,237 xml machine files was compared, with no differences identified. Conclusion: An independent QA/validation approach for ROIS upgrades was developed and implemented at our clinic. The success of this approach ensures a robust QA of ROIS upgrades without manual paper/electronic checks and associated intensive labor.« less

Semiautomated analysis of small-animal PET data.

PubMed

Kesner, Adam L; Dahlbom, Magnus; Huang, Sung-Cheng; Hsueh, Wei-Ann; Pio, Betty S; Czernin, Johannes; Kreissl, Michael; Wu, Hsiao-Ming; Silverman, Daniel H S

2006-07-01

The objective of the work reported here was to develop and test automated methods to calculate biodistribution of PET tracers using small-animal PET images. After developing software that uses visually distinguishable organs and other landmarks on a scan to semiautomatically coregister a digital mouse phantom with a small-animal PET scan, we elastically transformed the phantom to conform to those landmarks in 9 simulated scans and in 18 actual PET scans acquired of 9 mice. Tracer concentrations were automatically calculated in 22 regions of interest (ROIs) reflecting the whole body and 21 individual organs. To assess the accuracy of this approach, we compared the software-measured activities in the ROIs of simulated PET scans with the known activities, and we compared the software-measured activities in the ROIs of real PET scans both with manually established ROI activities in original scan data and with actual radioactivity content in immediately harvested tissues of imaged animals. PET/atlas coregistrations were successfully generated with minimal end-user input, allowing rapid quantification of 22 separate tissue ROIs. The simulated scan analysis found the method to be robust with respect to the overall size and shape of individual animal scans, with average activity values for all organs tested falling within the range of 98% +/- 3% of the organ activity measured in the unstretched phantom scan. Standardized uptake values (SUVs) measured from actual PET scans using this semiautomated method correlated reasonably well with radioactivity content measured in harvested organs (median r = 0.94) and compared favorably with conventional SUV correlations with harvested organ data (median r = 0.825). A semiautomated analytic approach involving coregistration of scan-derived images with atlas-type images can be used in small-animal whole-body radiotracer studies to estimate radioactivity concentrations in organs. This approach is rapid and less labor intensive than are traditional methods, without diminishing overall accuracy. Such techniques have the possibility of saving time, effort, and the number of animals needed for such assessments.

A preliminary study of DTI Fingerprinting on stroke analysis.

PubMed

Ma, Heather T; Ye, Chenfei; Wu, Jun; Yang, Pengfei; Chen, Xuhui; Yang, Zhengyi; Ma, Jingbo

2014-01-01

DTI (Diffusion Tensor Imaging) is a well-known MRI (Magnetic Resonance Imaging) technique which provides useful structural information about human brain. However, the quantitative measurement to physiological variation of subtypes of ischemic stroke is not available. An automatically quantitative method for DTI analysis will enhance the DTI application in clinics. In this study, we proposed a DTI Fingerprinting technology to quantitatively analyze white matter tissue, which was applied in stroke classification. The TBSS (Tract Based Spatial Statistics) method was employed to generate mask automatically. To evaluate the clustering performance of the automatic method, lesion ROI (Region of Interest) is manually drawn on the DWI images as a reference. The results from the DTI Fingerprinting were compared with those obtained from the reference ROIs. It indicates that the DTI Fingerprinting could identify different states of ischemic stroke and has promising potential to provide a more comprehensive measure of the DTI data. Further development should be carried out to improve DTI Fingerprinting technology in clinics.

Semiautomatic segmentation of liver metastases on volumetric CT images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yan, Jiayong; Schwartz, Lawrence H.; Zhao, Binsheng, E-mail: bz2166@cumc.columbia.edu

2015-11-15

Purpose: Accurate segmentation and quantification of liver metastases on CT images are critical to surgery/radiation treatment planning and therapy response assessment. To date, there are no reliable methods to perform such segmentation automatically. In this work, the authors present a method for semiautomatic delineation of liver metastases on contrast-enhanced volumetric CT images. Methods: The first step is to manually place a seed region-of-interest (ROI) in the lesion on an image. This ROI will (1) serve as an internal marker and (2) assist in automatically identifying an external marker. With these two markers, lesion contour on the image can be accuratelymore » delineated using traditional watershed transformation. Density information will then be extracted from the segmented 2D lesion and help determine the 3D connected object that is a candidate of the lesion volume. The authors have developed a robust strategy to automatically determine internal and external markers for marker-controlled watershed segmentation. By manually placing a seed region-of-interest in the lesion to be delineated on a reference image, the method can automatically determine dual threshold values to approximately separate the lesion from its surrounding structures and refine the thresholds from the segmented lesion for the accurate segmentation of the lesion volume. This method was applied to 69 liver metastases (1.1–10.3 cm in diameter) from a total of 15 patients. An independent radiologist manually delineated all lesions and the resultant lesion volumes served as the “gold standard” for validation of the method’s accuracy. Results: The algorithm received a median overlap, overestimation ratio, and underestimation ratio of 82.3%, 6.0%, and 11.5%, respectively, and a median average boundary distance of 1.2 mm. Conclusions: Preliminary results have shown that volumes of liver metastases on contrast-enhanced CT images can be accurately estimated by a semiautomatic segmentation method.« less

Correction for FDG PET dose extravasations: Monte Carlo validation and quantitative evaluation of patient studies.

PubMed

Silva-Rodríguez, Jesús; Aguiar, Pablo; Sánchez, Manuel; Mosquera, Javier; Luna-Vega, Víctor; Cortés, Julia; Garrido, Miguel; Pombar, Miguel; Ruibal, Alvaro

2014-05-01

Current procedure guidelines for whole body [18F]fluoro-2-deoxy-D-glucose (FDG)-positron emission tomography (PET) state that studies with visible dose extravasations should be rejected for quantification protocols. Our work is focused on the development and validation of methods for estimating extravasated doses in order to correct standard uptake value (SUV) values for this effect in clinical routine. One thousand three hundred sixty-seven consecutive whole body FDG-PET studies were visually inspected looking for extravasation cases. Two methods for estimating the extravasated dose were proposed and validated in different scenarios using Monte Carlo simulations. All visible extravasations were retrospectively evaluated using a manual ROI based method. In addition, the 50 patients with higher extravasated doses were also evaluated using a threshold-based method. Simulation studies showed that the proposed methods for estimating extravasated doses allow us to compensate the impact of extravasations on SUV values with an error below 5%. The quantitative evaluation of patient studies revealed that paravenous injection is a relatively frequent effect (18%) with a small fraction of patients presenting considerable extravasations ranging from 1% to a maximum of 22% of the injected dose. A criterion based on the extravasated volume and maximum concentration was established in order to identify this fraction of patients that might be corrected for paravenous injection effect. The authors propose the use of a manual ROI based method for estimating the effectively administered FDG dose and then correct SUV quantification in those patients fulfilling the proposed criterion.

A Multiatlas Segmentation Using Graph Cuts with Applications to Liver Segmentation in CT Scans

PubMed Central

2014-01-01

An atlas-based segmentation approach is presented that combines low-level operations, an affine probabilistic atlas, and a multiatlas-based segmentation. The proposed combination provides highly accurate segmentation due to registrations and atlas selections based on the regions of interest (ROIs) and coarse segmentations. Our approach shares the following common elements between the probabilistic atlas and multiatlas segmentation: (a) the spatial normalisation and (b) the segmentation method, which is based on minimising a discrete energy function using graph cuts. The method is evaluated for the segmentation of the liver in computed tomography (CT) images. Low-level operations define a ROI around the liver from an abdominal CT. We generate a probabilistic atlas using an affine registration based on geometry moments from manually labelled data. Next, a coarse segmentation of the liver is obtained from the probabilistic atlas with low computational effort. Then, a multiatlas segmentation approach improves the accuracy of the segmentation. Both the atlas selections and the nonrigid registrations of the multiatlas approach use a binary mask defined by coarse segmentation. We experimentally demonstrate that this approach performs better than atlas selections and nonrigid registrations in the entire ROI. The segmentation results are comparable to those obtained by human experts and to other recently published results. PMID:25276219

SU-C-BRA-04: Automated Segmentation of Head-And-Neck CT Images for Radiotherapy Treatment Planning Via Multi-Atlas Machine Learning (MAML)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ren, X; Gao, H; Sharp, G

Purpose: Accurate image segmentation is a crucial step during image guided radiation therapy. This work proposes multi-atlas machine learning (MAML) algorithm for automated segmentation of head-and-neck CT images. Methods: As the first step, the algorithm utilizes normalized mutual information as similarity metric, affine registration combined with multiresolution B-Spline registration, and then fuses together using the label fusion strategy via Plastimatch. As the second step, the following feature selection strategy is proposed to extract five feature components from reference or atlas images: intensity (I), distance map (D), box (B), center of gravity (C) and stable point (S). The box feature Bmore » is novel. It describes a relative position from each point to minimum inscribed rectangle of ROI. The center-of-gravity feature C is the 3D Euclidean distance from a sample point to the ROI center of gravity, and then S is the distance of the sample point to the landmarks. Then, we adopt random forest (RF) in Scikit-learn, a Python module integrating a wide range of state-of-the-art machine learning algorithms as classifier. Different feature and atlas strategies are used for different ROIs for improved performance, such as multi-atlas strategy with reference box for brainstem, and single-atlas strategy with reference landmark for optic chiasm. Results: The algorithm was validated on a set of 33 CT images with manual contours using a leave-one-out cross-validation strategy. Dice similarity coefficients between manual contours and automated contours were calculated: the proposed MAML method had an improvement from 0.79 to 0.83 for brainstem and 0.11 to 0.52 for optic chiasm with respect to multi-atlas segmentation method (MA). Conclusion: A MAML method has been proposed for automated segmentation of head-and-neck CT images with improved performance. It provides the comparable result in brainstem and the improved result in optic chiasm compared with MA. Xuhua Ren and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000), and the Shanghai Pujiang Talent Program (#14PJ1404500).« less

Anatomy-driven multiple trajectory planning (ADMTP) of intracranial electrodes for epilepsy surgery.

PubMed

Sparks, Rachel; Vakharia, Vejay; Rodionov, Roman; Vos, Sjoerd B; Diehl, Beate; Wehner, Tim; Miserocchi, Anna; McEvoy, Andrew W; Duncan, John S; Ourselin, Sebastien

2017-08-01

Epilepsy is potentially curable with resective surgery if the epileptogenic zone (EZ) can be identified. If non-invasive imaging is unable to elucidate the EZ, intracranial electrodes may be implanted to identify the EZ as well as map cortical function. In current clinical practice, each electrode trajectory is determined by time-consuming manual inspection of preoperative imaging to find a path that avoids blood vessels while traversing appropriate deep and superficial regions of interest (ROIs). We present anatomy-driven multiple trajectory planning (ADMTP) to find safe trajectories from a list of user-defined ROIs within minutes rather than the hours required for manual planning. Electrode trajectories are automatically computed in three steps: (1) Target Point Selection to identify appropriate target points within each ROI; (2) Trajectory Risk Scoring to quantify the cumulative distance to critical structures (blood vessels) along each trajectory, defined as the skull entry point to target point. (3) Implantation Plan Computation: to determine a feasible combination of low-risk trajectories for all electrodes. ADMTP was evaluated on 20 patients (190 electrodes). ADMTP lowered the quantitative risk score in 83% of electrodes. Qualitative results show ADMTP found suitable trajectories for 70% of electrodes; a similar portion of manual trajectories were considered suitable. Trajectory suitability for ADMTP was 95% if traversing sulci was not included in the safety criteria. ADMTP is computationally efficient, computing between 7 and 12 trajectories in 54.5 (17.3-191.9) s. ADMTP efficiently compute safe and surgically feasible electrode trajectories.

Diffusion-weighted magnetic resonance imaging in the characterization of testicular germ cell neoplasms: Effect of ROI methods on apparent diffusion coefficient values and interobserver variability.

PubMed

Tsili, Athina C; Ntorkou, Alexandra; Astrakas, Loukas; Xydis, Vasilis; Tsampalas, Stavros; Sofikitis, Nikolaos; Argyropoulou, Maria I

2017-04-01

To evaluate the difference in apparent diffusion coefficient (ADC) measurements at diffusion-weighted (DW) magnetic resonance imaging of differently shaped regions-of-interest (ROIs) in testicular germ cell neoplasms (TGCNS), the diagnostic ability of differently shaped ROIs in differentiating seminomas from nonseminomatous germ cell neoplasms (NSGCNs) and the interobserver variability. Thirty-three TGCNs were retrospectively evaluated. Patients underwent MR examinations, including DWI on a 1.5-T MR system. Two observers measured mean tumor ADCs using four distinct ROI methods: round, square, freehand and multiple small, round ROIs. The interclass correlation coefficient was analyzed to assess interobserver variability. Statistical analysis was used to compare mean ADC measurements among observers, methods and histologic types. All ROI methods showed excellent interobserver agreement, with excellent correlation (P<0.001). Multiple, small ROIs provided the lower mean ADC in TGCNs. Seminomas had lower mean ADC compared to NSGCNs for each ROI method (P<0.001). Round ROI proved the most accurate method in characterizing TGCNS. Interobserver variability in ADC measurement is excellent, irrespective of the ROI shape. Multiple, small round ROIs and round ROI proved the more accurate methods for ADC measurement in the characterization of TGCNs and in the differentiation between seminomas and NSGCNs, respectively. Copyright © 2017 Elsevier B.V. All rights reserved.

Assessing age-related gray matter decline with voxel-based morphometry depends significantly on segmentation and normalization procedures

PubMed Central

Callaert, Dorothée V.; Ribbens, Annemie; Maes, Frederik; Swinnen, Stephan P.; Wenderoth, Nicole

2014-01-01

Healthy ageing coincides with a progressive decline of brain gray matter (GM) ultimately affecting the entire brain. For a long time, manual delineation-based volumetry within predefined regions of interest (ROI) has been the gold standard for assessing such degeneration. Voxel-Based Morphometry (VBM) offers an automated alternative approach that, however, relies critically on the segmentation and spatial normalization of a large collection of images from different subjects. This can be achieved via different algorithms, with SPM5/SPM8, DARTEL of SPM8 and FSL tools (FAST, FNIRT) being three of the most frequently used. We complemented these voxel based measurements with a ROI based approach, whereby the ROIs are defined by transforms of an atlas (containing different tissue probability maps as well as predefined anatomic labels) to the individual subject images in order to obtain volumetric information at the level of the whole brain or within separate ROIs. Comparing GM decline between 21 young subjects (mean age 23) and 18 elderly (mean age 66) revealed that volumetric measurements differed significantly between methods. The unified segmentation/normalization of SPM5/SPM8 revealed the largest age-related differences and DARTEL the smallest, with FSL being more similar to the DARTEL approach. Method specific differences were substantial after segmentation and most pronounced for the cortical structures in close vicinity to major sulci and fissures. Our findings suggest that algorithms that provide only limited degrees of freedom for local deformations (such as the unified segmentation and normalization of SPM5/SPM8) tend to overestimate between-group differences in VBM results when compared to methods providing more flexible warping. This difference seems to be most pronounced if the anatomy of one of the groups deviates from custom templates, a finding that is of particular importance when results are compared across studies using different VBM methods. PMID:25002845

Can color-coded parametric maps improve dynamic enhancement pattern analysis in MR mammography?

PubMed

Baltzer, P A; Dietzel, M; Vag, T; Beger, S; Freiberg, C; Herzog, A B; Gajda, M; Camara, O; Kaiser, W A

2010-03-01

Post-contrast enhancement characteristics (PEC) are a major criterion for differential diagnosis in MR mammography (MRM). Manual placement of regions of interest (ROIs) to obtain time/signal intensity curves (TSIC) is the standard approach to assess dynamic enhancement data. Computers can automatically calculate the TSIC in every lesion voxel and combine this data to form one color-coded parametric map (CCPM). Thus, the TSIC of the whole lesion can be assessed. This investigation was conducted to compare the diagnostic accuracy (DA) of CCPM with TSIC for the assessment of PEC. 329 consecutive patients with 469 histologically verified lesions were examined. MRM was performed according to a standard protocol (1.5 T, 0.1 mmol/kgbw Gd-DTPA). ROIs were drawn manually within any lesion to calculate the TSIC. CCPMs were created in all patients using dedicated software (CAD Sciences). Both methods were rated by 2 observers in consensus on an ordinal scale. Receiver operating characteristics (ROC) analysis was used to compare both methods. The area under the curve (AUC) was significantly (p=0.026) higher for CCPM (0.829) than TSIC (0.749). The sensitivity was 88.5% (CCPM) vs. 82.8% (TSIC), whereas equal specificity levels were found (CCPM: 63.7%, TSIC: 63.0%). The color-coded parametric maps (CCPMs) showed a significantly higher DA compared to TSIC, in particular the sensitivity could be increased. Therefore, the CCPM method is a feasible approach to assessing dynamic data in MRM and condenses several imaging series into one parametric map. © Georg Thieme Verlag KG Stuttgart · New York.

An automated method for accurate vessel segmentation.

PubMed

Yang, Xin; Liu, Chaoyue; Le Minh, Hung; Wang, Zhiwei; Chien, Aichi; Cheng, Kwang-Ting Tim

2017-05-07

Vessel segmentation is a critical task for various medical applications, such as diagnosis assistance of diabetic retinopathy, quantification of cerebral aneurysm's growth, and guiding surgery in neurosurgical procedures. Despite technology advances in image segmentation, existing methods still suffer from low accuracy for vessel segmentation in the two challenging while common scenarios in clinical usage: (1) regions with a low signal-to-noise-ratio (SNR), and (2) at vessel boundaries disturbed by adjacent non-vessel pixels. In this paper, we present an automated system which can achieve highly accurate vessel segmentation for both 2D and 3D images even under these challenging scenarios. Three key contributions achieved by our system are: (1) a progressive contrast enhancement method to adaptively enhance contrast of challenging pixels that were otherwise indistinguishable, (2) a boundary refinement method to effectively improve segmentation accuracy at vessel borders based on Canny edge detection, and (3) a content-aware region-of-interests (ROI) adjustment method to automatically determine the locations and sizes of ROIs which contain ambiguous pixels and demand further verification. Extensive evaluation of our method is conducted on both 2D and 3D datasets. On a public 2D retinal dataset (named DRIVE (Staal 2004 IEEE Trans. Med. Imaging 23 501-9)) and our 2D clinical cerebral dataset, our approach achieves superior performance to the state-of-the-art methods including a vesselness based method (Frangi 1998 Int. Conf. on Medical Image Computing and Computer-Assisted Intervention) and an optimally oriented flux (OOF) based method (Law and Chung 2008 European Conf. on Computer Vision). An evaluation on 11 clinical 3D CTA cerebral datasets shows that our method can achieve 94% average accuracy with respect to the manual segmentation reference, which is 23% to 33% better than the five baseline methods (Yushkevich 2006 Neuroimage 31 1116-28; Law and Chung 2008 European Conf. on Computer Vision; Law and Chung 2009 IEEE Trans. Image Process. 18 596-612; Wang 2015 J. Neurosci. Methods 241 30-6) with manually optimized parameters. Our system has also been applied clinically for cerebral aneurysm development analysis. Experimental results on 10 patients' data, with two 3D CT scans per patient, show that our system's automatic diagnosis outcomes are consistent with clinicians' manual measurements.

An automated method for accurate vessel segmentation

NASA Astrophysics Data System (ADS)

Yang, Xin; Liu, Chaoyue; Le Minh, Hung; Wang, Zhiwei; Chien, Aichi; (Tim Cheng, Kwang-Ting

2017-05-01

Vessel segmentation is a critical task for various medical applications, such as diagnosis assistance of diabetic retinopathy, quantification of cerebral aneurysm’s growth, and guiding surgery in neurosurgical procedures. Despite technology advances in image segmentation, existing methods still suffer from low accuracy for vessel segmentation in the two challenging while common scenarios in clinical usage: (1) regions with a low signal-to-noise-ratio (SNR), and (2) at vessel boundaries disturbed by adjacent non-vessel pixels. In this paper, we present an automated system which can achieve highly accurate vessel segmentation for both 2D and 3D images even under these challenging scenarios. Three key contributions achieved by our system are: (1) a progressive contrast enhancement method to adaptively enhance contrast of challenging pixels that were otherwise indistinguishable, (2) a boundary refinement method to effectively improve segmentation accuracy at vessel borders based on Canny edge detection, and (3) a content-aware region-of-interests (ROI) adjustment method to automatically determine the locations and sizes of ROIs which contain ambiguous pixels and demand further verification. Extensive evaluation of our method is conducted on both 2D and 3D datasets. On a public 2D retinal dataset (named DRIVE (Staal 2004 IEEE Trans. Med. Imaging 23 501-9)) and our 2D clinical cerebral dataset, our approach achieves superior performance to the state-of-the-art methods including a vesselness based method (Frangi 1998 Int. Conf. on Medical Image Computing and Computer-Assisted Intervention) and an optimally oriented flux (OOF) based method (Law and Chung 2008 European Conf. on Computer Vision). An evaluation on 11 clinical 3D CTA cerebral datasets shows that our method can achieve 94% average accuracy with respect to the manual segmentation reference, which is 23% to 33% better than the five baseline methods (Yushkevich 2006 Neuroimage 31 1116-28; Law and Chung 2008 European Conf. on Computer Vision; Law and Chung 2009 IEEE Trans. Image Process. 18 596-612; Wang 2015 J. Neurosci. Methods 241 30-6) with manually optimized parameters. Our system has also been applied clinically for cerebral aneurysm development analysis. Experimental results on 10 patients’ data, with two 3D CT scans per patient, show that our system’s automatic diagnosis outcomes are consistent with clinicians’ manual measurements.

Development of WRF-ROI system by incorporating eigen-decomposition

NASA Astrophysics Data System (ADS)

Kim, S.; Noh, N.; Song, H.; Lim, G.

2011-12-01

This study presents the development of WRF-ROI system, which is the implementation of Retrospective Optimal Interpolation (ROI) to the Weather Research and Forecasting model (WRF). ROI is a new data assimilation algorithm introduced by Song et al. (2009) and Song and Lim (2009). The formulation of ROI is similar with that of Optimal Interpolation (OI), but ROI iteratively assimilates an observation set at a post analysis time into a prior analysis, possibly providing the high quality reanalysis data. ROI method assimilates the data at post analysis time using perturbation method (Errico and Raeder, 1999) without adjoint model. In previous study, ROI method is applied to Lorenz 40-variable model (Lorenz, 1996) to validate the algorithm and to investigate the capability. It is therefore required to apply this ROI method into a more realistic and complicated model framework such as WRF. In this research, the reduced-rank formulation of ROI is used instead of a reduced-resolution method. The computational costs can be reduced due to the eigen-decomposition of background error covariance in the reduced-rank method. When single profile of observations is assimilated in the WRF-ROI system by incorporating eigen-decomposition, the analysis error tends to be reduced if compared with the background error. The difference between forecast errors with assimilation and without assimilation is obviously increased as time passed, which means the improvement of forecast error by assimilation.

Automated Segmentation Methods of Drusen to Diagnose Age-Related Macular Degeneration Screening in Retinal Images.

PubMed

Kim, Young Jae; Kim, Kwang Gi

2018-01-01

Existing drusen measurement is difficult to use in clinic because it requires a lot of time and effort for visual inspection. In order to resolve this problem, we propose an automatic drusen detection method to help clinical diagnosis of age-related macular degeneration. First, we changed the fundus image to a green channel and extracted the ROI of the macular area based on the optic disk. Next, we detected the candidate group using the difference image of the median filter within the ROI. We also segmented vessels and removed them from the image. Finally, we detected the drusen through Renyi's entropy threshold algorithm. We performed comparisons and statistical analysis between the manual detection results and automatic detection results for 30 cases in order to verify validity. As a result, the average sensitivity was 93.37% (80.95%~100%) and the average DSC was 0.73 (0.3~0.98). In addition, the value of the ICC was 0.984 (CI: 0.967~0.993, p < 0.01), showing the high reliability of the proposed automatic method. We expect that the automatic drusen detection helps clinicians to improve the diagnostic performance in the detection of drusen on fundus image.

Landmark-based deep multi-instance learning for brain disease diagnosis.

PubMed

Liu, Mingxia; Zhang, Jun; Adeli, Ehsan; Shen, Dinggang

2018-01-01

In conventional Magnetic Resonance (MR) image based methods, two stages are often involved to capture brain structural information for disease diagnosis, i.e., 1) manually partitioning each MR image into a number of regions-of-interest (ROIs), and 2) extracting pre-defined features from each ROI for diagnosis with a certain classifier. However, these pre-defined features often limit the performance of the diagnosis, due to challenges in 1) defining the ROIs and 2) extracting effective disease-related features. In this paper, we propose a landmark-based deep multi-instance learning (LDMIL) framework for brain disease diagnosis. Specifically, we first adopt a data-driven learning approach to discover disease-related anatomical landmarks in the brain MR images, along with their nearby image patches. Then, our LDMIL framework learns an end-to-end MR image classifier for capturing both the local structural information conveyed by image patches located by landmarks and the global structural information derived from all detected landmarks. We have evaluated our proposed framework on 1526 subjects from three public datasets (i.e., ADNI-1, ADNI-2, and MIRIAD), and the experimental results show that our framework can achieve superior performance over state-of-the-art approaches. Copyright © 2017 Elsevier B.V. All rights reserved.

DWI-based neural fingerprinting technology: a preliminary study on stroke analysis.

PubMed

Ye, Chenfei; Ma, Heather Ting; Wu, Jun; Yang, Pengfei; Chen, Xuhui; Yang, Zhengyi; Ma, Jingbo

2014-01-01

Stroke is a common neural disorder in neurology clinics. Magnetic resonance imaging (MRI) has become an important tool to assess the neural physiological changes under stroke, such as diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI). Quantitative analysis of MRI images would help medical doctors to localize the stroke area in the diagnosis in terms of structural information and physiological characterization. However, current quantitative approaches can only provide localization of the disorder rather than measure physiological variation of subtypes of ischemic stroke. In the current study, we hypothesize that each kind of neural disorder would have its unique physiological characteristics, which could be reflected by DWI images on different gradients. Based on this hypothesis, a DWI-based neural fingerprinting technology was proposed to classify subtypes of ischemic stroke. The neural fingerprint was constructed by the signal intensity of the region of interest (ROI) on the DWI images under different gradients. The fingerprint derived from the manually drawn ROI could classify the subtypes with accuracy 100%. However, the classification accuracy was worse when using semiautomatic and automatic method in ROI segmentation. The preliminary results showed promising potential of DWI-based neural fingerprinting technology in stroke subtype classification. Further studies will be carried out for enhancing the fingerprinting accuracy and its application in other clinical practices.

Template-based automatic extraction of the joint space of foot bones from CT scan

NASA Astrophysics Data System (ADS)

Park, Eunbi; Kim, Taeho; Park, Jinah

2016-03-01

Clean bone segmentation is critical in studying the joint anatomy for measuring the spacing between the bones. However, separation of the coupled bones in CT images is sometimes difficult due to ambiguous gray values coming from the noise and the heterogeneity of bone materials as well as narrowing of the joint space. For fine reconstruction of the individual local boundaries, manual operation is a common practice where the segmentation remains to be a bottleneck. In this paper, we present an automatic method for extracting the joint space by applying graph cut on Markov random field model to the region of interest (ROI) which is identified by a template of 3D bone structures. The template includes encoded articular surface which identifies the tight region of the high-intensity bone boundaries together with the fuzzy joint area of interest. The localized shape information from the template model within the ROI effectively separates the bones nearby. By narrowing the ROI down to the region including two types of tissue, the object extraction problem was reduced to binary segmentation and solved via graph cut. Based on the shape of a joint space marked by the template, the hard constraint was set by the initial seeds which were automatically generated from thresholding and morphological operations. The performance and the robustness of the proposed method are evaluated on 12 volumes of ankle CT data, where each volume includes a set of 4 tarsal bones (calcaneus, talus, navicular and cuboid).

A semi-automated region of interest detection method in the scintigraphic glomerular filtration rate determination for patients with abnormal low renal function.

PubMed

Tian, Cancan; Zheng, Xiujuan; Han, Yuan; Sun, Xiaoguang; Chen, Kewei; Huang, Qiu

2013-11-01

This work presents a novel semi-automated renal region-of-interest (ROI) determination method that is user friendly, time saving, and yet provides a robust glomerular filtration rate (GFR) estimation highly consistent with the reference method. We reviewed data from 57 patients who underwent (99m)Tc-diethylenetriaminepentaacetic acid renal scintigraphy and were diagnosed with abnormal renal function. The renal and background ROIs were delineated by the proposed multi-step, semi-automated method, which integrates temporal/morphologic information via visual inspection and computer-aided calculations. The total GFR was estimated using the proposed method (sGFR) performed by 2 junior clinicians (A and B) with 1 and 3 years of experience, respectively (sGFR_a, sGFR_b), and compared with the reference total GFR (rGFR) estimated by a senior clinician with 20 years of experience who manually delineated the kidney and background ROIs. All GFR calculations herein were conducted using the Gates method. Data from 10 patients with unilateral or non-functioning kidneys were excluded from the analysis. For the remaining patients, sGFR correlated well with rGFR (r(s/rGFR_a) = 0.957, P < 0.001 and r(s/rGFR_b) = 0.951, P < 0.001) and sGFR_a correlated well with sGFR_b (r(a/b) = 0.997, P < 0.001). Moreover, the Bland-Altman plots for sGFR_a and sGFR_b confirm the high reproducibility of the proposed method between different operators. Finally, the proposed procedure is almost 3 times faster than the routinely used procedure in clinical practice. The results suggest that this method is easy to use, highly reproducible, and accurate in measuring the GFR of patients with low renal function. The method is being further extended to a fully automated procedure.

Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study.

PubMed

Mercan, Ezgi; Aksoy, Selim; Shapiro, Linda G; Weaver, Donald L; Brunyé, Tad T; Elmore, Joann G

2016-08-01

Whole slide digital imaging technology enables researchers to study pathologists' interpretive behavior as they view digital slides and gain new understanding of the diagnostic medical decision-making process. In this study, we propose a simple yet important analysis to extract diagnostically relevant regions of interest (ROIs) from tracking records using only pathologists' actions as they viewed biopsy specimens in the whole slide digital imaging format (zooming, panning, and fixating). We use these extracted regions in a visual bag-of-words model based on color and texture features to predict diagnostically relevant ROIs on whole slide images. Using a logistic regression classifier in a cross-validation setting on 240 digital breast biopsy slides and viewport tracking logs of three expert pathologists, we produce probability maps that show 74 % overlap with the actual regions at which pathologists looked. We compare different bag-of-words models by changing dictionary size, visual word definition (patches vs. superpixels), and training data (automatically extracted ROIs vs. manually marked ROIs). This study is a first step in understanding the scanning behaviors of pathologists and the underlying reasons for diagnostic errors.

[Development of a Computer-aided Diagnosis System to Distinguish between Benign and Malignant Mammary Tumors in Dynamic Magnetic Resonance Images: Automatic Detection of the Position with the Strongest Washout Effect in the Tumor].

PubMed

Miyazaki, Yoshiaki; Tabata, Nobuyuki; Taroura, Tomomi; Shinozaki, Kenji; Kubo, Yuichiro; Tokunaga, Eriko; Taguchi, Kenichi

We propose a computer-aided diagnostic (CAD) system that uses time-intensity curves to distinguish between benign and malignant mammary tumors. Many malignant tumors show a washout pattern in time-intensity curves. Therefore, we designed a program that automatically detects the position with the strongest washout effect using the technique, such as the subtraction technique, which extracts only the washout area in the tumor, and by scanning data in 2×2 pixel region of interest (ROI). Operation of this independently developed program was verified using a phantom system that simulated tumors. In three cases of malignant tumors, the washout pattern detection rate in images with manually set ROI was ≤6%, whereas the detection rate with our novel method was 100%. In one case of a benign tumor, when the same method was used, we checked that there was no washout effect and detected the persistent pattern. Thus, the distinction between benign and malignant tumors using our method was completely consistent with the pathological diagnoses made. Our novel method is therefore effective for differentiating between benign and malignant mammary tumors in dynamic magnetic resonance images.

Brain Network Regional Synchrony Analysis in Deafness

PubMed Central

Xu, Lei; Liang, Mao-Jin

2018-01-01

Deafness, the most common auditory disease, has greatly affected people for a long time. The major treatment for deafness is cochlear implantation (CI). However, till today, there is still a lack of objective and precise indicator serving as evaluation of the effectiveness of the cochlear implantation. The goal of this EEG-based study is to effectively distinguish CI children from those prelingual deafened children without cochlear implantation. The proposed method is based on the functional connectivity analysis, which focuses on the brain network regional synchrony. Specifically, we compute the functional connectivity between each channel pair first. Then, we quantify the brain network synchrony among regions of interests (ROIs), where both intraregional synchrony and interregional synchrony are computed. And finally the synchrony values are concatenated to form the feature vector for the SVM classifier. What is more, we develop a new ROI partition method of 128-channel EEG recording system. That is, both the existing ROI partition method and the proposed ROI partition method are used in the experiments. Compared with the existing EEG signal classification methods, our proposed method has achieved significant improvements as large as 87.20% and 86.30% when the existing ROI partition method and the proposed ROI partition method are used, respectively. It further demonstrates that the new ROI partition method is comparable to the existing ROI partition method. PMID:29854776

Optimizing methods for linking cinematic features to fMRI data.

PubMed

Kauttonen, Janne; Hlushchuk, Yevhen; Tikka, Pia

2015-04-15

One of the challenges of naturalistic neurosciences using movie-viewing experiments is how to interpret observed brain activations in relation to the multiplicity of time-locked stimulus features. As previous studies have shown less inter-subject synchronization across viewers of random video footage than story-driven films, new methods need to be developed for analysis of less story-driven contents. To optimize the linkage between our fMRI data collected during viewing of a deliberately non-narrative silent film 'At Land' by Maya Deren (1944) and its annotated content, we combined the method of elastic-net regularization with the model-driven linear regression and the well-established data-driven independent component analysis (ICA) and inter-subject correlation (ISC) methods. In the linear regression analysis, both IC and region-of-interest (ROI) time-series were fitted with time-series of a total of 36 binary-valued and one real-valued tactile annotation of film features. The elastic-net regularization and cross-validation were applied in the ordinary least-squares linear regression in order to avoid over-fitting due to the multicollinearity of regressors, the results were compared against both the partial least-squares (PLS) regression and the un-regularized full-model regression. Non-parametric permutation testing scheme was applied to evaluate the statistical significance of regression. We found statistically significant correlation between the annotation model and 9 ICs out of 40 ICs. Regression analysis was also repeated for a large set of cubic ROIs covering the grey matter. Both IC- and ROI-based regression analyses revealed activations in parietal and occipital regions, with additional smaller clusters in the frontal lobe. Furthermore, we found elastic-net based regression more sensitive than PLS and un-regularized regression since it detected a larger number of significant ICs and ROIs. Along with the ISC ranking methods, our regression analysis proved a feasible method for ordering the ICs based on their functional relevance to the annotated cinematic features. The novelty of our method is - in comparison to the hypothesis-driven manual pre-selection and observation of some individual regressors biased by choice - in applying data-driven approach to all content features simultaneously. We found especially the combination of regularized regression and ICA useful when analyzing fMRI data obtained using non-narrative movie stimulus with a large set of complex and correlated features. Copyright © 2015. Published by Elsevier Inc.

What’s Wrong with the Murals at the Mogao Grottoes: A Near-Infrared Hyperspectral Imaging Method

PubMed Central

Sun, Meijun; Zhang, Dong; Wang, Zheng; Ren, Jinchang; Chai, Bolong; Sun, Jizhou

2015-01-01

Although a significant amount of work has been performed to preserve the ancient murals in the Mogao Grottoes by Dunhuang Cultural Research, non-contact methods need to be developed to effectively evaluate the degree of flaking of the murals. In this study, we propose to evaluate the flaking by automatically analyzing hyperspectral images that were scanned at the site. Murals with various degrees of flaking were scanned in the 126th cave using a near-infrared (NIR) hyperspectral camera with a spectral range of approximately 900 to 1700 nm. The regions of interest (ROIs) of the murals were manually labeled and grouped into four levels: normal, slight, moderate, and severe. The average spectral data from each ROI and its group label were used to train our classification model. To predict the degree of flaking, we adopted four algorithms: deep belief networks (DBNs), partial least squares regression (PLSR), principal component analysis with a support vector machine (PCA + SVM) and principal component analysis with an artificial neural network (PCA + ANN). The experimental results show the effectiveness of our method. In particular, better results are obtained using DBNs when the training data contain a significant amount of striping noise. PMID:26394926

A study on the characteristics of retrospective optimal interpolation using an Observing System Simulation Experiment

NASA Astrophysics Data System (ADS)

Kim, Shin-Woo; Noh, Nam-Kyu; Lim, Gyu-Ho

2013-04-01

This study presents the introduction of retrospective optimal interpolation (ROI) and its application with Weather Research and Forecasting model (WRF). Song et al. (2009) suggested ROI method which is an optimal interpolation (OI) that gradually assimilates observations over the analysis window for variance-minimum estimate of an atmospheric state at the initial time of the analysis window. The assimilation window of ROI algorithm is gradually increased, similar with that of the quasi-static variational assimilation (QSVA; Pires et al., 1996). Unlike QSVA method, however, ROI method assimilates the data at post analysis time using perturbation method (Verlaan and Heemink, 1997) without adjoint model. Song and Lim (2011) improved this method by incorporating eigen-decomposition and covariance inflation. The computational costs for ROI can be reduced due to the eigen-decomposition of background error covariance which can concentrate ROI analyses on the error variances of governing eigenmodes by transforming the control variables into eigenspace. A total energy norm is used for the normalization of each control variables. In this study, ROI method is applied to WRF model with Observing System Simulation Experiment (OSSE) to validate the algorithm and to investigate the capability. Horizontal wind, pressure, potential temperature, and water vapor mixing ratio are used for control variables and observations. Firstly, 1-profile assimilation experiment is performed. Subsequently, OSSE's are performed using the virtual observing system which consists of synop, ship, and sonde data. The difference between forecast errors with assimilation and without assimilation is obviously increased as time passed, which means the improvement of forecast error with the assimilation by ROI. The characteristics and strength/weakness of ROI method are also investigated by conducting the experiments with 3D-Var (3-dimensional variational) method and 4D-Var (4-dimensional variational) method. In the initial time, ROI produces a larger forecast error than that of 4D-Var. However, the difference between the two experimental results is decreased gradually with time, and the ROI shows apparently better result (i.e., smaller forecast error) than that of 4D-Var after 9-hour forecast.

Graphics Processing Unit-Accelerated Nonrigid Registration of MR Images to CT Images During CT-Guided Percutaneous Liver Tumor Ablations.

PubMed

Tokuda, Junichi; Plishker, William; Torabi, Meysam; Olubiyi, Olutayo I; Zaki, George; Tatli, Servet; Silverman, Stuart G; Shekher, Raj; Hata, Nobuhiko

2015-06-01

Accuracy and speed are essential for the intraprocedural nonrigid magnetic resonance (MR) to computed tomography (CT) image registration in the assessment of tumor margins during CT-guided liver tumor ablations. Although both accuracy and speed can be improved by limiting the registration to a region of interest (ROI), manual contouring of the ROI prolongs the registration process substantially. To achieve accurate and fast registration without the use of an ROI, we combined a nonrigid registration technique on the basis of volume subdivision with hardware acceleration using a graphics processing unit (GPU). We compared the registration accuracy and processing time of GPU-accelerated volume subdivision-based nonrigid registration technique to the conventional nonrigid B-spline registration technique. Fourteen image data sets of preprocedural MR and intraprocedural CT images for percutaneous CT-guided liver tumor ablations were obtained. Each set of images was registered using the GPU-accelerated volume subdivision technique and the B-spline technique. Manual contouring of ROI was used only for the B-spline technique. Registration accuracies (Dice similarity coefficient [DSC] and 95% Hausdorff distance [HD]) and total processing time including contouring of ROIs and computation were compared using a paired Student t test. Accuracies of the GPU-accelerated registrations and B-spline registrations, respectively, were 88.3 ± 3.7% versus 89.3 ± 4.9% (P = .41) for DSC and 13.1 ± 5.2 versus 11.4 ± 6.3 mm (P = .15) for HD. Total processing time of the GPU-accelerated registration and B-spline registration techniques was 88 ± 14 versus 557 ± 116 seconds (P < .000000002), respectively; there was no significant difference in computation time despite the difference in the complexity of the algorithms (P = .71). The GPU-accelerated volume subdivision technique was as accurate as the B-spline technique and required significantly less processing time. The GPU-accelerated volume subdivision technique may enable the implementation of nonrigid registration into routine clinical practice. Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.

Balance the nodule shape and surroundings: a new multichannel image based convolutional neural network scheme on lung nodule diagnosis

NASA Astrophysics Data System (ADS)

Sun, Wenqing; Zheng, Bin; Huang, Xia; Qian, Wei

2017-03-01

Deep learning is a trending promising method in medical image analysis area, but how to efficiently prepare the input image for the deep learning algorithms remains a challenge. In this paper, we introduced a novel artificial multichannel region of interest (ROI) generation procedure for convolutional neural networks (CNN). From LIDC database, we collected 54880 benign nodule samples and 59848 malignant nodule samples based on the radiologists' annotations. The proposed CNN consists of three pairs of convolutional layers and two fully connected layers. For each original ROI, two new ROIs were generated: one contains the segmented nodule which highlighted the nodule shape, and the other one contains the gradient of the original ROI which highlighted the textures. By combining the three channel images into a pseudo color ROI, the CNN was trained and tested on the new multichannel ROIs (multichannel ROI II). For the comparison, we generated another type of multichannel image by replacing the gradient image channel with a ROI contains whitened background region (multichannel ROI I). With the 5-fold cross validation evaluation method, the CNN using multichannel ROI II achieved the ROI based area under the curve (AUC) of 0.8823+/-0.0177, compared to the AUC of 0.8484+/-0.0204 generated by the original ROI. By calculating the average of ROI scores from one nodule, the lesion based AUC using multichannel ROI was 0.8793+/-0.0210. By comparing the convolved features maps from CNN using different types of ROIs, it can be noted that multichannel ROI II contains more accurate nodule shapes and surrounding textures.

A study on characteristics of retrospective optimal interpolation with WRF testbed

NASA Astrophysics Data System (ADS)

Kim, S.; Noh, N.; Lim, G.

2012-12-01

This study presents the application of retrospective optimal interpolation (ROI) with Weather Research and Forecasting model (WRF). Song et al. (2009) suggest ROI method which is an optimal interpolation (OI) that gradually assimilates observations over the analysis window for variance-minimum estimate of an atmospheric state at the initial time of the analysis window. Song and Lim (2011) improve the method by incorporating eigen-decomposition and covariance inflation. ROI method assimilates the data at post analysis time using perturbation method (Errico and Raeder, 1999) without adjoint model. In this study, ROI method is applied to WRF model to validate the algorithm and to investigate the capability. The computational costs for ROI can be reduced due to the eigen-decomposition of background error covariance. Using the background error covariance in eigen-space, 1-profile assimilation experiment is performed. The difference between forecast errors with assimilation and without assimilation is obviously increased as time passed, which means the improvement of forecast error by assimilation. The characteristics and strength/weakness of ROI method are investigated by conducting the experiments with other data assimilation method.

Advanced 3D image processing techniques for liver and hepatic tumor location and volumetry

NASA Astrophysics Data System (ADS)

Chemouny, Stephane; Joyeux, Henri; Masson, Bruno; Borne, Frederic; Jaeger, Marc; Monga, Olivier

1999-05-01

To assist radiologists and physicians in diagnosing, and in treatment planning and evaluating in liver oncology, we have developed a fast and accurate segmentation of the liver and its lesions within CT-scan exams. The first step of our method is to reduce spatial resolution of CT images. This will have two effects: obtain near isotropic 3D data space and drastically decrease computational time for further processing. On a second step a 3D non-linear `edge- preserving' smoothing filtering is performed throughout the entire exam. On a third step the 3D regions coming out from the second step are homogeneous enough to allow a quite simple segmentation process, based on morphological operations, under supervisor control, ending up with accurate 3D regions of interest (ROI) of the liver and all the hepatic tumors. On a fourth step the ROIs are eventually set back into the original images, features like volume and location are immediately computed and displayed. The segmentation we get is as precise as a manual one but is much faster.

SU-E-I-96: A Study About the Influence of ROI Variation On Tumor Segmentation in PET

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, L; Tan, S; Lu, W

2014-06-01

Purpose: To study the influence of different regions of interest (ROI) on tumor segmentation in PET. Methods: The experiments were conducted on a cylindrical phantom. Six spheres with different volumes (0.5ml, 1ml, 6ml, 12ml, 16ml and 20 ml) were placed inside a cylindrical container to mimic tumors of different sizes. The spheres were filled with 11C solution as sources and the cylindrical container was filled with 18F-FDG solution as the background. The phantom was continuously scanned in a Biograph-40 True Point/True View PET/CT scanner, and 42 images were reconstructed with source-to-background ratio (SBR) ranging from 16:1 to 1.8:1. We tookmore » a large and a small ROI for each sphere, both of which contain the whole sphere and does not contain any other spheres. Six other ROIs of different sizes were then taken between the large and the small ROI. For each ROI, all images were segmented by eitht thresholding methods and eight advanced methods, respectively. The segmentation results were evaluated by dice similarity index (DSI), classification error (CE) and volume error (VE). The robustness of different methods to ROI variation was quantified using the interrun variation and a generalized Cohen's kappa. Results: With the change of ROI, the segmentation results of all tested methods changed more or less. Compared with all advanced methods, thresholding methods were less affected by the ROI change. In addition, most of the thresholding methods got more accurate segmentation results for all sphere sizes. Conclusion: The results showed that the segmentation performance of all tested methods was affected by the change of ROI. Thresholding methods were more robust to this change and they can segment the PET image more accurately. This work was supported in part by National Natural Science Foundation of China (NNSFC), under Grant Nos. 60971112 and 61375018, and Fundamental Research Funds for the Central Universities, under Grant No. 2012QN086. Wei Lu was supported in part by the National Institutes of Health (NIH) Grant No. R01 CA172638.« less

A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson's disease.

PubMed

Peng, Bo; Wang, Suhong; Zhou, Zhiyong; Liu, Yan; Tong, Baotong; Zhang, Tao; Dai, Yakang

2017-06-09

Machine learning methods have been widely used in recent years for detection of neuroimaging biomarkers in regions of interest (ROIs) and assisting diagnosis of neurodegenerative diseases. The innovation of this study is to use multilevel-ROI-features-based machine learning method to detect sensitive morphometric biomarkers in Parkinson's disease (PD). Specifically, the low-level ROI features (gray matter volume, cortical thickness, etc.) and high-level correlative features (connectivity between ROIs) are integrated to construct the multilevel ROI features. Filter- and wrapper- based feature selection method and multi-kernel support vector machine (SVM) are used in the classification algorithm. T1-weighted brain magnetic resonance (MR) images of 69 PD patients and 103 normal controls from the Parkinson's Progression Markers Initiative (PPMI) dataset are included in the study. The machine learning method performs well in classification between PD patients and normal controls with an accuracy of 85.78%, a specificity of 87.79%, and a sensitivity of 87.64%. The most sensitive biomarkers between PD patients and normal controls are mainly distributed in frontal lobe, parental lobe, limbic lobe, temporal lobe, and central region. The classification performance of our method with multilevel ROI features is significantly improved comparing with other classification methods using single-level features. The proposed method shows promising identification ability for detecting morphometric biomarkers in PD, thus confirming the potentiality of our method in assisting diagnosis of the disease. Copyright © 2017 Elsevier B.V. All rights reserved.

Robust finger vein ROI localization based on flexible segmentation.

PubMed

Lu, Yu; Xie, Shan Juan; Yoon, Sook; Yang, Jucheng; Park, Dong Sun

2013-10-24

Finger veins have been proved to be an effective biometric for personal identification in the recent years. However, finger vein images are easily affected by influences such as image translation, orientation, scale, scattering, finger structure, complicated background, uneven illumination, and collection posture. All these factors may contribute to inaccurate region of interest (ROI) definition, and so degrade the performance of finger vein identification system. To improve this problem, in this paper, we propose a finger vein ROI localization method that has high effectiveness and robustness against the above factors. The proposed method consists of a set of steps to localize ROIs accurately, namely segmentation, orientation correction, and ROI detection. Accurate finger region segmentation and correct calculated orientation can support each other to produce higher accuracy in localizing ROIs. Extensive experiments have been performed on the finger vein image database, MMCBNU_6000, to verify the robustness of the proposed method. The proposed method shows the segmentation accuracy of 100%. Furthermore, the average processing time of the proposed method is 22 ms for an acquired image, which satisfies the criterion of a real-time finger vein identification system.

Robust Finger Vein ROI Localization Based on Flexible Segmentation

PubMed Central

Lu, Yu; Xie, Shan Juan; Yoon, Sook; Yang, Jucheng; Park, Dong Sun

2013-01-01

Finger veins have been proved to be an effective biometric for personal identification in the recent years. However, finger vein images are easily affected by influences such as image translation, orientation, scale, scattering, finger structure, complicated background, uneven illumination, and collection posture. All these factors may contribute to inaccurate region of interest (ROI) definition, and so degrade the performance of finger vein identification system. To improve this problem, in this paper, we propose a finger vein ROI localization method that has high effectiveness and robustness against the above factors. The proposed method consists of a set of steps to localize ROIs accurately, namely segmentation, orientation correction, and ROI detection. Accurate finger region segmentation and correct calculated orientation can support each other to produce higher accuracy in localizing ROIs. Extensive experiments have been performed on the finger vein image database, MMCBNU_6000, to verify the robustness of the proposed method. The proposed method shows the segmentation accuracy of 100%. Furthermore, the average processing time of the proposed method is 22 ms for an acquired image, which satisfies the criterion of a real-time finger vein identification system. PMID:24284769

Population based MRI and DTI templates of the adult ferret brain and tools for voxelwise analysis.

PubMed

Hutchinson, E B; Schwerin, S C; Radomski, K L; Sadeghi, N; Jenkins, J; Komlosh, M E; Irfanoglu, M O; Juliano, S L; Pierpaoli, C

2017-05-15

Non-invasive imaging has the potential to play a crucial role in the characterization and translation of experimental animal models to investigate human brain development and disorders, especially when employed to study animal models that more accurately represent features of human neuroanatomy. The purpose of this study was to build and make available MRI and DTI templates and analysis tools for the ferret brain as the ferret is a well-suited species for pre-clinical MRI studies with folded cortical surface, relatively high white matter volume and body dimensions that allow imaging with pre-clinical MRI scanners. Four ferret brain templates were built in this study - in-vivo MRI and DTI and ex-vivo MRI and DTI - using brain images across many ferrets and region of interest (ROI) masks corresponding to established ferret neuroanatomy were generated by semi-automatic and manual segmentation. The templates and ROI masks were used to create a web-based ferret brain viewing software for browsing the MRI and DTI volumes with annotations based on the ROI masks. A second objective of this study was to provide a careful description of the imaging methods used for acquisition, processing, registration and template building and to demonstrate several voxelwise analysis methods including Jacobian analysis of morphometry differences between the female and male brain and bias-free identification of DTI abnormalities in an injured ferret brain. The templates, tools and methodological optimization presented in this study are intended to advance non-invasive imaging approaches for human-similar animal species that will enable the use of pre-clinical MRI studies for understanding and treating brain disorders. Published by Elsevier Inc.

Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform

PubMed Central

2012-01-01

Background Digital mammography is the most reliable imaging modality for breast carcinoma diagnosis and breast micro-calcifications is regarded as one of the most important signs on imaging diagnosis. In this paper, a computer-aided diagnosis (CAD) system is presented for breast micro-calcifications based on dual-tree complex wavelet transform (DT-CWT) to facilitate radiologists like double reading. Methods Firstly, 25 abnormal ROIs were extracted according to the center and diameter of the lesions manually and 25 normal ROIs were selected randomly. Then micro-calcifications were segmented by combining space and frequency domain techniques. We extracted three texture features based on wavelet (Haar, DB4, DT-CWT) transform. Totally 14 descriptors were introduced to define the characteristics of the suspicious micro-calcifications. Principal Component Analysis (PCA) was used to transform these descriptors to a compact and efficient vector expression. Support Vector Machine (SVM) classifier was used to classify potential micro-calcifications. Finally, we used the receiver operating characteristic (ROC) curve and free-response operating characteristic (FROC) curve to evaluate the performance of the CAD system. Results The results of SVM classifications based on different wavelets shows DT-CWT has a better performance. Compared with other results, DT-CWT method achieved an accuracy of 96% and 100% for the classification of normal and abnormal ROIs, and the classification of benign and malignant micro-calcifications respectively. In FROC analysis, our CAD system for clinical dataset detection achieved a sensitivity of 83.5% at a false positive per image of 1.85. Conclusions Compared with general wavelets, DT-CWT could describe the features more effectively, and our CAD system had a competitive performance. PMID:23253202

[A computer tomography assisted method for the automatic detection of region of interest in dynamic kidney images].

PubMed

Jing, Xueping; Zheng, Xiujuan; Song, Shaoli; Liu, Kai

2017-12-01

Glomerular filtration rate (GFR), which can be estimated by Gates method with dynamic kidney single photon emission computed tomography (SPECT) imaging, is a key indicator of renal function. In this paper, an automatic computer tomography (CT)-assisted detection method of kidney region of interest (ROI) is proposed to achieve the objective and accurate GFR calculation. In this method, the CT coronal projection image and the enhanced SPECT synthetic image are firstly generated and registered together. Then, the kidney ROIs are delineated using a modified level set algorithm. Meanwhile, the background ROIs are also obtained based on the kidney ROIs. Finally, the value of GFR is calculated via Gates method. Comparing with the clinical data, the GFR values estimated by the proposed method were consistent with the clinical reports. This automatic method can improve the accuracy and stability of kidney ROI detection for GFR calculation, especially when the kidney function has been severely damaged.

Deformable planning CT to cone-beam CT image registration in head-and-neck cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hou Jidong; Guerrero, Mariana; Chen, Wenjuan

2011-04-15

Purpose: The purpose of this work was to implement and validate a deformable CT to cone-beam computed tomography (CBCT) image registration method in head-and-neck cancer to eventually facilitate automatic target delineation on CBCT. Methods: Twelve head-and-neck cancer patients underwent a planning CT and weekly CBCT during the 5-7 week treatment period. The 12 planning CT images (moving images) of these patients were registered to their weekly CBCT images (fixed images) via the symmetric force Demons algorithm and using a multiresolution scheme. Histogram matching was used to compensate for the intensity difference between the two types of images. Using nine knownmore » anatomic points as registration targets, the accuracy of the registration was evaluated using the target registration error (TRE). In addition, region-of-interest (ROI) contours drawn on the planning CT were morphed to the CBCT images and the volume overlap index (VOI) between registered contours and manually delineated contours was evaluated. Results: The mean TRE value of the nine target points was less than 3.0 mm, the slice thickness of the planning CT. Of the 369 target points evaluated for registration accuracy, the average TRE value was 2.6{+-}0.6 mm. The mean TRE for bony tissue targets was 2.4{+-}0.2 mm, while the mean TRE for soft tissue targets was 2.8{+-}0.2 mm. The average VOI between the registered and manually delineated ROI contours was 76.2{+-}4.6%, which is consistent with that reported in previous studies. Conclusions: The authors have implemented and validated a deformable image registration method to register planning CT images to weekly CBCT images in head-and-neck cancer cases. The accuracy of the TRE values suggests that they can be used as a promising tool for automatic target delineation on CBCT.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Besemer, A; Marsh, I; Bednarz, B

Purpose: The calculation of 3D internal dose calculations in targeted radionuclide therapy requires the acquisition and temporal coregistration of a serial PET/CT or SPECT/CT images. This work investigates the dosimetric impact of different temporal coregistration methods commonly used for 3D internal dosimetry. Methods: PET/CT images of four mice were acquired at 1, 24, 48, 72, 96, 144 hrs post-injection of {sup 124}I-CLR1404. The therapeutic {sup 131}I-CLR1404 absorbed dose rate (ADR) was calculated at each time point using a Geant4-based MC dosimetry platform using three temporal image coregistration Methods: (1) no coregistration (NC), whole body sequential CT-CT affine coregistration (WBAC), andmore » individual sequential ROI-ROI affine coregistration (IRAC). For NC, only the ROI mean ADR was integrated to obtain ROI mean doses. For WBAC, the CT at each time point was coregistered to a single reference CT. The CT transformations were applied to the corresponding ADR images and the dose was calculated on a voxel-basis within the whole CT volume. For IRAC, each individual ROI was isolated and sequentially coregistered to a single reference ROI. The ROI transformations were applied to the corresponding ADR images and the dose was calculated on a voxel-basis within the ROI volumes. Results: The percent differences in the ROI mean doses were as large as 109%, 88%, and 32%, comparing the WBAC vs. IRAC, NC vs. IRAC, and NC vs. WBAC methods, respectively. The CoV in the mean dose between the all three methods ranged from 2–36%. The pronounced curvature of the spinal cord was not adequately coregistered using WBAC which resulted in large difference between the WBAC and IRAC. Conclusion: The method used for temporal image coregistration can result in large differences in 3D internal dosimetry calculations. Care must be taken to choose the most appropriate method depending on the imaging conditions, clinical site, and specific application. This work is partially funded by NIH Grant R21 CA198392-01.« less

Performance of shear-wave elastography for breast masses using different region-of-interest (ROI) settings.

PubMed

Youk, Ji Hyun; Son, Eun Ju; Han, Kyunghwa; Gweon, Hye Mi; Kim, Jeong-Ah

2018-07-01

Background Various size and shape of region of interest (ROI) can be applied for shear-wave elastography (SWE). Purpose To investigate the diagnostic performance of SWE according to ROI settings for breast masses. Material and Methods To measure elasticity for 142 lesions, ROIs were set as follows: circular ROIs 1 mm (ROI-1), 2 mm (ROI-2), and 3 mm (ROI-3) in diameter placed over the stiffest part of the mass; freehand ROIs drawn by tracing the border of mass (ROI-M) and the area of peritumoral increased stiffness (ROI-MR); and circular ROIs placed within the mass (ROI-C) and to encompass the area of peritumoral increased stiffness (ROI-CR). Mean (E mean ), maximum (E max ), and standard deviation (E SD ) of elasticity values and their areas under the receiver operating characteristic (ROC) curve (AUCs) for diagnostic performance were compared. Results Means of E mean and E SD significantly differed between ROI-1, ROI-2, and ROI-3 ( P < 0.0001), whereas means of E max did not ( P = 0.50). For E SD , ROI-1 (0.874) showed a lower AUC than ROI-2 (0.964) and ROI-3 (0.975) ( P < 0.002). The mean E SD was significantly different between ROI-M and ROI-MR and between ROI-C and ROI-CR ( P < 0.0001). The AUCs of E SD in ROI-M and ROI-C were significantly lower than in ROI-MR ( P = 0.041 and 0.015) and ROI-CR ( P = 0.007 and 0.004). Conclusion Shear-wave elasticity values and their diagnostic performance vary based on ROI settings and elasticity indices. E max is recommended for the ROIs over the stiffest part of mass and an ROI encompassing the peritumoral area of increased stiffness is recommended for elastic heterogeneity of mass.

Examining Brain Morphometry Associated with Self-Esteem in Young Adults Using Multilevel-ROI-Features-Based Classification Method

PubMed Central

Peng, Bo; Lu, Jieru; Saxena, Aditya; Zhou, Zhiyong; Zhang, Tao; Wang, Suhong; Dai, Yakang

2017-01-01

Purpose: This study is to exam self-esteem related brain morphometry on brain magnetic resonance (MR) images using multilevel-features-based classification method. Method: The multilevel region of interest (ROI) features consist of two types of features: (i) ROI features, which include gray matter volume, white matter volume, cerebrospinal fluid volume, cortical thickness, and cortical surface area, and (ii) similarity features, which are based on similarity calculation of cortical thickness between ROIs. For each feature type, a hybrid feature selection method, comprising of filter-based and wrapper-based algorithms, is used to select the most discriminating features. ROI features and similarity features are integrated by using multi-kernel support vector machines (SVMs) with appropriate weighting factor. Results: The classification performance is improved by using multilevel ROI features with an accuracy of 96.66%, a specificity of 96.62%, and a sensitivity of 95.67%. The most discriminating ROI features that are related to self-esteem spread over occipital lobe, frontal lobe, parietal lobe, limbic lobe, temporal lobe, and central region, mainly involving white matter and cortical thickness. The most discriminating similarity features are distributed in both the right and left hemisphere, including frontal lobe, occipital lobe, limbic lobe, parietal lobe, and central region, which conveys information of structural connections between different brain regions. Conclusion: By using ROI features and similarity features to exam self-esteem related brain morphometry, this paper provides a pilot evidence that self-esteem is linked to specific ROIs and structural connections between different brain regions. PMID:28588470

Examining Brain Morphometry Associated with Self-Esteem in Young Adults Using Multilevel-ROI-Features-Based Classification Method.

PubMed

Peng, Bo; Lu, Jieru; Saxena, Aditya; Zhou, Zhiyong; Zhang, Tao; Wang, Suhong; Dai, Yakang

2017-01-01

Purpose: This study is to exam self-esteem related brain morphometry on brain magnetic resonance (MR) images using multilevel-features-based classification method. Method: The multilevel region of interest (ROI) features consist of two types of features: (i) ROI features, which include gray matter volume, white matter volume, cerebrospinal fluid volume, cortical thickness, and cortical surface area, and (ii) similarity features, which are based on similarity calculation of cortical thickness between ROIs. For each feature type, a hybrid feature selection method, comprising of filter-based and wrapper-based algorithms, is used to select the most discriminating features. ROI features and similarity features are integrated by using multi-kernel support vector machines (SVMs) with appropriate weighting factor. Results: The classification performance is improved by using multilevel ROI features with an accuracy of 96.66%, a specificity of 96.62%, and a sensitivity of 95.67%. The most discriminating ROI features that are related to self-esteem spread over occipital lobe, frontal lobe, parietal lobe, limbic lobe, temporal lobe, and central region, mainly involving white matter and cortical thickness. The most discriminating similarity features are distributed in both the right and left hemisphere, including frontal lobe, occipital lobe, limbic lobe, parietal lobe, and central region, which conveys information of structural connections between different brain regions. Conclusion: By using ROI features and similarity features to exam self-esteem related brain morphometry, this paper provides a pilot evidence that self-esteem is linked to specific ROIs and structural connections between different brain regions.

Reference-tissue correction of T2-weighted signal intensity for prostate cancer detection

NASA Astrophysics Data System (ADS)

Peng, Yahui; Jiang, Yulei; Oto, Aytekin

2014-03-01

The purpose of this study was to investigate whether correction with respect to reference tissue of T2-weighted MRimage signal intensity (SI) improves its effectiveness for classification of regions of interest (ROIs) as prostate cancer (PCa) or normal prostatic tissue. Two image datasets collected retrospectively were used in this study: 71 cases acquired with GE scanners (dataset A), and 59 cases acquired with Philips scanners (dataset B). Through a consensus histology- MR correlation review, 175 PCa and 108 normal-tissue ROIs were identified and drawn manually. Reference-tissue ROIs were selected in each case from the levator ani muscle, urinary bladder, and pubic bone. T2-weighted image SI was corrected as the ratio of the average T2-weighted image SI within an ROI to that of a reference-tissue ROI. Area under the receiver operating characteristic curve (AUC) was used to evaluate the effectiveness of T2-weighted image SIs for differentiation of PCa from normal-tissue ROIs. AUC (+/- standard error) for uncorrected T2-weighted image SIs was 0.78+/-0.04 (datasets A) and 0.65+/-0.05 (datasets B). AUC for corrected T2-weighted image SIs with respect to muscle, bladder, and bone reference was 0.77+/-0.04 (p=1.0), 0.77+/-0.04 (p=1.0), and 0.75+/-0.04 (p=0.8), respectively, for dataset A; and 0.81+/-0.04 (p=0.002), 0.78+/-0.04 (p<0.001), and 0.79+/-0.04 (p<0.001), respectively, for dataset B. Correction in reference to the levator ani muscle yielded the most consistent results between GE and Phillips images. Correction of T2-weighted image SI in reference to three types of extra-prostatic tissue can improve its effectiveness for differentiation of PCa from normal-tissue ROIs, and correction in reference to the levator ani muscle produces consistent T2-weighted image SIs between GE and Phillips MR images.

Automatic segmentation of pulmonary fissures in x-ray CT images using anatomic guidance

NASA Astrophysics Data System (ADS)

Ukil, Soumik; Sonka, Milan; Reinhardt, Joseph M.

2006-03-01

The pulmonary lobes are the five distinct anatomic divisions of the human lungs. The physical boundaries between the lobes are called the lobar fissures. Detection of lobar fissure positions in pulmonary X-ray CT images is of increasing interest for the early detection of pathologies, and also for the regional functional analysis of the lungs. We have developed a two-step automatic method for the accurate segmentation of the three pulmonary fissures. In the first step, an approximation of the actual fissure locations is made using a 3-D watershed transform on the distance map of the segmented vasculature. Information from the anatomically labeled human airway tree is used to guide the watershed segmentation. These approximate fissure boundaries are then used to define the region of interest (ROI) for a more exact 3-D graph search to locate the fissures. Within the ROI the fissures are enhanced by computing a ridgeness measure, and this is used as the cost function for the graph search. The fissures are detected as the optimal surface within the graph defined by the cost function, which is computed by transforming the problem to the problem of finding a minimum s-t cut on a derived graph. The accuracy of the lobar borders is assessed by comparing the automatic results to manually traced lobe segments. The mean distance error between manually traced and computer detected left oblique, right oblique and right horizontal fissures is 2.3 +/- 0.8 mm, 2.3 +/- 0.7 mm and 1.0 +/- 0.1 mm, respectively.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shen, Z; Greskovich, J; Xia, P

Purpose: To generate virtual phantoms with clinically relevant deformation and use them to objectively evaluate geometric and dosimetric uncertainties of deformable image registration (DIR) algorithms. Methods: Ten lung cancer patients undergoing adaptive 3DCRT planning were selected. For each patient, a pair of planning CT (pCT) and replanning CT (rCT) were used as the basis for virtual phantom generation. Manually adjusted meshes were created for selected ROIs (e.g. PTV, lungs, spinal cord, esophagus, and heart) on pCT and rCT. The mesh vertices were input into a thin-plate spline algorithm to generate a reference displacement vector field (DVF). The reference DVF wasmore » used to deform pCT to generate a simulated replanning CT (srCT) that was closely matched to rCT. Three DIR algorithms (Demons, B-Spline, and intensity-based) were applied to these ten virtual phantoms. The images, ROIs, and doses were mapped from pCT to srCT using the DVFs computed by these three DIRs and compared to those mapped using the reference DVF. Results: The average Dice coefficients for selected ROIs were from 0.85 to 0.96 for Demons, from 0.86 to 0.97 for intensity-based, and from 0.76 to 0.95 for B-Spline. The average Hausdorff distances for selected ROIs were from 2.2 to 5.4 mm for Demons, from 2.3 to 6.8 mm for intensity-based, and from 2.4 to 11.4 mm for B-Spline. The average absolute dose errors for selected ROIs were from 0.2 to 0.6 Gy for Demons, from 0.1 to 0.5 Gy for intensity-based, and from 0.5 to 1.5 Gy for B-Spline. Conclusion: Virtual phantoms were modeled after patients with lung cancer and were clinically relevant for adaptive radiotherapy treatment replanning. Virtual phantoms with known DVFs serve as references and can provide a fair comparison when evaluating different DIRs. Demons and intensity-based DIRs were shown to have smaller geometric and dosimetric uncertainties than B-Spline. Z Shen: None; K Bzdusek: an employee of Philips Healthcare; J Greskovich: None; P Xia: received research grants from Philips Healthcare and Siemens Healthcare.« less

CT, MR, and ultrasound image artifacts from prostate brachytherapy seed implants: The impact of seed size

DOE Office of Scientific and Technical Information (OSTI.GOV)

Robertson, Andrew K. H.; Basran, Parminder S.; Thomas, Steven D.

Purpose: To investigate the effects of brachytherapy seed size on the quality of x-ray computed tomography (CT), ultrasound (US), and magnetic resonance (MR) images and seed localization through comparison of the 6711 and 9011 {sup 125}I sources. Methods: For CT images, an acrylic phantom mimicking a clinical implantation plan and embedded with low contrast regions of interest (ROIs) was designed for both the 0.774 mm diameter 6711 (standard) and the 0.508 mm diameter 9011 (thin) seed models (Oncura, Inc., and GE Healthcare, Arlington Heights, IL). Image quality metrics were assessed using the standard deviation of ROIs between the seeds andmore » the contrast to noise ratio (CNR) within the low contrast ROIs. For US images, water phantoms with both single and multiseed arrangements were constructed for both seed sizes. For MR images, both seeds were implanted into a porcine gel and imaged with pelvic imaging protocols. The standard deviation of ROIs and CNR values were used as metrics of artifact quantification. Seed localization within the CT images was assessed using the automated seed finder in a commercial brachytherapy treatment planning system. The number of erroneous seed placements and the average and maximum error in seed placements were recorded as metrics of the localization accuracy. Results: With the thin seeds, CT image noise was reduced from 48.5 {+-} 0.2 to 32.0 {+-} 0.2 HU and CNR improved by a median value of 74% when compared with the standard seeds. Ultrasound image noise was measured at 50.3 {+-} 17.1 dB for the thin seed images and 50.0 {+-} 19.8 dB for the standard seed images, and artifacts directly behind the seeds were smaller and less prominent with the thin seed model. For MR images, CNR of the standard seeds reduced on average 17% when using the thin seeds for all different imaging sequences and seed orientations, but these differences are not appreciable. Automated seed localization required an average ({+-}SD) of 7.0 {+-} 3.5 manual corrections in seed positions for the thin seed scans and 3.0 {+-} 1.2 manual corrections in seed positions for the standard seed scans. The average error in seed placement was 1.2 mm for both seed types and the maximum error in seed placement was 2.1 mm for the thin seed scans and 1.8 mm for the standard seed scans. Conclusions: The 9011 thin seeds yielded significantly improved image quality for CT and US images but no significant differences in MR image quality.« less

Data-driven region-of-interest selection without inflating Type I error rate.

PubMed

Brooks, Joseph L; Zoumpoulaki, Alexia; Bowman, Howard

2017-01-01

In ERP and other large multidimensional neuroscience data sets, researchers often select regions of interest (ROIs) for analysis. The method of ROI selection can critically affect the conclusions of a study by causing the researcher to miss effects in the data or to detect spurious effects. In practice, to avoid inflating Type I error rate (i.e., false positives), ROIs are often based on a priori hypotheses or independent information. However, this can be insensitive to experiment-specific variations in effect location (e.g., latency shifts) reducing power to detect effects. Data-driven ROI selection, in contrast, is nonindependent and uses the data under analysis to determine ROI positions. Therefore, it has potential to select ROIs based on experiment-specific information and increase power for detecting effects. However, data-driven methods have been criticized because they can substantially inflate Type I error rate. Here, we demonstrate, using simulations of simple ERP experiments, that data-driven ROI selection can indeed be more powerful than a priori hypotheses or independent information. Furthermore, we show that data-driven ROI selection using the aggregate grand average from trials (AGAT), despite being based on the data at hand, can be safely used for ROI selection under many circumstances. However, when there is a noise difference between conditions, using the AGAT can inflate Type I error and should be avoided. We identify critical assumptions for use of the AGAT and provide a basis for researchers to use, and reviewers to assess, data-driven methods of ROI localization in ERP and other studies. © 2016 Society for Psychophysiological Research.

An ROI multi-resolution compression method for 3D-HEVC

NASA Astrophysics Data System (ADS)

Ti, Chunli; Guan, Yudong; Xu, Guodong; Teng, Yidan; Miao, Xinyuan

2017-09-01

3D High Efficiency Video Coding (3D-HEVC) provides a significant potential on increasing the compression ratio of multi-view RGB-D videos. However, the bit rate still rises dramatically with the improvement of the video resolution, which will bring challenges to the transmission network, especially the mobile network. This paper propose an ROI multi-resolution compression method for 3D-HEVC to better preserve the information in ROI on condition of limited bandwidth. This is realized primarily through ROI extraction and compression multi-resolution preprocessed video as alternative data according to the network conditions. At first, the semantic contours are detected by the modified structured forests to restrain the color textures inside objects. The ROI is then determined utilizing the contour neighborhood along with the face region and foreground area of the scene. Secondly, the RGB-D videos are divided into slices and compressed via 3D-HEVC under different resolutions for selection by the audiences and applications. Afterwards, the reconstructed low-resolution videos from 3D-HEVC encoder are directly up-sampled via Laplace transformation and used to replace the non-ROI areas of the high-resolution videos. Finally, the ROI multi-resolution compressed slices are obtained by compressing the ROI preprocessed videos with 3D-HEVC. The temporal and special details of non-ROI are reduced in the low-resolution videos, so the ROI will be better preserved by the encoder automatically. Experiments indicate that the proposed method can keep the key high-frequency information with subjective significance while the bit rate is reduced.

Semi-automatic 10/20 Identification Method for MRI-Free Probe Placement in Transcranial Brain Mapping Techniques.

PubMed

Xiao, Xiang; Zhu, Hao; Liu, Wei-Jie; Yu, Xiao-Ting; Duan, Lian; Li, Zheng; Zhu, Chao-Zhe

2017-01-01

The International 10/20 system is an important head-surface-based positioning system for transcranial brain mapping techniques, e.g., fNIRS and TMS. As guidance for probe placement, the 10/20 system permits both proper ROI coverage and spatial consistency among multiple subjects and experiments in a MRI-free context. However, the traditional manual approach to the identification of 10/20 landmarks faces problems in reliability and time cost. In this study, we propose a semi-automatic method to address these problems. First, a novel head surface reconstruction algorithm reconstructs head geometry from a set of points uniformly and sparsely sampled on the subject's head. Second, virtual 10/20 landmarks are determined on the reconstructed head surface in computational space. Finally, a visually-guided real-time navigation system guides the experimenter to each of the identified 10/20 landmarks on the physical head of the subject. Compared with the traditional manual approach, our proposed method provides a significant improvement both in reliability and time cost and thus could contribute to improving both the effectiveness and efficiency of 10/20-guided MRI-free probe placement.

Correction for FDG PET dose extravasations: Monte Carlo validation and quantitative evaluation of patient studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Silva-Rodríguez, Jesús, E-mail: jesus.silva.rodriguez@sergas.es; Aguiar, Pablo, E-mail: pablo.aguiar.fernandez@sergas.es; Servicio de Medicina Nuclear, Complexo Hospitalario Universidade de Santiago de Compostela

Purpose: Current procedure guidelines for whole body [18F]fluoro-2-deoxy-D-glucose (FDG)-positron emission tomography (PET) state that studies with visible dose extravasations should be rejected for quantification protocols. Our work is focused on the development and validation of methods for estimating extravasated doses in order to correct standard uptake value (SUV) values for this effect in clinical routine. Methods: One thousand three hundred sixty-seven consecutive whole body FDG-PET studies were visually inspected looking for extravasation cases. Two methods for estimating the extravasated dose were proposed and validated in different scenarios using Monte Carlo simulations. All visible extravasations were retrospectively evaluated using a manualmore » ROI based method. In addition, the 50 patients with higher extravasated doses were also evaluated using a threshold-based method. Results: Simulation studies showed that the proposed methods for estimating extravasated doses allow us to compensate the impact of extravasations on SUV values with an error below 5%. The quantitative evaluation of patient studies revealed that paravenous injection is a relatively frequent effect (18%) with a small fraction of patients presenting considerable extravasations ranging from 1% to a maximum of 22% of the injected dose. A criterion based on the extravasated volume and maximum concentration was established in order to identify this fraction of patients that might be corrected for paravenous injection effect. Conclusions: The authors propose the use of a manual ROI based method for estimating the effectively administered FDG dose and then correct SUV quantification in those patients fulfilling the proposed criterion.« less

SU-F-J-156: The Feasibility of MR-Only IMRT Planning for Prostate Anatomy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vaitheeswaran, R; Sivaramakrishnan, KR; Kumar, Prashant

Purpose: For prostate anatomy, previous investigations have shown that simulated CT (sCT) generated from MR images can be used for accurate dose computation. In this study, we demonstrate the feasibility of MR-only IMRT planning for prostate case. Methods: Regular CT (rCT) and MR images of the same patient were acquired for prostate anatomy. Regions-of-interest (ROIs) i.e. target and risk structures are delineated on the rCT. A simulated CT (sCT) is generated from the MR image using the method described by Schadewaldt N et al. Their work establishes the clinical acceptability of dose calculation results on the sCT when compared tomore » rCT. rCT and sCT are rigidly registered to ensure proper alignment between the two images. rCT and sCT are overlaid on each other and slice-wise visual inspection confirms excellent agreement between the two images. ROIs on the rCT are copied over to sCT. Philips AutoPlanning solution is used for generating treatment plans. The same treatment technique protocol (plan parameters and clinical goals) is used to generate AutoPlan-rCT and AutoPlan-sCT respectively for rCT and and sCT. DVH comparison on ROIs and slice-wise evaluation of dose is performed between AutoPlan-rCT and AutoPlan-sCT. Delivery parameters i.e. beam and corresponding segments from the AutoPlan-sCT are copied over to rCT and dose is computed to get AutoPlan-sCT-on-rCT. Results: Plan evaluation is done based on Dose Volume Histogram (DVH) of ROIs and manual slice-wise inspection of dose distribution. Both AutoPlan-rCT and AutoPlan-sCT provide a clinically acceptable plan. Also, AutoPlan-sCT-on-rCT shows excellent agreement with AutoPlan-sCT. Conclusion: The study demonstrates that it is feasible to do IMRT planning on the simulated CT image obtained from MR image for prostate anatomy. The research is supported by Philips India Ltd.« less

Dual-resolution image reconstruction for region-of-interest CT scan

NASA Astrophysics Data System (ADS)

Jin, S. O.; Shin, K. Y.; Yoo, S. K.; Kim, J. G.; Kim, K. H.; Huh, Y.; Lee, S. Y.; Kwon, O.-K.

2014-07-01

In ordinary CT scan, so called full field-of-view (FFOV) scan, in which the x-ray beam span covers the whole section of the body, a large number of projections are necessary to reconstruct high resolution images. However, excessive x-ray dose is a great concern in FFOV scan. Region-of-interest (ROI) scan is a method to visualize the ROI in high resolution while reducing the x-ray dose. But, ROI scan suffers from bright-band artifacts which may hamper CT-number accuracy. In this study, we propose an image reconstruction method to eliminate the band artifacts in the ROI scan. In addition to the ROI scan with high sampling rate in the view direction, we get FFOV projection data with much lower sampling rate. Then, we reconstruct images in the compressed sensing (CS) framework with dual resolutions, that is, high resolution in the ROI and low resolution outside the ROI. For the dual-resolution image reconstruction, we implemented the dual-CS reconstruction algorithm in which data fidelity and total variation (TV) terms were enforced twice in the framework of adaptive steepest descent projection onto convex sets (ASD-POCS). The proposed method has remarkably reduced the bright-band artifacts at around the ROI boundary, and it has also effectively suppressed the streak artifacts over the entire image. We expect the proposed method can be greatly used for dual-resolution imaging with reducing the radiation dose, artifacts and scan time.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ranganathan, V; Kumar, P; Bzdusek, K

Purpose: We propose a novel data-driven method to predict the achievability of clinical objectives upfront before invoking the IMRT optimization. Methods: A new metric called “Geometric Complexity (GC)” is used to estimate the achievability of clinical objectives. Here, GC is the measure of the number of “unmodulated” beamlets or rays that intersect the Region-of-interest (ROI) and the target volume. We first compute the geometric complexity ratio (GCratio) between the GC of a ROI (say, parotid) in a reference plan and the GC of the same ROI in a given plan. The GCratio of a ROI indicates the relative geometric complexitymore » of the ROI as compared to the same ROI in the reference plan. Hence GCratio can be used to predict if a defined clinical objective associated with the ROI can be met by the optimizer for a given case. Basically a higher GCratio indicates a lesser likelihood for the optimizer to achieve the clinical objective defined for a given ROI. Similarly, a lower GCratio indicates a higher likelihood for the optimizer to achieve the clinical objective defined for the given ROI. We have evaluated the proposed method on four Head and Neck cases using Pinnacle3 (version 9.10.0) Treatment Planning System (TPS). Results: Out of the total of 28 clinical objectives from four head and neck cases included in the study, 25 were in agreement with the prediction, which implies an agreement of about 85% between predicted and obtained results. The Pearson correlation test shows a positive correlation between predicted and obtained results (Correlation = 0.82, r2 = 0.64, p < 0.005). Conclusion: The study demonstrates the feasibility of the proposed method in head and neck cases for predicting the achievability of clinical objectives with reasonable accuracy.« less

Assessing the skeletal age from a hand radiograph: automating the Tanner-Whitehouse method

NASA Astrophysics Data System (ADS)

Niemeijer, Meindert; van Ginneken, Bram; Maas, Casper A.; Beek, Frederik J. A.; Viergever, Max A.

2003-05-01

The skeletal maturity of children is usually assessed from a standard radiograph of the left hand and wrist. An established clinical method to determine the skeletal maturity is the Tanner-Whitehouse (TW2) method. This method divides the skeletal development into several stages (labelled A, B, ...,I). We are developing an automated system based on this method. In this work we focus on assigning a stage to one region of interest (ROI), the middle phalanx of the third finger. We classify each ROI as follows. A number of ROIs which have been assigned a certain stage by a radiologist are used to construct a mean image for that stage. For a new input ROI, landmarks are detected by using an Active Shape Model. These are used to align the mean images with the input image. Subsequently the correlation between each transformed mean stage image and the input is calculated. The input ROI can be assigned to the stage with the highest correlation directly, or the values can be used as features in a classifier. The method was tested on 71 cases ranging from stage E to I. The ROI was staged correctly in 73.2% of all cases and in 97.2% of all incorrectly staged cases the error was not more than one stage.

Voxel-based morphometry and automated lobar volumetry: The trade-off between spatial scale and statistical correction

PubMed Central

Voormolen, Eduard H.J.; Wei, Corie; Chow, Eva W.C.; Bassett, Anne S.; Mikulis, David J.; Crawley, Adrian P.

2011-01-01

Voxel-based morphometry (VBM) and automated lobar region of interest (ROI) volumetry are comprehensive and fast methods to detect differences in overall brain anatomy on magnetic resonance images. However, VBM and automated lobar ROI volumetry have detected dissimilar gray matter differences within identical image sets in our own experience and in previous reports. To gain more insight into how diverging results arise and to attempt to establish whether one method is superior to the other, we investigated how differences in spatial scale and in the need to statistically correct for multiple spatial comparisons influence the relative sensitivity of either technique to group differences in gray matter volumes. We assessed the performance of both techniques on a small dataset containing simulated gray matter deficits and additionally on a dataset of 22q11-deletion syndrome patients with schizophrenia (22q11DS-SZ) vs. matched controls. VBM was more sensitive to simulated focal deficits compared to automated ROI volumetry, and could detect global cortical deficits equally well. Moreover, theoretical calculations of VBM and ROI detection sensitivities to focal deficits showed that at increasing ROI size, ROI volumetry suffers more from loss in sensitivity than VBM. Furthermore, VBM and automated ROI found corresponding GM deficits in 22q11DS-SZ patients, except in the parietal lobe. Here, automated lobar ROI volumetry found a significant deficit only after a smaller subregion of interest was employed. Thus, sensitivity to focal differences is impaired relatively more by averaging over larger volumes in automated ROI methods than by the correction for multiple comparisons in VBM. These findings indicate that VBM is to be preferred over automated lobar-scale ROI volumetry for assessing gray matter volume differences between groups. PMID:19619660

Apparent diffusion coefficient measurement in glioma: Influence of region-of-interest determination methods on apparent diffusion coefficient values, interobserver variability, time efficiency, and diagnostic ability.

PubMed

Han, Xu; Suo, Shiteng; Sun, Yawen; Zu, Jinyan; Qu, Jianxun; Zhou, Yan; Chen, Zengai; Xu, Jianrong

2017-03-01

To compare four methods of region-of-interest (ROI) placement for apparent diffusion coefficient (ADC) measurements in distinguishing low-grade gliomas (LGGs) from high-grade gliomas (HGGs). Two independent readers measured ADC parameters using four ROI methods (single-slice [single-round, five-round and freehand] and whole-volume) on 43 patients (20 LGGs, 23 HGGs) who had undergone 3.0 Tesla diffusion-weighted imaging and time required for each method of ADC measurements was recorded. Intraclass correlation coefficients (ICCs) were used to assess interobserver variability of ADC measurements. Mean and minimum ADC values and time required were compared using paired Student's t-tests. All ADC parameters (mean/minimum ADC values of three single-slice methods, mean/minimum/standard deviation/skewness/kurtosis/the10 th and 25 th percentiles/median/maximum of whole-volume method) were correlated with tumor grade (low versus high) by unpaired Student's t-tests. Discriminative ability was determined by receiver operating characteristic curves. All ADC measurements except minimum, skewness, and kurtosis of whole-volume ROI differed significantly between LGGs and HGGs (all P < 0.05). Mean ADC value of single-round ROI had the highest effect size (0.72) and the greatest areas under the curve (0.872). Three single-slice methods had good to excellent ICCs (0.67-0.89) and the whole-volume method fair to excellent ICCs (0.32-0.96). Minimum ADC values differed significantly between whole-volume and single-round ROI (P = 0.003) and, between whole-volume and five-round ROI (P = 0.001). The whole-volume method took significantly longer than all single-slice methods (all P < 0.001). ADC measurements are influenced by ROI determination methods. Whole-volume histogram analysis did not yield better results than single-slice methods and took longer. Mean ADC value derived from single-round ROI is the most optimal parameter for differentiating LGGs from HGGs. 3 J. Magn. Reson. Imaging 2017;45:722-730. © 2016 International Society for Magnetic Resonance in Medicine.

SU-C-202-02: A Comprehensive Evaluation of Adaptive Daily Planning for Cervical Cancer HDR Brachytherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meerschaert, R; Paul, A; Zhuang, L

Purpose: To evaluate adaptive daily planning for cervical cancer patients who underwent high-dose-rate intra-cavitary brachytherapy (HDR-ICBT). Methods: This study included 22 cervical cancer patients who underwent 5 fractions of HDR ICBT. Regions of interest (ROIs) including high-risk clinical tumor volume (HR-CTV) and organs-at-risk (OARs) were manually contoured on daily CT images. All patients were treated with adaptive daily plans, which involved ROI delineation and dose optimization at each treatment fraction. Single treatment plans were retrospectively generated by applying the first treatment fraction’s dwell times adjusted for decay and dwell positions of the applicator to subsequent treatment fractions. Various existing similaritymore » metrics were calculated for the ROIs to quantify interfractional organ variations. A novel similarity score (JRARM) was established, which combined both volumetric overlap metrics (DSC, JSC, and RVD) and distance metrics (ASD, MSD, and RMSD). Linear regression was performed to determine a relationship between inter-fractional organ variations of various similarity metrics and D2cc variations from both plans. Wilcoxon Signed Rank Tests were used to assess adaptive daily plans and single plans by comparing EQD2 D2cc (α/β=3) for OARs. Results: For inter-fractional organ variations, the sigmoid demonstrated the greatest variations based on the JRARM and DSC similarity metrics. Comparisons between paired ROIs showed differences in JRARM scores and DSCs at each treatment fraction. RVD, MSD, and RMSD were found to be significantly correlated to D2cc variations for bladder and sigmoid. The comparison between plans found that adaptive daily planning provided lower EQD2 D2cc of OARs than single planning, specifically for the sigmoid (p=0.015). Conclusion: Substantial inter-fractional organ motion can occur during HDR-BT, which may significantly affect D2cc of OARs. Adaptive daily planning provides improved dose sparing for OARs compared to single planning.« less

Simplifying volumes-of-interest (VOIs) definition in quantitative SPECT: Beyond manual definition of 3D whole-organ VOIs.

PubMed

Vicente, Esther M; Lodge, Martin A; Rowe, Steven P; Wahl, Richard L; Frey, Eric C

2017-05-01

We investigated the feasibility of using simpler methods than manual whole-organ volume-of-interest (VOI) definition to estimate the organ activity concentration in single photon emission computed tomography (SPECT) in cases where the activity in the organ can be assumed to be uniformly distributed on the scale of the voxel size. In particular, we investigated an anatomic region-of-interest (ROI) defined in a single transaxial slice, and a single sphere placed inside the organ boundaries. The evaluation was carried out using Monte Carlo simulations based on patient indium 111 In pentetreotide SPECT and computed tomography (CT) images. We modeled constant activity concentrations in each organ, validating this assumption by comparing the distribution of voxel values inside the organ VOIs of the simulated data with the patient data. We simulated projection data corresponding to 100, 50, and 25% of the clinical count level to study the effects of noise level due to shortened acquisition time. Images were reconstructed using a previously validated quantitative SPECT reconstruction method. The evaluation was performed in terms of the accuracy and precision of the activity concentration estimates. The results demonstrated that the non-uniform image intensity observed in the reconstructed images in the organs with normal uptake was consistent with uniform activity concentration in the organs on the scale of the voxel size; observed non-uniformities in image intensity were due to a combination of partial-volume effects at the boundaries of the organ, artifacts in the reconstructed image due to collimator-detector response compensation, and noise. Using an ROI defined in a single transaxial slice produced similar biases compared to the three-dimensional (3D) whole-organ VOIs, provided that the transaxial slice was near the central plane of the organ and that the pixels from the organ boundaries were not included in the ROI. Although this slice method was sensitive to noise, biases were less than 10% for all the noise levels studied. The use of spherical VOIs was more sensitive to noise. The method was more accurate for larger spheres and larger organs such as the liver in comparison to the kidneys. Biases lower than 7% were found in the liver when using large enough spheres (radius ≥ 28 mm), regardless of the position, of the VOI inside the organ even with shortened acquisition times. The biases were more position-dependent for smaller organs. Both of the simpler methods provided suitable surrogates in terms of accuracy and precision. The results suggested that a spherical VOI was more appropriate for estimating the activity concentration in larger organs such as the liver, regardless of the position of the sphere inside the organ. Larger spheres resulted in better estimates. A single-slice ROI was more suitable for activity estimation in smaller organs such as the kidneys, providing that the transaxial slice selected was near the central plane of the organ and that voxels from the organ boundaries were excluded. Under those conditions, activity concentrations with biases lower than 5% were observed for all the studied count levels and coefficients of variation were less than 9% and 5% for the 25% and 100% count levels, respectively. © 2017 American Association of Physicists in Medicine.

Influence of region-of-interest designs on quantitative measurement of multimodal imaging of MR non-enhancing gliomas.

PubMed

Takano, Koji; Kinoshita, Manabu; Arita, Hideyuki; Okita, Yoshiko; Chiba, Yasuyoshi; Kagawa, Naoki; Watanabe, Yoshiyuki; Shimosegawa, Eku; Hatazawa, Jun; Hashimoto, Naoya; Fujimoto, Yasunori; Kishima, Haruhiko

2018-05-01

A number of studies have revealed the usefulness of multimodal imaging in gliomas. Although the results have been heavily affected by the method used for region of interest (ROI) design, the most discriminatory method for setting the ROI remains unclear. The aim of the present study was to determine the most suitable ROI design for 18 F-fluorodeoxyglucose (FDG) and 11 C-methionine (MET) positron emission tomography (PET), apparent diffusion coefficient (ADC), and fractional anisotropy (FA) obtained by diffusion tensor imaging (DTI) from the viewpoint of grades of non-enhancing gliomas. A total of 31 consecutive patients with newly diagnosed, histologically confirmed magnetic resonance (MR) non-enhancing gliomas who underwent FDG-PET, MET-PET and DTI were retrospectively investigated. Quantitative measurements were performed using four different ROIs; hotspot/tumor center and whole tumor, constructed in either two-dimensional (2D) or three-dimensional (3D). Histopathological grading of the tumor was considered as empirical truth and the quantitative measurements obtained from each ROI was correlated with the grade of the tumor. The most discriminating ROI for non-enhancing glioma grading was different according to the different imaging modalities. 2D-hotspot/center ROI was most discriminating for FDG-PET (P=0.087), ADC map (P=0.0083), and FA map (P=0.25), whereas 3D-whole tumor ROI was best for MET-PET (P=0.0050). In the majority of scenarios, 2D-ROIs performed better than 3D-ROIs. Results from the image analysis using FDG-PET, MET-PET, ADC and FA may be affected by ROI design and the most discriminating ROI for non-enhancing glioma grading was different according to the imaging modality.

Sliding Window-Based Region of Interest Extraction for Finger Vein Images

PubMed Central

Yang, Lu; Yang, Gongping; Yin, Yilong; Xiao, Rongyang

2013-01-01

Region of Interest (ROI) extraction is a crucial step in an automatic finger vein recognition system. The aim of ROI extraction is to decide which part of the image is suitable for finger vein feature extraction. This paper proposes a finger vein ROI extraction method which is robust to finger displacement and rotation. First, we determine the middle line of the finger, which will be used to correct the image skew. Then, a sliding window is used to detect the phalangeal joints and further to ascertain the height of ROI. Last, for the corrective image with certain height, we will obtain the ROI by using the internal tangents of finger edges as the left and right boundary. The experimental results show that the proposed method can extract ROI more accurately and effectively compared with other methods, and thus improve the performance of finger vein identification system. Besides, to acquire the high quality finger vein image during the capture process, we propose eight criteria for finger vein capture from different aspects and these criteria should be helpful to some extent for finger vein capture. PMID:23507824

Sulcal depth-based cortical shape analysis in normal healthy control and schizophrenia groups

NASA Astrophysics Data System (ADS)

Lyu, Ilwoo; Kang, Hakmook; Woodward, Neil D.; Landman, Bennett A.

2018-03-01

Sulcal depth is an important marker of brain anatomy in neuroscience/neurological function. Previously, sulcal depth has been explored at the region-of-interest (ROI) level to increase statistical sensitivity to group differences. In this paper, we present a fully automated method that enables inferences of ROI properties from a sulcal region- focused perspective consisting of two main components: 1) sulcal depth computation and 2) sulcal curve-based refined ROIs. In conventional statistical analysis, the average sulcal depth measurements are employed in several ROIs of the cortical surface. However, taking the average sulcal depth over the full ROI blurs overall sulcal depth measurements which may result in reduced sensitivity to detect sulcal depth changes in neurological and psychiatric disorders. To overcome such a blurring effect, we focus on sulcal fundic regions in each ROI by filtering out other gyral regions. Consequently, the proposed method results in more sensitive to group differences than a traditional ROI approach. In the experiment, we focused on a cortical morphological analysis to sulcal depth reduction in schizophrenia with a comparison to the normal healthy control group. We show that the proposed method is more sensitivity to abnormalities of sulcal depth in schizophrenia; sulcal depth is significantly smaller in most cortical lobes in schizophrenia compared to healthy controls (p < 0.05).

Breast tumour visualization using 3D quantitative ultrasound methods

NASA Astrophysics Data System (ADS)

Gangeh, Mehrdad J.; Raheem, Abdul; Tadayyon, Hadi; Liu, Simon; Hadizad, Farnoosh; Czarnota, Gregory J.

2016-04-01

Breast cancer is one of the most common cancer types accounting for 29% of all cancer cases. Early detection and treatment has a crucial impact on improving the survival of affected patients. Ultrasound (US) is non-ionizing, portable, inexpensive, and real-time imaging modality for screening and quantifying breast cancer. Due to these attractive attributes, the last decade has witnessed many studies on using quantitative ultrasound (QUS) methods in tissue characterization. However, these studies have mainly been limited to 2-D QUS methods using hand-held US (HHUS) scanners. With the availability of automated breast ultrasound (ABUS) technology, this study is the first to develop 3-D QUS methods for the ABUS visualization of breast tumours. Using an ABUS system, unlike the manual 2-D HHUS device, the whole patient's breast was scanned in an automated manner. The acquired frames were subsequently examined and a region of interest (ROI) was selected in each frame where tumour was identified. Standard 2-D QUS methods were used to compute spectral and backscatter coefficient (BSC) parametric maps on the selected ROIs. Next, the computed 2-D parameters were mapped to a Cartesian 3-D space, interpolated, and rendered to provide a transparent color-coded visualization of the entire breast tumour. Such 3-D visualization can potentially be used for further analysis of the breast tumours in terms of their size and extension. Moreover, the 3-D volumetric scans can be used for tissue characterization and the categorization of breast tumours as benign or malignant by quantifying the computed parametric maps over the whole tumour volume.

Automated PET-only quantification of amyloid deposition with adaptive template and empirically pre-defined ROI

NASA Astrophysics Data System (ADS)

Akamatsu, G.; Ikari, Y.; Ohnishi, A.; Nishida, H.; Aita, K.; Sasaki, M.; Yamamoto, Y.; Sasaki, M.; Senda, M.

2016-08-01

Amyloid PET is useful for early and/or differential diagnosis of Alzheimer’s disease (AD). Quantification of amyloid deposition using PET has been employed to improve diagnosis and to monitor AD therapy, particularly in research. Although MRI is often used for segmentation of gray matter and for spatial normalization into standard Montreal Neurological Institute (MNI) space where region-of-interest (ROI) template is defined, 3D MRI is not always available in clinical practice. The purpose of this study was to examine the feasibility of PET-only amyloid quantification with an adaptive template and a pre-defined standard ROI template that has been empirically generated from typical cases. A total of 68 subjects who underwent brain 11C-PiB PET were examined. The 11C-PiB images were non-linearly spatially normalized to the standard MNI T1 atlas using the same transformation parameters of MRI-based normalization. The automatic-anatomical-labeling-ROI (AAL-ROI) template was applied to the PET images. All voxel values were normalized by the mean value of cerebellar cortex to generate the SUVR-scaled images. Eleven typical positive images and eight typical negative images were normalized and averaged, respectively, and were used as the positive and negative template. Positive and negative masks which consist of voxels with SUVR  ⩾1.7 were extracted from both templates. Empirical PiB-prone ROI (EPP-ROI) was generated by subtracting the negative mask from the positive mask. The 11C-PiB image of each subject was non-rigidly normalized to the positive and negative template, respectively, and the one with higher cross-correlation was adopted. The EPP-ROI was then inversely transformed to individual PET images. We evaluated differences of SUVR between standard MRI-based method and PET-only method. We additionally evaluated whether the PET-only method would correctly categorize 11C-PiB scans as positive or negative. Significant correlation was observed between the SUVRs obtained with AAL-ROI and those with EPP-ROI when MRI-based normalization was used, the latter providing higher SUVR. When EPP-ROI was used, MRI-based method and PET-only method provided almost identical SUVR. All 11C-PiB scans were correctly categorized into positive and negative using a cutoff value of 1.7 as compared to visual interpretation. The 11C-PiB SUVR were 2.30  ±  0.24 and 1.25  ±  0.11 for the positive and negative images. PET-only amyloid quantification method with adaptive templates and EPP-ROI can provide accurate, robust and simple amyloid quantification without MRI.

The Relative Value of Skills, Knowledge, and Teaching Methods in Explaining Master of Business Administration (MBA) Program Return on Investment

ERIC Educational Resources Information Center

van Auken, Stuart; Wells, Ludmilla Gricenko; Chrysler, Earl

2005-01-01

In this article, the authors provide insight into alumni perceptions of Master of Business Administration (MBA) program return on investment (ROI). They sought to assess the relative value of skills, knowledge, and teaching methods in explaining ROI. By developing insight into the drivers of ROI, the real utility of MBA program ingredients can be…

A new background distribution-based active contour model for three-dimensional lesion segmentation in breast DCE-MRI

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Hui; Liu, Yiping; Qiu, Tianshuang

2014-08-15

Purpose: To develop and evaluate a computerized semiautomatic segmentation method for accurate extraction of three-dimensional lesions from dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) of the breast. Methods: The authors propose a new background distribution-based active contour model using level set (BDACMLS) to segment lesions in breast DCE-MRIs. The method starts with manual selection of a region of interest (ROI) that contains the entire lesion in a single slice where the lesion is enhanced. Then the lesion volume from the volume data of interest, which is captured automatically, is separated. The core idea of BDACMLS is a new signed pressure functionmore » which is based solely on the intensity distribution combined with pathophysiological basis. To compare the algorithm results, two experienced radiologists delineated all lesions jointly to obtain the ground truth. In addition, results generated by other different methods based on level set (LS) are also compared with the authors’ method. Finally, the performance of the proposed method is evaluated by several region-based metrics such as the overlap ratio. Results: Forty-two studies with 46 lesions that contain 29 benign and 17 malignant lesions are evaluated. The dataset includes various typical pathologies of the breast such as invasive ductal carcinoma, ductal carcinomain situ, scar carcinoma, phyllodes tumor, breast cysts, fibroadenoma, etc. The overlap ratio for BDACMLS with respect to manual segmentation is 79.55% ± 12.60% (mean ± s.d.). Conclusions: A new active contour model method has been developed and shown to successfully segment breast DCE-MRI three-dimensional lesions. The results from this model correspond more closely to manual segmentation, solve the weak-edge-passed problem, and improve the robustness in segmenting different lesions.« less

Validation of a rapid, semiautomatic image analysis tool for measurement of gastric accommodation and emptying by magnetic resonance imaging

PubMed Central

Dixit, Sudeepa; Fox, Mark; Pal, Anupam

2014-01-01

Magnetic resonance imaging (MRI) has advantages for the assessment of gastrointestinal structures and functions; however, processing MRI data is time consuming and this has limited uptake to a few specialist centers. This study introduces a semiautomatic image processing system for rapid analysis of gastrointestinal MRI. For assessment of simpler regions of interest (ROI) such as the stomach, the system generates virtual images along arbitrary planes that intersect the ROI edges in the original images. This generates seed points that are joined automatically to form contours on each adjacent two-dimensional image and reconstructed in three dimensions (3D). An alternative thresholding approach is available for rapid assessment of complex structures like the small intestine. For assessment of dynamic gastrointestinal function, such as gastric accommodation and emptying, the initial 3D reconstruction is used as reference to process adjacent image stacks automatically. This generates four-dimensional (4D) reconstructions of dynamic volume change over time. Compared with manual processing, this semiautomatic system reduced the user input required to analyze a MRI gastric emptying study (estimated 100 vs. 10,000 mouse clicks). This analysis was not subject to variation in volume measurements seen between three human observers. In conclusion, the image processing platform presented processed large volumes of MRI data, such as that produced by gastric accommodation and emptying studies, with minimal user input. 3D and 4D reconstructions of the stomach and, potentially, other gastrointestinal organs are produced faster and more accurately than manual methods. This system will facilitate the application of MRI in gastrointestinal research and clinical practice. PMID:25540229

Least-squares dual characterization for ROI assessment in emission tomography

NASA Astrophysics Data System (ADS)

Ben Bouallègue, F.; Crouzet, J. F.; Dubois, A.; Buvat, I.; Mariano-Goulart, D.

2013-06-01

Our aim is to describe an original method for estimating the statistical properties of regions of interest (ROIs) in emission tomography. Drawn upon the works of Louis on the approximate inverse, we propose a dual formulation of the ROI estimation problem to derive the ROI activity and variance directly from the measured data without any image reconstruction. The method requires the definition of an ROI characteristic function that can be extracted from a co-registered morphological image. This characteristic function can be smoothed to optimize the resolution-variance tradeoff. An iterative procedure is detailed for the solution of the dual problem in the least-squares sense (least-squares dual (LSD) characterization), and a linear extrapolation scheme is described to compensate for sampling partial volume effect and reduce the estimation bias (LSD-ex). LSD and LSD-ex are compared with classical ROI estimation using pixel summation after image reconstruction and with Huesman's method. For this comparison, we used Monte Carlo simulations (GATE simulation tool) of 2D PET data of a Hoffman brain phantom containing three small uniform high-contrast ROIs and a large non-uniform low-contrast ROI. Our results show that the performances of LSD characterization are at least as good as those of the classical methods in terms of root mean square (RMS) error. For the three small tumor regions, LSD-ex allows a reduction in the estimation bias by up to 14%, resulting in a reduction in the RMS error of up to 8.5%, compared with the optimal classical estimation. For the large non-specific region, LSD using appropriate smoothing could intuitively and efficiently handle the resolution-variance tradeoff.

Annotating image ROIs with text descriptions for multimodal biomedical document retrieval

NASA Astrophysics Data System (ADS)

You, Daekeun; Simpson, Matthew; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

2013-01-01

Regions of interest (ROIs) that are pointed to by overlaid markers (arrows, asterisks, etc.) in biomedical images are expected to contain more important and relevant information than other regions for biomedical article indexing and retrieval. We have developed several algorithms that localize and extract the ROIs by recognizing markers on images. Cropped ROIs then need to be annotated with contents describing them best. In most cases accurate textual descriptions of the ROIs can be found from figure captions, and these need to be combined with image ROIs for annotation. The annotated ROIs can then be used to, for example, train classifiers that separate ROIs into known categories (medical concepts), or to build visual ontologies, for indexing and retrieval of biomedical articles. We propose an algorithm that pairs visual and textual ROIs that are extracted from images and figure captions, respectively. This algorithm based on dynamic time warping (DTW) clusters recognized pointers into groups, each of which contains pointers with identical visual properties (shape, size, color, etc.). Then a rule-based matching algorithm finds the best matching group for each textual ROI mention. Our method yields a precision and recall of 96% and 79%, respectively, when ground truth textual ROI data is used.

A texture analysis method for MR images of airway dilator muscles: a feasibility study

PubMed Central

Järnstedt, J; Sikiö, M; Viik, J; Dastidar, P; Peltomäki, T; Eskola, H

2014-01-01

Objectives: Airway dilator muscles play an important role in the analysis of breathing-related symptoms, such as obstructive sleep apnoea. Texture analysis (TA) provides a new non-invasive method for analysing airway dilator muscles. In this study, we propose a TA methodology for airway dilator muscles and prove the robustness of this method. Methods: 15 orthognathic surgery patients underwent 3-T MRI. Computerized TA was performed on 20 regions of interest (ROIs) in the patients' airway dilator muscles. 53 texture parameters were calculated for all ROIs. The robustness of the TA method was analysed by altering the locations, sizes and shapes of the ROIs. Results: Our study shows that there is significant difference in TA results as the size or shape of ROI changes. The change of location of the ROI inside the studied muscle does not affect the TA results. Conclusions: The TA method is valid for airway dilator muscles. We propose a methodology in which the number of co-occurrence parameters is reduced by using mean values from four different directions (0°, 45°, 90° and 135°) with pixel spacing of 1 pixel. PMID:24773626

Reconstructing cone-beam CT with spatially varying qualities for adaptive radiotherapy: a proof-of-principle study.

PubMed

Lu, Wenting; Yan, Hao; Gu, Xuejun; Tian, Zhen; Luo, Ouyang; Yang, Liu; Zhou, Linghong; Cervino, Laura; Wang, Jing; Jiang, Steve; Jia, Xun

2014-10-21

With the aim of maximally reducing imaging dose while meeting requirements for adaptive radiation therapy (ART), we propose in this paper a new cone beam CT (CBCT) acquisition and reconstruction method that delivers images with a low noise level inside a region of interest (ROI) and a relatively high noise level outside the ROI. The acquired projection images include two groups: densely sampled projections at a low exposure with a large field of view (FOV) and sparsely sampled projections at a high exposure with a small FOV corresponding to the ROI. A new algorithm combining the conventional filtered back-projection algorithm and the tight-frame iterative reconstruction algorithm is also designed to reconstruct the CBCT based on these projection data. We have validated our method on a simulated head-and-neck (HN) patient case, a semi-real experiment conducted on a HN cancer patient under a full-fan scan mode, as well as a Catphan phantom under a half-fan scan mode. Relative root-mean-square errors (RRMSEs) of less than 3% for the entire image and ~1% within the ROI compared to the ground truth have been observed. These numbers demonstrate the ability of our proposed method to reconstruct high-quality images inside the ROI. As for the part outside ROI, although the images are relatively noisy, it can still provide sufficient information for radiation dose calculations in ART. Dose distributions calculated on our CBCT image and on a standard CBCT image are in agreement, with a mean relative difference of 0.082% inside the ROI and 0.038% outside the ROI. Compared with the standard clinical CBCT scheme, an imaging dose reduction of approximately 3-6 times inside the ROI was achieved, as well as an 8 times outside the ROI. Regarding computational efficiency, it takes 1-3 min to reconstruct a CBCT image depending on the number of projections used. These results indicate that the proposed method has the potential for application in ART.

An application of Chan-Vese method used to determine the ROI area in CT lung screening

NASA Astrophysics Data System (ADS)

Prokop, Paweł; Surtel, Wojciech

2016-09-01

The article presents two approaches of determining the ROI area in CT lung screening. First approach is based on a classic method of framing the image in order to determine the ROI by using a MaZda tool. Second approach is based on segmentation of CT images of the lungs and reducing the redundant information from the image. Of the two approaches of an Active Contour, it was decided to choose the Chan-Vese method. In order to determine the effectiveness of the approach, it was performed an analysis of received ROI texture and extraction of textural features. In order to determine the effectiveness of the method, it was performed an analysis of the received ROI textures and extraction of the texture features, by using a Mazda tool. The results were compared and presented in the form of the radar graphs. The second approach proved to be effective and appropriate and consequently it is used for further analysis of CT images, in the computer-aided diagnosis of sarcoidosis.

Comparing brain graphs in which nodes are regions of interest or independent components: A simulation study.

PubMed

Yu, Qingbao; Du, Yuhui; Chen, Jiayu; He, Hao; Sui, Jing; Pearlson, Godfrey; Calhoun, Vince D

2017-11-01

A key challenge in building a brain graph using fMRI data is how to define the nodes. Spatial brain components estimated by independent components analysis (ICA) and regions of interest (ROIs) determined by brain atlas are two popular methods to define nodes in brain graphs. It is difficult to evaluate which method is better in real fMRI data. Here we perform a simulation study and evaluate the accuracies of a few graph metrics in graphs with nodes of ICA components, ROIs, or modified ROIs in four simulation scenarios. Graph measures with ICA nodes are more accurate than graphs with ROI nodes in all cases. Graph measures with modified ROI nodes are modulated by artifacts. The correlations of graph metrics across subjects between graphs with ICA nodes and ground truth are higher than the correlations between graphs with ROI nodes and ground truth in scenarios with large overlapped spatial sources. Moreover, moving the location of ROIs would largely decrease the correlations in all scenarios. Evaluating graphs with different nodes is promising in simulated data rather than real data because different scenarios can be simulated and measures of different graphs can be compared with a known ground truth. Since ROIs defined using brain atlas may not correspond well to real functional boundaries, overall findings of this work suggest that it is more appropriate to define nodes using data-driven ICA than ROI approaches in real fMRI data. Copyright © 2017 Elsevier B.V. All rights reserved.

A software platform for the analysis of dermatology images

NASA Astrophysics Data System (ADS)

Vlassi, Maria; Mavraganis, Vlasios; Asvestas, Panteleimon

2017-11-01

The purpose of this paper is to present a software platform developed in Python programming environment that can be used for the processing and analysis of dermatology images. The platform provides the capability for reading a file that contains a dermatology image. The platform supports image formats such as Windows bitmaps, JPEG, JPEG2000, portable network graphics, TIFF. Furthermore, it provides suitable tools for selecting, either manually or automatically, a region of interest (ROI) on the image. The automated selection of a ROI includes filtering for smoothing the image and thresholding. The proposed software platform has a friendly and clear graphical user interface and could be a useful second-opinion tool to a dermatologist. Furthermore, it could be used to classify images including from other anatomical parts such as breast or lung, after proper re-training of the classification algorithms.

SU-E-J-119: Head-And-Neck Digital Phantoms for Geometric and Dosimetric Uncertainty Evaluation of CT-CBCT Deformable Image Registration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shen, Z; Koyfman, S; Xia, P

2015-06-15

Purpose: To evaluate geometric and dosimetric uncertainties of CT-CBCT deformable image registration (DIR) algorithms using digital phantoms generated from real patients. Methods: We selected ten H&N cancer patients with adaptive IMRT. For each patient, a planning CT (CT1), a replanning CT (CT2), and a pretreatment CBCT (CBCT1) were used as the basis for digital phantom creation. Manually adjusted meshes were created for selected ROIs (e.g. PTVs, brainstem, spinal cord, mandible, and parotids) on CT1 and CT2. The mesh vertices were input into a thin-plate spline algorithm to generate a reference displacement vector field (DVF). The reference DVF was applied tomore » CBCT1 to create a simulated mid-treatment CBCT (CBCT2). The CT-CBCT digital phantom consisted of CT1 and CBCT2, which were linked by the reference DVF. Three DIR algorithms (Demons, B-Spline, and intensity-based) were applied to these ten digital phantoms. The images, ROIs, and volumetric doses were mapped from CT1 to CBCT2 using the DVFs computed by these three DIRs and compared to those mapped using the reference DVF. Results: The average Dice coefficients for selected ROIs were from 0.83 to 0.94 for Demons, from 0.82 to 0.95 for B-Spline, and from 0.67 to 0.89 for intensity-based DIR. The average Hausdorff distances for selected ROIs were from 2.4 to 6.2 mm for Demons, from 1.8 to 5.9 mm for B-Spline, and from 2.8 to 11.2 mm for intensity-based DIR. The average absolute dose errors for selected ROIs were from 0.7 to 2.1 Gy for Demons, from 0.7 to 2.9 Gy for B- Spline, and from 1.3 to 4.5 Gy for intensity-based DIR. Conclusion: Using clinically realistic CT-CBCT digital phantoms, Demons and B-Spline were shown to have similar geometric and dosimetric uncertainties while intensity-based DIR had the worst uncertainties. CT-CBCT DIR has the potential to provide accurate CBCT-based dose verification for H&N adaptive radiotherapy. Z Shen: None; K Bzdusek: an employee of Philips Healthcare; S Koyfman: None; P Xia: received research grants from Philips Healthcare and Siemens Healthcare.« less

Detection by voxel-wise statistical analysis of significant changes in regional cerebral glucose uptake in an APP/PS1 transgenic mouse model of Alzheimer's disease.

PubMed

Dubois, Albertine; Hérard, Anne-Sophie; Delatour, Benoît; Hantraye, Philippe; Bonvento, Gilles; Dhenain, Marc; Delzescaux, Thierry

2010-06-01

Biomarkers and technologies similar to those used in humans are essential for the follow-up of Alzheimer's disease (AD) animal models, particularly for the clarification of mechanisms and the screening and validation of new candidate treatments. In humans, changes in brain metabolism can be detected by 1-deoxy-2-[(18)F] fluoro-D-glucose PET (FDG-PET) and assessed in a user-independent manner with dedicated software, such as Statistical Parametric Mapping (SPM). FDG-PET can be carried out in small animals, but its resolution is low as compared to the size of rodent brain structures. In mouse models of AD, changes in cerebral glucose utilization are usually detected by [(14)C]-2-deoxyglucose (2DG) autoradiography, but this requires prior manual outlining of regions of interest (ROI) on selected sections. Here, we evaluate the feasibility of applying the SPM method to 3D autoradiographic data sets mapping brain metabolic activity in a transgenic mouse model of AD. We report the preliminary results obtained with 4 APP/PS1 (64+/-1 weeks) and 3 PS1 (65+/-2 weeks) mice. We also describe new procedures for the acquisition and use of "blockface" photographs and provide the first demonstration of their value for the 3D reconstruction and spatial normalization of post mortem mouse brain volumes. Despite this limited sample size, our results appear to be meaningful, consistent, and more comprehensive than findings from previously published studies based on conventional ROI-based methods. The establishment of statistical significance at the voxel level, rather than with a user-defined ROI, makes it possible to detect more reliably subtle differences in geometrically complex regions, such as the hippocampus. Our approach is generic and could be easily applied to other biomarkers and extended to other species and applications. Copyright 2010 Elsevier Inc. All rights reserved.

Joint Labeling Of Multiple Regions of Interest (Rois) By Enhanced Auto Context Models.

PubMed

Kim, Minjeong; Wu, Guorong; Guo, Yanrong; Shen, Dinggang

2015-04-01

Accurate segmentation of a set of regions of interest (ROIs) in the brain images is a key step in many neuroscience studies. Due to the complexity of image patterns, many learning-based segmentation methods have been proposed, including auto context model (ACM) that can capture high-level contextual information for guiding segmentation. However, since current ACM can only handle one ROI at a time, neighboring ROIs have to be labeled separately with different ACMs that are trained independently without communicating each other. To address this, we enhance the current single-ROI learning ACM to multi-ROI learning ACM for joint labeling of multiple neighboring ROIs (called e ACM). First, we extend current independently-trained single-ROI ACMs to a set of jointly-trained cross-ROI ACMs, by simultaneous training of ACMs for all spatially-connected ROIs to let them to share their respective intermediate outputs for coordinated labeling of each image point. Then, the context features in each ACM can capture the cross-ROI dependence information from the outputs of other ACMs that are designed for neighboring ROIs. Second, we upgrade the output labeling map of each ACM with the multi-scale representation, thus both local and global context information can be effectively used to increase the robustness in characterizing geometric relationship among neighboring ROIs. Third, we integrate ACM into a multi-atlases segmentation paradigm, for encompassing high variations among subjects. Experiments on LONI LPBA40 dataset show much better performance by our e ACM, compared to the conventional ACM.

Interior region-of-interest reconstruction using a small, nearly piecewise constant subregion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Taguchi, Katsuyuki; Xu Jingyan; Srivastava, Somesh

2011-03-15

Purpose: To develop a method to reconstruct an interior region-of-interest (ROI) image with sufficient accuracy that uses differentiated backprojection (DBP) projection onto convex sets (POCS) [H. Kudo et al., ''Tiny a priori knowledge solves the interior problem in computed tomography'', Phys. Med. Biol. 53, 2207-2231 (2008)] and a tiny knowledge that there exists a nearly piecewise constant subregion. Methods: The proposed method first employs filtered backprojection to reconstruct an image on which a tiny region P with a small variation in the pixel values is identified inside the ROI. Total variation minimization [H. Yu and G. Wang, ''Compressed sensing basedmore » interior tomography'', Phys. Med. Biol. 54, 2791-2805 (2009); W. Han et al., ''A general total variation minimization theorem for compressed sensing based interior tomography'', Int. J. Biomed. Imaging 2009, Article 125871 (2009)] is then employed to obtain pixel values in the subregion P, which serve as a priori knowledge in the next step. Finally, DBP-POCS is performed to reconstruct f(x,y) inside the ROI. Clinical data and the reconstructed image obtained by an x-ray computed tomography system (SOMATOM Definition; Siemens Healthcare) were used to validate the proposed method. The detector covers an object with a diameter of {approx}500 mm. The projection data were truncated either moderately to limit the detector coverage to diameter 350 mm of the object or severely to cover diameter 199 mm. Images were reconstructed using the proposed method. Results: The proposed method provided ROI images with correct pixel values in all areas except near the edge of the ROI. The coefficient of variation, i.e., the root mean square error divided by the mean pixel values, was less than 2.0% or 4.5% with the moderate or severe truncation cases, respectively, except near the boundary of the ROI. Conclusions: The proposed method allows for reconstructing interior ROI images with sufficient accuracy with a tiny knowledge that there exists a nearly piecewise constant subregion.« less

Dual energy X-ray absorptiometry spine scans to determine abdominal fat in post-menopausal women

PubMed Central

Bea, J. W.; Blew, R. M.; Going, S. B.; Hsu, C-H; Lee, M. C.; Lee, V. R.; Caan, B.J.; Kwan, M.L.; Lohman, T. G.

2016-01-01

Body composition may be a better predictor of chronic disease risk than body mass index (BMI) in older populations. Objectives We sought to validate spine fat fraction (%) from dual energy X-ray absorptiometry (DXA) spine scans as a proxy for total abdominal fat. Methods Total body DXA scan abdominal fat regions of interest (ROI) that have been previously validated by magnetic resonance imaging were assessed among healthy, postmenopausal women who also had antero-posterior spine scans (n=103). ROIs were 1) lumbar vertebrae L2-L4 and 2) L2-Iliac Crest (L2-IC), manually selected by two independent raters, and 3) trunk, auto-selected by DXA software. Intra-class correlation coefficients evaluated intra and inter-rater reliability on a random subset (N=25). Linear regression models, validated by bootstrapping, assessed the relationship between spine fat fraction (%) and total abdominal fat (%) ROIs. Results Mean age, BMI and total body fat were: 66.1 ± 4.8y, 25.8 ± 3.8kg/m2 and 40.0 ± 6.6%, respectively. There were no significant differences within or between raters. Linear regression models adjusted for several participant and scan characteristics were equivalent to using only BMI and spine fat fraction. The model predicted L2-L4 (Adj. R2: 0.83) and L2-IC (Adj.R2:0.84) abdominal fat (%) well; the adjusted R2 for trunk fat (%) was 0.78. Model validation demonstrated minimal over-fitting (Adj. R2: 0.82, 0.83, and 0.77 for L2-L4, L2-IC, and trunk fat respectively). Conclusions The strong correlation between spine fat fraction and DXA abdominal fat measures make it suitable for further development in post-menopausal chronic disease risk prediction models. PMID:27416964

Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform.

PubMed

Jian, Wushuai; Sun, Xueyan; Luo, Shuqian

2012-12-19

Digital mammography is the most reliable imaging modality for breast carcinoma diagnosis and breast micro-calcifications is regarded as one of the most important signs on imaging diagnosis. In this paper, a computer-aided diagnosis (CAD) system is presented for breast micro-calcifications based on dual-tree complex wavelet transform (DT-CWT) to facilitate radiologists like double reading. Firstly, 25 abnormal ROIs were extracted according to the center and diameter of the lesions manually and 25 normal ROIs were selected randomly. Then micro-calcifications were segmented by combining space and frequency domain techniques. We extracted three texture features based on wavelet (Haar, DB4, DT-CWT) transform. Totally 14 descriptors were introduced to define the characteristics of the suspicious micro-calcifications. Principal Component Analysis (PCA) was used to transform these descriptors to a compact and efficient vector expression. Support Vector Machine (SVM) classifier was used to classify potential micro-calcifications. Finally, we used the receiver operating characteristic (ROC) curve and free-response operating characteristic (FROC) curve to evaluate the performance of the CAD system. The results of SVM classifications based on different wavelets shows DT-CWT has a better performance. Compared with other results, DT-CWT method achieved an accuracy of 96% and 100% for the classification of normal and abnormal ROIs, and the classification of benign and malignant micro-calcifications respectively. In FROC analysis, our CAD system for clinical dataset detection achieved a sensitivity of 83.5% at a false positive per image of 1.85. Compared with general wavelets, DT-CWT could describe the features more effectively, and our CAD system had a competitive performance.

Automatic estimation of elasticity parameters in breast tissue

NASA Astrophysics Data System (ADS)

Skerl, Katrin; Cochran, Sandy; Evans, Andrew

2014-03-01

Shear wave elastography (SWE), a novel ultrasound imaging technique, can provide unique information about cancerous tissue. To estimate elasticity parameters, a region of interest (ROI) is manually positioned over the stiffest part of the shear wave image (SWI). The aim of this work is to estimate the elasticity parameters i.e. mean elasticity, maximal elasticity and standard deviation, fully automatically. Ultrasonic SWI of a breast elastography phantom and breast tissue in vivo were acquired using the Aixplorer system (SuperSonic Imagine, Aix-en-Provence, France). First, the SWI within the ultrasonic B-mode image was detected using MATLAB then the elasticity values were extracted. The ROI was automatically positioned over the stiffest part of the SWI and the elasticity parameters were calculated. Finally all values were saved in a spreadsheet which also contains the patient's study ID. This spreadsheet is easily available for physicians and clinical staff for further evaluation and so increase efficiency. Therewith the efficiency is increased. This algorithm simplifies the handling, especially for the performance and evaluation of clinical trials. The SWE processing method allows physicians easy access to the elasticity parameters of the examinations from their own and other institutions. This reduces clinical time and effort and simplifies evaluation of data in clinical trials. Furthermore, reproducibility will be improved.

SU-E-I-37: Low-Dose Real-Time Region-Of-Interest X-Ray Fluoroscopic Imaging with a GPU-Accelerated Spatially Different Bilateral Filtering

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chung, H; Lee, J; Pua, R

2014-06-01

Purpose: The purpose of our study is to reduce imaging radiation dose while maintaining image quality of region of interest (ROI) in X-ray fluoroscopy. A low-dose real-time ROI fluoroscopic imaging technique which includes graphics-processing-unit- (GPU-) accelerated image processing for brightness compensation and noise filtering was developed in this study. Methods: In our ROI fluoroscopic imaging, a copper filter is placed in front of the X-ray tube. The filter contains a round aperture to reduce radiation dose to outside of the aperture. To equalize the brightness difference between inner and outer ROI regions, brightness compensation was performed by use of amore » simple weighting method that applies selectively to the inner ROI, the outer ROI, and the boundary zone. A bilateral filtering was applied to the images to reduce relatively high noise in the outer ROI images. To speed up the calculation of our technique for real-time application, the GPU-acceleration was applied to the image processing algorithm. We performed a dosimetric measurement using an ion-chamber dosimeter to evaluate the amount of radiation dose reduction. The reduction of calculation time compared to a CPU-only computation was also measured, and the assessment of image quality in terms of image noise and spatial resolution was conducted. Results: More than 80% of dose was reduced by use of the ROI filter. The reduction rate depended on the thickness of the filter and the size of ROI aperture. The image noise outside the ROI was remarkably reduced by the bilateral filtering technique. The computation time for processing each frame image was reduced from 3.43 seconds with single CPU to 9.85 milliseconds with GPU-acceleration. Conclusion: The proposed technique for X-ray fluoroscopy can substantially reduce imaging radiation dose to the patient while maintaining image quality particularly in the ROI region in real-time.« less

Transforming the back office with a single keystroke.

PubMed

Kaplan, Charles

2011-09-01

Intelligent data capture is an application that can provide electronic access to data contained in any type of document, including clinical records and financial documents, thereby reducing or eliminating the need for manual data entry. Before implementing this technology, however, healthcare leaders should: Evaluate technology in the context of a business case to ensure a measurable ROI. Communicate with employees throughout the process so they are prepared for and embrace the inevitable changes that will come with automation. Implement the solution in phases, focusing on those document types that can deliver for a safer, less disruptive approach. Achieve maximum benefit to the organization by reengineering business processes to fully leverage technology rather than simply automating existing manual processes.

Patient-specific model-based segmentation of brain tumors in 3D intraoperative ultrasound images.

PubMed

Ilunga-Mbuyamba, Elisee; Avina-Cervantes, Juan Gabriel; Lindner, Dirk; Arlt, Felix; Ituna-Yudonago, Jean Fulbert; Chalopin, Claire

2018-03-01

Intraoperative ultrasound (iUS) imaging is commonly used to support brain tumor operation. The tumor segmentation in the iUS images is a difficult task and still under improvement because of the low signal-to-noise ratio. The success of automatic methods is also limited due to the high noise sensibility. Therefore, an alternative brain tumor segmentation method in 3D-iUS data using a tumor model obtained from magnetic resonance (MR) data for local MR-iUS registration is presented in this paper. The aim is to enhance the visualization of the brain tumor contours in iUS. A multistep approach is proposed. First, a region of interest (ROI) based on the specific patient tumor model is defined. Second, hyperechogenic structures, mainly tumor tissues, are extracted from the ROI of both modalities by using automatic thresholding techniques. Third, the registration is performed over the extracted binary sub-volumes using a similarity measure based on gradient values, and rigid and affine transformations. Finally, the tumor model is aligned with the 3D-iUS data, and its contours are represented. Experiments were successfully conducted on a dataset of 33 patients. The method was evaluated by comparing the tumor segmentation with expert manual delineations using two binary metrics: contour mean distance and Dice index. The proposed segmentation method using local and binary registration was compared with two grayscale-based approaches. The outcomes showed that our approach reached better results in terms of computational time and accuracy than the comparative methods. The proposed approach requires limited interaction and reduced computation time, making it relevant for intraoperative use. Experimental results and evaluations were performed offline. The developed tool could be useful for brain tumor resection supporting neurosurgeons to improve tumor border visualization in the iUS volumes.

Effect of various binning methods and ROI sizes on the accuracy of the automatic classification system for differentiation between diffuse infiltrative lung diseases on the basis of texture features at HRCT

NASA Astrophysics Data System (ADS)

Kim, Namkug; Seo, Joon Beom; Sung, Yu Sub; Park, Bum-Woo; Lee, Youngjoo; Park, Seong Hoon; Lee, Young Kyung; Kang, Suk-Ho

2008-03-01

To find optimal binning, variable binning size linear binning (LB) and non-linear binning (NLB) methods were tested. In case of small binning size (Q <= 10), NLB shows significant better accuracy than the LB. K-means NLB (Q = 26) is statistically significant better than every LB. To find optimal binning method and ROI size of the automatic classification system for differentiation between diffuse infiltrative lung diseases on the basis of textural analysis at HRCT Six-hundred circular regions of interest (ROI) with 10, 20, and 30 pixel diameter, comprising of each 100 ROIs representing six regional disease patterns (normal, NL; ground-glass opacity, GGO; reticular opacity, RO; honeycombing, HC; emphysema, EMPH; and consolidation, CONS) were marked by an experienced radiologist from HRCT images. Histogram (mean) and co-occurrence matrix (mean and SD of angular second moment, contrast, correlation, entropy, and inverse difference momentum) features were employed to test binning and ROI effects. To find optimal binning, variable binning size LB (bin size Q: 4~30, 32, 64, 128, 144, 196, 256, 384) and NLB (Q: 4~30) methods (K-means, and Fuzzy C-means clustering) were tested. For automated classification, a SVM classifier was implemented. To assess cross-validation of the system, a five-folding method was used. Each test was repeatedly performed twenty times. Overall accuracies with every combination of variable ROIs, and binning sizes were statistically compared. In case of small binning size (Q <= 10), NLB shows significant better accuracy than the LB. K-means NLB (Q = 26) is statistically significant better than every LB. In case of 30x30 ROI size and most of binning size, the K-means method showed better than other NLB and LB methods. When optimal binning and other parameters were set, overall sensitivity of the classifier was 92.85%. The sensitivity and specificity of the system for each class were as follows: NL, 95%, 97.9%; GGO, 80%, 98.9%; RO 85%, 96.9%; HC, 94.7%, 97%; EMPH, 100%, 100%; and CONS, 100%, 100%, respectively. We determined the optimal binning method and ROI size of the automatic classification system for differentiation between diffuse infiltrative lung diseases on the basis of texture features at HRCT.

Iterative Region-of-Interest Reconstruction from Limited Data Using Prior Information

NASA Astrophysics Data System (ADS)

Vogelgesang, Jonas; Schorr, Christian

2017-12-01

In practice, computed tomography and computed laminography applications suffer from incomplete data. In particular, when inspecting large objects with extremely different diameters in longitudinal and transversal directions or when high resolution reconstructions are desired, the physical conditions of the scanning system lead to restricted data and truncated projections, also known as the interior or region-of-interest (ROI) problem. To recover the searched-for density function of the inspected object, we derive a semi-discrete model of the ROI problem that inherently allows the incorporation of geometrical prior information in an abstract Hilbert space setting for bounded linear operators. Assuming that the attenuation inside the object is approximately constant, as for fibre reinforced plastics parts or homogeneous objects where one is interested in locating defects like cracks or porosities, we apply the semi-discrete Landweber-Kaczmarz method to recover the inner structure of the object inside the ROI from the measured data resulting in a semi-discrete iteration method. Finally, numerical experiments for three-dimensional tomographic applications with both an inherent restricted source and ROI problem are provided to verify the proposed method for the ROI reconstruction.

Breast mass segmentation in mammography using plane fitting and dynamic programming.

PubMed

Song, Enmin; Jiang, Luan; Jin, Renchao; Zhang, Lin; Yuan, Yuan; Li, Qiang

2009-07-01

Segmentation is an important and challenging task in a computer-aided diagnosis (CAD) system. Accurate segmentation could improve the accuracy in lesion detection and characterization. The objective of this study is to develop and test a new segmentation method that aims at improving the performance level of breast mass segmentation in mammography, which could be used to provide accurate features for classification. This automated segmentation method consists of two main steps and combines the edge gradient, the pixel intensity, as well as the shape characteristics of the lesions to achieve good segmentation results. First, a plane fitting method was applied to a background-trend corrected region-of-interest (ROI) of a mass to obtain the edge candidate points. Second, dynamic programming technique was used to find the "optimal" contour of the mass from the edge candidate points. Area-based similarity measures based on the radiologist's manually marked annotation and the segmented region were employed as criteria to evaluate the performance level of the segmentation method. With the evaluation criteria, the new method was compared with 1) the dynamic programming method developed by Timp and Karssemeijer, and 2) the normalized cut segmentation method, based on 337 ROIs extracted from a publicly available image database. The experimental results indicate that our segmentation method can achieve a higher performance level than the other two methods, and the improvements in segmentation performance level were statistically significant. For instance, the mean overlap percentage for the new algorithm was 0.71, whereas those for Timp's dynamic programming method and the normalized cut segmentation method were 0.63 (P < .001) and 0.61 (P < .001), respectively. We developed a new segmentation method by use of plane fitting and dynamic programming, which achieved a relatively high performance level. The new segmentation method would be useful for improving the accuracy of computerized detection and classification of breast cancer in mammography.

Ultrasound estimates of muscle quality in older adults: reliability and comparison of Photoshop and ImageJ for the grayscale analysis of muscle echogenicity

PubMed Central

Seamon, Bryant A.; Teixeira, Carla; Ismail, Catheeja

2016-01-01

Background. Quantitative diagnostic ultrasound imaging has been proposed as a method of estimating muscle quality using measures of echogenicity. The Rectangular Marquee Tool (RMT) and the Free Hand Tool (FHT) are two types of editing features used in Photoshop and ImageJ for determining a region of interest (ROI) within an ultrasound image. The primary objective of this study is to determine the intrarater and interrater reliability of Photoshop and ImageJ for the estimate of muscle tissue echogenicity in older adults via grayscale histogram analysis. The secondary objective is to compare the mean grayscale values obtained using both the RMT and FHT methods across both image analysis platforms. Methods. This cross-sectional observational study features 18 community-dwelling men (age = 61.5 ± 2.32 years). Longitudinal views of the rectus femoris were captured using B-mode ultrasound. The ROI for each scan was selected by 2 examiners using the RMT and FHT methods from each software program. Their reliability is assessed using intraclass correlation coefficients (ICCs) and the standard error of the measurement (SEM). Measurement agreement for these values is depicted using Bland-Altman plots. A paired t-test is used to determine mean differences in echogenicity expressed as grayscale values using the RMT and FHT methods to select the post-image acquisition ROI. The degree of association among ROI selection methods and image analysis platforms is analyzed using the coefficient of determination (R2). Results. The raters demonstrated excellent intrarater and interrater reliability using the RMT and FHT methods across both platforms (lower bound 95% CI ICC = .97–.99, p < .001). Mean differences between the echogenicity estimates obtained with the RMT and FHT methods was .87 grayscale levels (95% CI [.54–1.21], p < .0001) using data obtained with both programs. The SEM for Photoshop was .97 and 1.05 grayscale levels when using the RMT and FHT ROI selection methods, respectively. Comparatively, the SEM values were .72 and .81 grayscale levels, respectively, when using the RMT and FHT ROI selection methods in ImageJ. Uniform coefficients of determination (R2 = .96–.99, p < .001) indicate strong positive associations among the grayscale histogram analysis measurement conditions independent of the ROI selection methods and imaging platform. Conclusion. Our method for evaluating muscle echogenicity demonstrated a high degree of intrarater and interrater reliability using both the RMT and FHT methods across 2 common image analysis platforms. The minimal measurement error exhibited by the examiners demonstrates that the ROI selection methods used with Photoshop and ImageJ are suitable for the post-acquisition image analysis of tissue echogenicity in older adults. PMID:26925339

Medical image retrieval system using multiple features from 3D ROIs

NASA Astrophysics Data System (ADS)

Lu, Hongbing; Wang, Weiwei; Liao, Qimei; Zhang, Guopeng; Zhou, Zhiming

2012-02-01

Compared to a retrieval using global image features, features extracted from regions of interest (ROIs) that reflect distribution patterns of abnormalities would benefit more for content-based medical image retrieval (CBMIR) systems. Currently, most CBMIR systems have been designed for 2D ROIs, which cannot reflect 3D anatomical features and region distribution of lesions comprehensively. To further improve the accuracy of image retrieval, we proposed a retrieval method with 3D features including both geometric features such as Shape Index (SI) and Curvedness (CV) and texture features derived from 3D Gray Level Co-occurrence Matrix, which were extracted from 3D ROIs, based on our previous 2D medical images retrieval system. The system was evaluated with 20 volume CT datasets for colon polyp detection. Preliminary experiments indicated that the integration of morphological features with texture features could improve retrieval performance greatly. The retrieval result using features extracted from 3D ROIs accorded better with the diagnosis from optical colonoscopy than that based on features from 2D ROIs. With the test database of images, the average accuracy rate for 3D retrieval method was 76.6%, indicating its potential value in clinical application.

Evaluation of the accuracy and clinical practicality of a calculation system for patient positional displacement in carbon ion radiotherapy at five sites.

PubMed

Kubota, Yoshiki; Hayashi, Hayato; Abe, Satoshi; Souda, Saki; Okada, Ryosuke; Ishii, Takayoshi; Tashiro, Mutsumi; Torikoshi, Masami; Kanai, Tatsuaki; Ohno, Tatsuya; Nakano, Takashi

2018-03-01

We developed a system for calculating patient positional displacement between digital radiography images (DRs) and digitally reconstructed radiography images (DRRs) to reduce patient radiation exposure, minimize individual differences between radiological technologists in patient positioning, and decrease positioning time. The accuracy of this system at five sites was evaluated with clinical data from cancer patients. The dependence of calculation accuracy on the size of the region of interest (ROI) and initial position was evaluated for clinical use. For a preliminary verification, treatment planning and positioning data from eight setup patterns using a head and neck phantom were evaluated. Following this, data from 50 patients with prostate, lung, head and neck, liver, or pancreatic cancer (n = 10 each) were evaluated. Root mean square errors (RMSEs) between the results calculated by our system and the reference positions were assessed. The reference positions were manually determined by two radiological technologists to best-matching positions with orthogonal DRs and DRRs in six axial directions. The ROI size dependence was evaluated by comparing RMSEs for three different ROI sizes. Additionally, dependence on initial position parameters was evaluated by comparing RMSEs for four position patterns. For the phantom study, the average (± standard deviation) translation error was 0.17 ± 0.05, rotation error was 0.17 ± 0.07, and ΔD was 0.14 ± 0.05. Using the optimal ROI size for each patient site, all cases of prostate, lung, and head and neck cancer with initial position parameters of 10 mm or under were acceptable in our tolerance. However, only four liver cancer cases and three pancreatic cancer cases were acceptable, because of low-reproducibility regions in the ROIs. Our system has clinical practicality for prostate, lung, and head and neck cancer cases. Additionally, our findings suggest ROI size dependence in some cases. © 2018 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

A Fully Automated Method for Quantifying and Localizing White Matter Hyperintensities on MR Images

PubMed Central

Wu, Minjie; Rosano, Caterina; Butters, Meryl; Whyte, Ellen; Nable, Megan; Crooks, Ryan; Meltzer, Carolyn C.; Reynolds, Charles F.; Aizenstein3, Howard J.

2006-01-01

White matter hyperintensities (WMH), commonly found on T2-weighted FLAIR brain MR images in the elderly, are associated with a number of neuropsychiatric disorders, including vascular dementia, Alzheimer’s disease, and late-life depression. Previous MRI studies of WMHs have primarily relied on the subjective and global (i.e., full-brain) ratings of WMH grade. In the current study we implement and validate an automated method for quantifying and localizing WMHs. We adapt a fuzzy connected algorithm to automate the segmentation of WMHs and use a demons-based image registration to automate the anatomic localization of the WMHs using the Johns Hopkins University White Matter Atlas. The method is validated using the brain MR images acquired from eleven elderly subjects with late-onset late-life depression (LLD) and eight elderly controls. This dataset was chosen because LLD subjects are known to have significant WMH burden. The volumes of WMH identified in our automated method are compared with the accepted gold standard (manual ratings). A significant correlation of the automated method and the manual ratings is found (P<0.0001), thus demonstrating similar WMH quantifications of both methods. As has been shown in other studies e.g. (Taylor, et al. 2003)), we found there was a significantly greater WMH burden in the LLD subjects versus the controls for both the manual and automated method. The effect size was greater for the automated method, suggesting that it is a more specific measure. Additionally, we describe the anatomic localization of the WMHs in LLD subjects as well as in the control subjects, and detect the regions of interest (ROIs) specific for the WMH burden of LLD patients. Given the emergence of large neuroimage databases, techniques, such as that described here, will allow for a better understanding of the relationship between WMHs and neuropsychiatric disorders. PMID:17097277

A study of T2-weighted MR image texture features and diffusion-weighted MR image features for computer-aided diagnosis of prostate cancer

NASA Astrophysics Data System (ADS)

Peng, Yahui; Jiang, Yulei; Antic, Tatjana; Giger, Maryellen L.; Eggener, Scott; Oto, Aytekin

2013-02-01

The purpose of this study was to study T2-weighted magnetic resonance (MR) image texture features and diffusionweighted (DW) MR image features in distinguishing prostate cancer (PCa) from normal tissue. We collected two image datasets: 23 PCa patients (25 PCa and 23 normal tissue regions of interest [ROIs]) imaged with Philips MR scanners, and 30 PCa patients (41 PCa and 26 normal tissue ROIs) imaged with GE MR scanners. A radiologist drew ROIs manually via consensus histology-MR correlation conference with a pathologist. A number of T2-weighted texture features and apparent diffusion coefficient (ADC) features were investigated, and linear discriminant analysis (LDA) was used to combine select strong image features. Area under the receiver operating characteristic (ROC) curve (AUC) was used to characterize feature effectiveness in distinguishing PCa from normal tissue ROIs. Of the features studied, ADC 10th percentile, ADC average, and T2-weighted sum average yielded AUC values (+/-standard error) of 0.95+/-0.03, 0.94+/-0.03, and 0.85+/-0.05 on the Phillips images, and 0.91+/-0.04, 0.89+/-0.04, and 0.70+/-0.06 on the GE images, respectively. The three-feature combination yielded AUC values of 0.94+/-0.03 and 0.89+/-0.04 on the Phillips and GE images, respectively. ADC 10th percentile, ADC average, and T2-weighted sum average, are effective in distinguishing PCa from normal tissue, and appear robust in images acquired from Phillips and GE MR scanners.

Quantifying Digital Ulcers in Systemic Sclerosis: Reliability of Computer-Assisted Planimetry in Measuring Lesion Size.

PubMed

Simpson, V; Hughes, M; Wilkinson, J; Herrick, A L; Dinsdale, G

2018-03-01

Digital ulcers are a major problem in patients with systemic sclerosis (SSc), causing severe pain and impairment of hand function. In addition, digital ulcers heal slowly and sometimes become infected, which can lead to gangrene and necessitate amputation if appropriate intervention is not taken. A reliable, objective method for assessing digital ulcer healing or progression is needed in both the clinical and research arenas. This study was undertaken to compare 2 computer-assisted planimetry methods of measurement of digital ulcer area on photographs (ellipse and freehand regions of interest [ROIs]), and to assess the reliability of photographic calibration and the 2 methods of area measurement. Photographs were taken of 107 digital ulcers in 36 patients with SSc spectrum disease. Three raters assessed the photographs. Custom software allowed raters to calibrate photograph dimensions and draw ellipse or freehand ROIs. The shapes and dimensions of the ROIs were saved for further analysis. Calibration (by a single rater performing 5 repeats per image) produced an intraclass correlation coefficient (intrarater reliability) of 0.99. The mean ± SD areas of digital ulcers assessed using ellipse and freehand ROIs were 18.7 ± 20.2 mm 2 and 17.6 ± 19.3 mm 2 , respectively. Intrarater and interrater reliability of the ellipse ROI were 0.97 and 0.77, respectively. For the freehand ROI, the intrarater and interrater reliability were 0.98 and 0.76, respectively. Our findings indicate that computer-assisted planimetry methods applied to SSc-related digital ulcers can be extremely reliable. Further work is needed to move toward applying these methods as outcome measures for clinical trials and in clinical settings. © 2017, American College of Rheumatology.

AutoTag and AutoSnap: Standardized, semi-automatic capture of regions of interest from whole slide images

PubMed Central

Marien, Koen M.; Andries, Luc; De Schepper, Stefanie; Kockx, Mark M.; De Meyer, Guido R.Y.

2015-01-01

Tumor angiogenesis is measured by counting microvessels in tissue sections at high power magnification as a potential prognostic or predictive biomarker. Until now, regions of interest1 (ROIs) were selected by manual operations within a tumor by using a systematic uniform random sampling2 (SURS) approach. Although SURS is the most reliable sampling method, it implies a high workload. However, SURS can be semi-automated and in this way contribute to the development of a validated quantification method for microvessel counting in the clinical setting. Here, we report a method to use semi-automated SURS for microvessel counting: • Whole slide imaging with Pannoramic SCAN (3DHISTECH) • Computer-assisted sampling in Pannoramic Viewer (3DHISTECH) extended by two self-written AutoHotkey applications (AutoTag and AutoSnap) • The use of digital grids in Photoshop® and Bridge® (Adobe Systems) This rapid procedure allows traceability essential for high throughput protein analysis of immunohistochemically stained tissue. PMID:26150998

SU-F-J-183: Interior Region-Of-Interest Tomography by Using Inverse Geometry System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, K; Kim, D; Kang, S

2016-06-15

Purpose: The inverse geometry computed tomography (IGCT) composed of multiple source and small size detector has several merits such as reduction of scatter effect and large volumetric imaging within one rotation without cone-beam artifact, compared to conventional cone-beam computed tomography (CBCT). By using this multi-source characteristics, we intend to present a selective and multiple interior region-of-interest (ROI) imaging method by using a designed source on-off sequence of IGCT. Methods: All of the IGCT sources are operated one by one sequentially, and each projection in the shape of narrow cone-beam covers its own partial volume of full field of view (FOV)more » determined from system geometry. Thus, through controlling multi source operation, limited irradiation within ROI is possible and selective radon space data for ROI imaging can be acquired without additional X-ray filtration. With this feature, we designed a source on-off sequence for multi ROI-IGCT imaging, and projections of ROI-IGCT were generated by using the on-off sequence. Multi ROI-IGCT images were reconstructed by using filtered back-projection algorithm. All these imaging process of our study has been performed by utilizing digital phantom and patient CT data. ROI-IGCT images of the phantom were compared to CBCT image and the phantom data for the image quality evaluation. Results: Image quality of ROI-IGCT was comparable to that of CBCT. However, the distal axial-plane from the FOV center, large cone-angle region, ROI-IGCT showed uniform image quality without significant cone-beam artifact contrary to CBCT. Conclusion: ROI-IGCT showed comparable image quality and has the capability to provide multi ROI image within a rotation. Projection of ROI-IGCT is performed by selective irradiation, hence unnecessary imaging dose to non-interest region can be reduced. In this regard, it seems to be useful for diagnostic or image guidance purpose in radiotherapy such as low dose target localization and patient alignment. This research was supported by the Mid-career Researcher Program through NRF funded by the Ministry of Science, ICT & Future Planning of Korea (NRF-2014R1A2A1A10050270) and by the Radiation Technology R&D program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (No. 2013M2A2A7038291)« less

Comparison of 133 xenon ventilation equilibrium scan (XV) and 99m technetium transmission (TT) scan for use in regional lung analysis by 2D gamma scintigraphy in healthy and cystic fibrosis lungs.

PubMed

Zeman, Kirby L; Wu, Jihong; Donaldson, Scott H; Bennett, William D

2013-04-01

Quantification of particle deposition in the lung by gamma scintigraphy requires a reference image for location of regions of interest (ROIs) and normalization to lung thickness. In various laboratories, the reference image is made by a transmission scan ((57)Co or (99m)Tc) or gas ventilation scan ((133)Xe or (81)Kr). There has not been a direct comparison of measures from the two methods. We compared (99m)Tc transmission scans to (133)Xe equilibrium ventilation scans as reference images for 38 healthy subjects and 14 cystic fibrosis (CF) patients for their effects on measures of regional particle deposition: the central-to-peripheral ratio of lung counts (C/P); and ROI area versus forced vital capacity. Whole right lung ROI was based on either an isocontour threshold of three times the soft tissue transmission (TT) or a threshold of 20% of peak xenon ventilation counts (XV). We used a central ROI drawn to 50% of height and of width of the whole right lung ROI and placed along the left lung margin and centered vertically. In general, the correlation of normalized C/P (nC/P) between the two methods was strong. However, the value of nC/P was significantly smaller for the XV method than the TT method. Regression equations for the relationship of nC/P between the two methods were, for healthy subjects, y=0.75x+0.61, R(2)=0.64 using rectangular ROIs and y=0.76x+0.45, R(2)=0.66 using isocontour ROIs; and for CF patients, y=0.94x+0.46, R(2)=0.43 and y=0.85x+0.42, R(2)=0.41, respectively. (1) A transmission scan with an isocontour outline in combination with a rectangular central region to define the lung borders may be more useful than a ventilation scan. (2) Close correlation of nC/Ps measured by transmission or gas ventilation should allow confident comparison of values determined by the two methods.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kang, H; Malin, M; Chmura, S

Purpose: For African-American patients receiving breast radiotherapy with a bolus, skin darkening can affect the surface visualization when using optical imaging for daily positioning and gating at deep-inspiration breath holds (DIBH). Our goal is to identify a region-of-interest (ROI) that is robust against deteriorating surface image quality due to skin darkening. Methods: We study four patients whose post-mastectomy surfaces are imaged daily with AlignRT (VisionRT, UK) for DIBH radiotherapy and whose surface image quality is degraded toward the end of treatment. To simulate the effects of skin darkening, surfaces from the first ten fractions of each patient are systematically degradedmore » by 25–35%, 40–50% and 65–75% of the total area of the clinically used ROI-ipsilateral-chestwall. The degraded surfaces are registered to the reference surface in six degrees-of-freedom. To identify a robust ROI, three additional reference ROIs — ROI-chest+abdomen, ROI-bilateral-chest and ROI-extended-ipsilateral-chestwall are created and registered to the degraded surfaces. Differences in registration using these ROIs are compared to that using ROI-ipsilateral-chestwall. Results: For three patients, the deviations in the registrations to ROI-ipsilateral-chestwall are > 2.0, 3.1 and 7.9mm on average for 25–35%, 40–50% and 65–75% degraded surfaces, respectively. Rotational deviations reach 11.1° in pitch. For the last patient, registration is consistent to within 2.6mm even on the 65–75% degraded surfaces, possibly because the surface topography has more distinct features. For ROI-bilateral-chest and ROI-extended-ipsilateral-chest registrations deviate in a similar pattern. However, registration on ROI-chest+abdomen is robust to deteriorating image qualities to within 4.2mm for all four patients. Conclusion: Registration deviations using ROI-ipsilateral-chestwall can reach 9.8mm on the 40–50% degraded surfaces. Caution is required when using AlignRT for patients experiencing skin darkening since the accuracy of AlignRT registration deteriorates. To avoid this inaccuracy, we recommend use of ROI-chest+abdomen, on which registration is consistent within 4.2mm even for highly degraded surfaces.« less

Semi-automatic assessment of skin capillary density: proof of principle and validation.

PubMed

Gronenschild, E H B M; Muris, D M J; Schram, M T; Karaca, U; Stehouwer, C D A; Houben, A J H M

2013-11-01

Skin capillary density and recruitment have been proven to be relevant measures of microvascular function. Unfortunately, the assessment of skin capillary density from movie files is very time-consuming, since this is done manually. This impedes the use of this technique in large-scale studies. We aimed to develop a (semi-) automated assessment of skin capillary density. CapiAna (Capillary Analysis) is a newly developed semi-automatic image analysis application. The technique involves four steps: 1) movement correction, 2) selection of the frame range and positioning of the region of interest (ROI), 3) automatic detection of capillaries, and 4) manual correction of detected capillaries. To gain insight into the performance of the technique, skin capillary density was measured in twenty participants (ten women; mean age 56.2 [42-72] years). To investigate the agreement between CapiAna and the classic manual counting procedure, we used weighted Deming regression and Bland-Altman analyses. In addition, intra- and inter-observer coefficients of variation (CVs), and differences in analysis time were assessed. We found a good agreement between CapiAna and the classic manual method, with a Pearson's correlation coefficient (r) of 0.95 (P<0.001) and a Deming regression coefficient of 1.01 (95%CI: 0.91; 1.10). In addition, we found no significant differences between the two methods, with an intercept of the Deming regression of 1.75 (-6.04; 9.54), while the Bland-Altman analysis showed a mean difference (bias) of 2.0 (-13.5; 18.4) capillaries/mm(2). The intra- and inter-observer CVs of CapiAna were 2.5% and 5.6% respectively, while for the classic manual counting procedure these were 3.2% and 7.2%, respectively. Finally, the analysis time for CapiAna ranged between 25 and 35min versus 80 and 95min for the manual counting procedure. We have developed a semi-automatic image analysis application (CapiAna) for the assessment of skin capillary density, which agrees well with the classic manual counting procedure, is time-saving, and has a better reproducibility as compared to the classic manual counting procedure. As a result, the use of skin capillaroscopy is feasible in large-scale studies, which importantly extends the possibilities to perform microcirculation research in humans. © 2013.

Robust Estimation of Mahalanobis Distances in Hyperspectral Images

DTIC Science & Technology

2006-12-01

each method used to fit the MD distribution from the DFC ROI. No- tice how the F -mixture is affected by the last two data points (points most unlike...bottom spectra are the minimum and maximum in magnitude. Notice the decrease in variability compared to DFC and MCFC. For this ROI, the variability is...performance for DFC MD Data (ROI = 11,557 pix- els). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 6.5. Summary of performance

Region-of-interest image reconstruction in circular cone-beam microCT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cho, Seungryong; Bian, Junguo; Pelizzari, Charles A.

2007-12-15

Cone-beam microcomputed tomography (microCT) is one of the most popular choices for small animal imaging which is becoming an important tool for studying animal models with transplanted diseases. Region-of-interest (ROI) imaging techniques in CT, which can reconstruct an ROI image from the projection data set of the ROI, can be used not only for reducing imaging-radiation exposure to the subject and scatters to the detector but also for potentially increasing spatial resolution of the reconstructed images. Increasing spatial resolution in microCT images can facilitate improved accuracy in many assessment tasks. A method proposed previously for increasing CT image spatial resolutionmore » entails the exploitation of the geometric magnification in cone-beam CT. Due to finite detector size, however, this method can lead to data truncation for a large geometric magnification. The Feldkamp-Davis-Kress (FDK) algorithm yields images with artifacts when truncated data are used, whereas the recently developed backprojection filtration (BPF) algorithm is capable of reconstructing ROI images without truncation artifacts from truncated cone-beam data. We apply the BPF algorithm to reconstructing ROI images from truncated data of three different objects acquired by our circular cone-beam microCT system. Reconstructed images by use of the FDK and BPF algorithms from both truncated and nontruncated cone-beam data are compared. The results of the experimental studies demonstrate that, from certain truncated data, the BPF algorithm can reconstruct ROI images with quality comparable to that reconstructed from nontruncated data. In contrast, the FDK algorithm yields ROI images with truncation artifacts. Therefore, an implication of the studies is that, when truncated data are acquired with a configuration of a large geometric magnification, the BPF algorithm can be used for effective enhancement of the spatial resolution of a ROI image.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, J; Balter, P; Court, L

Purpose: To evaluate the performance of commercially available automatic segmentation tools built into treatment planning systems (TPS) in terms of their segmentation accuracy and flexibility in customization. Methods: Twelve head-and-neck cancer patients and twelve thoracic cancer patients were retrospectively selected to benchmark the model-based segmentation (MBS) and atlas-based segmentation (ABS) in RayStation TPS and the Smart Probabilistic Image Contouring Engine (SPICE) in Pinnacle TPS. Multi-atlas contouring service (MACS) that was developed in-house as a plug-in of Pinnacle TPS was evaluated as well. Manual contours used in clinic were reviewed and modified for consistency and served as ground truth for themore » evaluation. Head-and-neck evaluation included six regions of interest (ROIs): left and right parotid glands, brainstem, spinal cord, mandible, and submandibular glands. Thoracic evaluation includes seven ROIs: left and right lungs, spinal cord, heart, esophagus, and left and right brachial plexus. Auto-segmented contours were compared with the manual contours using the Dice similarity coefficient (DSC) and the mean surface distance (MSD). Results: In head- and-neck evaluation, only mandible has a high accuracy in all segmentations (DSC>85%); SPICE achieved DSC>70% for parotid glands; MACS achieved this for both parotid glands and submandibular glands; and RayStation ABS achieved this for spinal cord. In thoracic evaluation, SPICE achieved the best in lung and heart segmentation, while MACS achieved the best for all other structures. The less distinguishable structures on CT images, such as brainstem, spinal cord, parotid glands, submandibular glands, esophagus, and brachial plexus, showed great variability in different segmentation tools (mostly DSC<70% and MSD>3mm). The template for RayStation ABS can be easily customized by users, while RayStation MBS and SPICE rely on the vendors to provide the templates/models. Conclusion: Great variability was observed in different segmentation tools applied to different structures. These commercially-available segmentation tools should be carefully evaluated before clinical use.« less

Open versus percutaneous instrumentation in thoracolumbar fractures: magnetic resonance imaging comparison of paravertebral muscles after implant removal.

PubMed

Ntilikina, Yves; Bahlau, David; Garnon, Julien; Schuller, Sébastien; Walter, Axel; Schaeffer, Mickaël; Steib, Jean-Paul; Charles, Yann Philippe

2017-08-01

OBJECTIVE Percutaneous instrumentation in thoracolumbar fractures is intended to decrease paravertebral muscle damage by avoiding dissection. The aim of this study was to compare muscles at instrumented levels in patients who were treated by open or percutaneous surgery. METHODS Twenty-seven patients underwent open instrumentation, and 65 were treated percutaneously. A standardized MRI protocol using axial T1-weighted sequences was performed at a minimum 1-year follow-up after implant removal. Two independent observers measured cross-sectional areas (CSAs, in cm 2 ) and region of interest (ROI) signal intensity (in pixels) of paravertebral muscles by using OsiriX at the fracture level, and at cranial and caudal instrumented pedicle levels. An interobserver comparison was made using the Bland-Altman method. Reference ROI muscle was assessed in the psoas and ROI fat subcutaneously. The ratio ROI-CSA/ROI-fat was compared for patients treated with open versus percutaneous procedures by using a linear mixed model. A linear regression analyzed additional factors: age, sex, body mass index (BMI), Pfirrmann grade of adjacent discs, and duration of instrumentation in situ. RESULTS The interobserver agreement was good for all CSAs. The average CSA for the entire spine was 15.7 cm 2 in the open surgery group and 18.5 cm 2 in the percutaneous group (p = 0.0234). The average ROI-fat and ROI-muscle signal intensities were comparable: 497.1 versus 483.9 pixels for ROI-fat and 120.4 versus 111.7 pixels for ROI-muscle in open versus percutaneous groups. The ROI-CSA varied between 154 and 226 for open, and between 154 and 195 for percutaneous procedures, depending on instrumented levels. A significant difference of the ROI-CSA/ROI-fat ratio (0.4 vs 0.3) was present at fracture levels T12-L1 (p = 0.0329) and at adjacent cranial (p = 0.0139) and caudal (p = 0.0100) instrumented levels. Differences were not significant at thoracic levels. When adjusting based on age, BMI, and Pfirrmann grade, a significant difference between open and percutaneous procedures regarding the ROI-CSA/ROI-fat ratio was present in the lumbar spine (p < 0.01). Sex and duration of instrumentation had no significant influence. CONCLUSIONS Percutaneous instrumentation decreased muscle atrophy compared with open surgery. The MRI signal differences for T-12 and L-1 fractures indicated less fat infiltration within CSAs in patients who received percutaneous treatment. Differences were not evidenced at thoracic levels, where CSAs were smaller. Fat infiltration was not significantly different at lumbar levels with either procedure in elderly patients with associated discopathy and higher BMI. In younger patients, there was less fat infiltration of lumbar paravertebral muscles with percutaneous procedures.

Comparison of Two Methods of Noise Power Spectrum Determinations of Medical Radiography Systems

NASA Astrophysics Data System (ADS)

Hassan, Wan Muhamad Saridan Wan; Ahmed Darwish, Zeki

2011-03-01

Noise in medical images is recognized as an important factor that determines the image quality. Image noise is characterized by noise power spectrum (NPS). We compared two methods of NPS determination namely the methods of Wagner and Dobbins on Lanex Regular TMG screen-film system and Hologic Lorad Selenia full field digital mammography system, with the aim of choosing the better method to use. The methods differ in terms of various parametric choices and algorithm implementations. These parameters include the low pass filtering, low frequency filtering, windowing, smoothing, aperture correction, overlapping of region of interest (ROI), length of fast Fourier transform, ROI size, method of ROI normalization, and slice selection of the NPS. Overall, the two methods agreed to the practical value of noise power spectrum between 10-3-10-6 mm2 over spatial frequency range 0-10 mm-1.

Region of interest extraction based on multiscale visual saliency analysis for remote sensing images

NASA Astrophysics Data System (ADS)

Zhang, Yinggang; Zhang, Libao; Yu, Xianchuan

2015-01-01

Region of interest (ROI) extraction is an important component of remote sensing image processing. However, traditional ROI extraction methods are usually prior knowledge-based and depend on classification, segmentation, and a global searching solution, which are time-consuming and computationally complex. We propose a more efficient ROI extraction model for remote sensing images based on multiscale visual saliency analysis (MVS), implemented in the CIE L*a*b* color space, which is similar to visual perception of the human eye. We first extract the intensity, orientation, and color feature of the image using different methods: the visual attention mechanism is used to eliminate the intensity feature using a difference of Gaussian template; the integer wavelet transform is used to extract the orientation feature; and color information content analysis is used to obtain the color feature. Then, a new feature-competition method is proposed that addresses the different contributions of each feature map to calculate the weight of each feature image for combining them into the final saliency map. Qualitative and quantitative experimental results of the MVS model as compared with those of other models show that it is more effective and provides more accurate ROI extraction results with fewer holes inside the ROI.

The male to female ratio at birth in the Republic of Ireland and Northern Ireland: influence of societal stress

PubMed Central

2015-01-01

Introduction Male live births occur slightly in excess of female births. The ratio of male divided by total births is referred to as M/F. Many factors reduce M/F including toxins, stress, and privation, with excess male foetal loss. “The Troubles” (1969-1998) of Northern Ireland (NI) and the economic downturn of Republic of Ireland (ROI) from 2007 posed stresses with corresponding controls. This study analysed M/F in NI and ROI. Methods Annual male and female live births in NI and the ROI were compared using chi tests. Results M/F was significantly higher in NI than in ROI. M/F in NI dropped after 1974. M/F rose in ROI up to 1994, then fell. Discussion Violence-related stress may have been the cause for the M/F drop in NI. Economic improvement followed by recession may have caused parallel M/F changes in ROI. These findings agree with the stress hypothesis of M/F. PMID:26668416

Classification of visual signs in abdominal CT image figures in biomedical literature

NASA Astrophysics Data System (ADS)

Xue, Zhiyun; You, Daekeun; Antani, Sameer; Long, L. Rodney; Demner-Fushman, Dina; Thoma, George R.

2014-03-01

"Imaging signs" are a critical part of radiology's language. They not only are important for conveying diagnosis, but may also aid in indexing radiology literature and retrieving relevant cases and images. Here we report our work towards representing and categorizing imaging signs of abdominal abnormalities in figures in the radiology literature. Given a region-of-interest (ROI) from a figure, our goal was to assign a correct imaging sign label to that ROI from the following seven: accordion, comb, ring, sandwich, small bowel feces, target, or whirl. As training and test data, we created our own "gold standard" dataset of regions containing imaging signs. We computed 2997 feature attributes to represent imaging sign characteristics for each ROI in training and test sets. Following feature selection they were reduced to 70 attributes and were input to a Support Vector Machine classifier. We applied image-enhancement methods to compensate for variable quality of the images in radiology articles. In particular we developed a method for automatic detection and removal of pointers/markers (arrows, arrowheads, and asterisk symbols) on the images. These pointers/markers are valuable for approximately locating ROIs; however, they degrade the classification because they are often (partially) included in the training ROIs. On a test set of 283 ROIs, our method achieved an overall accuracy of 70% in labeling the seven signs, which we believe is a promising result for using imaging signs to search/retrieve radiology literature. This work is also potentially valuable for the creation of a visual ontology of biomedical imaging entities.

Classification of mass and normal breast tissue: A convolution neural network classifier with spatial domain and texture images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sahiner, B.; Chan, H.P.; Petrick, N.

1996-10-01

The authors investigated the classification of regions of interest (ROI`s) on mammograms as either mass or normal tissue using a convolution neural network (CNN). A CNN is a back-propagation neural network with two-dimensional (2-D) weight kernels that operate on images. A generalized, fast and stable implementation of the CNN was developed. The input images to the CNN were obtained form the ROI`s using two techniques. The first technique employed averaging and subsampling. The second technique employed texture feature extraction methods applied to small subregions inside the ROI. Features computed over different subregions were arranged as texture images, which were subsequentlymore » used as CNN inputs. The effects of CNN architecture and texture feature parameters on classification accuracy were studied. Receiver operating characteristic (ROC) methodology was used to evaluate the classification accuracy. A data set consisting of 168 ROI`s containing biopsy-proven masses and 504 ROI`s containing normal breast tissue was extracted from 168 mammograms by radiologists experienced in mammography. This data set was used for training and testing the CNN. With the best combination of CNN architecture and texture feature parameters, the area under the test ROC curve reached 0.87, which corresponded to a true-positive fraction of 90% at a false positive fraction of 31%. The results demonstrate the feasibility of using a CNN for classification of masses and normal tissue on mammograms.« less

Selective visual region of interest to enhance medical video conferencing

NASA Astrophysics Data System (ADS)

Bonneau, Walt, Jr.; Read, Christopher J.; Shirali, Girish

1998-06-01

The continued economic pressure that is being placed upon the healthcare industry creates both challenge and opportunity to develop cost effective healthcare tools. Tools that provide improvements in the quality of medical care at the same time improve the distribution of efficient care will create product demand. Video Conferencing systems are one of the latest product technologies that are evolving their way into healthcare applications. The systems that provide quality Bi- directional video and imaging at the lowest system and communication cost are creating many possible options for the healthcare industry. A method to use only 128k bits/sec. of ISDN bandwidth while providing quality video images in selected regions will be applied to echocardiograms using a low cost video conferencing system operating within a basic rate ISDN line bandwidth. Within a given display area (frame) it has been observed that only selected informational areas of the frame of are of value when viewing for detail and precision within an image. Much in the same manner that a photograph is cropped. If a method to accomplish Region Of Interest (ROI) was applied to video conferencing using H.320 with H.263 (compression) and H.281 (camera control) international standards, medical image quality could be achieved in a cost-effective manner. For example, the cardiologist could be provided with a selectable three to eight end-point viewable ROI polygon that defines the ROI in the image. This is achieved by the video system calculating the selected regional end-points and creating an alpha mask to signify the importance of the ROI to the compression processor. This region is then applied to the compression algorithm in a manner that the majority of the video conferencing processor cycles are focused on the ROI of the image. An occasional update of the non-ROI area is processed to maintain total image coherence. The user could control the non-ROI area updates. Providing encoder side ROI specification is of value. However, the power of this capability is improved if remote access and selection of the ROI is also provided. Using the H.281 camera standard and proposing an additional option to the standard to allow for remote ROI selection would make this possible. When ROI is applied the ability to reach the equivalent of 384K bits/sec ISDN rates may be achieved or exceeded depending upon the size of the selected ROI using 128K bits/sec. This opens additional opportunity to establish international calling and reduced call rates by up to sixty- six percent making reoccurring communication costs attractive. Rates of twenty to thirty quality ROI updates could be achieved. It is however important to understand that this technique is still under development.

Region of interest processing for iterative reconstruction in x-ray computed tomography

NASA Astrophysics Data System (ADS)

Kopp, Felix K.; Nasirudin, Radin A.; Mei, Kai; Fehringer, Andreas; Pfeiffer, Franz; Rummeny, Ernst J.; Noël, Peter B.

2015-03-01

The recent advancements in the graphics card technology raised the performance of parallel computing and contributed to the introduction of iterative reconstruction methods for x-ray computed tomography in clinical CT scanners. Iterative maximum likelihood (ML) based reconstruction methods are known to reduce image noise and to improve the diagnostic quality of low-dose CT. However, iterative reconstruction of a region of interest (ROI), especially ML based, is challenging. But for some clinical procedures, like cardiac CT, only a ROI is needed for diagnostics. A high-resolution reconstruction of the full field of view (FOV) consumes unnecessary computation effort that results in a slower reconstruction than clinically acceptable. In this work, we present an extension and evaluation of an existing ROI processing algorithm. Especially improvements for the equalization between regions inside and outside of a ROI are proposed. The evaluation was done on data collected from a clinical CT scanner. The performance of the different algorithms is qualitatively and quantitatively assessed. Our solution to the ROI problem provides an increase in signal-to-noise ratio and leads to visually less noise in the final reconstruction. The reconstruction speed of our technique was observed to be comparable with other previous proposed techniques. The development of ROI processing algorithms in combination with iterative reconstruction will provide higher diagnostic quality in the near future.

Influence of ROI definition on the heart-to-mediastinum ratio in planar 123I-MIBG imaging.

PubMed

Klene, Christiane; Jungen, Christiane; Okuda, Koichi; Kobayashi, Yuske; Helberg, Annabelle; Mester, Janos; Meyer, Christian; Nakajima, Kenichi

2018-02-01

Iodine-123-metaiodobenzylguanidine ( 123 I-MIBG) imaging with estimation of the heart-to-mediastinum ratio (HMR) has been established for risk assessment in patients with chronic heart failure. Our aim was to evaluate the effect of different methods of ROI definition on the renderability of HMR to normal or decreased sympathetic innervation. The results of three different methods of ROI definition (clinical routine (CLI), simple standardization (STA), and semi-automated (AUT) were compared. Ranges of 95% limits of agreement (LoA) of inter-observer variabilities were 0.28 and 0.13 for STA and AUT, respectively. Considering a HMR of 1.60 as the lower limit of normal, 13 of 32 (41%) for method STA and 5 of 32 (16%) for method AUT of all HMR measurements could not be classified to normal or pathologic. Ranges of 95% LoA of inter-method variabilities were 0.72 for CLI vs AUT, 0.65 for CLI vs STA, and 0.31 for STA vs AUT. Different methods of ROI definition result in different ranges of the LoA of the measured HMR with relevance for rendering the results to normal or pathological innervation. We could demonstrate that standardized protocols can help keep methodological variabilities limited, narrowing the gray zone of renderability.

SU-E-J-129: Atlas Development for Cardiac Automatic Contouring Using Multi-Atlas Segmentation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, R; Yang, J; Pan, T

Purpose: To develop a set of atlases for automatic contouring of cardiac structures to determine heart radiation dose and the associated toxicity. Methods: Six thoracic cancer patients with both contrast and non-contrast CT images were acquired for this study. Eight radiation oncologists manually and independently delineated cardiac contours on the non-contrast CT by referring to the fused contrast CT and following the RTOG 1106 atlas contouring guideline. Fifteen regions of interest (ROIs) were delineated, including heart, four chambers, four coronary arteries, pulmonary artery and vein, inferior and superior vena cava, and ascending and descending aorta. Individual expert contours were fusedmore » using the simultaneous truth and performance level estimation (STAPLE) algorithm for each ROI and each patient. The fused contours became atlases for an in-house multi-atlas segmentation. Using leave-one-out test, we generated auto-segmented contours for each ROI and each patient. The auto-segmented contours were compared with the fused contours using the Dice similarity coefficient (DSC) and the mean surface distance (MSD). Results: Inter-observer variability was not obvious for heart, chambers, and aorta but was large for other structures that were not clearly distinguishable on CT image. The average DSC between individual expert contours and the fused contours were less than 50% for coronary arteries and pulmonary vein, and the average MSD were greater than 4.0 mm. The largest MSD of expert contours deviating from the fused contours was 2.5 cm. The mean DSC and MSD of auto-segmented contours were within one standard deviation of expert contouring variability except the right coronary artery. The coronary arteries, vena cava, and pulmonary vein had DSC<70% and MSD>3.0 mm. Conclusion: A set of cardiac atlases was created for cardiac automatic contouring, the accuracy of which was comparable to the variability in expert contouring. However, substantial modification may need for auto-segmented contours of indistinguishable small structures.« less

Look@NanoSIMS--a tool for the analysis of nanoSIMS data in environmental microbiology.

PubMed

Polerecky, Lubos; Adam, Birgit; Milucka, Jana; Musat, Niculina; Vagner, Tomas; Kuypers, Marcel M M

2012-04-01

We describe an open-source freeware programme for high throughput analysis of nanoSIMS (nanometre-scale secondary ion mass spectrometry) data. The programme implements basic data processing and analytical functions, including display and drift-corrected accumulation of scanned planes, interactive and semi-automated definition of regions of interest (ROIs), and export of the ROIs' elemental and isotopic composition in graphical and text-based formats. Additionally, the programme offers new functions that were custom-designed to address the needs of environmental microbiologists. Specifically, it allows manual and automated classification of ROIs based on the information that is derived either from the nanoSIMS dataset itself (e.g. from labelling achieved by halogen in situ hybridization) or is provided externally (e.g. as a fluorescence in situ hybridization image). Moreover, by implementing post-processing routines coupled to built-in statistical tools, the programme allows rapid synthesis and comparative analysis of results from many different datasets. After validation of the programme, we illustrate how these new processing and analytical functions increase flexibility, efficiency and depth of the nanoSIMS data analysis. Through its custom-made and open-source design, the programme provides an efficient, reliable and easily expandable tool that can help a growing community of environmental microbiologists and researchers from other disciplines process and analyse their nanoSIMS data. © 2012 Society for Applied Microbiology and Blackwell Publishing Ltd.

A new efficient method for color image compression based on visual attention mechanism

NASA Astrophysics Data System (ADS)

Shao, Xiaoguang; Gao, Kun; Lv, Lily; Ni, Guoqiang

2010-11-01

One of the key procedures in color image compression is to extract its region of interests (ROIs) and evaluate different compression ratios. A new non-uniform color image compression algorithm with high efficiency is proposed in this paper by using a biology-motivated selective attention model for the effective extraction of ROIs in natural images. When the ROIs have been extracted and labeled in the image, the subsequent work is to encode the ROIs and other regions with different compression ratios via popular JPEG algorithm. Furthermore, experiment results and quantitative and qualitative analysis in the paper show perfect performance when comparing with other traditional color image compression approaches.

Infrared thermography of the pig thorax: an assessment of selected regions of interest by computed tomographical and anatomical parameters.

PubMed

Menzel, A; Siewert, C; Gasse, H; Seifert, H; Hoeltig, D; Hennig-Pauka, I

2015-04-01

Current methods of diagnosis of respiratory diseases in swine are invasive, time-consuming and expensive. Infrared thermography (IRT) of the thorax might provide a new method of high specificity to select swine affected with lung alterations for further diagnostics. In this study, layer thickness of different tissues was determined in frozen thorax slices (FTS) by computed tomography (CT) and then related to skin temperatures measured by IRT in healthy pigs. The aim was to determine appropriate regions of interest (ROI) for evaluation of IRT images. Organ layer thicknesses measured in CT images correspond to those measured in FTS. Temperature differences between lung ROIs and abdomen ROIs were positively correlated with lung layer thickness at certain localizations, and negatively correlated with the thickness of the thorax wall and of inner organ layers. Reference values of differences between skin temperatures were established for two ROIs on the thorax with potential practical use for lung health status determination. Respective ROIs were located on vertical lines crossing the 7th (right) and the 10th (left) thoracic vertebrae. The presence of ribs affected skin temperature significantly. © 2014 Blackwell Verlag GmbH.

Detection scheme for a partially occluded pedestrian based on occluded depth in lidar-radar sensor fusion

NASA Astrophysics Data System (ADS)

Kwon, Seong Kyung; Hyun, Eugin; Lee, Jin-Hee; Lee, Jonghun; Son, Sang Hyuk

2017-11-01

Object detections are critical technologies for the safety of pedestrians and drivers in autonomous vehicles. Above all, occluded pedestrian detection is still a challenging topic. We propose a new detection scheme for occluded pedestrian detection by means of lidar-radar sensor fusion. In the proposed method, the lidar and radar regions of interest (RoIs) have been selected based on the respective sensor measurement. Occluded depth is a new means to determine whether an occluded target exists or not. The occluded depth is a region projected out by expanding the longitudinal distance with maintaining the angle formed by the outermost two end points of the lidar RoI. The occlusion RoI is the overlapped region made by superimposing the radar RoI and the occluded depth. The object within the occlusion RoI is detected by the radar measurement information and the occluded object is estimated as a pedestrian based on human Doppler distribution. Additionally, various experiments are performed in detecting a partially occluded pedestrian in outdoor as well as indoor environments. According to experimental results, the proposed sensor fusion scheme has much better detection performance compared to the case without our proposed method.

Cost and Return on Investment of a Work-Family Intervention in the Extended Care Industry: Evidence From the Work, Family, and Health Network.

PubMed

Dowd, William N; Bray, Jeremy W; Barbosa, Carolina; Brockwood, Krista; Kaiser, David J; Mills, Michael J; Hurtado, David A; Wipfli, Brad

2017-10-01

To estimate the cost and return on investment (ROI) of an intervention targeting work-family conflict (WFC) in the extended care industry. Costs to deliver the intervention during a group-randomized controlled trial were estimated, and data on organizational costs-presenteeism, health care costs, voluntary termination, and sick time-were collected from interviews and administrative data. Generalized linear models were used to estimate the intervention's impact on organizational costs. Combined, these results produced ROI estimates. A cluster-robust confidence interval (CI) was estimated around the ROI estimate. The per-participant cost of the intervention was $767. The ROI was -1.54 (95% CI: -4.31 to 2.18). The intervention was associated with a $668 reduction in health care costs (P < 0.05). This paper builds upon and expands prior ROI estimation methods to a new setting.

Semi-automatic measures of activity in selected south polar regions of Mars using morphological image analysis

NASA Astrophysics Data System (ADS)

Aye, Klaus-Michael; Portyankina, Ganna; Pommerol, Antoine; Thomas, Nicolas

The High Resolution Imaging Science Experiment (HiRISE) onboard Mars Reconnaissance Orbiter (MRO) has been used to monitor the seasonal evolution of several regions at high southern latitudes. Of particular interest have been jet-like activities that may result from the process described by Kieffer (2007), involving translucent CO2 ice. These jets are assumed to create fan-shaped ground features, as studied e.g. in Hansen et.al. (2010) and Portyankina et.al. (2010). In Thomas et.al. (2009), a small region of interest (ROI) inside the south polar Inca City region (81° S, 296° E) was defined for which the seasonal change of the number of fans was determined. This ROI was chosen for its strong visual variability in ground features. The mostly manual counting work showed, that the number of apparent fans increases monotonously for a considerable amount of time from the beginning of the spring time observations at Ls of 178° until approx. 230° , following the increase of available solar energy for the aforementioned processes of the Kieffer model. This fact indicates that the number of visual fan features can be used as an activity measure for the seasonal evolution of this area, in addition to commonly used evolution studies of surface reflectance. Motivated by these results, we would like to determine the fan count evolution for more south polar areas like Ithaca, Manhattan, Giza and others. To increase the reproducibility of the results by avoiding potential variability in fan shape recognition by human eye and to increase the production efficiency, efforts are being undertaken to automise the fan counting procedure. The techniques used, cleanly separated in different stages of the procedure, the difficulties for each stage and an overview of the tools used at each step will be presented. After showing a proof of concept in Aye et.al. (2010), for a ROI that is comparable to the one previously used for manual counting in Thomas et.al. (2009), we now will show results of these semi-automatically determined seasonal fan count evolutions for Inca City, Ithaca and Manhattan ROIs, compare these evolutionary patterns with each other and with surface reflectance evolutions of both HiRISE and CRISM for the same locations. References: Aye, K.-M. et. al. (2010), LPSC 2010, 2707 Hansen, C. et. al (2010) Icarus, 205, Issue 1, p. 283-295 Kieffer, H.H. (2007), JGR 112 Portyankina, G. et. al. (2010), Icarus, 205, Issue 1, p. 311-320 Thomas, N. et. Al. (2009), Vol. 4, EPSC2009-478

Effects of 99mTc-TRODAT-1 drug template on image quantitative analysis

PubMed Central

Yang, Bang-Hung; Chou, Yuan-Hwa; Wang, Shyh-Jen; Chen, Jyh-Cheng

2018-01-01

99mTc-TRODAT-1 is a type of drug that can bind to dopamine transporters in living organisms and is often used in SPCT imaging for observation of changes in the activity uptake of dopamine in the striatum. Therefore, it is currently widely used in studies on clinical diagnosis of Parkinson’s disease (PD) and movement-related disorders. In conventional 99mTc-TRODAT-1 SPECT image evaluation, visual inspection or manual selection of ROI for semiquantitative analysis is mainly used to observe and evaluate the degree of striatal defects. However, these methods are dependent on the subjective opinions of observers, which lead to human errors, have shortcomings such as long duration, increased effort, and have low reproducibility. To solve this problem, this study aimed to establish an automatic semiquantitative analytical method for 99mTc-TRODAT-1. This method combines three drug templates (one built-in SPECT template in SPM software and two self-generated MRI-based and HMPAO-based TRODAT-1 templates) for the semiquantitative analysis of the striatal phantom and clinical images. At the same time, the results of automatic analysis of the three templates were compared with results from a conventional manual analysis for examining the feasibility of automatic analysis and the effects of drug templates on automatic semiquantitative analysis results. After comparison, it was found that the MRI-based TRODAT-1 template generated from MRI images is the most suitable template for 99mTc-TRODAT-1 automatic semiquantitative analysis. PMID:29543874

Thoracic cavity definition for 3D PET/CT analysis and visualization.

PubMed

Cheirsilp, Ronnarit; Bascom, Rebecca; Allen, Thomas W; Higgins, William E

2015-07-01

X-ray computed tomography (CT) and positron emission tomography (PET) serve as the standard imaging modalities for lung-cancer management. CT gives anatomical details on diagnostic regions of interest (ROIs), while PET gives highly specific functional information. During the lung-cancer management process, a patient receives a co-registered whole-body PET/CT scan pair and a dedicated high-resolution chest CT scan. With these data, multimodal PET/CT ROI information can be gleaned to facilitate disease management. Effective image segmentation of the thoracic cavity, however, is needed to focus attention on the central chest. We present an automatic method for thoracic cavity segmentation from 3D CT scans. We then demonstrate how the method facilitates 3D ROI localization and visualization in patient multimodal imaging studies. Our segmentation method draws upon digital topological and morphological operations, active-contour analysis, and key organ landmarks. Using a large patient database, the method showed high agreement to ground-truth regions, with a mean coverage=99.2% and leakage=0.52%. Furthermore, it enabled extremely fast computation. For PET/CT lesion analysis, the segmentation method reduced ROI search space by 97.7% for a whole-body scan, or nearly 3 times greater than that achieved by a lung mask. Despite this reduction, we achieved 100% true-positive ROI detection, while also reducing the false-positive (FP) detection rate by >5 times over that achieved with a lung mask. Finally, the method greatly improved PET/CT visualization by eliminating false PET-avid obscurations arising from the heart, bones, and liver. In particular, PET MIP views and fused PET/CT renderings depicted unprecedented clarity of the lesions and neighboring anatomical structures truly relevant to lung-cancer assessment. Copyright © 2015 Elsevier Ltd. All rights reserved.

Thoracic Cavity Definition for 3D PET/CT Analysis and Visualization

PubMed Central

Cheirsilp, Ronnarit; Bascom, Rebecca; Allen, Thomas W.; Higgins, William E.

2015-01-01

X-ray computed tomography (CT) and positron emission tomography (PET) serve as the standard imaging modalities for lung-cancer management. CT gives anatomical detail on diagnostic regions of interest (ROIs), while PET gives highly specific functional information. During the lung-cancer management process, a patient receives a co-registered whole-body PET/CT scan pair and a dedicated high-resolution chest CT scan. With these data, multimodal PET/CT ROI information can be gleaned to facilitate disease management. Effective image segmentation of the thoracic cavity, however, is needed to focus attention on the central chest. We present an automatic method for thoracic cavity segmentation from 3D CT scans. We then demonstrate how the method facilitates 3D ROI localization and visualization in patient multimodal imaging studies. Our segmentation method draws upon digital topological and morphological operations, active-contour analysis, and key organ landmarks. Using a large patient database, the method showed high agreement to ground-truth regions, with a mean coverage = 99.2% and leakage = 0.52%. Furthermore, it enabled extremely fast computation. For PET/CT lesion analysis, the segmentation method reduced ROI search space by 97.7% for a whole-body scan, or nearly 3 times greater than that achieved by a lung mask. Despite this reduction, we achieved 100% true-positive ROI detection, while also reducing the false-positive (FP) detection rate by >5 times over that achieved with a lung mask. Finally, the method greatly improved PET/CT visualization by eliminating false PET-avid obscurations arising from the heart, bones, and liver. In particular, PET MIP views and fused PET/CT renderings depicted unprecedented clarity of the lesions and neighboring anatomical structures truly relevant to lung-cancer assessment. PMID:25957746

SU-C-BRA-05: Delineating High-Dose Clinical Target Volumes for Head and Neck Tumors Using Machine Learning Algorithms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cardenas, C; The University of Texas Graduate School of Biomedical Sciences, Houston, TX; Wong, A

Purpose: To develop and test population-based machine learning algorithms for delineating high-dose clinical target volumes (CTVs) in H&N tumors. Automating and standardizing the contouring of CTVs can reduce both physician contouring time and inter-physician variability, which is one of the largest sources of uncertainty in H&N radiotherapy. Methods: Twenty-five node-negative patients treated with definitive radiotherapy were selected (6 right base of tongue, 11 left and 9 right tonsil). All patients had GTV and CTVs manually contoured by an experienced radiation oncologist prior to treatment. This contouring process, which is driven by anatomical, pathological, and patient specific information, typically results inmore » non-uniform margin expansions about the GTV. Therefore, we tested two methods to delineate high-dose CTV given a manually-contoured GTV: (1) regression-support vector machines(SVM) and (2) classification-SVM. These models were trained and tested on each patient group using leave-one-out cross-validation. The volume difference(VD) and Dice similarity coefficient(DSC) between the manual and auto-contoured CTV were calculated to evaluate the results. Distances from GTV-to-CTV were computed about each patient’s GTV and these distances, in addition to distances from GTV to surrounding anatomy in the expansion direction, were utilized in the regression-SVM method. The classification-SVM method used categorical voxel-information (GTV, selected anatomical structures, else) from a 3×3×3cm3 ROI centered about the voxel to classify voxels as CTV. Results: Volumes for the auto-contoured CTVs ranged from 17.1 to 149.1cc and 17.4 to 151.9cc; the average(range) VD between manual and auto-contoured CTV were 0.93 (0.48–1.59) and 1.16(0.48–1.97); while average(range) DSC values were 0.75(0.59–0.88) and 0.74(0.59–0.81) for the regression-SVM and classification-SVM methods, respectively. Conclusion: We developed two novel machine learning methods to delineate high-dose CTV for H&N patients. Both methods showed promising results that hint to a solution to the standardization of the contouring process of clinical target volumes. Varian Medical Systems grant.« less

Medical Ultrasound Video Coding with H.265/HEVC Based on ROI Extraction

PubMed Central

Wu, Yueying; Liu, Pengyu; Gao, Yuan; Jia, Kebin

2016-01-01

High-efficiency video compression technology is of primary importance to the storage and transmission of digital medical video in modern medical communication systems. To further improve the compression performance of medical ultrasound video, two innovative technologies based on diagnostic region-of-interest (ROI) extraction using the high efficiency video coding (H.265/HEVC) standard are presented in this paper. First, an effective ROI extraction algorithm based on image textural features is proposed to strengthen the applicability of ROI detection results in the H.265/HEVC quad-tree coding structure. Second, a hierarchical coding method based on transform coefficient adjustment and a quantization parameter (QP) selection process is designed to implement the otherness encoding for ROIs and non-ROIs. Experimental results demonstrate that the proposed optimization strategy significantly improves the coding performance by achieving a BD-BR reduction of 13.52% and a BD-PSNR gain of 1.16 dB on average compared to H.265/HEVC (HM15.0). The proposed medical video coding algorithm is expected to satisfy low bit-rate compression requirements for modern medical communication systems. PMID:27814367

Medical Ultrasound Video Coding with H.265/HEVC Based on ROI Extraction.

PubMed

Wu, Yueying; Liu, Pengyu; Gao, Yuan; Jia, Kebin

2016-01-01

High-efficiency video compression technology is of primary importance to the storage and transmission of digital medical video in modern medical communication systems. To further improve the compression performance of medical ultrasound video, two innovative technologies based on diagnostic region-of-interest (ROI) extraction using the high efficiency video coding (H.265/HEVC) standard are presented in this paper. First, an effective ROI extraction algorithm based on image textural features is proposed to strengthen the applicability of ROI detection results in the H.265/HEVC quad-tree coding structure. Second, a hierarchical coding method based on transform coefficient adjustment and a quantization parameter (QP) selection process is designed to implement the otherness encoding for ROIs and non-ROIs. Experimental results demonstrate that the proposed optimization strategy significantly improves the coding performance by achieving a BD-BR reduction of 13.52% and a BD-PSNR gain of 1.16 dB on average compared to H.265/HEVC (HM15.0). The proposed medical video coding algorithm is expected to satisfy low bit-rate compression requirements for modern medical communication systems.

SU-D-202-03: Statistical Segmentation On Quantitative CT for Assessing Spatial Tumor Response During Radiation Therapy Delivery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schott, D; Chen, X; Klawikowski, S

2016-06-15

Purpose: Develop a method to segment regions of interest (ROIs) in tumor with statistically similar Hounsfield unit (HU) values and/or HU changes during chemoradiation therapy (CRT) delivery, to assess spatial tumor treatment response based on daily CTs during CRT delivery. Methods: Generate a three region map of ROIs with differential HUs, by sampling neighboring voxels around a selected voxel and comparing to the mean of the entire ROI using a t-test. The cumulative distribution function, P, is calculated from the t-test. The P value is assigned to be the value at the selected voxel, and this is repeated over allmore » voxels in the initial ROI. Three regions are defined as: (1-P) < 0.00001 (mid region), and 0.00001 < (1-P) (mean greater than baseline and mean lower than baseline). The test is then expanded to compare daily CT sets acquired during routine CT-guided RT delivery using a CT-on-rails. The first fraction CT is used as the baseline for comparison. We tested 15 pancreatic head tumor cases undergoing CRT, to identify the ROIs and changes corresponding to normal, fibrotic, and tumor tissue. The obtained ROIs were compared with MRI-ADC maps acquired pre- and post-CRT. Results: The ROIs in 13 out of 15 patients’ first fraction CTs and pre-CRT MRIs matched the general region and slices covered, as well as in 6 out of the 9 patients with post-CRT MRIs. The high HU region designated by the t-test was seen to correlate with the tumor region in MR, and these ROIs are positioned within the same region over the course of treatment. In patients with poorly delineated tumors in MR, the t-test was inconclusive. Conclusion: The proposed statistical segmentation technique shows the potential to identify regions in tumor with differential HUs and HU changes during CRT delivery for patients with pancreas head cancer.« less

Research on infrared ship detection method in sea-sky background

NASA Astrophysics Data System (ADS)

Tang, Da; Sun, Gang; Wang, Ding-he; Niu, Zhao-dong; Chen, Zeng-ping

2013-09-01

An approach to infrared ship detection based on sea-sky-line(SSL) detection, ROI extraction and feature recognition is proposed in this paper. Firstly, considering that far ships are expected to be adjacent to the SSL, SSL is detected to find potential target areas. Radon transform is performed on gradient image to choose candidate SSLs, and detection result is given by fuzzy synthetic evaluation values. Secondly, in view of recognizable condition that there should be enough differences between target and background in infrared image, two gradient masks have been created and improved as practical guidelines in eliminating false alarm. Thirdly, extract ROI near the SSL by using multi-grade segmentation and fusion method after image sharpening, and unsuitable candidates are screened out according to the gradient masks and ROI shape. Finally, we segment the rest of ROIs by two-stage modified OTSU, and calculate target confidence as a standard measuring the facticity of target. Compared with other ship detection methods, proposed method is suitable for bipolar targets, which offers a good practicability and accuracy, and achieves a satisfying detection speed. Detection experiments with 200 thousand frames show that the proposed method is widely applicable, powerful in resistance to interferences and noises with a detection rate of above 95%, which satisfies the engineering needs commendably.

Nodule Detection in a Lung Region that's Segmented with Using Genetic Cellular Neural Networks and 3D Template Matching with Fuzzy Rule Based Thresholding

PubMed Central

Osman, Onur; Ucan, Osman N.

2008-01-01

Objective The purpose of this study was to develop a new method for automated lung nodule detection in serial section CT images with using the characteristics of the 3D appearance of the nodules that distinguish themselves from the vessels. Materials and Methods Lung nodules were detected in four steps. First, to reduce the number of region of interests (ROIs) and the computation time, the lung regions of the CTs were segmented using Genetic Cellular Neural Networks (G-CNN). Then, for each lung region, ROIs were specified with using the 8 directional search; +1 or -1 values were assigned to each voxel. The 3D ROI image was obtained by combining all the 2-Dimensional (2D) ROI images. A 3D template was created to find the nodule-like structures on the 3D ROI image. Convolution of the 3D ROI image with the proposed template strengthens the shapes that are similar to those of the template and it weakens the other ones. Finally, fuzzy rule based thresholding was applied and the ROI's were found. To test the system's efficiency, we used 16 cases with a total of 425 slices, which were taken from the Lung Image Database Consortium (LIDC) dataset. Results The computer aided diagnosis (CAD) system achieved 100% sensitivity with 13.375 FPs per case when the nodule thickness was greater than or equal to 5.625 mm. Conclusion Our results indicate that the detection performance of our algorithm is satisfactory, and this may well improve the performance of computer-aided detection of lung nodules. PMID:18253070

Improved operator agreement and efficiency using the minimum area contour change method for delineation of hyperintense multiple sclerosis lesions on FLAIR MRI

PubMed Central

2013-01-01

Background Activity of disease in patients with multiple sclerosis (MS) is monitored by detecting and delineating hyper-intense lesions on MRI scans. The Minimum Area Contour Change (MACC) algorithm has been created with two main goals: a) to improve inter-operator agreement on outlining regions of interest (ROIs) and b) to automatically propagate longitudinal ROIs from the baseline scan to a follow-up scan. Methods The MACC algorithm first identifies an outer bound for the solution path, forms a high number of iso-contour curves based on equally spaced contour values, and then selects the best contour value to outline the lesion. The MACC software was tested on a set of 17 FLAIR MRI images evaluated by a pair of human experts and a longitudinal dataset of 12 pairs of T2-weighted Fluid Attenuated Inversion Recovery (FLAIR) images that had lesion analysis ROIs drawn by a single expert operator. Results In the tests where two human experts evaluated the same MRI images, the MACC program demonstrated that it could markedly reduce inter-operator outline error. In the longitudinal part of the study, the MACC program created ROIs on follow-up scans that were in close agreement to the original expert’s ROIs. Finally, in a post-hoc analysis of 424 follow-up scans 91% of propagated MACC were accepted by an expert and only 9% of the final accepted ROIS had to be created or edited by the expert. Conclusion When used with an expert operator's verification of automatically created ROIs, MACC can be used to improve inter- operator agreement and decrease analysis time, which should improve data collected and analyzed in multicenter clinical trials. PMID:24004511

Use of spectral analysis with iterative filter for voxelwise determination of regional rates of cerebral protein synthesis with L-[1-11C]leucine PET.

PubMed

Veronese, Mattia; Schmidt, Kathleen C; Smith, Carolyn Beebe; Bertoldo, Alessandra

2012-06-01

A spectral analysis approach was used to estimate kinetic parameters of the L-[1-(11)C]leucine positron emission tomography (PET) method and regional rates of cerebral protein synthesis (rCPS) on a voxel-by-voxel basis. Spectral analysis applies to both heterogeneous and homogeneous tissues; it does not require prior assumptions concerning number of tissue compartments. Parameters estimated with spectral analysis can be strongly affected by noise, but numerical filters improve estimation performance. Spectral analysis with iterative filter (SAIF) was originally developed to improve estimation of leucine kinetic parameters and rCPS in region-of-interest (ROI) data analyses. In the present study, we optimized SAIF for application at the voxel level. In measured L-[1-(11)C]leucine PET data, voxel-level SAIF parameter estimates averaged over all voxels within a ROI (mean voxel-SAIF) generally agreed well with corresponding estimates derived by applying the originally developed SAIF to ROI time-activity curves (ROI-SAIF). Region-of-interest-SAIF and mean voxel-SAIF estimates of rCPS were highly correlated. Simulations showed that mean voxel-SAIF rCPS estimates were less biased and less variable than ROI-SAIF estimates in the whole brain and cortex; biases were similar in white matter. We conclude that estimation of rCPS with SAIF is improved when the method is applied at voxel level than in ROI analysis.

Optimization of focality and direction in dense electrode array transcranial direct current stimulation (tDCS)

NASA Astrophysics Data System (ADS)

Guler, Seyhmus; Dannhauer, Moritz; Erem, Burak; Macleod, Rob; Tucker, Don; Turovets, Sergei; Luu, Phan; Erdogmus, Deniz; Brooks, Dana H.

2016-06-01

Objective. Transcranial direct current stimulation (tDCS) aims to alter brain function non-invasively via electrodes placed on the scalp. Conventional tDCS uses two relatively large patch electrodes to deliver electrical current to the brain region of interest (ROI). Recent studies have shown that using dense arrays containing up to 512 smaller electrodes may increase the precision of targeting ROIs. However, this creates a need for methods to determine effective and safe stimulus patterns as the number of degrees of freedom is much higher with such arrays. Several approaches to this problem have appeared in the literature. In this paper, we describe a new method for calculating optimal electrode stimulus patterns for targeted and directional modulation in dense array tDCS which differs in some important aspects with methods reported to date. Approach. We optimize stimulus pattern of dense arrays with fixed electrode placement to maximize the current density in a particular direction in the ROI. We impose a flexible set of safety constraints on the current power in the brain, individual electrode currents, and total injected current, to protect subject safety. The proposed optimization problem is convex and thus efficiently solved using existing optimization software to find unique and globally optimal electrode stimulus patterns. Main results. Solutions for four anatomical ROIs based on a realistic head model are shown as exemplary results. To illustrate the differences between our approach and previously introduced methods, we compare our method with two of the other leading methods in the literature. We also report on extensive simulations that show the effect of the values chosen for each proposed safety constraint bound on the optimized stimulus patterns. Significance. The proposed optimization approach employs volume based ROIs, easily adapts to different sets of safety constraints, and takes negligible time to compute. An in-depth comparison study gives insight into the relationship between different objective criteria and optimized stimulus patterns. In addition, the analysis of the interaction between optimized stimulus patterns and safety constraint bounds suggests that more precise current localization in the ROI, with improved safety criterion, may be achieved by careful selection of the constraint bounds.

Intrinsic feature-based pose measurement for imaging motion compensation

DOEpatents

Baba, Justin S.; Goddard, Jr., James Samuel

2014-08-19

Systems and methods for generating motion corrected tomographic images are provided. A method includes obtaining first images of a region of interest (ROI) to be imaged and associated with a first time, where the first images are associated with different positions and orientations with respect to the ROI. The method also includes defining an active region in the each of the first images and selecting intrinsic features in each of the first images based on the active region. Second, identifying a portion of the intrinsic features temporally and spatially matching intrinsic features in corresponding ones of second images of the ROI associated with a second time prior to the first time and computing three-dimensional (3D) coordinates for the portion of the intrinsic features. Finally, the method includes computing a relative pose for the first images based on the 3D coordinates.

SU-E-J-32: Dosimetric Evaluation Based On Pre-Treatment Cone Beam CT for Spine Stereotactic Body Radiotherapy: Does Region of Interest Focus Matter?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Magnelli, A; Xia, P

2015-06-15

Purpose: Spine stereotactic body radiotherapy requires very conformal dose distributions and precise delivery. Prior to treatment, a KV cone-beam CT (KV-CBCT) is registered to the planning CT to provide image-guided positional corrections, which depend on selection of the region of interest (ROI) because of imperfect patient positioning and anatomical deformation. Our objective is to determine the dosimetric impact of ROI selections. Methods: Twelve patients were selected for this study with the treatment regions varied from C-spine to T-spine. For each patient, the KV-CBCT was registered to the planning CT three times using distinct ROIs: one encompassing the entire patient, amore » large ROI containing large bony anatomy, and a small target-focused ROI. Each registered CBCT volume, saved as an aligned dataset, was then sent to the planning system. The treated plan was applied to each dataset and dose was recalculated. The tumor dose coverage (percentage of target volume receiving prescription dose), maximum point dose to 0.03 cc of the spinal cord, and dose to 10% of the spinal cord volume (V10) for each alignment were compared to the original plan. Results: The average magnitude of tumor coverage deviation was 3.9%±5.8% with external contour, 1.5%±1.1% with large ROI, 1.3%±1.1% with small ROI. Spinal cord V10 deviation from plan was 6.6%±6.6% with external contour, 3.5%±3.1% with large ROI, and 1.2%±1.0% with small ROI. Spinal cord max point dose deviation from plan was: 12.2%±13.3% with external contour, 8.5%±8.4% with large ROI, and 3.7%±2.8% with small ROI. Conclusion: A small ROI focused on the target results in the smallest deviation from planned dose to target and cord although rotations at large distances from the targets were observed. It is recommended that image fusion during CBCT focus narrowly on the target volume to minimize dosimetric error. Improvement in patient setups may further reduce residual errors.« less

Detection of MRI artifacts produced by intrinsic heart motion using a saliency model

NASA Astrophysics Data System (ADS)

Salguero, Jennifer; Velasco, Nelson; Romero, Eduardo

2017-11-01

Cardiac Magnetic Resonance (CMR) requires synchronization with the ECG to correct many types of noise. However, the complex heart motion frequently produces displaced slices that have to be either ignored or manually corrected since the ECG correction is useless in this case. This work presents a novel methodology that detects the motion artifacts in CMR using a saliency method that highlights the region where the heart chambers are located. Once the Region of Interest (RoI) is set, its center of gravity is determined for the set of slices composing the volume. The deviation of the gravity center is an estimation of the coherence between the slices and is used to find out slices with certain displacement. Validation was performed with distorted real images where a slice is artificially misaligned with respect to set of slices. The displaced slice is found with a Recall of 84% and F Score of 68%.

A Surface-based Analysis of Language Lateralization and Cortical Asymmetry

PubMed Central

Greve, Douglas N.; Van der Haegen, Lise; Cai, Qing; Stufflebeam, Steven; Sabuncu, Mert R.; Fischl, Bruce; Bysbaert, Marc

2013-01-01

Among brain functions, language is one of the most lateralized. Cortical language areas are also some of the most asymmetrical in the brain. An open question is whether the asymmetry in function is linked to the asymmetry in anatomy. To address this question, we measured anatomical asymmetry in 34 participants shown with fMRI to have language dominance of the left hemisphere (LLD) and 21 participants shown to have atypical right hemisphere dominance (RLD). All participants were healthy and left-handed, and most (80%) were female. Gray matter (GM) volume asymmetry was measured using an automated surface-based technique in both ROIs and exploratory analyses. In the ROI analysis, a significant difference between LLD and RLD was found in the insula. No differences were found in planum temporale (PT), pars opercularis (POp), pars triangularis (PTr), or Heschl’s gyrus (HG). The PT, POp, insula, and HG were all significantly left lateralized in both LLD and RLD participants. Both the positive and negative ROI findings replicate a previous study using manually labeled ROIs in a different cohort [Keller, S. S., Roberts, N., Garcia-Finana, M., Mohammadi, S., Ringelstein, E. B., Knecht, S., et al. Can the language-dominant hemisphere be predicted by brain anatomy? Journal of Cognitive Neuroscience, 23, 2013–2029, 2011]. The exploratory analysis was accomplished using a new surface-based registration that aligns cortical folding patterns across both subject and hemisphere. A small but significant cluster was found in the superior temporal gyrus that overlapped with the PT. A cluster was also found in the ventral occipitotemporal cortex corresponding to the visual word recognition area. The surface-based analysis also makes it possible to disentangle the effects of GM volume, thickness, and surface area while removing the effects of curvature. For both the ROI and exploratory analyses, the difference between LLD and RLD volume laterality was most strongly driven by differences in surface area and not cortical thickness. Overall, there were surprisingly few differences in GM volume asymmetry between LLD and RLD indicating that gross morphometric asymmetry is only subtly related to functional language laterality. PMID:23701459

Automatic segementation of histological structures in normal and neoplastic mammary gland tissue sections

NASA Astrophysics Data System (ADS)

Fernandez-Gonzalez, Rodrigo; Deschamps, Thomas; Idica, Adam; Malladi, Ravikanth; Ortiz de Solorzano, Carlos

2003-07-01

In this paper we present a scheme for real time segmentation of histological structures in microscopic images of normal and neoplastic mammary gland sections. Paraffin embedded or frozen tissue blocks are sliced, and sections are stained with hematoxylin and eosin (H&E). The sections are then imaged using conventional bright field microscopy. The background of the images is corrected by arithmetic manipulation using a "phantom." Then we use the fast marching method with a speed function that depends on the brightness gradient of the image to obtain a preliminary approximation to the boundaries of the structures of interest within a region of interest (ROI) of the entire section manually selected by the user. We use the result of the fast marching method as the initial condition for the level set motion equation. We run this last method for a few steps and obtain the final result of the segmentation. These results can be connected from section to section to build a three-dimensional reconstruction of the entire tissue block that we are studying.

Financial analysis of technology acquisition using fractionated lasers as a model.

PubMed

Jutkowitz, Eric; Carniol, Paul J; Carniol, Alan R

2010-08-01

Ablative fractional lasers are among the most advanced and costly devices on the market. Yet, there is a dearth of published literature on the cost and potential return on investment (ROI) of such devices. The objective of this study was to provide a methodological framework for physicians to evaluate ROI. To facilitate this analysis, we conducted a case study on the potential ROI of eight ablative fractional lasers. In the base case analysis, a 5-year lease and a 3-year lease were assumed as the purchase option with a $0 down payment and 3-month payment deferral. In addition to lease payments, service contracts, labor cost, and disposables were included in the total cost estimate. Revenue was estimated as price per procedure multiplied by total number of procedures in a year. Sensitivity analyses were performed to account for variability in model assumptions. Based on the assumptions of the model, all lasers had higher ROI under the 5-year lease agreement compared with that for the 3-year lease agreement. When comparing results between lasers, those with lower operating and purchase cost delivered a higher ROI. Sensitivity analysis indicates the model is most sensitive to purchase method. If physicians opt to purchase the device rather than lease, they can significantly enhance ROI. ROI analysis is an important tool for physicians who are considering making an expensive device acquisition. However, physicians should not rely solely on ROI and must also consider the clinical benefits of a laser. (c) Thieme Medical Publishers.

Multi-resolution analysis for region of interest extraction in thermographic nondestructive evaluation

NASA Astrophysics Data System (ADS)

Ortiz-Jaramillo, B.; Fandiño Toro, H. A.; Benitez-Restrepo, H. D.; Orjuela-Vargas, S. A.; Castellanos-Domínguez, G.; Philips, W.

2012-03-01

Infrared Non-Destructive Testing (INDT) is known as an effective and rapid method for nondestructive inspection. It can detect a broad range of near-surface structuring flaws in metallic and composite components. Those flaws are modeled as a smooth contour centered at peaks of stored thermal energy, termed Regions of Interest (ROI). Dedicated methodologies must detect the presence of those ROIs. In this paper, we present a methodology for ROI extraction in INDT tasks. The methodology deals with the difficulties due to the non-uniform heating. The non-uniform heating affects low spatial/frequencies and hinders the detection of relevant points in the image. In this paper, a methodology for ROI extraction in INDT using multi-resolution analysis is proposed, which is robust to ROI low contrast and non-uniform heating. The former methodology includes local correlation, Gaussian scale analysis and local edge detection. In this methodology local correlation between image and Gaussian window provides interest points related to ROIs. We use a Gaussian window because thermal behavior is well modeled by Gaussian smooth contours. Also, the Gaussian scale is used to analyze details in the image using multi-resolution analysis avoiding low contrast, non-uniform heating and selection of the Gaussian window size. Finally, local edge detection is used to provide a good estimation of the boundaries in the ROI. Thus, we provide a methodology for ROI extraction based on multi-resolution analysis that is better or equal compared with the other dedicate algorithms proposed in the state of art.

An Implementation of Privacy Protection for a Surveillance Camera Using ROI Coding of JPEG2000 with Face Detection

NASA Astrophysics Data System (ADS)

Muneyasu, Mitsuji; Odani, Shuhei; Kitaura, Yoshihiro; Namba, Hitoshi

On the use of a surveillance camera, there is a case where privacy protection should be considered. This paper proposes a new privacy protection method by automatically degrading the face region in surveillance images. The proposed method consists of ROI coding of JPEG2000 and a face detection method based on template matching. The experimental result shows that the face region can be detected and hidden correctly.

Application of newly developed Fluoro-QC software for image quality evaluation in cardiac X-ray systems.

PubMed

Oliveira, M; Lopez, G; Geambastiani, P; Ubeda, C

2018-05-01

A quality assurance (QA) program is a valuable tool for the continuous production of optimal quality images. The aim of this paper is to assess a newly developed automatic computer software for image quality (IR) evaluation in fluoroscopy X-ray systems. Test object images were acquired using one fluoroscopy system, Siemens Axiom Artis model (Siemens AG, Medical Solutions Erlangen, Germany). The software was developed as an ImageJ plugin. Two image quality parameters were assessed: high-contrast spatial resolution (HCSR) and signal-to-noise ratio (SNR). The time between manual and automatic image quality assessment procedures were compared. The paired t-test was used to assess the data. p Values of less than 0.05 were considered significant. The Fluoro-QC software generated faster IQ evaluation results (mean = 0.31 ± 0.08 min) than manual procedure (mean = 4.68 ± 0.09 min). The mean difference between techniques was 4.36 min. Discrepancies were identified in the region of interest (ROI) areas drawn manually with evidence of user dependence. The new software presented the results of two tests (HCSR = 3.06, SNR = 5.17) and also collected information from the DICOM header. Significant differences were not identified between manual and automatic measures of SNR (p value = 0.22) and HCRS (p value = 0.46). The Fluoro-QC software is a feasible, fast and free to use method for evaluating imaging quality parameters on fluoroscopy systems. Copyright © 2017 The College of Radiographers. Published by Elsevier Ltd. All rights reserved.

A voxel-based approach to gray matter asymmetries.

PubMed

Luders, E; Gaser, C; Jancke, L; Schlaug, G

2004-06-01

Voxel-based morphometry (VBM) was used to analyze gray matter (GM) asymmetries in a large sample (n = 60) of male and female professional musicians with and without absolute pitch (AP). We chose to examine these particular groups because previous studies using traditional region-of-interest (ROI) analyses have shown differences in hemispheric asymmetry related to AP and gender. Voxel-based methods may have advantages over traditional ROI-based methods since the analysis can be performed across the whole brain with minimal user bias. After determining that the VBM method was sufficiently sensitive for the detection of differences in GM asymmetries between groups, we found that male AP musicians were more leftward lateralized in the anterior region of the planum temporale (PT) than male non-AP musicians. This confirmed the results of previous studies using ROI-based methods that showed an association between PT asymmetry and the AP phenotype. We further observed that male non-AP musicians revealed an increased leftward GM asymmetry in the postcentral gyrus compared to female non-AP musicians, again corroborating results of a previously published study using ROI-based methods. By analyzing hemispheric GM differences across our entire sample, we were able to partially confirm findings of previous studies using traditional morphometric techniques, as well as more recent, voxel-based analyses. In addition, we found some unusually pronounced GM asymmetries in our musician sample not previously detected in subjects unselected for musical training. Since we were able to validate gender- and AP-related brain asymmetries previously described using traditional ROI-based morphometric techniques, the results of our analyses support the use of VBM for examinations of GM asymmetries.

Spatiotemporal alignment of in utero BOLD-MRI series.

PubMed

Turk, Esra Abaci; Luo, Jie; Gagoski, Borjan; Pascau, Javier; Bibbo, Carolina; Robinson, Julian N; Grant, P Ellen; Adalsteinsson, Elfar; Golland, Polina; Malpica, Norberto

2017-08-01

To present a method for spatiotemporal alignment of in-utero magnetic resonance imaging (MRI) time series acquired during maternal hyperoxia for enabling improved quantitative tracking of blood oxygen level-dependent (BOLD) signal changes that characterize oxygen transport through the placenta to fetal organs. The proposed pipeline for spatiotemporal alignment of images acquired with a single-shot gradient echo echo-planar imaging includes 1) signal nonuniformity correction, 2) intravolume motion correction based on nonrigid registration, 3) correction of motion and nonrigid deformations across volumes, and 4) detection of the outlier volumes to be discarded from subsequent analysis. BOLD MRI time series collected from 10 pregnant women during 3T scans were analyzed using this pipeline. To assess pipeline performance, signal fluctuations between consecutive timepoints were examined. In addition, volume overlap and distance between manual region of interest (ROI) delineations in a subset of frames and the delineations obtained through propagation of the ROIs from the reference frame were used to quantify alignment accuracy. A previously demonstrated rigid registration approach was used for comparison. The proposed pipeline improved anatomical alignment of placenta and fetal organs over the state-of-the-art rigid motion correction methods. In particular, unexpected temporal signal fluctuations during the first normoxia period were significantly decreased (P < 0.01) and volume overlap and distance between region boundaries measures were significantly improved (P < 0.01). The proposed approach to align MRI time series enables more accurate quantitative studies of placental function by improving spatiotemporal alignment across placenta and fetal organs. 1 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:403-412. © 2017 International Society for Magnetic Resonance in Medicine.

Interface and permittivity simultaneous reconstruction in electrical capacitance tomography based on boundary and finite-elements coupling method

PubMed Central

Ren, Shangjie; Dong, Feng

2016-01-01

Electrical capacitance tomography (ECT) is a non-destructive detection technique for imaging the permittivity distributions inside an observed domain from the capacitances measurements on its boundary. Owing to its advantages of non-contact, non-radiation, high speed and low cost, ECT is promising in the measurements of many industrial or biological processes. However, in the practical industrial or biological systems, a deposit is normally seen in the inner wall of its pipe or vessel. As the actual region of interest (ROI) of ECT is surrounded by the deposit layer, the capacitance measurements become weakly sensitive to the permittivity perturbation occurring at the ROI. When there is a major permittivity difference between the deposit and the ROI, this kind of shielding effect is significant, and the permittivity reconstruction becomes challenging. To deal with the issue, an interface and permittivity simultaneous reconstruction approach is proposed. Both the permittivity at the ROI and the geometry of the deposit layer are recovered using the block coordinate descent method. The boundary and finite-elements coupling method is employed to improve the computational efficiency. The performance of the proposed method is evaluated with the simulation tests. This article is part of the themed issue ‘Supersensing through industrial process tomography’. PMID:27185960

Remote pedestrians detection at night time in FIR Image using contrast filtering and locally projected region based CNN

NASA Astrophysics Data System (ADS)

Kim, Taehwan; Kim, Sungho

2017-02-01

This paper presents a novel method to detect the remote pedestrians. After producing the human temperature based brightness enhancement image using the temperature data input, we generates the regions of interest (ROIs) by the multiscale contrast filtering based approach including the biased hysteresis threshold and clustering, remote pedestrian's height, pixel area and central position information. Afterwards, we conduct local vertical and horizontal projection based ROI refinement and weak aspect ratio based ROI limitation to solve the problem of region expansion in the contrast filtering stage. Finally, we detect the remote pedestrians by validating the final ROIs using transfer learning with convolutional neural network (CNN) feature, following non-maximal suppression (NMS) with strong aspect ratio limitation to improve the detection performance. In the experimental results, we confirmed that the proposed contrast filtering and locally projected region based CNN (CFLP-CNN) outperforms the baseline method by 8% in term of logaveraged miss rate. Also, the proposed method is more effective than the baseline approach and the proposed method provides the better regions that are suitably adjusted to the shape and appearance of remote pedestrians, which makes it detect the pedestrian that didn't find in the baseline approach and are able to help detect pedestrians by splitting the people group into a person.

Automatic segmentation of the prostate on CT images using deep learning and multi-atlas fusion

NASA Astrophysics Data System (ADS)

Ma, Ling; Guo, Rongrong; Zhang, Guoyi; Tade, Funmilayo; Schuster, David M.; Nieh, Peter; Master, Viraj; Fei, Baowei

2017-02-01

Automatic segmentation of the prostate on CT images has many applications in prostate cancer diagnosis and therapy. However, prostate CT image segmentation is challenging because of the low contrast of soft tissue on CT images. In this paper, we propose an automatic segmentation method by combining a deep learning method and multi-atlas refinement. First, instead of segmenting the whole image, we extract the region of interesting (ROI) to delete irrelevant regions. Then, we use the convolutional neural networks (CNN) to learn the deep features for distinguishing the prostate pixels from the non-prostate pixels in order to obtain the preliminary segmentation results. CNN can automatically learn the deep features adapting to the data, which are different from some handcrafted features. Finally, we select some similar atlases to refine the initial segmentation results. The proposed method has been evaluated on a dataset of 92 prostate CT images. Experimental results show that our method achieved a Dice similarity coefficient of 86.80% as compared to the manual segmentation. The deep learning based method can provide a useful tool for automatic segmentation of the prostate on CT images and thus can have a variety of clinical applications.

Roi-Orientated Sensor Correction Based on Virtual Steady Reimaging Model for Wide Swath High Resolution Optical Satellite Imagery

NASA Astrophysics Data System (ADS)

Zhu, Y.; Jin, S.; Tian, Y.; Wang, M.

2017-09-01

To meet the requirement of high accuracy and high speed processing for wide swath high resolution optical satellite imagery under emergency situation in both ground processing system and on-board processing system. This paper proposed a ROI-orientated sensor correction algorithm based on virtual steady reimaging model for wide swath high resolution optical satellite imagery. Firstly, the imaging time and spatial window of the ROI is determined by a dynamic search method. Then, the dynamic ROI sensor correction model based on virtual steady reimaging model is constructed. Finally, the corrected image corresponding to the ROI is generated based on the coordinates mapping relationship which is established by the dynamic sensor correction model for corrected image and rigours imaging model for original image. Two experimental results show that the image registration between panchromatic and multispectral images can be well achieved and the image distortion caused by satellite jitter can be also corrected efficiently.

A study on the measurement of the core body temperature change after radiofrequency ablation (RFA) through MR temperature mapping

NASA Astrophysics Data System (ADS)

Kim, Chang-Bok; Dong, Kyung-Rae; Yu, Young; Chung, Woon-Kwan; Cho, Jae-Hwan; Joo, Kyu-Ji

2013-09-01

This study examined the change in the heat generated during radiofrequency ablation (RFA) using a self-manufactured phantom and used magnetic resonance imaging (MRI) to analyze the change in the temperature of the core body and the tissues surrounding the phantom. In this experiment, the image and the phase image were obtained simultaneously from a gradient echo-based sequence using 1.5-Tesla MRI equipment and a 12-channel head coil. The temperature mapping technique was used to calculate the change in temperature. The regions of interest (ROIs) (ROI 1 - ROI 6) were set with a focus on the area where the RFA was performed, according to the temperature distribution, before monitoring the temperature change for one hour in time intervals of five minutes. The results showed that the temperature change in the ROI with time was largest in the ROI 1 and smallest in the ROI 5. In addition, after the RFA procedure, the temperature decreased from the initial value to 0 °C in one hour. The temperature changes in the core body and the surrounding tissues were confirmed by MRI temperature mapping, which is a noninvasive method.

Assessment of the Spectral Stability of Libya 4, Libya 1, and Mauritania 2 Sites Using Earth Observing One Hyperion

NASA Technical Reports Server (NTRS)

Choi, Taeyoung; Xiong, Xiaoxiong; Angal, Amit; Chander, Gyanesh; Qu, John J.

2014-01-01

The objective of this paper is to formulate a methodology to assess the spectral stability of the Libya 4, Libya 1, and Mauritania 2 pseudo-invariant calibration sites (PICS) using Earth Observing One (EO-1) Hyperion sensor. All the available Hyperion collections, downloaded from the Earth Explorer website, were utilized for the three PICS. In each site, a reference spectrum is selected at a specific day in the vicinity of the region of interest (ROI) defined by Committee on Earth Observation Satellites (CEOS). A series of ROIs are predefined in the along-track direction with 196 spectral top-of-atmosphere reflectance values in each ROI. Based on the reference ROI spectrum, the spectral stability of these ROIs is evaluated by average deviations (ADs) and spectral angle mapper (SAM) methods in the specific ranges of time and geo-spatial locations. Time and ROI location-dependent SAM and AD results are very stable within +/- 2 deg and +/-1.7% of 1sigma standard deviations. Consequently, the Libya 4, Mauritania 2, and Libya 1 CEOS selected PICS are spectrally stable targets within the time and spatial swath ranges of the Hyperion collections.

Automated segmentation of ultrasonic breast lesions using statistical texture classification and active contour based on probability distance.

PubMed

Liu, Bo; Cheng, H D; Huang, Jianhua; Tian, Jiawei; Liu, Jiafeng; Tang, Xianglong

2009-08-01

Because of its complicated structure, low signal/noise ratio, low contrast and blurry boundaries, fully automated segmentation of a breast ultrasound (BUS) image is a difficult task. In this paper, a novel segmentation method for BUS images without human intervention is proposed. Unlike most published approaches, the proposed method handles the segmentation problem by using a two-step strategy: ROI generation and ROI segmentation. First, a well-trained texture classifier categorizes the tissues into different classes, and the background knowledge rules are used for selecting the regions of interest (ROIs) from them. Second, a novel probability distance-based active contour model is applied for segmenting the ROIs and finding the accurate positions of the breast tumors. The active contour model combines both global statistical information and local edge information, using a level set approach. The proposed segmentation method was performed on 103 BUS images (48 benign and 55 malignant). To validate the performance, the results were compared with the corresponding tumor regions marked by an experienced radiologist. Three error metrics, true-positive ratio (TP), false-negative ratio (FN) and false-positive ratio (FP) were used for measuring the performance of the proposed method. The final results (TP = 91.31%, FN = 8.69% and FP = 7.26%) demonstrate that the proposed method can segment BUS images efficiently, quickly and automatically.

Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases.

PubMed

Rondina, Jane Maryam; Ferreira, Luiz Kobuti; de Souza Duran, Fabio Luis; Kubo, Rodrigo; Ono, Carla Rachel; Leite, Claudia Costa; Smid, Jerusa; Nitrini, Ricardo; Buchpiguel, Carlos Alberto; Busatto, Geraldo F

2018-01-01

Machine learning techniques such as support vector machine (SVM) have been applied recently in order to accurately classify individuals with neuropsychiatric disorders such as Alzheimer's disease (AD) based on neuroimaging data. However, the multivariate nature of the SVM approach often precludes the identification of the brain regions that contribute most to classification accuracy. Multiple kernel learning (MKL) is a sparse machine learning method that allows the identification of the most relevant sources for the classification. By parcelating the brain into regions of interest (ROI) it is possible to use each ROI as a source to MKL (ROI-MKL). We applied MKL to multimodal neuroimaging data in order to: 1) compare the diagnostic performance of ROI-MKL and whole-brain SVM in discriminating patients with AD from demographically matched healthy controls and 2) identify the most relevant brain regions to the classification. We used two atlases (AAL and Brodmann's) to parcelate the brain into ROIs and applied ROI-MKL to structural (T1) MRI, 18 F-FDG-PET and regional cerebral blood flow SPECT (rCBF-SPECT) data acquired from the same subjects (20 patients with early AD and 18 controls). In ROI-MKL, each ROI received a weight (ROI-weight) that indicated the region's relevance to the classification. For each ROI, we also calculated whether there was a predominance of voxels indicating decreased or increased regional activity (for 18 F-FDG-PET and rCBF-SPECT) or volume (for T1-MRI) in AD patients. Compared to whole-brain SVM, the ROI-MKL approach resulted in better accuracies (with either atlas) for classification using 18 F-FDG-PET (92.5% accuracy for ROI-MKL versus 84% for whole-brain), but not when using rCBF-SPECT or T1-MRI. Although several cortical and subcortical regions contributed to discrimination, high ROI-weights and predominance of hypometabolism and atrophy were identified specially in medial parietal and temporo-limbic cortical regions. Also, the weight of discrimination due to a pattern of increased voxel-weight values in AD individuals was surprisingly high (ranging from approximately 20% to 40% depending on the imaging modality), located mainly in primary sensorimotor and visual cortices and subcortical nuclei. The MKL-ROI approach highlights the high discriminative weight of a subset of brain regions of known relevance to AD, the selection of which contributes to increased classification accuracy when applied to 18 F-FDG-PET data. Moreover, the MKL-ROI approach demonstrates that brain regions typically spared in mild stages of AD also contribute substantially in the individual discrimination of AD patients from controls.

MR-perfusion (MRP) and diffusion-weighted imaging (DWI) in prostate cancer: quantitative and model-based gadobenate dimeglumine MRP parameters in detection of prostate cancer.

PubMed

Scherr, M K; Seitz, M; Müller-Lisse, U G; Ingrisch, M; Reiser, M F; Müller-Lisse, U L

2010-12-01

Various MR methods, including MR-spectroscopy (MRS), dynamic, contrast-enhanced MRI (DCE-MRI), and diffusion-weighted imaging (DWI) have been applied to improve test quality of standard MRI of the prostate. To determine if quantitative, model-based MR-perfusion (MRP) with gadobenate dimeglumine (Gd-BOPTA) discriminates between prostate cancer, benign tissue, and transitional zone (TZ) tissue. 27 patients (age, 65±4 years; PSA 11.0±6.1 ng/ml) with clinical suspicion of prostate cancer underwent standard MRI, 3D MR-spectroscopy (MRS), and MRP with Gd-BOPTA. Based on results of combined MRI/MRS and subsequent guided prostate biopsy alone (17/27), biopsy and radical prostatectomy (9/27), or sufficient negative follow-up (7/27), maps of model-free, deconvolution-based mean transit time (dMTT) were generated for 29 benign regions (bROIs), 14 cancer regions (cROIs), and 18 regions of transitional zone (tzROIs). Applying a 2-compartment exchange model, quantitative perfusion analysis was performed including as parameters: plasma flow (PF), plasma volume (PV), plasma mean transit time (PMTT), extraction flow (EFL), extraction fraction (EFR), interstitial volume (IV) and interstitial mean transit time (IMTT). Two-sided T-tests (significance level p<0.05) discriminated bROIs vs. cROIs and cROIs vs. tzROIs, respectively. PMTT discriminated best between bROIs (11.8±3.0 s) and cROIs (24.3±9.6 s) (p<0.0001), while PF, PV, PS, EFR, IV, IMTT also differed significantly (p 0.00002-0.0136). Discrimination between cROIs and tzROIs was insignificant for all parameters except PV (14.3±2.5 ml vs. 17.6±2.6 ml, p<0.05). Besides MRI, MRS and DWI quantitative, 2-compartment MRP with Gd-BOPTA discriminates between prostate cancer and benign tissue with several parameters. However, distinction of prostate cancer and TZ does not appear to be reliable. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Unsupervised classification of cirrhotic livers using MRI data

NASA Astrophysics Data System (ADS)

Lee, Gobert; Kanematsu, Masayuki; Kato, Hiroki; Kondo, Hiroshi; Zhou, Xiangrong; Hara, Takeshi; Fujita, Hiroshi; Hoshi, Hiroaki

2008-03-01

Cirrhosis of the liver is a chronic disease. It is characterized by the presence of widespread nodules and fibrosis in the liver which results in characteristic texture patterns. Computerized analysis of hepatic texture patterns is usually based on regions-of-interest (ROIs). However, not all ROIs are typical representatives of the disease stage of the liver from which the ROIs originated. This leads to uncertainties in the ROI labels (diseased or non-diseased). On the other hand, supervised classifiers are commonly used in determining the assignment rule. This presents a problem as the training of a supervised classifier requires the correct labels of the ROIs. The main purpose of this paper is to investigate the use of an unsupervised classifier, the k-means clustering, in classifying ROI based data. In addition, a procedure for generating a receiver operating characteristic (ROC) curve depicting the classification performance of k-means clustering is also reported. Hepatic MRI images of 44 patients (16 cirrhotic; 28 non-cirrhotic) are used in this study. The MRI data are derived from gadolinium-enhanced equilibrium phase images. For each patient, 10 ROIs selected by an experienced radiologist and 7 texture features measured on each ROI are included in the MRI data. Results of the k-means classifier are depicted using an ROC curve. The area under the curve (AUC) has a value of 0.704. This is slightly lower than but comparable to that of LDA and ANN classifiers which have values 0.781 and 0.801, respectively. Methods in constructing ROC curve in relation to k-means clustering have not been previously reported in the literature.

Surface-Based fMRI-Driven Diffusion Tractography in the Presence of Significant Brain Pathology: A Study Linking Structure and Function in Cerebral Palsy

PubMed Central

Cunnington, Ross; Boyd, Roslyn N.; Rose, Stephen E.

2016-01-01

Diffusion MRI (dMRI) tractography analyses are difficult to perform in the presence of brain pathology. Automated methods that rely on cortical parcellation for structural connectivity studies often fail, while manually defining regions is extremely time consuming and can introduce human error. Both methods also make assumptions about structure-function relationships that may not hold after cortical reorganisation. Seeding tractography with functional-MRI (fMRI) activation is an emerging method that reduces these confounds, but inherent smoothing of fMRI signal may result in the inclusion of irrelevant pathways. This paper describes a novel fMRI-seeded dMRI-analysis pipeline based on surface-meshes that reduces these issues and utilises machine-learning to generate task specific white matter pathways, minimising the requirement for manually-drawn ROIs. We directly compared this new strategy to a standard voxelwise fMRI-dMRI approach, by investigating correlations between clinical scores and dMRI metrics of thalamocortical and corticomotor tracts in 31 children with unilateral cerebral palsy. The surface-based approach successfully processed more participants (87%) than the voxel-based approach (65%), and provided significantly more-coherent tractography. Significant correlations between dMRI metrics and five clinical scores of function were found for the more superior regions of these tracts. These significant correlations were stronger and more frequently found with the surface-based method (15/20 investigated were significant; R2 = 0.43–0.73) than the voxelwise analysis (2 sig. correlations; 0.38 & 0.49). More restricted fMRI signal, better-constrained tractography, and the novel track-classification method all appeared to contribute toward these differences. PMID:27487011

Breast mass segmentation in mammograms combining fuzzy c-means and active contours

NASA Astrophysics Data System (ADS)

Hmida, Marwa; Hamrouni, Kamel; Solaiman, Basel; Boussetta, Sana

2018-04-01

Segmentation of breast masses in mammograms is a challenging issue due to the nature of mammography and the characteristics of masses. In fact, mammographic images are poor in contrast and breast masses have various shapes and densities with fuzzy and ill-defined borders. In this paper, we propose a method based on a modified Chan-Vese active contour model for mass segmentation in mammograms. We conduct the experiment on mass Regions of Interest (ROI) extracted from the MIAS database. The proposed method consists of mainly three stages: Firstly, the ROI is preprocessed to enhance the contrast. Next, two fuzzy membership maps are generated from the preprocessed ROI based on fuzzy C-Means algorithm. These fuzzy membership maps are finally used to modify the energy of the Chan-Vese model and to perform the final segmentation. Experimental results indicate that the proposed method yields good mass segmentation results.

Absolute myocardial flow quantification with (82)Rb PET/CT: comparison of different software packages and methods.

PubMed

Tahari, Abdel K; Lee, Andy; Rajaram, Mahadevan; Fukushima, Kenji; Lodge, Martin A; Lee, Benjamin C; Ficaro, Edward P; Nekolla, Stephan; Klein, Ran; deKemp, Robert A; Wahl, Richard L; Bengel, Frank M; Bravo, Paco E

2014-01-01

In clinical cardiac (82)Rb PET, globally impaired coronary flow reserve (CFR) is a relevant marker for predicting short-term cardiovascular events. However, there are limited data on the impact of different software and methods for estimation of myocardial blood flow (MBF) and CFR. Our objective was to compare quantitative results obtained from previously validated software tools. We retrospectively analyzed cardiac (82)Rb PET/CT data from 25 subjects (group 1, 62 ± 11 years) with low-to-intermediate probability of coronary artery disease (CAD) and 26 patients (group 2, 57 ± 10 years; P=0.07) with known CAD. Resting and vasodilator-stress MBF and CFR were derived using three software applications: (1) Corridor4DM (4DM) based on factor analysis (FA) and kinetic modeling, (2) 4DM based on region-of-interest (ROI) and kinetic modeling, (3) MunichHeart (MH), which uses a simplified ROI-based retention model approach, and (4) FlowQuant (FQ) based on ROI and compartmental modeling with constant distribution volume. Resting and stress MBF values (in milliliters per minute per gram) derived using the different methods were significantly different: using 4DM-FA, 4DM-ROI, FQ, and MH resting MBF values were 1.47 ± 0.59, 1.16 ± 0.51, 0.91 ± 0.39, and 0.90 ± 0.44, respectively (P<0.001), and stress MBF values were 3.05 ± 1.66, 2.26 ± 1.01, 1.90 ± 0.82, and 1.83 ± 0.81, respectively (P<0.001). However, there were no statistically significant differences among the CFR values (2.15 ± 1.08, 2.05 ± 0.83, 2.23 ± 0.89, and 2.21 ± 0.90, respectively; P=0.17). Regional MBF and CFR according to vascular territories showed similar results. Linear correlation coefficient for global CFR varied between 0.71 (MH vs. 4DM-ROI) and 0.90 (FQ vs. 4DM-ROI). Using a cut-off value of 2.0 for abnormal CFR, the agreement among the software programs ranged between 76 % (MH vs. FQ) and 90 % (FQ vs. 4DM-ROI). Interobserver agreement was in general excellent with all software packages. Quantitative assessment of resting and stress MBF with (82)Rb PET is dependent on the software and methods used, whereas CFR appears to be more comparable. Follow-up and treatment assessment should be done with the same software and method.

What Is Professional Development Worth? Calculating the Value of Onboarding Programs in Extension

ERIC Educational Resources Information Center

Harder, Amy; Hodges, Alan; Zelaya, Priscilla

2017-01-01

Return on investment (ROI) is a commonly used metric for organizations concerned with demonstrating the value of their investments; it can be used to determine whether funds spent providing professional development programs for Extension professionals are good investments. This article presents a method for calculating ROI for an onboarding…

FISSA: A neuropil decontamination toolbox for calcium imaging signals.

PubMed

Keemink, Sander W; Lowe, Scott C; Pakan, Janelle M P; Dylda, Evelyn; van Rossum, Mark C W; Rochefort, Nathalie L

2018-02-22

In vivo calcium imaging has become a method of choice to image neuronal population activity throughout the nervous system. These experiments generate large sequences of images. Their analysis is computationally intensive and typically involves motion correction, image segmentation into regions of interest (ROIs), and extraction of fluorescence traces from each ROI. Out of focus fluorescence from surrounding neuropil and other cells can strongly contaminate the signal assigned to a given ROI. In this study, we introduce the FISSA toolbox (Fast Image Signal Separation Analysis) for neuropil decontamination. Given pre-defined ROIs, the FISSA toolbox automatically extracts the surrounding local neuropil and performs blind-source separation with non-negative matrix factorization. Using both simulated and in vivo data, we show that this toolbox performs similarly or better than existing published methods. FISSA requires only little RAM, and allows for fast processing of large datasets even on a standard laptop. The FISSA toolbox is available in Python, with an option for MATLAB format outputs, and can easily be integrated into existing workflows. It is available from Github and the standard Python repositories.

Comparison of demons deformable registration-based methods for texture analysis of serial thoracic CT scans

NASA Astrophysics Data System (ADS)

Cunliffe, Alexandra R.; Al-Hallaq, Hania A.; Fei, Xianhan M.; Tuohy, Rachel E.; Armato, Samuel G.

2013-02-01

To determine how 19 image texture features may be altered by three image registration methods, "normal" baseline and follow-up computed tomography (CT) scans from 27 patients were analyzed. Nineteen texture feature values were calculated in over 1,000 32x32-pixel regions of interest (ROIs) randomly placed in each baseline scan. All three methods used demons registration to map baseline scan ROIs to anatomically matched locations in the corresponding transformed follow-up scan. For the first method, the follow-up scan transformation was subsampled to achieve a voxel size identical to that of the baseline scan. For the second method, the follow-up scan was transformed through affine registration to achieve global alignment with the baseline scan. For the third method, the follow-up scan was directly deformed to the baseline scan using demons deformable registration. Feature values in matched ROIs were compared using Bland- Altman 95% limits of agreement. For each feature, the range spanned by the 95% limits was normalized to the mean feature value to obtain the normalized range of agreement, nRoA. Wilcoxon signed-rank tests were used to compare nRoA values across features for the three methods. Significance for individual tests was adjusted using the Bonferroni method. nRoA was significantly smaller for affine-registered scans than for the resampled scans (p=0.003), indicating lower feature value variability between baseline and follow-up scan ROIs using this method. For both of these methods, however, nRoA was significantly higher than when feature values were calculated directly on demons-deformed followup scans (p<0.001). Across features and methods, nRoA values remained below 26%.

Dynamic intensity-weighted region of interest imaging for conebeam CT

PubMed Central

Pearson, Erik; Pan, Xiaochuan; Pelizzari, Charles

2017-01-01

BACKGROUND Patient dose from image guidance in radiotherapy is small compared to the treatment dose. However, the imaging beam is untargeted and deposits dose equally in tumor and healthy tissues. It is desirable to minimize imaging dose while maintaining efficacy. OBJECTIVE Image guidance typically does not require full image quality throughout the patient. Dynamic filtration of the kV beam allows local control of CT image noise for high quality around the target volume and lower quality elsewhere, with substantial dose sparing and reduced scatter fluence on the detector. METHODS The dynamic Intensity-Weighted Region of Interest (dIWROI) technique spatially varies beam intensity during acquisition with copper filter collimation. Fluence is reduced by 95% under the filters with the aperture conformed dynamically to the ROI during cone-beam CT scanning. Preprocessing to account for physical effects of the collimator before reconstruction is described. RESULTS Reconstructions show image quality comparable to a standard scan in the ROI, with higher noise and streak artifacts in the outer region but still adequate quality for patient localization. Monte Carlo modeling shows dose reduction by 10–15% in the ROI due to reduced scatter, and up to 75% outside. CONCLUSIONS The presented technique offers a method to reduce imaging dose by accepting increased image noise outside the ROI, while maintaining full image quality inside the ROI. PMID:27257875

Application of neuroanatomical features to tractography clustering.

PubMed

Wang, Qian; Yap, Pew-Thian; Wu, Guorong; Shen, Dinggang

2013-09-01

Diffusion tensor imaging allows unprecedented insight into brain neural connectivity in vivo by allowing reconstruction of neuronal tracts via captured patterns of water diffusion in white matter microstructures. However, tractography algorithms often output hundreds of thousands of fibers, rendering subsequent data analysis intractable. As a remedy, fiber clustering techniques are able to group fibers into dozens of bundles and thus facilitate analyses. Most existing fiber clustering methods rely on geometrical information of fibers, by viewing them as curves in 3D Euclidean space. The important neuroanatomical aspect of fibers, however, is ignored. In this article, the neuroanatomical information of each fiber is encapsulated in the associativity vector, which functions as the unique "fingerprint" of the fiber. Specifically, each entry in the associativity vector describes the relationship between the fiber and a certain anatomical ROI in a fuzzy manner. The value of the entry approaches 1 if the fiber is spatially related to the ROI at high confidence; on the contrary, the value drops closer to 0. The confidence of the ROI is calculated by diffusing the ROI according to the underlying fibers from tractography. In particular, we have adopted the fast marching method for simulation of ROI diffusion. Using the associativity vectors of fibers, we further model fibers as observations sampled from multivariate Gaussian mixtures in the feature space. To group all fibers into relevant major bundles, an expectation-maximization clustering approach is employed. Experimental results indicate that our method results in anatomically meaningful bundles that are highly consistent across subjects. Copyright © 2012 Wiley Periodicals, Inc., a Wiley company.

Methodological considerations in region of interest definitions for paraspinal muscles in axial MRIs of the lumbar spine.

PubMed

Berry, David B; Padwal, Jennifer; Johnson, Seth; Parra, Callan L; Ward, Samuel R; Shahidi, Bahar

2018-05-07

Magnetic Resonance Imaging (MRI) is commonly used to assess the health of the lumbar spine and supporting structures. Studies have suggested that fatty infiltration of the posterior lumbar muscles is important in predicting responses to treatment for low back pain. However, methodological differences exist in defining the region of interest (ROI) of a muscle, which limits the ability to compare data between studies. The purpose of this study was to determine reliability and systematic differences within and between two commonly utilized methodologies for ROI definitions of lumbar paraspinal muscle. T2-weighted MRIs of the mid-L4 vertebrae from 37 patients with low back pain who were scheduled for lumbar spine surgery were included from a hospital database. Fatty infiltration for these patients ranged from low to high, based on Kjaer criteria. Two methods were used to define ROI: 1) segmentation of the multifidus and erector spinae based on fascial planes including epimuscular fat, and 2) segmentation of the multifidus and erector spinae based on visible muscle boundaries, which did not include epimuscular fat. Total cross sectional area (tCSA), fat signal fraction (FSF), muscle cross sectional area, and fat cross sectional area were measured. Degree of agreement between raters for each parameter was assessed using intra-class correlation coefficients (ICC) and area fraction of overlapping voxels. Excellent inter-rater agreement (ICC > 0.75) was observed for all measures for both methods. There was no significant difference between area fraction overlap of ROIs between methods. Method 1 demonstrated a greater tCSA for both the erector spinae (14-15%, p < 0.001) and multifidus (4%, p < 0.016) but a greater FSF only for the erector spinae (11-13%, p < 0.001). The two methods of defining lumbar spine muscle ROIs demonstrated excellent inter-rater reliability, although significant differences exist as method 1 showed larger CSA and FSF values compared to method 2. The results of this study confirm the validity of using either method to measure lumbar paraspinal musculature, and that method should be selected based on the primary outcome variables of interest.

Opacity annotation of diffuse lung diseases using deep convolutional neural network with multi-channel information

NASA Astrophysics Data System (ADS)

Mabu, Shingo; Kido, Shoji; Hashimoto, Noriaki; Hirano, Yasushi; Kuremoto, Takashi

2018-02-01

This research proposes a multi-channel deep convolutional neural network (DCNN) for computer-aided diagnosis (CAD) that classifies normal and abnormal opacities of diffuse lung diseases in Computed Tomography (CT) images. Because CT images are gray scale, DCNN usually uses one channel for inputting image data. On the other hand, this research uses multi-channel DCNN where each channel corresponds to the original raw image or the images transformed by some preprocessing techniques. In fact, the information obtained only from raw images is limited and some conventional research suggested that preprocessing of images contributes to improving the classification accuracy. Thus, the combination of the original and preprocessed images is expected to show higher accuracy. The proposed method realizes region of interest (ROI)-based opacity annotation. We used lung CT images taken in Yamaguchi University Hospital, Japan, and they are divided into 32 × 32 ROI images. The ROIs contain six kinds of opacities: consolidation, ground-glass opacity (GGO), emphysema, honeycombing, nodular, and normal. The aim of the proposed method is to classify each ROI into one of the six opacities (classes). The DCNN structure is based on VGG network that secured the first and second places in ImageNet ILSVRC-2014. From the experimental results, the classification accuracy of the proposed method was better than the conventional method with single channel, and there was a significant difference between them.

Arbitrary shape region-of-interest fluoroscopy system

NASA Astrophysics Data System (ADS)

Xu, Tong; Le, Huy; Molloi, Sabee Y.

2002-05-01

Region-of-interest (ROI) fluoroscopy has previously been investigated as a method to reduce x-ray exposure to the patient and the operator. This ROI fluoroscopy technique allows the operator to arbitrarily determine the shape, size, and location of the ROI. A device was used to generate patient specific x-ray beam filters. The device is comprised of 18 step-motors that control a 16 X 16 matrix of pistons to form the filter from a deformable attenuating material. Patient exposure reductions were measured to be 84 percent for a 65 kVp beam. Operator exposure reduction was measured to be 69 percent. Due to the reduced x-ray scatter, image contrast was improved by 23 percent inside the ROI. The reduced gray level in the periphery was corrected using an experimentally determined compensation ratio. A running average interpolation technique was used to eliminate the artifacts from the ROI edge. As expected, the final corrected images show increased noise in the periphery. However, the anatomical structures in the periphery could still be visualized. This arbitrary shaped region of interest fluoroscopic technique was shown to be effective in terms of its ability to reduce patient and operator exposure without significant reduction in image quality. The ability to define an arbitrary shaped ROI should make the technique more clinically feasible.

Improvement of Reliability of Diffusion Tensor Metrics in Thigh Skeletal Muscles.

PubMed

Keller, Sarah; Chhabra, Avneesh; Ahmed, Shaheen; Kim, Anne C; Chia, Jonathan M; Yamamura, Jin; Wang, Zhiyue J

2018-05-01

Quantitative diffusion tensor imaging (DTI) of skeletal muscles is challenging due to the bias in DTI metrics, such as fractional anisotropy (FA) and mean diffusivity (MD), related to insufficient signal-to-noise ratio (SNR). This study compares the bias of DTI metrics in skeletal muscles via pixel-based and region-of-interest (ROI)-based analysis. DTI of the thigh muscles was conducted on a 3.0-T system in N = 11 volunteers using a fat-suppressed single-shot spin-echo echo planar imaging (SS SE-EPI) sequence with eight repetitions (number of signal averages (NSA) = 4 or 8 for each repeat). The SNR was calculated for different NSAs and estimated for the composite images combining all data (effective NSA = 48) as standard reference. The bias of MD and FA derived by pixel-based and ROI-based quantification were compared at different NSAs. An "intra-ROI diffusion direction dispersion angle (IRDDDA)" was calculated to assess the uniformity of diffusion within the ROI. Using our standard reference image with NSA = 48, the ROI-based and pixel-based measurements agreed for FA and MD. Larger disagreements were observed for the pixel-based quantification at NSA = 4. MD was less sensitive than FA to the noise level. The IRDDDA decreased with higher NSA. At NSA = 4, ROI-based FA showed a lower average bias (0.9% vs. 37.4%) and narrower 95% limits of agreement compared to the pixel-based method. The ROI-based estimation of FA is less prone to bias than the pixel-based estimations when SNR is low. The IRDDDA can be applied as a quantitative quality measure to assess reliability of ROI-based DTI metrics. Copyright © 2018 Elsevier B.V. All rights reserved.

Detection of lobular structures in normal breast tissue.

PubMed

Apou, Grégory; Schaadt, Nadine S; Naegel, Benoît; Forestier, Germain; Schönmeyer, Ralf; Feuerhake, Friedrich; Wemmert, Cédric; Grote, Anne

2016-07-01

Ongoing research into inflammatory conditions raises an increasing need to evaluate immune cells in histological sections in biologically relevant regions of interest (ROIs). Herein, we compare different approaches to automatically detect lobular structures in human normal breast tissue in digitized whole slide images (WSIs). This automation is required to perform objective and consistent quantitative studies on large data sets. In normal breast tissue from nine healthy patients immunohistochemically stained for different markers, we evaluated and compared three different image analysis methods to automatically detect lobular structures in WSIs: (1) a bottom-up approach using the cell-based data for subsequent tissue level classification, (2) a top-down method starting with texture classification at tissue level analysis of cell densities in specific ROIs, and (3) a direct texture classification using deep learning technology. All three methods result in comparable overall quality allowing automated detection of lobular structures with minor advantage in sensitivity (approach 3), specificity (approach 2), or processing time (approach 1). Combining the outputs of the approaches further improved the precision. Different approaches of automated ROI detection are feasible and should be selected according to the individual needs of biomarker research. Additionally, detected ROIs could be used as a basis for quantification of immune infiltration in lobular structures. Copyright © 2016 Elsevier Ltd. All rights reserved.

A diffusion tensor imaging study of suicide attempters

PubMed Central

Thapa-Chhetry, Binod; Sublette, M. Elizabeth; Sullivan, Gregory M.; Oquendo, Maria A.; Mann, J. John; Parsey, Ramin V.

2014-01-01

Background Few studies have examined white matter abnormalities in suicide attempters using diffusion tensor imaging (DTI). This study sought to identify white matter regions altered in individuals with a prior suicide attempt. Methods DTI scans were acquired in 13 suicide attempters with major depressive disorder (MDD), 39 non-attempters with MDD, and 46 healthy participants (HP). Fractional anisotropy (FA) and apparent diffusion coefficient (ADC) was determined in the brain using two methods: region of interest (ROI) and tract-based spatial statistics (TBSS). ROIs were limited a priori to white matter adjacent to the caudal anterior cingulate cortex, rostral anterior cingulate cortex, dorsomedial prefrontal cortex, and medial orbitofrontal cortex. Results Using the ROI approach, suicide attempters had lower FA than MDD non-attempters and HP in the dorsomedial prefrontal cortex. Uncorrected TBSS results confirmed a significant cluster within the right dorsomedial prefrontal cortex indicating lower FA in suicide attempters compared to non-attempters. There were no differences in ADC when comparing suicide attempters, non-attempters and HP groups using ROI or TBSS methods. Conclusions Low FA in the dorsomedial prefrontal cortex was associated with a suicide attempt history. Converging findings from other imaging modalities support this finding, making this region of potential interest in determining the diathesis for suicidal behavior. PMID:24462041

Region-of-interest determination and bit-rate conversion for H.264 video transcoding

NASA Astrophysics Data System (ADS)

Huang, Shu-Fen; Chen, Mei-Juan; Tai, Kuang-Han; Li, Mian-Shiuan

2013-12-01

This paper presents a video bit-rate transcoder for baseline profile in H.264/AVC standard to fit the available channel bandwidth for the client when transmitting video bit-streams via communication channels. To maintain visual quality for low bit-rate video efficiently, this study analyzes the decoded information in the transcoder and proposes a Bayesian theorem-based region-of-interest (ROI) determination algorithm. In addition, a curve fitting scheme is employed to find the models of video bit-rate conversion. The transcoded video will conform to the target bit-rate by re-quantization according to our proposed models. After integrating the ROI detection method and the bit-rate transcoding models, the ROI-based transcoder allocates more coding bits to ROI regions and reduces the complexity of the re-encoding procedure for non-ROI regions. Hence, it not only keeps the coding quality but improves the efficiency of the video transcoding for low target bit-rates and makes the real-time transcoding more practical. Experimental results show that the proposed framework gets significantly better visual quality.

Diagnostic accuracy of automatic normalization of CBV in glioma grading using T1- weighted DCE-MRI.

PubMed

Sahoo, Prativa; Gupta, Rakesh K; Gupta, Pradeep K; Awasthi, Ashish; Pandey, Chandra M; Gupta, Mudit; Patir, Rana; Vaishya, Sandeep; Ahlawat, Sunita; Saha, Indrajit

2017-12-01

Aim of this retrospective study was to compare diagnostic accuracy of proposed automatic normalization method to quantify the relative cerebral blood volume (rCBV) with existing contra-lateral region of interest (ROI) based CBV normalization method for glioma grading using T1-weighted dynamic contrast enhanced MRI (DCE-MRI). Sixty patients with histologically confirmed gliomas were included in this study retrospectively. CBV maps were generated using T1-weighted DCE-MRI and are normalized by contralateral ROI based method (rCBV_contra), unaffected white matter (rCBV_WM) and unaffected gray matter (rCBV_GM), the latter two of these were generated automatically. An expert radiologist with >10years of experience in DCE-MRI and a non-expert with one year experience were used independently to measure rCBVs. Cutoff values for glioma grading were decided from ROC analysis. Agreement of histology with rCBV_WM, rCBV_GM and rCBV_contra respectively was studied using Kappa statistics and intra-class correlation coefficient (ICC). The diagnostic accuracy of glioma grading using the measured rCBV_contra by expert radiologist was found to be high (sensitivity=1.00, specificity=0.96, p<0.001) compared to the non-expert user (sensitivity=0.65, specificity=0.78, p<0.001). On the other hand, both the expert and non-expert user showed similar diagnostic accuracy for automatic rCBV_WM (sensitivity=0.89, specificity=0.87, p=0.001) and rCBV_GM (sensitivity=0.81, specificity=0.78, p=0.001) measures. Further, it was also observed that, contralateral based method by expert user showed highest agreement with histological grading of tumor (kappa=0.96, agreement 98.33%, p<0.001), however; automatic normalization method showed same percentage of agreement for both expert and non-expert user. rCBV_WM showed an agreement of 88.33% (kappa=0.76,p<0.001) with histopathological grading. It was inferred from this study that, in the absence of expert user, automated normalization of CBV using the proposed method could provide better diagnostic accuracy compared to the manual contralateral based approach. Copyright © 2017 Elsevier Inc. All rights reserved.

T2* Mapping Provides Information That Is Statistically Comparable to an Arthroscopic Evaluation of Acetabular Cartilage.

PubMed

Morgan, Patrick; Nissi, Mikko J; Hughes, John; Mortazavi, Shabnam; Ellerman, Jutta

2017-07-01

Objectives The purpose of this study was to validate T2* mapping as an objective, noninvasive method for the prediction of acetabular cartilage damage. Methods This is the second step in the validation of T2*. In a previous study, we established a quantitative predictive model for identifying and grading acetabular cartilage damage. In this study, the model was applied to a second cohort of 27 consecutive hips to validate the model. A clinical 3.0-T imaging protocol with T2* mapping was used. Acetabular regions of interest (ROI) were identified on magnetic resonance and graded using the previously established model. Each ROI was then graded in a blinded fashion by arthroscopy. Accurate surgical location of ROIs was facilitated with a 2-dimensional map projection of the acetabulum. A total of 459 ROIs were studied. Results When T2* mapping and arthroscopic assessment were compared, 82% of ROIs were within 1 Beck group (of a total 6 possible) and 32% of ROIs were classified identically. Disease prediction based on receiver operating characteristic curve analysis demonstrated a sensitivity of 0.713 and a specificity of 0.804. Model stability evaluation required no significant changes to the predictive model produced in the initial study. Conclusions These results validate that T2* mapping provides statistically comparable information regarding acetabular cartilage when compared to arthroscopy. In contrast to arthroscopy, T2* mapping is quantitative, noninvasive, and can be used in follow-up. Unlike research quantitative magnetic resonance protocols, T2* takes little time and does not require a contrast agent. This may facilitate its use in the clinical sphere.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, G; Nishino, T; Greene, T

Purpose: To determine the consistency of digital detector (DR) tests recommended by AAPM TG150 and tests provided by commercially available DirectView Total Quality Tool (TQT). Methods: The DR tests recommended by the TG150 Detector Subgroup[1] were performed on 4 new Carestream DRX-Revolution and one Carestream DRX1C retrofit of a GE AMX-4 that had been in service for three years. After detector calibration, flat-field images plus images of two bar patterns oriented parallel and perpendicular to the A-C axis, were acquired at conditions recommended by TG150. Raw images were harvested and then analyzed using a MATLAB software previously validated[2,3,4]. Data weremore » analyzed using ROIs of two different dimensions: 1) 128 x 128 ROIs matching the detector electronics; and 2) 256 x 256 ROIs, each including 4 adjacent smaller ROIs. TG150 metrics from 128 x 128 ROIs were compared to TQT metrics, which are also obtained from 128 x 128 ROIs[5]. Results: The results show that both TG150 and TQT measurements were consistent among these detectors. Differences between TG150 and TQT values appear systematic. Compared with 128 x 128 ROIs, noise and SNR non-uniformity were lower with 256 x 256 ROIs, although signal non-uniformity was similar, indicating detectors were appropriately calibrated for gain and offset. MTF of the retrofit unit remained essentially the same between 2012 and 2015, but was inferior to the new units. The older generator focal spot is smaller (0.75mm vs. 1.2mm), and the SID for acquisition is 182cm as well, so focal spot dimensions cannot explain the difference. The difference in MTF may be secondary to differences in generator X-ray spectrum or by unannounced changes in detector architecture. Further investigation is needed. Conclusion: The study shows that both TG150 and TQT tests are consistent. The numerical value of some metrics are dependent on ROI size.« less

SU-F-R-14: PET Based Radiomics to Predict Outcomes in Patients with Hodgkin Lymphoma

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, J; Aristophanous, M; Akhtari, M

Purpose: To identify PET-based radiomics features associated with high refractory/relapsed disease risk for Hodgkin lymphoma patients. Methods: A total of 251 Hodgkin lymphoma patients including 19 primary refractory and 9 relapsed patients were investigated. All patients underwent an initial pre-treatment diagnostic FDG PET/CT scan. All cancerous lymph node regions (ROIs) were delineated by an experienced physician based on thresholding each volume of disease in the anatomical regions to SUV>2.5. We extracted 122 image features and evaluated the effect of ROI selection (the largest ROI, the ROI with highest mean SUV, merged ROI, and a single anatomic region [e.g. mediastinum]) onmore » classification accuracy. Random forest was used as a classifier and ROC analysis was used to assess the relationship between selected features and patient’s outcome status. Results: Each patient had between 1 and 9 separate ROIs, with much intra-patient variability in PET features. The best model, which used features from a single anatomic region (the mediastinal ROI, only volumes>5cc: 169 patients with 12 primary refractory) had a classification accuracy of 80.5% for primary refractory disease. The top five features, based on Gini index, consist of shape features (max 3D-diameter and volume) and texture features (correlation and information measure of correlation1&2). In the ROC analysis, sensitivity and specificity of the best model were 0.92 and 0.80, respectively. The area under the ROC (AUC) and the accuracy were 0.86 and 0.86, respectively. The classification accuracy was less than 60% for other ROI models or when ROIs less than 5cc were included. Conclusion: This study showed that PET-based radiomics features from the mediastinal lymph region are associated with primary refractory disease and therefore may play an important role in predicting outcomes in Hodgkin lymphoma patients. These features could be additive beyond baseline tumor and clinical characteristics, and may warrant more aggressive treatment.« less

Discrimination between patients with mild Alzheimer's disease and healthy subjects based on cerebral blood flow images of the lateral views in xenon-enhanced computed tomography.

PubMed

Sase, Shigeru; Yamamoto, Homaro; Kawashima, Ena; Tan, Xin; Sawa, Yutaka

2018-01-01

Quantitative cerebral blood flow (CBF) measurement is expected to help early detection of functional abnormalities caused by Alzheimer's disease (AD) and enable AD treatment to begin in its early stages. Recently, a technique of layer analysis was reported that allowed CBF to be analyzed from the outer to inner layers of the brain. The aim of this work was to develop methods for discriminating between patients with mild AD and healthy subjects based on CBF images of the lateral views created with the layer analysis technique in xenon-enhanced computed tomography. Xenon-enhanced computed tomography using a wide-volume CT was performed on 17 patients with mild AD aged 75 or older and on 15 healthy age-matched volunteers. For each subject, we created CBF images of the right and left lateral views with a depth of 10-15 mm from the surface of the brain. Ten circular regions of interest (ROI) were placed on each image, and CBF was calculated for each ROI. We determined discriminant ROI that had CBF that could be used to differentiate between the AD and volunteer groups. AD patients' CBF range (mean - SD to mean + SD) and healthy volunteers' CBF range (mean - SD to mean + SD) were obtained for each ROI. Receiver-operator curves were created to identify patients with AD for each of the discriminant ROI and for the AD patients' and healthy volunteers' CBF ranges. We selected an ROI on both the right and left temporal lobes as the discriminant ROI. Areas under the receiver-operator curve were 93.3% using the ROI on the right temporal lobe, 95.3% using the ROI on the left temporal lobe, and 92.4% using the AD patients' and healthy volunteers' CBF ranges. We could effectively discriminate between patients with mild AD and healthy subjects using ROI placed on CBF images of the lateral views in xenon-enhanced computed tomography. © 2017 Japanese Psychogeriatric Society.

Joint penalized-likelihood reconstruction of time-activity curves and regions-of-interest from projection data in brain PET

NASA Astrophysics Data System (ADS)

Krestyannikov, E.; Tohka, J.; Ruotsalainen, U.

2008-06-01

This paper presents a novel statistical approach for joint estimation of regions-of-interest (ROIs) and the corresponding time-activity curves (TACs) from dynamic positron emission tomography (PET) brain projection data. It is based on optimizing the joint objective function that consists of a data log-likelihood term and two penalty terms reflecting the available a priori information about the human brain anatomy. The developed local optimization strategy iteratively updates both the ROI and TAC parameters and is guaranteed to monotonically increase the objective function. The quantitative evaluation of the algorithm is performed with numerically and Monte Carlo-simulated dynamic PET brain data of the 11C-Raclopride and 18F-FDG tracers. The results demonstrate that the method outperforms the existing sequential ROI quantification approaches in terms of accuracy, and can noticeably reduce the errors in TACs arising due to the finite spatial resolution and ROI delineation.

Single-shot full resolution region-of-interest (ROI) reconstruction in image plane digital holographic microscopy

NASA Astrophysics Data System (ADS)

Singh, Mandeep; Khare, Kedar

2018-05-01

We describe a numerical processing technique that allows single-shot region-of-interest (ROI) reconstruction in image plane digital holographic microscopy with full pixel resolution. The ROI reconstruction is modelled as an optimization problem where the cost function to be minimized consists of an L2-norm squared data fitting term and a modified Huber penalty term that are minimized alternately in an adaptive fashion. The technique can provide full pixel resolution complex-valued images of the selected ROI which is not possible to achieve with the commonly used Fourier transform method. The technique can facilitate holographic reconstruction of individual cells of interest from a large field-of-view digital holographic microscopy data. The complementary phase information in addition to the usual absorption information already available in the form of bright field microscopy can make the methodology attractive to the biomedical user community.

Development of adaptive observation strategy using retrospective optimal interpolation

NASA Astrophysics Data System (ADS)

Noh, N.; Kim, S.; Song, H.; Lim, G.

2011-12-01

Retrospective optimal interpolation (ROI) is a method that is used to minimize cost functions with multiple minima without using adjoint models. Song and Lim (2011) perform the experiments to reduce the computational costs for implementing ROI by transforming the control variables into eigenvectors of background error covariance. We adapt the ROI algorithm to compute sensitivity estimates of severe weather events over the Korean peninsula. The eigenvectors of the ROI algorithm is modified every time the observations are assimilated. This implies that the modified eigenvectors shows the error distribution of control variables which are updated by assimilating observations. So, We can estimate the effects of the specific observations. In order to verify the adaptive observation strategy, High-impact weather over the Korean peninsula is simulated and interpreted using WRF modeling system and sensitive regions for each high-impact weather is calculated. The effects of assimilation for each observation type is discussed.

A block-based JPEG-LS compression technique with lossless region of interest

NASA Astrophysics Data System (ADS)

Deng, Lihua; Huang, Zhenghua; Yao, Shoukui

2018-03-01

JPEG-LS lossless compression algorithm is used in many specialized applications that emphasize on the attainment of high fidelity for its lower complexity and better compression ratios than the lossless JPEG standard. But it cannot prevent error diffusion because of the context dependence of the algorithm, and have low compression rate when compared to lossy compression. In this paper, we firstly divide the image into two parts: ROI regions and non-ROI regions. Then we adopt a block-based image compression technique to decrease the range of error diffusion. We provide JPEG-LS lossless compression for the image blocks which include the whole or part region of interest (ROI) and JPEG-LS near lossless compression for the image blocks which are included in the non-ROI (unimportant) regions. Finally, a set of experiments are designed to assess the effectiveness of the proposed compression method.

[Return on investment for occupational health: review of methods and recommendations].

PubMed

Rydlewska-Liszkowska, Izabela

2010-01-01

The impact of the population health on the national economy, occupational risk factors and economic consequences of workers' behaviors in the workplace are the subject of health economics studies. Conditions and behaviors at work are recognized as one of the major economic factors. These relations are analyzed and evaluated with use of methods that combine economic consequences of working conditions, management and enterprise finances. The methods of assessing this impact are well known but the ability to implement them in practice is still limited. There are two main types of methods: methods for measuring economic relations between the work environment and enterprise management and methods for the economic analysis of investments in the work environment and occupational health. Methods for assessing economic effectiveness of working conditions limited to measurements of costs of absenteeism at work are also used. One of the methodological options in this regard is return on investment (ROI) for occupational health. ROI has been applied in many firms all over the world. Depending on the analytical assumptions and the scope of research, the ROI value ranged between more than 1 and 13. This means that the increase in return on investment for occupational health has been observed. There are examples that the return on invested resources cannot be always obtained. ROI accounting gives employers an opportunity of increasing effectiveness. Evidence of the real value of health investment enables to provide the platform of discussion among managers and other persons responsible for occupational health management. The results of the studies cannot be overestimated as an element of economic incentives system. Based on the review of the methods and results of their implementation some recommendations can be formulated.

Quantitative Immunofluorescence Analysis of Nucleolus-Associated Chromatin.

PubMed

Dillinger, Stefan; Németh, Attila

2016-01-01

The nuclear distribution of eu- and heterochromatin is nonrandom, heterogeneous, and dynamic, which is mirrored by specific spatiotemporal arrangements of histone posttranslational modifications (PTMs). Here we describe a semiautomated method for the analysis of histone PTM localization patterns within the mammalian nucleus using confocal laser scanning microscope images of fixed, immunofluorescence stained cells as data source. The ImageJ-based process includes the segmentation of the nucleus, furthermore measurements of total fluorescence intensities, the heterogeneity of the staining, and the frequency of the brightest pixels in the region of interest (ROI). In the presented image analysis pipeline, the perinucleolar chromatin is selected as primary ROI, and the nuclear periphery as secondary ROI.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao, B; Maquilan, G; Anders, M

Purpose: Full face and neck thermoplastic masks provide standard-of-care immobilization for patients receiving H&N IMRT. However, these masks are uncomfortable and increase skin dose. The purpose of this pilot study was to investigate the feasibility and setup accuracy of open face and neck mask immobilization with OIG. Methods: Ten patients were consented and enrolled to this IRB-approved protocol. Patients were immobilized with open masks securing only forehead and chin. Standard IMRT to 60–70 Gy in 30 fractions were delivered in all cases. Patient simulation information, including isocenter location and CT skin contours, were imported to a commercial OIG system. Onmore » the first day of treatment, patients were initially set up to surface markings and then OIG referenced to face and neck skin regions of interest (ROI) localized on simulation CT images, followed by in-room CBCT. CBCTs were acquired at least weekly while planar OBI was acquired on the days without CBCT. Following 6D robotic couch correction with kV imaging, a new optical real-time surface image was acquired to track intrafraction motion and to serve as a reference surface for setup at the next treatment fraction. Therapists manually recorded total treatment time as well as couch shifts based on kV imaging. Intrafractional ROI motion tracking was automatically recorded. Results: Setup accuracy of OIG was compared with CBCT results. The setup error based on OIG was represented as a 6D shift (vertical/longitudinal/lateral/rotation/pitch/roll). Mean error values were −0.70±3.04mm, −0.69±2.77mm, 0.33±2.67 mm, −0.14±0.94 o, −0.15±1.10o and 0.12±0.82o, respectively for the cohort. Average treatment time was 24.1±9.2 minutes, comparable to standard immobilization. The amplitude of intrafractional ROI motion was 0.69±0.36 mm, driven primarily by respiratory neck motion. Conclusion: OGI can potentially provide accurate setup and treatment tracking for open face and neck immobilization. Study accrual and patient/provider satisfaction survey collection remain ongoing. This study is supported by VisionRT, Ltd.« less

SU-G-IeP1-07: Inaccuracy of Lesion Blood Flow Quantification Related to the Proton Density Reference Image in Arterial Spin Labeling MRI of Brain Tumors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jen, M; Johnson, J; Hou, P

Purpose: Cerebral blood flow quantification in arterial spin labeling (ASL) MRI requires an estimate of the equilibrium magnetization of blood, which is often obtained by a set of proton density (PD) reference image. Normally, a constant blood-brain partition coefficient is assumed across the brain. However, this assumption may not be valid for brain lesions. This study aimed to evaluate the impact of lesion-related PD variations on ASL quantification in patients with brain tumors. Methods: MR images for posttreatment evaluation of 42 patients with brain tumors were retrospectively analyzed. These images were acquired on a 3T MRI scanner, including T2-weighted FLAIR,more » 3D pseudo-continuous ASL and post-contrast T1-weighted images. Anatomical images were coregistered with ASL images using the SPM software. Regions of interest (ROIs) of the enhancing and FLAIR lesions were manually drawn on the coregistered images. ROIs of the contralateral normal appearing tissues were also determined, with the consideration of approximating coil sensitivity patterns in lesion ROIs. Relative lesion blood flow (lesion/contralateral tissue) was calculated from both the CBF map (dependent on the PD) and the ΔM map for comparison. Results: The signal intensities in both enhancing and FLAIR lesions were significantly different than contralateral tissues on the PD reference image (p<0.001). The percent signal difference ranged from −15.9 to 19.2%, with a mean of 5.4% for the enhancing lesion, and from −2.8 to 22.9% with a mean of 10.1% for the FLAIR lesion. The high/low lesion-related PD signal resulted in inversely proportional under-/over-estimation of blood flow in both enhancing and FLAIR lesions. Conclusion: Significant signal differences were found between lesions and contralateral tissues in the PD reference image, which introduced errors in blood flow quantification in ASL. The error can be up to 20% in individual patients with an average of 5- 10% for the group of patients with brain tumors.« less

Reduced field-of-view imaging for single-shot MRI with an amplitude-modulated chirp pulse excitation and Fourier transform reconstruction.

PubMed

Li, Jing; Zhang, Miao; Chen, Lin; Cai, Congbo; Sun, Huijun; Cai, Shuhui

2015-06-01

We employ an amplitude-modulated chirp pulse to selectively excite spins in one or more regions of interest (ROIs) to realize reduced field-of-view (rFOV) imaging based on single-shot spatiotemporally encoded (SPEN) sequence and Fourier transform reconstruction. The proposed rFOV imaging method was theoretically analyzed and illustrated with numerical simulation and tested with phantom experiments and in vivo rat experiments. In addition, point spread function was applied to demonstrate the feasibility of the proposed method. To evaluate the proposed method, the rFOV results were compared with those obtained using the EPI method with orthogonal RF excitation. The simulation and experimental results show that the proposed method can image one or two separated ROIs along the SPEN dimension in a single shot with higher spatial resolution, less sensitive to field inhomogeneity, and practically no aliasing artifacts. In addition, the proposed method may produce rFOV images with comparable signal-to-noise ratio to the rFOV EPI images. The proposed method is promising for the applications under severe susceptibility heterogeneities and for imaging separate ROIs simultaneously. Copyright © 2015 Elsevier Inc. All rights reserved.

Automated processing of first-pass radionuclide angiocardiography by factor analysis of dynamic structures.

PubMed

Cavailloles, F; Bazin, J P; Capderou, A; Valette, H; Herbert, J L; Di Paola, R

1987-05-01

A method for automatic processing of cardiac first-pass radionuclide study is presented. This technique, factor analysis of dynamic structures (FADS) provides an automatic separation of anatomical structures according to their different temporal behaviour, even if they are superimposed. FADS has been applied to 76 studies. A description of factor patterns obtained in various pathological categories is presented. FADS provides easy diagnosis of shunts and tricuspid insufficiency. Quantitative information derived from the factors (cardiac output and mean transit time) were compared to those obtained by the region of interest method. Using FADS, a higher correlation with cardiac catheterization was found for cardiac output calculation. Thus compared to the ROI method, FADS presents obvious advantages: a good separation of overlapping cardiac chambers is obtained; this operator independant method provides more objective and reproducible results. A number of parameters of the cardio-pulmonary function can be assessed by first-pass radionuclide angiocardiography (RNA) [1,2]. Usually, they are calculated using time-activity curves (TAC) from regions of interest (ROI) drawn on the cardiac chambers and the lungs. This method has two main drawbacks: (1) the lack of inter and intra-observers reproducibility; (2) the problem of crosstalk which affects the evaluation of the cardio-pulmonary performance. The crosstalk on planar imaging is due to anatomical superimposition of the cardiac chambers and lungs. The activity measured in any ROI is the sum of the activity in several organs and 'decontamination' of the TAC cannot easily be performed using the ROI method [3]. Factor analysis of dynamic structures (FADS) [4,5] can solve the two problems mentioned above. It provides an automatic separation of anatomical structures according to their different temporal behaviour, even if they are superimposed. The resulting factors are estimates of the time evolution of the activity in each structure (underlying physiological components), and the associated factor images are estimates of the spatial distribution of each factor. The aim of this study was to assess the reliability of FADS in first pass RNA and compare the results to those obtained by the ROI method which is generally considered as the routine procedure.

Analyzing multimodality tomographic images and associated regions of interest with MIDAS

NASA Astrophysics Data System (ADS)

Tsui, Wai-Hon; Rusinek, Henry; Van Gelder, Peter; Lebedev, Sergey

2001-07-01

This paper outlines the design and features incorporated in a software package for analyzing multi-modality tomographic images. The package MIDAS has been evolving for the past 15 years and is in wide use by researchers at New York University School of Medicine and a number of collaborating research sites. It was written in the C language and runs on Sun workstations and Intel PCs under the Solaris operating system. A unique strength of the MIDAS package lies in its ability to generate, manipulate and analyze a practically unlimited number of regions of interest (ROIs). These regions are automatically saved in an efficient data structure and linked to associated images. A wide selection of set theoretical (e.g. union, xor, difference), geometrical (e.g. move, rotate) and morphological (grow, peel) operators can be applied to an arbitrary selection of ROIs. ROIs are constructed as a result of image segmentation algorithms incorporated in MIDAS; they also can be drawn interactively. These ROI editing operations can be applied in either 2D or 3D mode. ROI statistics generated by MIDAS include means, standard deviations, centroids and histograms. Other image manipulation tools incorporated in MIDAS are multimodality and within modality coregistration methods (including landmark matching, surface fitting and Woods' correlation methods) and image reformatting methods (using nearest-neighbor, tri-linear or sinc interpolation). Applications of MIDAS include: (1) neuroanatomy research: marking anatomical structures in one orientation, reformatting marks to another orientation; (2) tissue volume measurements: brain structures (PET, MRI, CT), lung nodules (low dose CT), breast density (MRI); (3) analysis of functional (SPECT, PET) experiments by overlaying corresponding structural scans; (4) longitudinal studies: regional measurement of atrophy.

TO "LIMITATIONS OF ROI TESTING FOR VENTING DESIGN: DESCRIPTION OF AN ALTERNATIVE APPROACH BASED ON ATTAINMENT OF A CRITICAL PORE-GAS VELOCITY IN CONTAMINATED MEDIA

EPA Science Inventory

In this paper, we describe the limitations of radius of influence (ROI) evaluation for venting design in more detail than has been done previously and propose an alternative method based on specification and attainment of critical pore-gas velocities in contaminated subsurface me...

Skeletal maturity determination from hand radiograph by model-based analysis

NASA Astrophysics Data System (ADS)

Vogelsang, Frank; Kohnen, Michael; Schneider, Hansgerd; Weiler, Frank; Kilbinger, Markus W.; Wein, Berthold B.; Guenther, Rolf W.

2000-06-01

Derived from a model based segmentation algorithm for hand radiographs proposed in our former work we now present a method to determine skeletal maturity by an automated analysis of regions of interest (ROI). These ROIs including the epiphyseal and carpal bones, which are most important for skeletal maturity determination, can be extracted out of the radiograph by knowledge based algorithms.

A novel background field removal method for MRI using projection onto dipole fields (PDF).

PubMed

Liu, Tian; Khalidov, Ildar; de Rochefort, Ludovic; Spincemaille, Pascal; Liu, Jing; Tsiouris, A John; Wang, Yi

2011-11-01

For optimal image quality in susceptibility-weighted imaging and accurate quantification of susceptibility, it is necessary to isolate the local field generated by local magnetic sources (such as iron) from the background field that arises from imperfect shimming and variations in magnetic susceptibility of surrounding tissues (including air). Previous background removal techniques have limited effectiveness depending on the accuracy of model assumptions or information input. In this article, we report an observation that the magnetic field for a dipole outside a given region of interest (ROI) is approximately orthogonal to the magnetic field of a dipole inside the ROI. Accordingly, we propose a nonparametric background field removal technique based on projection onto dipole fields (PDF). In this PDF technique, the background field inside an ROI is decomposed into a field originating from dipoles outside the ROI using the projection theorem in Hilbert space. This novel PDF background removal technique was validated on a numerical simulation and a phantom experiment and was applied in human brain imaging, demonstrating substantial improvement in background field removal compared with the commonly used high-pass filtering method. Copyright © 2011 John Wiley & Sons, Ltd.

Classification of cirrhotic liver in Gadolinium-enhanced MR images

NASA Astrophysics Data System (ADS)

Lee, Gobert; Uchiyama, Yoshikazu; Zhang, Xuejun; Kanematsu, Masayuki; Zhou, Xiangrong; Hara, Takeshi; Kato, Hiroki; Kondo, Hiroshi; Fujita, Hiroshi; Hoshi, Hiroaki

2007-03-01

Cirrhosis of the liver is characterized by the presence of widespread nodules and fibrosis in the liver. The fibrosis and nodules formation causes distortion of the normal liver architecture, resulting in characteristic texture patterns. Texture patterns are commonly analyzed with the use of co-occurrence matrix based features measured on regions-of-interest (ROIs). A classifier is subsequently used for the classification of cirrhotic or non-cirrhotic livers. Problem arises if the classifier employed falls into the category of supervised classifier which is a popular choice. This is because the 'true disease states' of the ROIs are required for the training of the classifier but is, generally, not available. A common approach is to adopt the 'true disease state' of the liver as the 'true disease state' of all ROIs in that liver. This paper investigates the use of a nonsupervised classifier, the k-means clustering method in classifying livers as cirrhotic or non-cirrhotic using unlabelled ROI data. A preliminary result with a sensitivity and specificity of 72% and 60%, respectively, demonstrates the feasibility of using the k-means non-supervised clustering method in generating a characteristic cluster structure that could facilitate the classification of cirrhotic and non-cirrhotic livers.

Automated segmentation and reconstruction of patient-specific cardiac anatomy and pathology from in vivo MRI*

NASA Astrophysics Data System (ADS)

Ringenberg, Jordan; Deo, Makarand; Devabhaktuni, Vijay; Filgueiras-Rama, David; Pizarro, Gonzalo; Ibañez, Borja; Berenfeld, Omer; Boyers, Pamela; Gold, Jeffrey

2012-12-01

This paper presents an automated method to segment left ventricle (LV) tissues from functional and delayed-enhancement (DE) cardiac magnetic resonance imaging (MRI) scans using a sequential multi-step approach. First, a region of interest (ROI) is computed to create a subvolume around the LV using morphological operations and image arithmetic. From the subvolume, the myocardial contours are automatically delineated using difference of Gaussians (DoG) filters and GSV snakes. These contours are used as a mask to identify pathological tissues, such as fibrosis or scar, within the DE-MRI. The presented automated technique is able to accurately delineate the myocardium and identify the pathological tissue in patient sets. The results were validated by two expert cardiologists, and in one set the automated results are quantitatively and qualitatively compared with expert manual delineation. Furthermore, the method is patient-specific, performed on an entire patient MRI series. Thus, in addition to providing a quick analysis of individual MRI scans, the fully automated segmentation method is used for effectively tagging regions in order to reconstruct computerized patient-specific 3D cardiac models. These models can then be used in electrophysiological studies and surgical strategy planning.

MRI-determined liver proton density fat fraction, with MRS validation: Comparison of regions of interest sampling methods in patients with type 2 diabetes.

PubMed

Vu, Kim-Nhien; Gilbert, Guillaume; Chalut, Marianne; Chagnon, Miguel; Chartrand, Gabriel; Tang, An

2016-05-01

To assess the agreement between published magnetic resonance imaging (MRI)-based regions of interest (ROI) sampling methods using liver mean proton density fat fraction (PDFF) as the reference standard. This retrospective, internal review board-approved study was conducted in 35 patients with type 2 diabetes. Liver PDFF was measured by magnetic resonance spectroscopy (MRS) using a stimulated-echo acquisition mode sequence and MRI using a multiecho spoiled gradient-recalled echo sequence at 3.0T. ROI sampling methods reported in the literature were reproduced and liver mean PDFF obtained by whole-liver segmentation was used as the reference standard. Intraclass correlation coefficients (ICCs), Bland-Altman analysis, repeated-measures analysis of variance (ANOVA), and paired t-tests were performed. ICC between MRS and MRI-PDFF was 0.916. Bland-Altman analysis showed excellent intermethod agreement with a bias of -1.5 ± 2.8%. The repeated-measures ANOVA found no systematic variation of PDFF among the nine liver segments. The correlation between liver mean PDFF and ROI sampling methods was very good to excellent (0.873 to 0.975). Paired t-tests revealed significant differences (P < 0.05) with ROI sampling methods that exclusively or predominantly sampled the right lobe. Significant correlations with mean PDFF were found with sampling methods that included higher number of segments, total area equal or larger than 5 cm(2) , or sampled both lobes (P = 0.001, 0.023, and 0.002, respectively). MRI-PDFF quantification methods should sample each liver segment in both lobes and include a total surface area equal or larger than 5 cm(2) to provide a close estimate of the liver mean PDFF. © 2015 Wiley Periodicals, Inc.

A longitudinal magnetic resonance elastography study of murine brain tumors following radiation therapy

NASA Astrophysics Data System (ADS)

Feng, Y.; Clayton, E. H.; Okamoto, R. J.; Engelbach, J.; Bayly, P. V.; Garbow, J. R.

2016-08-01

An accurate and noninvasive method for assessing treatment response following radiotherapy is needed for both treatment monitoring and planning. Measurement of solid tumor volume alone is not sufficient for reliable early detection of therapeutic response, since changes in physiological and/or biomechanical properties can precede tumor volume change following therapy. In this study, we use magnetic resonance elastography to evaluate the treatment effect after radiotherapy in a murine brain tumor model. Shear modulus was calculated and compared between the delineated tumor region of interest (ROI) and its contralateral, mirrored counterpart. We also compared the shear modulus from both the irradiated and non-irradiated tumor and mirror ROIs longitudinally, sampling four time points spanning 9-19 d post tumor implant. Results showed that the tumor ROI had a lower shear modulus than that of the mirror ROI, independent of radiation. The shear modulus of the tumor ROI decreased over time for both the treated and untreated groups. By contrast, the shear modulus of the mirror ROI appeared to be relatively constant for the treated group, while an increasing trend was observed for the untreated group. The results provide insights into the tumor properties after radiation treatment and demonstrate the potential of using the mechanical properties of the tumor as a biomarker. In future studies, more closely spaced time points will be employed for detailed analysis of the radiation effect.

SPECT and PET analysis of subthalamic stimulation in Parkinson's disease: analysis using a manual segmentation.

PubMed

Haegelen, Claire; García-Lorenzo, Daniel; Le Jeune, Florence; Péron, Julie; Gibaud, Bernard; Riffaud, Laurent; Brassier, Gilles; Barillot, Christian; Vérin, Marc; Morandi, Xavier

2010-03-01

The subthalamic nucleus (STN) has become an effective target of deep-brain stimulation (DBS) in severely disabled patients with advanced Parkinson's disease (PD). Clinical studies have reported DBS-induced adverse effects on cognitive functions, mood, emotion and behavior. STN DBS seems to interfere with the limbic functions of the basal ganglia, but the limbic effects of STN DBS are controversial. We measured prospectively resting regional cerebral metabolism (rCMb) with 18-fluorodeoxyglucose and PET, and resting regional cerebral blood flow (rCBF) with HMPAO and SPECT in six patients with Parkinson's disease. We compared PET and SPECT 1 month before and 3 months after STN DBS. On cerebral MRI, 13 regions of interest (ROI) were manually delineated slice by slice in frontal and limbic lobes. We obtained mean rCBF and rCMb values for each ROI and the whole brain. We normalized rCBF and rCMB values to ones for the whole brain volume, which we compared before and following STN DBS. No significant difference emerged in the SPECT analysis. PET analysis revealed a significant decrease in rCMb following STN DBS in the superior frontal gyri and left and right dorsolateral prefrontal cortex (p < 0.05). A non-significant decrease in rCMb in the left anterior cingulate gyrus appeared following STN DBS (p = 0.075). Our prospective SPECT and PET study revealed significantly decreased glucose metabolism of the two superior frontal gyri without any attendant perfusion changes following STN DBS. These results suggest that STN DBS may change medial prefrontal function and therefore the integration of limbic information, either by disrupting emotional processes within the STN, or by hampering the normal function of a limbic circuit.

Factoring economic costs into conservation planning may not improve agreement over priorities for protection.

PubMed

Armsworth, Paul R; Jackson, Heather B; Cho, Seong-Hoon; Clark, Melissa; Fargione, Joseph E; Iacona, Gwenllian D; Kim, Taeyoung; Larson, Eric R; Minney, Thomas; Sutton, Nathan A

2017-12-21

Conservation organizations must redouble efforts to protect habitat given continuing biodiversity declines. Prioritization of future areas for protection is hampered by disagreements over what the ecological targets of conservation should be. Here we test the claim that such disagreements will become less important as conservation moves away from prioritizing areas for protection based only on ecological considerations and accounts for varying costs of protection using return-on-investment (ROI) methods. We combine a simulation approach with a case study of forests in the eastern United States, paying particular attention to how covariation between ecological benefits and economic costs influences agreement levels. For many conservation goals, agreement over spatial priorities improves with ROI methods. However, we also show that a reliance on ROI-based prioritization can sometimes exacerbate disagreements over priorities. As such, accounting for costs in conservation planning does not enable society to sidestep careful consideration of the ecological goals of conservation.

panelcn.MOPS: Copy-number detection in targeted NGS panel data for clinical diagnostics.

PubMed

Povysil, Gundula; Tzika, Antigoni; Vogt, Julia; Haunschmid, Verena; Messiaen, Ludwine; Zschocke, Johannes; Klambauer, Günter; Hochreiter, Sepp; Wimmer, Katharina

2017-07-01

Targeted next-generation-sequencing (NGS) panels have largely replaced Sanger sequencing in clinical diagnostics. They allow for the detection of copy-number variations (CNVs) in addition to single-nucleotide variants and small insertions/deletions. However, existing computational CNV detection methods have shortcomings regarding accuracy, quality control (QC), incidental findings, and user-friendliness. We developed panelcn.MOPS, a novel pipeline for detecting CNVs in targeted NGS panel data. Using data from 180 samples, we compared panelcn.MOPS with five state-of-the-art methods. With panelcn.MOPS leading the field, most methods achieved comparably high accuracy. panelcn.MOPS reliably detected CNVs ranging in size from part of a region of interest (ROI), to whole genes, which may comprise all ROIs investigated in a given sample. The latter is enabled by analyzing reads from all ROIs of the panel, but presenting results exclusively for user-selected genes, thus avoiding incidental findings. Additionally, panelcn.MOPS offers QC criteria not only for samples, but also for individual ROIs within a sample, which increases the confidence in called CNVs. panelcn.MOPS is freely available both as R package and standalone software with graphical user interface that is easy to use for clinical geneticists without any programming experience. panelcn.MOPS combines high sensitivity and specificity with user-friendliness rendering it highly suitable for routine clinical diagnostics. © 2017 The Authors. Human Mutation published by Wiley Periodicals, Inc.

panelcn.MOPS: Copy‐number detection in targeted NGS panel data for clinical diagnostics

PubMed Central

Povysil, Gundula; Tzika, Antigoni; Vogt, Julia; Haunschmid, Verena; Messiaen, Ludwine; Zschocke, Johannes; Klambauer, Günter; Wimmer, Katharina

2017-01-01

Abstract Targeted next‐generation‐sequencing (NGS) panels have largely replaced Sanger sequencing in clinical diagnostics. They allow for the detection of copy‐number variations (CNVs) in addition to single‐nucleotide variants and small insertions/deletions. However, existing computational CNV detection methods have shortcomings regarding accuracy, quality control (QC), incidental findings, and user‐friendliness. We developed panelcn.MOPS, a novel pipeline for detecting CNVs in targeted NGS panel data. Using data from 180 samples, we compared panelcn.MOPS with five state‐of‐the‐art methods. With panelcn.MOPS leading the field, most methods achieved comparably high accuracy. panelcn.MOPS reliably detected CNVs ranging in size from part of a region of interest (ROI), to whole genes, which may comprise all ROIs investigated in a given sample. The latter is enabled by analyzing reads from all ROIs of the panel, but presenting results exclusively for user‐selected genes, thus avoiding incidental findings. Additionally, panelcn.MOPS offers QC criteria not only for samples, but also for individual ROIs within a sample, which increases the confidence in called CNVs. panelcn.MOPS is freely available both as R package and standalone software with graphical user interface that is easy to use for clinical geneticists without any programming experience. panelcn.MOPS combines high sensitivity and specificity with user‐friendliness rendering it highly suitable for routine clinical diagnostics. PMID:28449315

Effects of global signal regression and subtraction methods on resting-state functional connectivity using arterial spin labeling data.

PubMed

Silva, João Paulo Santos; Mônaco, Luciana da Mata; Paschoal, André Monteiro; Oliveira, Ícaro Agenor Ferreira de; Leoni, Renata Ferranti

2018-05-16

Arterial spin labeling (ASL) is an established magnetic resonance imaging (MRI) technique that is finding broader applications in functional studies of the healthy and diseased brain. To promote improvement in cerebral blood flow (CBF) signal specificity, many algorithms and imaging procedures, such as subtraction methods, were proposed to eliminate or, at least, minimize noise sources. Therefore, this study addressed the main considerations of how CBF functional connectivity (FC) is changed, regarding resting brain network (RBN) identification and correlations between regions of interest (ROI), by different subtraction methods and removal of residual motion artifacts and global signal fluctuations (RMAGSF). Twenty young healthy participants (13 M/7F, mean age = 25 ± 3 years) underwent an MRI protocol with a pseudo-continuous ASL (pCASL) sequence. Perfusion-based images were obtained using simple, sinc and running subtraction. RMAGSF removal was applied to all CBF time series. Independent Component Analysis (ICA) was used for RBN identification, while Pearson' correlation was performed for ROI-based FC analysis. Temporal signal-to-noise ratio (tSNR) was higher in CBF maps obtained by sinc subtraction, although RMAGSF removal had a significant effect on maps obtained with simple and running subtractions. Neither the subtraction method nor the RMAGSF removal directly affected the identification of RBNs. However, the number of correlated and anti-correlated voxels varied for different subtraction and filtering methods. In an ROI-to-ROI level, changes were prominent in FC values and their statistical significance. Our study showed that both RMAGSF filtering and subtraction method might influence resting-state FC results, especially in an ROI level, consequently affecting FC analysis and its interpretation. Taking our results and the whole discussion together, we understand that for an exploratory assessment of the brain, one could avoid removing RMAGSF to not bias FC measures, but could use sinc subtraction to minimize low-frequency contamination. However, CBF signal specificity and frequency range for filtering purposes still need to be assessed in future studies. Copyright © 2018 Elsevier Inc. All rights reserved.

Generalized whole-body Patlak parametric imaging for enhanced quantification in clinical PET.

PubMed

Karakatsanis, Nicolas A; Zhou, Yun; Lodge, Martin A; Casey, Michael E; Wahl, Richard L; Zaidi, Habib; Rahmim, Arman

2015-11-21

We recently developed a dynamic multi-bed PET data acquisition framework to translate the quantitative benefits of Patlak voxel-wise analysis to the domain of routine clinical whole-body (WB) imaging. The standard Patlak (sPatlak) linear graphical analysis assumes irreversible PET tracer uptake, ignoring the effect of FDG dephosphorylation, which has been suggested by a number of PET studies. In this work: (i) a non-linear generalized Patlak (gPatlak) model is utilized, including a net efflux rate constant kloss, and (ii) a hybrid (s/g)Patlak (hPatlak) imaging technique is introduced to enhance contrast to noise ratios (CNRs) of uptake rate Ki images. Representative set of kinetic parameter values and the XCAT phantom were employed to generate realistic 4D simulation PET data, and the proposed methods were additionally evaluated on 11 WB dynamic PET patient studies. Quantitative analysis on the simulated Ki images over 2 groups of regions-of-interest (ROIs), with low (ROI A) or high (ROI B) true kloss relative to Ki, suggested superior accuracy for gPatlak. Bias of sPatlak was found to be 16-18% and 20-40% poorer than gPatlak for ROIs A and B, respectively. By contrast, gPatlak exhibited, on average, 10% higher noise than sPatlak. Meanwhile, the bias and noise levels for hPatlak always ranged between the other two methods. In general, hPatlak was seen to outperform all methods in terms of target-to-background ratio (TBR) and CNR for all ROIs. Validation on patient datasets demonstrated clinical feasibility for all Patlak methods, while TBR and CNR evaluations confirmed our simulation findings, and suggested presence of non-negligible kloss reversibility in clinical data. As such, we recommend gPatlak for highly quantitative imaging tasks, while, for tasks emphasizing lesion detectability (e.g. TBR, CNR) over quantification, or for high levels of noise, hPatlak is instead preferred. Finally, gPatlak and hPatlak CNR was systematically higher compared to routine SUV values.

Generalized whole-body Patlak parametric imaging for enhanced quantification in clinical PET

NASA Astrophysics Data System (ADS)

Karakatsanis, Nicolas A.; Zhou, Yun; Lodge, Martin A.; Casey, Michael E.; Wahl, Richard L.; Zaidi, Habib; Rahmim, Arman

2015-11-01

We recently developed a dynamic multi-bed PET data acquisition framework to translate the quantitative benefits of Patlak voxel-wise analysis to the domain of routine clinical whole-body (WB) imaging. The standard Patlak (sPatlak) linear graphical analysis assumes irreversible PET tracer uptake, ignoring the effect of FDG dephosphorylation, which has been suggested by a number of PET studies. In this work: (i) a non-linear generalized Patlak (gPatlak) model is utilized, including a net efflux rate constant kloss, and (ii) a hybrid (s/g)Patlak (hPatlak) imaging technique is introduced to enhance contrast to noise ratios (CNRs) of uptake rate Ki images. Representative set of kinetic parameter values and the XCAT phantom were employed to generate realistic 4D simulation PET data, and the proposed methods were additionally evaluated on 11 WB dynamic PET patient studies. Quantitative analysis on the simulated Ki images over 2 groups of regions-of-interest (ROIs), with low (ROI A) or high (ROI B) true kloss relative to Ki, suggested superior accuracy for gPatlak. Bias of sPatlak was found to be 16-18% and 20-40% poorer than gPatlak for ROIs A and B, respectively. By contrast, gPatlak exhibited, on average, 10% higher noise than sPatlak. Meanwhile, the bias and noise levels for hPatlak always ranged between the other two methods. In general, hPatlak was seen to outperform all methods in terms of target-to-background ratio (TBR) and CNR for all ROIs. Validation on patient datasets demonstrated clinical feasibility for all Patlak methods, while TBR and CNR evaluations confirmed our simulation findings, and suggested presence of non-negligible kloss reversibility in clinical data. As such, we recommend gPatlak for highly quantitative imaging tasks, while, for tasks emphasizing lesion detectability (e.g. TBR, CNR) over quantification, or for high levels of noise, hPatlak is instead preferred. Finally, gPatlak and hPatlak CNR was systematically higher compared to routine SUV values.

Local Kernel for Brains Classification in Schizophrenia

NASA Astrophysics Data System (ADS)

Castellani, U.; Rossato, E.; Murino, V.; Bellani, M.; Rambaldelli, G.; Tansella, M.; Brambilla, P.

In this paper a novel framework for brain classification is proposed in the context of mental health research. A learning by example method is introduced by combining local measurements with non linear Support Vector Machine. Instead of considering a voxel-by-voxel comparison between patients and controls, we focus on landmark points which are characterized by local region descriptors, namely Scale Invariance Feature Transform (SIFT). Then, matching is obtained by introducing the local kernel for which the samples are represented by unordered set of features. Moreover, a new weighting approach is proposed to take into account the discriminative relevance of the detected groups of features. Experiments have been performed including a set of 54 patients with schizophrenia and 54 normal controls on which region of interest (ROI) have been manually traced by experts. Preliminary results on Dorso-lateral PreFrontal Cortex (DLPFC) region are promising since up to 75% of successful classification rate has been obtained with this technique and the performance has improved up to 85% when the subjects have been stratified by sex.

Dental panoramic image analysis for enhancement biomarker of mandibular condyle for osteoporosis early detection

NASA Astrophysics Data System (ADS)

Suprijanto; Azhari; Juliastuti, E.; Septyvergy, A.; Setyagar, N. P. P.

2016-03-01

Osteoporosis is a degenerative disease characterized by low Bone Mineral Density (BMD). Currently, a BMD level is determined by Dual Energy X-ray Absorptiometry (DXA) at the lumbar vertebrae and femur. Previous studies reported that dental panoramic radiography image has potential information for early osteoporosis detection. This work reported alternative scheme, that consists of the determination of the Region of Interest (ROI) the condyle mandibular in the image as biomarker and feature extraction from ROI and classification of bone conditions. The minimum value of intensity in the cavity area is used to compensate an offset on the ROI. For feature extraction, the fraction of intensity values in the ROI that represent high bone density and the ROI total area is perfomed. The classification will be evaluated from the ability of each feature and its combinations for the BMD detection in 2 classes (normal and abnormal), with the artificial neural network method. The evaluation system used 105 panoramic image data from menopause women which consist of 36 training data and 69 test data that were divided into 2 classes. The 2 classes of classification obtained 88.0% accuracy rate and 88.0% sensitivity rate.

The diminishing dominance of the dominant hemisphere: Language fMRI in focal epilepsy.

PubMed

Tailby, Chris; Abbott, David F; Jackson, Graeme D

2017-01-01

"Which is the dominant hemisphere?" is a question that arises frequently in patients considered for neurosurgery. The concept of the dominant hemisphere implies uniformity of language lateralisation throughout the brain. It is increasingly recognised that this is not the case in the healthy control brain, and it is especially not so in neurological diseases such as epilepsy. In the present work we adapt our published objective lateralisation method (based on the construction of laterality curves) for use with sub-lobar cortical, subcortical and cerebellar regions of interest (ROIs). We apply this method to investigate regional lateralisation of language activation in 12 healthy controls and 18 focal epilepsy patients, using three different block design language fMRI paradigms, each tapping different aspects of language processing. We compared lateralisation within each ROI across tasks, and investigated how the quantity of data collected affected the ability to robustly estimate laterality across ROIs. In controls, lateralisation was stronger, and the variance across individuals smaller, in cortical ROIs, particularly in the Inferior Frontal (Broca) region. Lateralisation within temporal ROIs was dependent on the nature of the language task employed. One of the healthy controls was left lateralised anteriorly and right lateralised posteriorly. Consistent with previous work, departures from normality occurred in ~ 15-50% of focal epilepsy patients across the different ROIs, with atypicality most common in the Lateral Temporal (Wernicke) region. Across tasks and ROIs the absolute magnitude of the laterality estimate increased and its across participant variance decreased as more cycles of task and rest were included, stabilising at ~ 4 cycles (~ 4 min of data collection). Our data highlight the importance of considering language as a complex task where lateralisation varies at the subhemispheric scale. This is especially important for presurgical planning for focal resections where the concept of 'hemispheric dominance' may be misleading. This is a precision medicine approach that enables objective evaluation of language dominance within specific brain regions and can reveal surprising and unexpected anomalies that may be clinically important for individual cases.

Optimization of region-of-interest sampling strategies for hepatic MRI proton density fat fraction quantification.

PubMed

Hong, Cheng William; Wolfson, Tanya; Sy, Ethan Z; Schlein, Alexandra N; Hooker, Jonathan C; Fazeli Dehkordy, Soudabeh; Hamilton, Gavin; Reeder, Scott B; Loomba, Rohit; Sirlin, Claude B

2018-04-01

Clinical trials utilizing proton density fat fraction (PDFF) as an imaging biomarker for hepatic steatosis have used a laborious region-of-interest (ROI) sampling strategy of placing an ROI in each hepatic segment. To identify a strategy with the fewest ROIs that consistently achieves close agreement with the nine-ROI strategy. Retrospective secondary analysis of prospectively acquired clinical research data. A total of 391 adults (173 men, 218 women) with known or suspected NAFLD. Confounder-corrected chemical-shift-encoded 3T MRI using a 2D multiecho gradient-recalled echo technique. An ROI was placed in each hepatic segment. Mean nine-ROI PDFF and segmental PDFF standard deviation were computed. Segmental and lobar PDFF were compared. PDFF was estimated using every combinatorial subset of ROIs and compared to the nine-ROI average. Mean nine-ROI PDFF and segmental PDFF standard deviation were summarized descriptively. Segmental PDFF was compared using a one-way analysis of variance, and lobar PDFF was compared using a paired t-test and a Bland-Altman analysis. The PDFF estimated by every subset of ROIs was informally compared to the nine-ROI average using median intraclass correlation coefficients (ICCs) and Bland-Altman analyses. The study population's mean whole-liver PDFF was 10.1 ± 8.9% (range: 1.1-44.1%). Although there was no significant difference in average segmental (P = 0.452) or lobar (P = 0.154) PDFF, left and right lobe PDFF differed by at least 1.5 percentage points in 25.1% (98/391) of patients. Any strategy with ≥4 ROIs had ICC >0.995. 115 of 126 four-ROI strategies (91%) had limits of agreement (LOA) <1.5%, including four-ROI strategies with two ROIs from each lobe, which all had LOA <1.5%. 14/36 (39%) of two-ROI strategies and 74/84 (88%) of three-ROI strategies had ICC >0.995, and 2/36 (6%) of two-ROI strategies and 46/84 (55%) of three-ROI strategies had LOA <1.5%. Four-ROI sampling strategies with two ROIs in the left and right lobes achieve close agreement with nine-ROI PDFF. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:988-994. © 2017 International Society for Magnetic Resonance in Medicine.

Optimization of Region-of-Interest Sampling Strategies for Hepatic MRI Proton Density Fat Fraction Quantification

PubMed Central

Hong, Cheng William; Wolfson, Tanya; Sy, Ethan Z.; Schlein, Alexandra N.; Hooker, Jonathan C.; Dehkordy, Soudabeh Fazeli; Hamilton, Gavin; Reeder, Scott B.; Loomba, Rohit; Sirlin, Claude B.

2017-01-01

BACKGROUND Clinical trials utilizing proton density fat fraction (PDFF) as an imaging biomarker for hepatic steatosis have used a laborious region-of-interest (ROI) sampling strategy of placing an ROI in each hepatic segment. PURPOSE To identify a strategy with the fewest ROIs that consistently achieves close agreement with the nine-ROI strategy. STUDY TYPE Retrospective secondary analysis of prospectively acquired clinical research data. POPULATION A total of 391 adults (173 men, 218 women) with known or suspected NAFLD. FIELD STRENGTH/SEQUENCE Confounder-corrected chemical-shift-encoded 3T MRI using a 2D multiecho gradientrecalled echo technique. ASSESSMENT An ROI was placed in each hepatic segment. Mean nine-ROI PDFF and segmental PDFF standard deviation were computed. Segmental and lobar PDFF were compared. PDFF was estimated using every combinatorial subset of ROIs and compared to the nine-ROI average. STATISTICAL TESTING Mean nine-ROI PDFF and segmental PDFF standard deviation were summarized descriptively. Segmental PDFF was compared using a one-way analysis of variance, and lobar PDFF was compared using a paired t-test and a Bland–Altman analysis. The PDFF estimated by every subset of ROIs was informally compared to the nine-ROI average using median intraclass correlation coefficients (ICCs) and Bland–Altman analyses. RESULTS The study population’s mean whole-liver PDFF was 10.1±8.9% (range: 1.1–44.1%). Although there was no significant difference in average segmental (P=0.452) or lobar (P=0.154) PDFF, left and right lobe PDFF differed by at least 1.5 percentage points in 25.1% (98/391) of patients. Any strategy with ≥ 4 ROIs had ICC >0.995. 115 of 126 four-ROI strategies (91%) had limits of agreement (LOA) <1.5%, including four-ROI strategies with two ROIs from each lobe, which all had LOA <1.5%. 14/36 (39%) of two-ROI strategies and 74/84 (88%) of three-ROI strategies had ICC >0.995, and 2/36 (6%) of two-ROI strategies and 46/84 (55%) of three-ROI strategies had LOA <1.5%. DATA CONCLUSION Four-ROI sampling strategies with two ROIs in the left and right lobes achieve close agreement with nine-ROI PDFF. Level of Evidence 3 Technical Efficacy Stage 2 PMID:28842937

A method to estimate the fractional fat volume within a ROI of a breast biopsy for WAXS applications: Animal tissue evaluation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tang, Robert Y., E-mail: rx-tang@laurentian.ca; McDonald, Nancy, E-mail: mcdnancye@gmail.com; Laamanen, Curtis, E-mail: cx-laamanen@laurentian.ca

Purpose: To develop a method to estimate the mean fractional volume of fat (ν{sup ¯}{sub fat}) within a region of interest (ROI) of a tissue sample for wide-angle x-ray scatter (WAXS) applications. A scatter signal from the ROI was obtained and use of ν{sup ¯}{sub fat} in a WAXS fat subtraction model provided a way to estimate the differential linear scattering coefficient μ{sub s} of the remaining fatless tissue. Methods: The efficacy of the method was tested using animal tissue from a local butcher shop. Formalin fixed samples, 5 mm in diameter 4 mm thick, were prepared. The two mainmore » tissue types were fat and meat (fibrous). Pure as well as composite samples consisting of a mixture of the two tissue types were analyzed. For the latter samples, ν{sub fat} for the tissue columns of interest were extracted from corresponding pixels in CCD digital x-ray images using a calibration curve. The means ν{sup ¯}{sub fat} were then calculated for use in a WAXS fat subtraction model. For the WAXS measurements, the samples were interrogated with a 2.7 mm diameter 50 kV beam and the 6° scattered photons were detected with a CdTe detector subtending a solid angle of 7.75 × 10{sup −5} sr. Using the scatter spectrum, an estimate of the incident spectrum, and a scatter model, μ{sub s} was determined for the tissue in the ROI. For the composite samples, a WAXS fat subtraction model was used to estimate the μ{sub s} of the fibrous tissue in the ROI. This signal was compared to μ{sub s} of fibrous tissue obtained using a pure fibrous sample. Results: For chicken and beef composites, ν{sup ¯}{sub fat}=0.33±0.05 and 0.32 ± 0.05, respectively. The subtractions of these fat components from the WAXS composite signals provided estimates of μ{sub s} for chicken and beef fibrous tissue. The differences between the estimates and μ{sub s} of fibrous obtained with a pure sample were calculated as a function of the momentum transfer x. A t-test showed that the mean of the differences did not vary from zero in a statistically significant way thereby validating the methods. Conclusions: The methodology to estimate ν{sup ¯}{sub fat} in a ROI of a tissue sample via CCD x-ray imaging was quantitatively accurate. The WAXS fat subtraction model allowed μ{sub s} of fibrous tissue to be obtained from a ROI which had some fat. The fat estimation method coupled with the WAXS models can be used to compare μ{sub s} coefficients of fibroglandular and cancerous breast tissue.« less

Variability in CT lung-nodule quantification: Effects of dose reduction and reconstruction methods on density and texture based features.

PubMed

Lo, P; Young, S; Kim, H J; Brown, M S; McNitt-Gray, M F

2016-08-01

To investigate the effects of dose level and reconstruction method on density and texture based features computed from CT lung nodules. This study had two major components. In the first component, a uniform water phantom was scanned at three dose levels and images were reconstructed using four conventional filtered backprojection (FBP) and four iterative reconstruction (IR) methods for a total of 24 different combinations of acquisition and reconstruction conditions. In the second component, raw projection (sinogram) data were obtained for 33 lung nodules from patients scanned as a part of their clinical practice, where low dose acquisitions were simulated by adding noise to sinograms acquired at clinical dose levels (a total of four dose levels) and reconstructed using one FBP kernel and two IR kernels for a total of 12 conditions. For the water phantom, spherical regions of interest (ROIs) were created at multiple locations within the water phantom on one reference image obtained at a reference condition. For the lung nodule cases, the ROI of each nodule was contoured semiautomatically (with manual editing) from images obtained at a reference condition. All ROIs were applied to their corresponding images reconstructed at different conditions. For 17 of the nodule cases, repeat contours were performed to assess repeatability. Histogram (eight features) and gray level co-occurrence matrix (GLCM) based texture features (34 features) were computed for all ROIs. For the lung nodule cases, the reference condition was selected to be 100% of clinical dose with FBP reconstruction using the B45f kernel; feature values calculated from other conditions were compared to this reference condition. A measure was introduced, which the authors refer to as Q, to assess the stability of features across different conditions, which is defined as the ratio of reproducibility (across conditions) to repeatability (across repeat contours) of each feature. The water phantom results demonstrated substantial variability among feature values calculated across conditions, with the exception of histogram mean. Features calculated from lung nodules demonstrated similar results with histogram mean as the most robust feature (Q ≤ 1), having a mean and standard deviation Q of 0.37 and 0.22, respectively. Surprisingly, histogram standard deviation and variance features were also quite robust. Some GLCM features were also quite robust across conditions, namely, diff. variance, sum variance, sum average, variance, and mean. Except for histogram mean, all features have a Q of larger than one in at least one of the 3% dose level conditions. As expected, the histogram mean is the most robust feature in their study. The effects of acquisition and reconstruction conditions on GLCM features vary widely, though trending toward features involving summation of product between intensities and probabilities being more robust, barring a few exceptions. Overall, care should be taken into account for variation in density and texture features if a variety of dose and reconstruction conditions are used for the quantification of lung nodules in CT, otherwise changes in quantification results may be more reflective of changes due to acquisition and reconstruction conditions than in the nodule itself.

Basic Investigation on Medical Ultrasonic Echo Image Compression by JPEG2000 - Availability of Wavelet Transform and ROI Method

DTIC Science & Technology

2001-10-25

Table III. In spite of the same quality in ROI, it is decided that the images in the cases where QF is 1.3, 1.5 or 2.0 are not good for diagnosis. Of...but (b) is not good for diagnosis by decision of ultrasonographer. Results reveal that wavelet transform achieves higher quality of image compared

Relative equilibrium plot improves graphical analysis and allows bias correction of SUVR in quantitative [11C]PiB PET studies

PubMed Central

Zhou, Yun; Sojkova, Jitka; Resnick, Susan M.; Wong, Dean F.

2012-01-01

Both the standardized uptake value ratio (SUVR) and the Logan plot result in biased distribution volume ratios (DVR) in ligand-receptor dynamic PET studies. The objective of this study is to use a recently developed relative equilibrium-based graphical plot (RE plot) method to improve and simplify the two commonly used methods for quantification of [11C]PiB PET. Methods The overestimation of DVR in SUVR was analyzed theoretically using the Logan and the RE plots. A bias-corrected SUVR (bcSUVR) was derived from the RE plot. Seventy-eight [11C]PiB dynamic PET scans (66 from controls and 12 from mildly cognitively impaired participants (MCI) from the Baltimore Longitudinal Study of Aging (BLSA)) were acquired over 90 minutes. Regions of interest (ROIs) were defined on coregistered MRIs. Both the ROI and pixelwise time activity curves (TACs) were used to evaluate the estimates of DVR. DVRs obtained using the Logan plot applied to ROI TACs were used as a reference for comparison of DVR estimates. Results Results from the theoretical analysis were confirmed by human studies. ROI estimates from the RE plot and the bcSUVR were nearly identical to those from the Logan plot with ROI TACs. In contrast, ROI estimates from DVR images in frontal, temporal, parietal, cingulate regions, and the striatum were underestimated by the Logan plot (controls 4 – 12%; MCI 9 – 16%) and overestimated by the SUVR (controls 8 – 16%; MCI 16 – 24%). This bias was higher in the MCI group than in controls (p < 0.01) but was not present when data were analyzed using either the RE plot or the bcSUVR. Conclusion The RE plot improves pixel-wise quantification of [11C]PiB dynamic PET compared to the conventional Logan plot. The bcSUVR results in lower bias and higher consistency of DVR estimates compared to SUVR. The RE plot and the bcSUVR are practical quantitative approaches that improve the analysis of [11C]PiB studies. PMID:22414634

Slow-rotation dynamic SPECT with a temporal second derivative constraint.

PubMed

Humphries, T; Celler, A; Trummer, M

2011-08-01

Dynamic tracer behavior in the human body arises as a result of continuous physiological processes. Hence, the change in tracer concentration within a region of interest (ROI) should follow a smooth curve. The authors propose a modification to an existing slow-rotation dynamic SPECT reconstruction algorithm (dSPECT) with the goal of improving the smoothness of time activity curves (TACs) and other properties of the reconstructed image. The new method, denoted d2EM, imposes a constraint on the second derivative (concavity) of the TAC in every voxel of the reconstructed image, allowing it to change sign at most once. Further constraints are enforced to prevent other nonphysical behaviors from arising. The new method is compared with dSPECT using digital phantom simulations and experimental dynamic 99mTc -DTPA renal SPECT data, to assess any improvement in image quality. In both phantom simulations and healthy volunteer experiments, the d2EM method provides smoother TACs than dSPECT, with more consistent shapes in regions with dynamic behavior. Magnitudes of TACs within an ROI still vary noticeably in both dSPECT and d2EM images, but also in images produced using an OSEM approach that reconstructs each time frame individually, based on much more complete projection data. TACs produced by averaging over a region are similar using either method, even for small ROIs. Results for experimental renal data show expected behavior in images produced by both methods, with d2EM providing somewhat smoother mean TACs and more consistent TAC shapes. The d2EM method is successful in improving the smoothness of time activity curves obtained from the reconstruction, as well as improving consistency of TAC shapes within ROIs.

TU-AB-BRA-12: Impact of Image Registration Algorithms On the Prediction of Pathological Response with Radiomic Textures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yip, S; Coroller, T; Niu, N

2015-06-15

Purpose: Tumor regions-of-interest (ROI) can be propagated from the pre-onto the post-treatment PET/CT images using image registration of their CT counterparts, providing an automatic way to compute texture features on longitudinal scans. This exploratory study assessed the impact of image registration algorithms on textures to predict pathological response. Methods: Forty-six esophageal cancer patients (1 tumor/patient) underwent PET/CT scans before and after chemoradiotherapy. Patients were classified into responders and non-responders after the surgery. Physician-defined tumor ROIs on pre-treatment PET were propagated onto the post-treatment PET using rigid and ten deformable registration algorithms. One co-occurrence, two run-length and size zone matrix texturesmore » were computed within all ROIs. The relative difference of each texture at different treatment time-points was used to predict the pathologic responders. Their predictive value was assessed using the area under the receiver-operating-characteristic curve (AUC). Propagated ROIs and texture quantification resulting from different algorithms were compared using overlap volume (OV) and coefficient of variation (CoV), respectively. Results: Tumor volumes were better captured by ROIs propagated by deformable rather than the rigid registration. The OV between rigidly and deformably propagated ROIs were 69%. The deformably propagated ROIs were found to be similar (OV∼80%) except for fast-demons (OV∼60%). Rigidly propagated ROIs with run-length matrix textures failed to significantly differentiate between responders and non-responders (AUC=0.65, p=0.07), while the differentiation was significant with other textures (AUC=0.69–0.72, p<0.03). Among the deformable algorithms, fast-demons was the least predictive (AUC=0.68–0.71, p<0.04). ROIs propagated by all other deformable algorithms with any texture significantly predicted pathologic responders (AUC=0.71–0.78, p<0.01) despite substantial variation in texture quantification (CoV>70%). Conclusion: Propagated ROIs using deformable registration for all textures can lead to accurate prediction of pathologic response, potentially expediting the temporal texture analysis process. However, rigid and fast-demons deformable algorithms are not recommended due to their inferior performance compared to other algorithms. The project was supported in part by a Kaye Scholar Award.« less

SU-D-202-04: Validation of Deformable Image Registration Algorithms for Head and Neck Adaptive Radiotherapy in Routine Clinical Setting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, L; Pi, Y; Chen, Z

2016-06-15

Purpose: To evaluate the ROI contours and accumulated dose difference using different deformable image registration (DIR) algorithms for head and neck (H&N) adaptive radiotherapy. Methods: Eight H&N cancer patients were randomly selected from the affiliated hospital. During the treatment, patients were rescanned every week with ROIs well delineated by radiation oncologist on each weekly CT. New weekly treatment plans were also re-designed with consistent dose prescription on the rescanned CT and executed for one week on Siemens CT-on-rails accelerator. At the end, we got six weekly CT scans from CT1 to CT6 including six weekly treatment plans for each patient.more » The primary CT1 was set as the reference CT for DIR proceeding with the left five weekly CTs using ANACONDA and MORFEUS algorithms separately in RayStation and the external skin ROI was set to be the controlling ROI both. The entire calculated weekly dose were deformed and accumulated on corresponding reference CT1 according to the deformation vector field (DVFs) generated by the two different DIR algorithms respectively. Thus we got both the ANACONDA-based and MORFEUS-based accumulated total dose on CT1 for each patient. At the same time, we mapped the ROIs on CT1 to generate the corresponding ROIs on CT6 using ANACONDA and MORFEUS DIR algorithms. DICE coefficients between the DIR deformed and radiation oncologist delineated ROIs on CT6 were calculated. Results: For DIR accumulated dose, PTV D95 and Left-Eyeball Dmax show significant differences with 67.13 cGy and 109.29 cGy respectively (Table1). For DIR mapped ROIs, PTV, Spinal cord and Left-Optic nerve show difference with −0.025, −0.127 and −0.124 (Table2). Conclusion: Even two excellent DIR algorithms can give divergent results for ROI deformation and dose accumulation. As more and more TPS get DIR module integrated, there is an urgent need to realize the potential risk using DIR in clinical.« less

MO-FG-CAMPUS-JeP3-02: A Novel Setup Approach to Improve C-Spine Curvature Reproducibility for Head and Neck Radiotherapy Using Optical Surface Imaging with Two Regions of Interest

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ryan, K; Gil, M; Li, G

Purpose: To develop a novel approach to improve cervical spine (c-spine) curvature reproducibility for head and neck (HN) patients using optical surface imaging (OSI) with two regions of interests (ROIs). Methods: The OSI-guided, two-step setup procedure requires two ROIs: ROI-1 of the shoulders and ROI-2 of the face. The neck can be stretched or squeezed in superior-inferior (SI) direction using a specially-designed sliding head support. We hypothesize that when these two ROIs are aligned, the c-spine should fall into a naturally reproducible position under same setup conditions. An anthropomorphous phantom test was performed to examine neck pitch angles comparing withmore » the calculated angles. Three volunteers participated in the experiments, which start with conventional HN setup using skin markers and room lasers. An OSI image and lateral photo-picture were acquired as the references. In each of the three replicate tests, conventional setup was first applied after volunteers got on the couch. ROI-1 was aligned by moving the body, followed by ROI-2 alignment via adjusting head position and orientation under real-time OSI guidance. A final static OSI image and lateral picture were taken to evaluate both anterior and posterior surface alignments. Three degrees of freedom can be adjusted if an open-face mask was applied, including head SI shift using the sliding head support and pitch-and-roll rotations using a commercial couch extension. Surface alignment was analyzed comparing with conventional setup. Results: The neck pitch angle measured by OSI is consistent with the calculated (0.2±0.6°). Volunteer study illustrated improved c-spine setup reproducibility using OSI comparing with conventional setup. ROI alignments with 2mm/1° tolerance are achieved within 3 minutes. Identical knee support is important to achieve ROI-1 pitch alignment. Conclusion: The feasibility of this novel approach has been demonstrated for c-spine curvature setup reproducibility. Further evaluation is necessary with bony alignment variation in patient studies. This study is in part supported by the NIH (U54CA137788).« less

Lymph node segmentation by dynamic programming and active contours.

PubMed

Tan, Yongqiang; Lu, Lin; Bonde, Apurva; Wang, Deling; Qi, Jing; Schwartz, Lawrence H; Zhao, Binsheng

2018-03-03

Enlarged lymph nodes are indicators of cancer staging, and the change in their size is a reflection of treatment response. Automatic lymph node segmentation is challenging, as the boundary can be unclear and the surrounding structures complex. This work communicates a new three-dimensional algorithm for the segmentation of enlarged lymph nodes. The algorithm requires a user to draw a region of interest (ROI) enclosing the lymph node. Rays are cast from the center of the ROI, and the intersections of the rays and the boundary of the lymph node form a triangle mesh. The intersection points are determined by dynamic programming. The triangle mesh initializes an active contour which evolves to low-energy boundary. Three radiologists independently delineated the contours of 54 lesions from 48 patients. Dice coefficient was used to evaluate the algorithm's performance. The mean Dice coefficient between computer and the majority vote results was 83.2%. The mean Dice coefficients between the three radiologists' manual segmentations were 84.6%, 86.2%, and 88.3%. The performance of this segmentation algorithm suggests its potential clinical value for quantifying enlarged lymph nodes. © 2018 American Association of Physicists in Medicine.

Myocardial perfusion assessment with contrast echocardiography

NASA Astrophysics Data System (ADS)

Desco, Manuel; Ledesma-Carbayo, Maria J.; Santos, Andres; Garcia-Fernandez, Miguel A.; Marcos-Alberca, Pedro; Malpica, Norberto; Antoranz, Jose C.; Garcia-Barreno, Pedro

2001-05-01

Assessment of intramyocardial perfusion by contrast echocardiography is a promising new technique that allows to obtain quantitative parameters for the assessment of ischemic disease. In this work, a new methodology and a software prototype developed for this task are presented. It has been validated with Coherent Contrast Imaging (CCI) images acquired with an Acuson Sequoia scanner. Contrast (Optison microbubbles) is injected continuously during the scan. 150 images are acquired using low mechanical index U/S pulses. A burst of high mechanical index pulses is used to destroy bubbles, thus allowing to detect the contrast wash-in. The stud is performed in two conditions: rest and pharmacologically induced stress. The software developed allows to visualized the study (cine) and to select several ROIs within the heart wall. The position of these ROIs along the cardiac cycle is automatically corrected on the basis of the gradient field, and they can also be manually corrected in case the automatic procedure fails. Time curves are analyzed according to a parametric model that incorporates both contrast inflow rate and cyclic variations. Preliminary clinical results on 80 patients have allowed us to identify normal and pathological patterns and to establish the correlation of quantitative parameters with the real diagnosis.

Initial development of a computer-aided diagnosis tool for solitary pulmonary nodules

NASA Astrophysics Data System (ADS)

Catarious, David M., Jr.; Baydush, Alan H.; Floyd, Carey E., Jr.

2001-07-01

This paper describes the development of a computer-aided diagnosis (CAD) tool for solitary pulmonary nodules. This CAD tool is built upon physically meaningful features that were selected because of their relevance to shape and texture. These features included a modified version of the Hotelling statistic (HS), a channelized HS, three measures of fractal properties, two measures of spicularity, and three manually measured shape features. These features were measured from a difficult database consisting of 237 regions of interest (ROIs) extracted from digitized chest radiographs. The center of each 256x256 pixel ROI contained a suspicious lesion which was sent to follow-up by a radiologist and whose nature was later clinically determined. Linear discriminant analysis (LDA) was used to search the feature space via sequential forward search using percentage correct as the performance metric. An optimized feature subset, selected for the highest accuracy, was then fed into a three layer artificial neural network (ANN). The ANN's performance was assessed by receiver operating characteristic (ROC) analysis. A leave-one-out testing/training methodology was employed for the ROC analysis. The performance of this system is competitive with that of three radiologists on the same database.

People detection method using graphics processing units for a mobile robot with an omnidirectional camera

NASA Astrophysics Data System (ADS)

Kang, Sungil; Roh, Annah; Nam, Bodam; Hong, Hyunki

2011-12-01

This paper presents a novel vision system for people detection using an omnidirectional camera mounted on a mobile robot. In order to determine regions of interest (ROI), we compute a dense optical flow map using graphics processing units, which enable us to examine compliance with the ego-motion of the robot in a dynamic environment. Shape-based classification algorithms are employed to sort ROIs into human beings and nonhumans. The experimental results show that the proposed system detects people more precisely than previous methods.

Interactive lesion segmentation on dynamic contrast enhanced breast MRI using a Markov model

NASA Astrophysics Data System (ADS)

Wu, Qiu; Salganicoff, Marcos; Krishnan, Arun; Fussell, Donald S.; Markey, Mia K.

2006-03-01

The purpose of this study is to develop a method for segmenting lesions on Dynamic Contrast-Enhanced (DCE) breast MRI. DCE breast MRI, in which the breast is imaged before, during, and after the administration of a contrast agent, enables a truly 3D examination of breast tissues. This functional angiogenic imaging technique provides noninvasive assessment of microcirculatory characteristics of tissues in addition to traditional anatomical structure information. Since morphological features and kinetic curves from segmented lesions are to be used for diagnosis and treatment decisions, lesion segmentation is a key pre-processing step for classification. In our study, the ROI is defined by a bounding box containing the enhancement region in the subtraction image, which is generated by subtracting the pre-contrast image from 1st post-contrast image. A maximum a posteriori (MAP) estimate of the class membership (lesion vs. non-lesion) for each voxel is obtained using the Iterative Conditional Mode (ICM) method. The prior distribution of the class membership is modeled as a multi-level logistic model, a Markov Random Field model in which the class membership of each voxel is assumed to depend upon its nearest neighbors only. The likelihood distribution is assumed to be Gaussian. The parameters of each Gaussian distribution are estimated from a dozen voxels manually selected as representative of the class. The experimental segmentation results demonstrate anatomically plausible breast tissue segmentation and the predicted class membership of voxels from the interactive segmentation algorithm agrees with the manual classifications made by inspection of the kinetic enhancement curves. The proposed method is advantageous in that it is efficient, flexible, and robust.

Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography

PubMed Central

Chan, Heang-Ping; Hadjiiski, Lubomir; Helvie, Mark A.; Wei, Jun; Cha, Kenny

2016-01-01

Purpose: Develop a computer-aided detection (CAD) system for masses in digital breast tomosynthesis (DBT) volume using a deep convolutional neural network (DCNN) with transfer learning from mammograms. Methods: A data set containing 2282 digitized film and digital mammograms and 324 DBT volumes were collected with IRB approval. The mass of interest on the images was marked by an experienced breast radiologist as reference standard. The data set was partitioned into a training set (2282 mammograms with 2461 masses and 230 DBT views with 228 masses) and an independent test set (94 DBT views with 89 masses). For DCNN training, the region of interest (ROI) containing the mass (true positive) was extracted from each image. False positive (FP) ROIs were identified at prescreening by their previously developed CAD systems. After data augmentation, a total of 45 072 mammographic ROIs and 37 450 DBT ROIs were obtained. Data normalization and reduction of non-uniformity in the ROIs across heterogeneous data was achieved using a background correction method applied to each ROI. A DCNN with four convolutional layers and three fully connected (FC) layers was first trained on the mammography data. Jittering and dropout techniques were used to reduce overfitting. After training with the mammographic ROIs, all weights in the first three convolutional layers were frozen, and only the last convolution layer and the FC layers were randomly initialized again and trained using the DBT training ROIs. The authors compared the performances of two CAD systems for mass detection in DBT: one used the DCNN-based approach and the other used their previously developed feature-based approach for FP reduction. The prescreening stage was identical in both systems, passing the same set of mass candidates to the FP reduction stage. For the feature-based CAD system, 3D clustering and active contour method was used for segmentation; morphological, gray level, and texture features were extracted and merged with a linear discriminant classifier to score the detected masses. For the DCNN-based CAD system, ROIs from five consecutive slices centered at each candidate were passed through the trained DCNN and a mass likelihood score was generated. The performances of the CAD systems were evaluated using free-response ROC curves and the performance difference was analyzed using a non-parametric method. Results: Before transfer learning, the DCNN trained only on mammograms with an AUC of 0.99 classified DBT masses with an AUC of 0.81 in the DBT training set. After transfer learning with DBT, the AUC improved to 0.90. For breast-based CAD detection in the test set, the sensitivity for the feature-based and the DCNN-based CAD systems was 83% and 91%, respectively, at 1 FP/DBT volume. The difference between the performances for the two systems was statistically significant (p-value < 0.05). Conclusions: The image patterns learned from the mammograms were transferred to the mass detection on DBT slices through the DCNN. This study demonstrated that large data sets collected from mammography are useful for developing new CAD systems for DBT, alleviating the problem and effort of collecting entirely new large data sets for the new modality. PMID:27908154

The effects of nail rigidity on fracture healing in rats with osteoporosis

PubMed Central

Sha, Mo; Fu, Jun; Li, Jing; Fan Yuan, Chao; Shi, Lei; Jun Li, Shu

2009-01-01

Background and purpose Stress shielding from rigid internal fixation may lead to refracture after removal of the osteosynthesis material. We investigated the effect of a low-rigidity (Ti-24Nb-4Zr-7.9Sn) intramedullary nail regarding stress shielding and bone healing of osteoporotic fractures in the rat. Methods 40 female Sprague-Dawley rats, aged 3 months, were divided into the following groups: sham-operation (SHAM) (n = 10), ovariectomized (OVX) (n = 10) and OVX-fracture (n = 20). 10 SHAM rats and 10 OVX rats were killed after 12 weeks to provide biomechanical data. Ovariectomy was performed 12 weeks before fracturing both femurs in 20 rats. The left fracture was stabilized with a high-rigidity titanium alloy pin (Ti-6Al-4V; elastic modulus 110 GPa) and the right with a low-rigidity (Ti-24Nb-4Zr-7.9Sn; elastic modulus 33 GPa). The bony calluses were examined by micro-CT at 6 and 12 weeks after fracture, bone volume (BV) and total volume (TV) were determined at the callus region (ROI1) and the total femur (ROI2). Subsequently, the bones were tested mechanically by a three-point bending test. Results In the low-rigidity group, TV (ROI1) increased at 6 weeks, but BV (ROI1), BV (ROI2) were similar but maximum load increased. At 12 weeks, the maximum load and also BV (ROI1, ROI2) were increased in the low-rigidity group. Interpretation The low-rigidity nail manufactured from Ti-24Nb-4Zr-7.9Sn showed better external callus formation, seemed to reduce effects of stress shielding, and reduced bone resorption better than the stiffer nail. The low-rigidity nail was strong enough to maintain alignment of the fracture in the osteoporotic rat model without delayed union. PMID:19297794

Mapping thalamocortical functional connectivity in chronic and early stages of psychotic disorders

PubMed Central

Woodward, Neil D.; Heckers, Stephan

2015-01-01

Objective There is considerable evidence that the thalamus is abnormal in psychotic disorders. Resting-state fMRI (RS-fMRI) has revealed an intriguing pattern of thalamic dysconnectivity in psychosis characterized by reduced prefrontal cortex (PFC) connectivity and increased somatomotor-thalamic connectivity. However, critical knowledge gaps remain with respect to the onset, anatomical specificity, and clinical correlates of thalamic dysconnectivity in psychosis. Method RS-fMRI was collected on 105 healthy subjects and 148 individuals with psychosis, including 53 early stage psychosis patients. Using all 253 subjects, the thalamus was parceled into functional regions-of-interest (ROIs) on the basis of connectivity with six a-priori defined cortical ROIs covering most of the cortical mantle. Functional connectivity between each cortical ROI and its corresponding thalamic ROI was quantified and compared across groups. Significant differences in the ROI-to-ROI analysis were followed up with voxel-wise seed-based analyses to further localize thalamic dysconnectivity. Results ROI analysis revealed reduced PFC-thalamic connectivity and increased somatomotor-thalamic connectivity in both chronic and early stages psychosis patients. PFC hypo-connectivity and motor cortex hyper-connectivity correlated in patients suggesting they result from a common pathophysiological mechanism. Seed-based analyses revealed thalamic hypo-connectivity in psychosis localized to dorsolateral PFC, medial PFC, and cerebellar areas of the well-described ‘executive control’ network. Across all subjects, thalamic connectivity with areas of the fronto-parietal network correlated with cognitive functioning, including verbal learning and memory. Conclusions Thalamocortical dysconnectivity is present in both chronic and early stages of psychosis, includes reduced thalamic connectivity with the executive control network, and is related to cognitive impairment. PMID:26248537

Image patch-based method for automated classification and detection of focal liver lesions on CT

NASA Astrophysics Data System (ADS)

Safdari, Mustafa; Pasari, Raghav; Rubin, Daniel; Greenspan, Hayit

2013-03-01

We developed a method for automated classification and detection of liver lesions in CT images based on image patch representation and bag-of-visual-words (BoVW). BoVW analysis has been extensively used in the computer vision domain to analyze scenery images. In the current work we discuss how it can be used for liver lesion classification and detection. The methodology includes building a dictionary for a training set using local descriptors and representing a region in the image using a visual word histogram. Two tasks are described: a classification task, for lesion characterization, and a detection task in which a scan window moves across the image and is determined to be normal liver tissue or a lesion. Data: In the classification task 73 CT images of liver lesions were used, 25 images having cysts, 24 having metastasis and 24 having hemangiomas. A radiologist circumscribed the lesions, creating a region of interest (ROI), in each of the images. He then provided the diagnosis, which was established either by biopsy or clinical follow-up. Thus our data set comprises 73 images and 73 ROIs. In the detection task, a radiologist drew ROIs around each liver lesion and two regions of normal liver, for a total of 159 liver lesion ROIs and 146 normal liver ROIs. The radiologist also demarcated the liver boundary. Results: Classification results of more than 95% were obtained. In the detection task, F1 results obtained is 0.76. Recall is 84%, with precision of 73%. Results show the ability to detect lesions, regardless of shape.

WE-G-204-07: Automated Characterization of Perceptual Quality of Clinical Chest Radiographs: Improvements in Lung, Spine, and Hardware Detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wells, J; Zhang, L; Samei, E

Purpose: To develop and validate more robust methods for automated lung, spine, and hardware detection in AP/PA chest images. This work is part of a continuing effort to automatically characterize the perceptual image quality of clinical radiographs. [Y. Lin et al. Med. Phys. 39, 7019–7031 (2012)] Methods: Our previous implementation of lung/spine identification was applicable to only one vendor. A more generalized routine was devised based on three primary components: lung boundary detection, fuzzy c-means (FCM) clustering, and a clinically-derived lung pixel probability map. Boundary detection was used to constrain the lung segmentations. FCM clustering produced grayscale- and neighborhood-based pixelmore » classification probabilities which are weighted by the clinically-derived probability maps to generate a final lung segmentation. Lung centerlines were set along the left-right lung midpoints. Spine centerlines were estimated as a weighted average of body contour, lateral lung contour, and intensity-based centerline estimates. Centerline estimation was tested on 900 clinical AP/PA chest radiographs which included inpatient/outpatient, upright/bedside, men/women, and adult/pediatric images from multiple imaging systems. Our previous implementation further did not account for the presence of medical hardware (pacemakers, wires, implants, staples, stents, etc.) potentially biasing image quality analysis. A hardware detection algorithm was developed using a gradient-based thresholding method. The training and testing paradigm used a set of 48 images from which 1920 51×51 pixel{sup 2} ROIs with and 1920 ROIs without hardware were manually selected. Results: Acceptable lung centerlines were generated in 98.7% of radiographs while spine centerlines were acceptable in 99.1% of radiographs. Following threshold optimization, the hardware detection software yielded average true positive and true negative rates of 92.7% and 96.9%, respectively. Conclusion: Updated segmentation and centerline estimation methods in addition to new gradient-based hardware detection software provide improved data integrity control and error-checking for automated clinical chest image quality characterization across multiple radiography systems.« less

Chaotic Image Encryption of Regions of Interest

NASA Astrophysics Data System (ADS)

Xiao, Di; Fu, Qingqing; Xiang, Tao; Zhang, Yushu

Since different regions of an image have different importance, therefore only the important information of the image regions, which the users are really interested in, needs to be encrypted and protected emphatically in some special multimedia applications. However, the regions of interest (ROI) are always some irregular parts, such as the face and the eyes. Assuming the bulk data in transmission without being damaged, we propose a chaotic image encryption algorithm for ROI. ROI with irregular shapes are chosen and detected arbitrarily. Then the chaos-based image encryption algorithm with scrambling, S-box and diffusion parts is used to encrypt the ROI. Further, the whole image is compressed with Huffman coding. At last, a message authentication code (MAC) of the compressed image is generated based on chaotic maps. The simulation results show that the encryption algorithm has a good security level and can resist various attacks. Moreover, the compression method improves the storage and transmission efficiency to some extent, and the MAC ensures the integrity of the transmission data.

Non-contact measurement of oxygen saturation with an RGB camera

PubMed Central

Guazzi, Alessandro R.; Villarroel, Mauricio; Jorge, João; Daly, Jonathan; Frise, Matthew C.; Robbins, Peter A.; Tarassenko, Lionel

2015-01-01

A novel method (Sophia) is presented to track oxygen saturation changes in a controlled environment using an RGB camera placed approximately 1.5 m away from the subject. The method is evaluated on five healthy volunteers (Fitzpatrick skin phenotypes II, III, and IV) whose oxygen saturations were varied between 80% and 100% in a purpose-built chamber over 40 minutes each. The method carefully selects regions of interest (ROI) in the camera image by calculating signal-to-noise ratios for each ROI. This allows it to track changes in oxygen saturation accurately with respect to a conventional pulse oximeter (median coefficient of determination, 0.85). PMID:26417504

Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography.

PubMed

Samala, Ravi K; Chan, Heang-Ping; Hadjiiski, Lubomir; Helvie, Mark A; Wei, Jun; Cha, Kenny

2016-12-01

Develop a computer-aided detection (CAD) system for masses in digital breast tomosynthesis (DBT) volume using a deep convolutional neural network (DCNN) with transfer learning from mammograms. A data set containing 2282 digitized film and digital mammograms and 324 DBT volumes were collected with IRB approval. The mass of interest on the images was marked by an experienced breast radiologist as reference standard. The data set was partitioned into a training set (2282 mammograms with 2461 masses and 230 DBT views with 228 masses) and an independent test set (94 DBT views with 89 masses). For DCNN training, the region of interest (ROI) containing the mass (true positive) was extracted from each image. False positive (FP) ROIs were identified at prescreening by their previously developed CAD systems. After data augmentation, a total of 45 072 mammographic ROIs and 37 450 DBT ROIs were obtained. Data normalization and reduction of non-uniformity in the ROIs across heterogeneous data was achieved using a background correction method applied to each ROI. A DCNN with four convolutional layers and three fully connected (FC) layers was first trained on the mammography data. Jittering and dropout techniques were used to reduce overfitting. After training with the mammographic ROIs, all weights in the first three convolutional layers were frozen, and only the last convolution layer and the FC layers were randomly initialized again and trained using the DBT training ROIs. The authors compared the performances of two CAD systems for mass detection in DBT: one used the DCNN-based approach and the other used their previously developed feature-based approach for FP reduction. The prescreening stage was identical in both systems, passing the same set of mass candidates to the FP reduction stage. For the feature-based CAD system, 3D clustering and active contour method was used for segmentation; morphological, gray level, and texture features were extracted and merged with a linear discriminant classifier to score the detected masses. For the DCNN-based CAD system, ROIs from five consecutive slices centered at each candidate were passed through the trained DCNN and a mass likelihood score was generated. The performances of the CAD systems were evaluated using free-response ROC curves and the performance difference was analyzed using a non-parametric method. Before transfer learning, the DCNN trained only on mammograms with an AUC of 0.99 classified DBT masses with an AUC of 0.81 in the DBT training set. After transfer learning with DBT, the AUC improved to 0.90. For breast-based CAD detection in the test set, the sensitivity for the feature-based and the DCNN-based CAD systems was 83% and 91%, respectively, at 1 FP/DBT volume. The difference between the performances for the two systems was statistically significant (p-value < 0.05). The image patterns learned from the mammograms were transferred to the mass detection on DBT slices through the DCNN. This study demonstrated that large data sets collected from mammography are useful for developing new CAD systems for DBT, alleviating the problem and effort of collecting entirely new large data sets for the new modality.

FIJI Macro 3D ART VeSElecT: 3D Automated Reconstruction Tool for Vesicle Structures of Electron Tomograms

PubMed Central

Kaltdorf, Kristin Verena; Schulze, Katja; Helmprobst, Frederik; Kollmannsberger, Philip; Stigloher, Christian

2017-01-01

Automatic image reconstruction is critical to cope with steadily increasing data from advanced microscopy. We describe here the Fiji macro 3D ART VeSElecT which we developed to study synaptic vesicles in electron tomograms. We apply this tool to quantify vesicle properties (i) in embryonic Danio rerio 4 and 8 days past fertilization (dpf) and (ii) to compare Caenorhabditis elegans N2 neuromuscular junctions (NMJ) wild-type and its septin mutant (unc-59(e261)). We demonstrate development-specific and mutant-specific changes in synaptic vesicle pools in both models. We confirm the functionality of our macro by applying our 3D ART VeSElecT on zebrafish NMJ showing smaller vesicles in 8 dpf embryos then 4 dpf, which was validated by manual reconstruction of the vesicle pool. Furthermore, we analyze the impact of C. elegans septin mutant unc-59(e261) on vesicle pool formation and vesicle size. Automated vesicle registration and characterization was implemented in Fiji as two macros (registration and measurement). This flexible arrangement allows in particular reducing false positives by an optional manual revision step. Preprocessing and contrast enhancement work on image-stacks of 1nm/pixel in x and y direction. Semi-automated cell selection was integrated. 3D ART VeSElecT removes interfering components, detects vesicles by 3D segmentation and calculates vesicle volume and diameter (spherical approximation, inner/outer diameter). Results are collected in color using the RoiManager plugin including the possibility of manual removal of non-matching confounder vesicles. Detailed evaluation considered performance (detected vesicles) and specificity (true vesicles) as well as precision and recall. We furthermore show gain in segmentation and morphological filtering compared to learning based methods and a large time gain compared to manual segmentation. 3D ART VeSElecT shows small error rates and its speed gain can be up to 68 times faster in comparison to manual annotation. Both automatic and semi-automatic modes are explained including a tutorial. PMID:28056033

Return on Investment in Electronic Health Records in Primary Care Practices: A Mixed-Methods Study

PubMed Central

Sanche, Steven

2014-01-01

Background The use of electronic health records (EHR) in clinical settings is considered pivotal to a patient-centered health care delivery system. However, uncertainty in cost recovery from EHR investments remains a significant concern in primary care practices. Objective Guided by the question of “When implemented in primary care practices, what will be the return on investment (ROI) from an EHR implementation?”, the objectives of this study are two-fold: (1) to assess ROI from EHR in primary care practices and (2) to identify principal factors affecting the realization of positive ROI from EHR. We used a break-even point, that is, the time required to achieve cost recovery from an EHR investment, as an ROI indicator of an EHR investment. Methods Given the complexity exhibited by most EHR implementation projects, this study adopted a retrospective mixed-method research approach, particularly a multiphase study design approach. For this study, data were collected from community-based primary care clinics using EHR systems. Results We collected data from 17 primary care clinics using EHR systems. Our data show that the sampled primary care clinics recovered their EHR investments within an average period of 10 months (95% CI 6.2-17.4 months), seeing more patients with an average increase of 27% in the active-patients-to-clinician-FTE (full time equivalent) ratio and an average increase of 10% in the active-patients-to-clinical-support-staff-FTE ratio after an EHR implementation. Our analysis suggests, with a 95% confidence level, that the increase in the number of active patients (P=.006), the increase in the active-patients-to-clinician-FTE ratio (P<.001), and the increase in the clinic net revenue (P<.001) are positively associated with the EHR implementation, likely contributing substantially to an average break-even point of 10 months. Conclusions We found that primary care clinics can realize a positive ROI with EHR. Our analysis of the variances in the time required to achieve cost recovery from EHR investments suggests that a positive ROI does not appear automatically upon implementing an EHR and that a clinic’s ability to leverage EHR for process changes seems to play a role. Policies that provide support to help primary care practices successfully make EHR-enabled changes, such as support of clinic workflow optimization with an EHR system, could facilitate the realization of positive ROI from EHR in primary care practices. PMID:25600508

SU-F-I-41: Calibration-Free Material Decomposition for Dual-Energy CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao, W; Xing, L; Zhang, Q

2016-06-15

Purpose: To eliminate tedious phantom calibration or manually region of interest (ROI) selection as required in dual-energy CT material decomposition, we establish a new projection-domain material decomposition framework with incorporation of energy spectrum. Methods: Similar to the case of dual-energy CT, the integral of the basis material image in our model is expressed as a linear combination of basis functions, which are the polynomials of high- and low-energy raw projection data. To yield the unknown coefficients of the linear combination, the proposed algorithm minimizes the quadratic error between the high- and low-energy raw projection data and the projection calculated usingmore » material images. We evaluate the algorithm with an iodine concentration numerical phantom at different dose and iodine concentration levels. The x-ray energy spectra of the high and low energy are estimated using an indirect transmission method. The derived monochromatic images are compared with the high- and low-energy CT images to demonstrate beam hardening artifacts reduction. Quantitative results were measured and compared to the true values. Results: The differences between the true density value used for simulation and that were obtained from the monochromatic images, are 1.8%, 1.3%, 2.3%, and 2.9% for the dose levels from standard dose to 1/8 dose, and are 0.4%, 0.7%, 1.5%, and 1.8% for the four iodine concentration levels from 6 mg/mL to 24 mg/mL. For all of the cases, beam hardening artifacts, especially streaks shown between dense inserts, are almost completely removed in the monochromatic images. Conclusion: The proposed algorithm provides an effective way to yield material images and artifacts-free monochromatic images at different dose levels without the need for phantom calibration or ROI selection. Furthermore, the approach also yields accurate results when the concentration of the iodine concentrate insert is very low, suggesting the algorithm is robust with respect to the low-contrast scenario.« less

A novel method based on learning automata for automatic lesion detection in breast magnetic resonance imaging.

PubMed

Salehi, Leila; Azmi, Reza

2014-07-01

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. In this way, magnetic resonance imaging (MRI) is emerging as a powerful tool for the detection of breast cancer. Breast MRI presently has two major challenges. First, its specificity is relatively poor, and it detects many false positives (FPs). Second, the method involves acquiring several high-resolution image volumes before, during, and after the injection of a contrast agent. The large volume of data makes the task of interpretation by the radiologist both complex and time-consuming. These challenges have led to the development of the computer-aided detection systems to improve the efficiency and accuracy of the interpretation process. Detection of suspicious regions of interests (ROIs) is a critical preprocessing step in dynamic contrast-enhanced (DCE)-MRI data evaluation. In this regard, this paper introduces a new automatic method to detect the suspicious ROIs for breast DCE-MRI based on region growing. The results indicate that the proposed method is thoroughly able to identify suspicious regions (accuracy of 75.39 ± 3.37 on PIDER breast MRI dataset). Furthermore, the FP per image in this method is averagely 7.92, which shows considerable improvement comparing to other methods like ROI hunter.

Finger crease pattern recognition using Legendre moments and principal component analysis

NASA Astrophysics Data System (ADS)

Luo, Rongfang; Lin, Tusheng

2007-03-01

The finger joint lines defined as finger creases and its distribution can identify a person. In this paper, we propose a new finger crease pattern recognition method based on Legendre moments and principal component analysis (PCA). After obtaining the region of interest (ROI) for each finger image in the pre-processing stage, Legendre moments under Radon transform are applied to construct a moment feature matrix from the ROI, which greatly decreases the dimensionality of ROI and can represent principal components of the finger creases quite well. Then, an approach to finger crease pattern recognition is designed based on Karhunen-Loeve (K-L) transform. The method applies PCA to a moment feature matrix rather than the original image matrix to achieve the feature vector. The proposed method has been tested on a database of 824 images from 103 individuals using the nearest neighbor classifier. The accuracy up to 98.584% has been obtained when using 4 samples per class for training. The experimental results demonstrate that our proposed approach is feasible and effective in biometrics.

High-performance Chinese multiclass traffic sign detection via coarse-to-fine cascade and parallel support vector machine detectors

NASA Astrophysics Data System (ADS)

Chang, Faliang; Liu, Chunsheng

2017-09-01

The high variability of sign colors and shapes in uncontrolled environments has made the detection of traffic signs a challenging problem in computer vision. We propose a traffic sign detection (TSD) method based on coarse-to-fine cascade and parallel support vector machine (SVM) detectors to detect Chinese warning and danger traffic signs. First, a region of interest (ROI) extraction method is proposed to extract ROIs using color contrast features in local regions. The ROI extraction can reduce scanning regions and save detection time. For multiclass TSD, we propose a structure that combines a coarse-to-fine cascaded tree with a parallel structure of histogram of oriented gradients (HOG) + SVM detectors. The cascaded tree is designed to detect different types of traffic signs in a coarse-to-fine process. The parallel HOG + SVM detectors are designed to do fine detection of different types of traffic signs. The experiments demonstrate the proposed TSD method can rapidly detect multiclass traffic signs with different colors and shapes in high accuracy.

A whole brain atlas with sub-parcellation of cortical gyri using resting fMRI

NASA Astrophysics Data System (ADS)

Joshi, Anand A.; Choi, Soyoung; Sonkar, Gaurav; Chong, Minqi; Gonzalez-Martinez, Jorge; Nair, Dileep; Shattuck, David W.; Damasio, Hanna; Leahy, Richard M.

2017-02-01

The new hybrid-BCI-DNI atlas is a high-resolution MPRAGE, single-subject atlas, constructed using both anatomical and functional information to guide the parcellation of the cerebral cortex. Anatomical labeling was performed manually on coronal single-slice images guided by sulcal and gyral landmarks to generate the original (non-hybrid) BCI-DNI atlas. Functional sub-parcellations of the gyral ROIs were then generated from 40 minimally preprocessed resting fMRI datasets from the HCP database. Gyral ROIs were transferred from the BCI-DNI atlas to the 40 subjects using the HCP grayordinate space as a reference. For each subject, each gyral ROI was subdivided using the fMRI data by applying spectral clustering to a similarity matrix computed from the fMRI time-series correlations between each vertex pair. The sub-parcellations were then transferred back to the original cortical mesh to create the subparcellated hBCI-DNI atlas with a total of 67 cortical regions per hemisphere. To assess the stability of the gyral subdivisons, a separate set of 60 HCP datasets were processed as follows: 1) coregistration of the structural scans to the hBCI-DNI atlas; 2) coregistration of the anatomical BCI-DNI atlas without functional subdivisions, followed by sub-parcellation of each subject's resting fMRI data as described above. We then computed consistency between the anatomically-driven delineation of each gyral subdivision and that obtained per subject using individual fMRI data. The gyral sub-parcellations generated by atlas-based registration show variable but generally good overlap of the confidence intervals with the resting fMRI-based subdivisions. These consistency measures will provide a quantitative measure of reliability of each subdivision to users of the atlas.

Signal quality and Bayesian signal processing in neurofeedback based on real-time fMRI.

PubMed

Koush, Yury; Zvyagintsev, Mikhail; Dyck, Miriam; Mathiak, Krystyna A; Mathiak, Klaus

2012-01-02

Real-time fMRI allows analysis and visualization of the brain activity online, i.e. within one repetition time. It can be used in neurofeedback applications where subjects attempt to control an activation level in a specified region of interest (ROI) of their brain. The signal derived from the ROI is contaminated with noise and artifacts, namely with physiological noise from breathing and heart beat, scanner drift, motion-related artifacts and measurement noise. We developed a Bayesian approach to reduce noise and to remove artifacts in real-time using a modified Kalman filter. The system performs several signal processing operations: subtraction of constant and low-frequency signal components, spike removal and signal smoothing. Quantitative feedback signal quality analysis was used to estimate the quality of the neurofeedback time series and performance of the applied signal processing on different ROIs. The signal-to-noise ratio (SNR) across the entire time series and the group event-related SNR (eSNR) were significantly higher for the processed time series in comparison to the raw data. Applied signal processing improved the t-statistic increasing the significance of blood oxygen level-dependent (BOLD) signal changes. Accordingly, the contrast-to-noise ratio (CNR) of the feedback time series was improved as well. In addition, the data revealed increase of localized self-control across feedback sessions. The new signal processing approach provided reliable neurofeedback, performed precise artifacts removal, reduced noise, and required minimal manual adjustments of parameters. Advanced and fast online signal processing algorithms considerably increased the quality as well as the information content of the control signal which in turn resulted in higher contingency in the neurofeedback loop. Copyright © 2011 Elsevier Inc. All rights reserved.

Return on investment of public health interventions: a systematic review

PubMed Central

Masters, Rebecca; Anwar, Elspeth; Collins, Brendan; Cookson, Richard; Capewell, Simon

2017-01-01

Background Public sector austerity measures in many high-income countries mean that public health budgets are reducing year on year. To help inform the potential impact of these proposed disinvestments in public health, we set out to determine the return on investment (ROI) from a range of existing public health interventions. Methods We conducted systematic searches on all relevant databases (including MEDLINE; EMBASE; CINAHL; AMED; PubMed, Cochrane and Scopus) to identify studies that calculated a ROI or cost-benefit ratio (CBR) for public health interventions in high-income countries. Results We identified 2957 titles, and included 52 studies. The median ROI for public health interventions was 14.3 to 1, and median CBR was 8.3. The median ROI for all 29 local public health interventions was 4.1 to 1, and median CBR was 10.3. Even larger benefits were reported in 28 studies analysing nationwide public health interventions; the median ROI was 27.2, and median CBR was 17.5. Conclusions This systematic review suggests that local and national public health interventions are highly cost-saving. Cuts to public health budgets in high income countries therefore represent a false economy, and are likely to generate billions of pounds of additional costs to health services and the wider economy. PMID:28356325

Semantic transcoding of video based on regions of interest

NASA Astrophysics Data System (ADS)

Lim, Jeongyeon; Kim, Munchurl; Kim, Jong-Nam; Kim, Kyeongsoo

2003-06-01

Traditional transcoding on multimedia has been performed from the perspectives of user terminal capabilities such as display sizes and decoding processing power, and network resources such as available network bandwidth and quality of services (QoS) etc. The adaptation (or transcoding) of multimedia contents to given such constraints has been made by frame dropping and resizing of audiovisual, as well as reduction of SNR (Signal-to-Noise Ratio) values by saving the resulting bitrates. Not only such traditional transcoding is performed from the perspective of user"s environment, but also we incorporate a method of semantic transcoding of audiovisual based on region of interest (ROI) from user"s perspective. Users can designate their interested parts in images or video so that the corresponding video contents can be adapted focused on the user"s ROI. We incorporate the MPEG-21 DIA (Digital Item Adaptation) framework in which such semantic information of the user"s ROI is represented and delivered to the content provider side as XDI (context digital item). Representation schema of our semantic information of the user"s ROI has been adopted in MPEG-21 DIA Adaptation Model. In this paper, we present the usage of semantic information of user"s ROI for transcoding and show our system implementation with experimental results.

System and method for generating motion corrected tomographic images

DOEpatents

Gleason, Shaun S [Knoxville, TN; Goddard, Jr., James S.

2012-05-01

A method and related system for generating motion corrected tomographic images includes the steps of illuminating a region of interest (ROI) to be imaged being part of an unrestrained live subject and having at least three spaced apart optical markers thereon. Simultaneous images are acquired from a first and a second camera of the markers from different angles. Motion data comprising 3D position and orientation of the markers relative to an initial reference position is then calculated. Motion corrected tomographic data obtained from the ROI using the motion data is then obtained, where motion corrected tomographic images obtained therefrom.

Dynamic scanpaths: eye movement analysis methods

NASA Astrophysics Data System (ADS)

Blackmon, Theodore T.; Ho, Yeuk F.; Chernyak, Dimitri A.; Azzariti, Michela; Stark, Lawrence W.

1999-05-01

An eye movements sequence, or scanpath, during viewing of a stationary stimulus has been described as a set of fixations onto regions-of-interest, ROIs, and the saccades or transitions between them. Such scanpaths have high similarity for the same subject and stimulus both in the spatial loci of the ROIs and their sequence; scanpaths also take place during recollection of a previously viewed stimulus, suggesting that they play a similar role in visual memory and recall.

Bladder cancer treatment response assessment in CT urography using two-channel deep-learning network

NASA Astrophysics Data System (ADS)

Cha, Kenny H.; Hadjiiski, Lubomir M.; Chan, Heang-Ping; Samala, Ravi K.; Cohan, Richard H.; Caoili, Elaine M.; Weizer, Alon Z.; Alva, Ajjai

2018-02-01

We are developing a CAD system for bladder cancer treatment response assessment in CT. We trained a 2- Channel Deep-learning Convolution Neural Network (2Ch-DCNN) to identify responders (T0 disease) and nonresponders to chemotherapy. The 87 lesions from 82 cases generated 18,600 training paired ROIs that were extracted from segmented bladder lesions in the pre- and post-treatment CT scans and partitioned for 2-fold cross validation. The paired ROIs were input to two parallel channels of the 2Ch-DCNN. We compared the 2Ch-DCNN with our hybrid prepost- treatment ROI DCNN method and the assessments by 2 experienced abdominal radiologists. The radiologist estimated the likelihood of stage T0 after viewing each pre-post-treatment CT pair. Receiver operating characteristic analysis was performed and the area under the curve (AUC) and the partial AUC at sensitivity <90% (AUC0.9) were compared. The test AUCs were 0.76+/-0.07 and 0.75+/-0.07 for the 2 partitions, respectively, for the 2Ch-DCNN, and were 0.75+/-0.08 and 0.75+/-0.07 for the hybrid ROI method. The AUCs for Radiologist 1 were 0.67+/-0.09 and 0.75+/-0.07 for the 2 partitions, respectively, and were 0.79+/-0.07 and 0.70+/-0.09 for Radiologist 2. For the 2Ch-DCNN, the AUC0.9s were 0.43 and 0.39 for the 2 partitions, respectively, and were 0.19 and 0.28 for the hybrid ROI method. For Radiologist 1, the AUC0.9s were 0.14 and 0.34 for partition 1 and 2, respectively, and were 0.33 and 0.23 for Radiologist 2. Our study demonstrated the feasibility of using a 2Ch-DCNN for the estimation of bladder cancer treatment response in CT.

Detection of the nipple in automated 3D breast ultrasound using coronal slab-average-projection and cumulative probability map

NASA Astrophysics Data System (ADS)

Kim, Hannah; Hong, Helen

2014-03-01

We propose an automatic method for nipple detection on 3D automated breast ultrasound (3D ABUS) images using coronal slab-average-projection and cumulative probability map. First, to identify coronal images that appeared remarkable distinction between nipple-areola region and skin, skewness of each coronal image is measured and the negatively skewed images are selected. Then, coronal slab-average-projection image is reformatted from selected images. Second, to localize nipple-areola region, elliptical ROI covering nipple-areola region is detected using Hough ellipse transform in coronal slab-average-projection image. Finally, to separate the nipple from areola region, 3D Otsu's thresholding is applied to the elliptical ROI and cumulative probability map in the elliptical ROI is generated by assigning high probability to low intensity region. False detected small components are eliminated using morphological opening and the center point of detected nipple region is calculated. Experimental results show that our method provides 94.4% nipple detection rate.

Assessment of myocardial metabolic rate of glucose by means of Bayesian ICA and Markov Chain Monte Carlo methods in small animal PET imaging

NASA Astrophysics Data System (ADS)

Berradja, Khadidja; Boughanmi, Nabil

2016-09-01

In dynamic cardiac PET FDG studies the assessment of myocardial metabolic rate of glucose (MMRG) requires the knowledge of the blood input function (IF). IF can be obtained by manual or automatic blood sampling and cross calibrated with PET. These procedures are cumbersome, invasive and generate uncertainties. The IF is contaminated by spillover of radioactivity from the adjacent myocardium and this could cause important error in the estimated MMRG. In this study, we show that the IF can be extracted from the images in a rat heart study with 18F-fluorodeoxyglucose (18F-FDG) by means of Independent Component Analysis (ICA) based on Bayesian theory and Markov Chain Monte Carlo (MCMC) sampling method (BICA). Images of the heart from rats were acquired with the Sherbrooke small animal PET scanner. A region of interest (ROI) was drawn around the rat image and decomposed into blood and tissue using BICA. The Statistical study showed that there is a significant difference (p < 0.05) between MMRG obtained with IF extracted by BICA with respect to IF extracted from measured images corrupted with spillover.

Estimating abdominal adipose tissue with DXA and anthropometry.

PubMed

Hill, Alison M; LaForgia, Joe; Coates, Alison M; Buckley, Jonathan D; Howe, Peter R C

2007-02-01

To identify an anatomically defined region of interest (ROI) from DXA assessment of body composition that when combined with anthropometry can be used to accurately predict intra-abdominal adipose tissue (IAAT) in overweight/obese individuals. Forty-one postmenopausal women (age, 49 to 66 years; BMI, 26 to 37 kg/m(2)) underwent anthropometric and body composition assessments. ROI were defined as quadrilateral boxes extending 5 or 10 cm above the iliac crest and laterally to the edges of the abdominal soft tissue. A single-slice computed tomography (CT) scan was measured at the L3 to L4 intervertebral space, and abdominal skinfolds were taken. Forward step-wise regression revealed the best predictor model of IAAT area measured by CT (r(2) = 0.68, standard error of estimate = 17%) to be: IAAT area (centimeters squared) = 51.844 + DXA 10-cm ROI (grams) (0.031) + abdominal skinfold (millimeters) (1.342). Interobserver reliability for fat mass (r = 0.994; coefficient of variation, 2.60%) and lean mass (r = 0.986, coefficient of variation, 2.67%) in the DXA 10-cm ROI was excellent. This study has identified a DXA ROI that can be reliably measured using prominent anatomical landmarks, in this case, the iliac crest. Using this ROI, combined with an abdominal skinfold measurement, we have derived an equation to predict IAAT in overweight/obese postmenopausal women. This approach offers a simpler, safer, and more cost-effective method than CT for assessing the efficacy of lifestyle interventions aimed at reducing IAAT. However, this warrants further investigation and validation with an independent cohort.

Computer-aided classification of mammographic masses using the deep learning technology: a preliminary study

NASA Astrophysics Data System (ADS)

Qiu, Yuchen; Yan, Shiju; Tan, Maxine; Cheng, Samuel; Liu, Hong; Zheng, Bin

2016-03-01

Although mammography is the only clinically acceptable imaging modality used in the population-based breast cancer screening, its efficacy is quite controversy. One of the major challenges is how to help radiologists more accurately classify between benign and malignant lesions. The purpose of this study is to investigate a new mammographic mass classification scheme based on a deep learning method. In this study, we used an image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms, which includes 280 malignant and 280 benign mass ROIs, respectively. An eight layer deep learning network was applied, which employs three pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perception (MLP) classifier for feature categorization. In order to improve robustness of selected features, each convolution layer is connected with a max-pooling layer. A number of 20, 10, and 5 feature maps were utilized for the 1st, 2nd and 3rd convolution layer, respectively. The convolution networks are followed by a MLP classifier, which generates a classification score to predict likelihood of a ROI depicting a malignant mass. Among 560 ROIs, 420 ROIs were used as a training dataset and the remaining 140 ROIs were used as a validation dataset. The result shows that the new deep learning based classifier yielded an area under the receiver operation characteristic curve (AUC) of 0.810+/-0.036. This study demonstrated the potential superiority of using a deep learning based classifier to distinguish malignant and benign breast masses without segmenting the lesions and extracting the pre-defined image features.

A simple derivation and analysis of a helical cone beam tomographic algorithm for long object imaging via a novel definition of region of interest

NASA Astrophysics Data System (ADS)

Hu, Jicun; Tam, Kwok; Johnson, Roger H.

2004-01-01

We derive and analyse a simple algorithm first proposed by Kudo et al (2001 Proc. 2001 Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine (Pacific Grove, CA) pp 7-10) for long object imaging from truncated helical cone beam data via a novel definition of region of interest (ROI). Our approach is based on the theory of short object imaging by Kudo et al (1998 Phys. Med. Biol. 43 2885-909). One of the key findings in their work is that filtering of the truncated projection can be divided into two parts: one, finite in the axial direction, results from ramp filtering the data within the Tam window. The other, infinite in the z direction, results from unbounded filtering of ray sums over PI lines only. We show that for an ROI defined by PI lines emanating from the initial and final source positions on a helical segment, the boundary data which would otherwise contaminate the reconstruction of the ROI can be completely excluded. This novel definition of the ROI leads to a simple algorithm for long object imaging. The overscan of the algorithm is analytically calculated and it is the same as that of the zero boundary method. The reconstructed ROI can be divided into two regions: one is minimally contaminated by the portion outside the ROI, while the other is reconstructed free of contamination. We validate the algorithm with a 3D Shepp-Logan phantom and a disc phantom.

Dynamic Contrast-Enhanced MRI in Head-and-Neck Cancer: The Impact of Region of Interest Selection on the Intra- and Interpatient Variability of Pharmacokinetic Parameters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Craciunescu, Oana I., E-mail: oana.craciunescu@duke.edu; Yoo, David S.; Cleland, Esi

2012-03-01

Purpose: Dynamic contrast-enhanced (DCE) MRI-extracted parameters measure tumor microvascular physiology and are usually calculated from an intratumor region of interest (ROI). Optimal ROI delineation is not established. The valid clinical use of DCE-MRI requires that the variation for any given parameter measured within a tumor be less than that observed between tumors in different patients. This work evaluates the impact of tumor ROI selection on the assessment of intra- and interpatient variability. Method and Materials: Head and neck cancer patients received initial targeted therapy (TT) treatment with erlotinib and/or bevacizumab, followed by radiotherapy and concurrent cisplatin with synchronous TT. DCE-MRImore » data from Baseline and the end of the TT regimen (Lead-In) were analyzed to generate the vascular transfer function (K{sup trans}), the extracellular volume fraction (v{sub e}), and the initial area under the concentration time curve (iAUC{sub 1min}). Four ROI sampling strategies were used: whole tumor or lymph node (Whole), the slice containing the most enhancing voxels (SliceMax), three slices centered in SliceMax (Partial), and the 5% most enhancing contiguous voxels within SliceMax (95Max). The average coefficient of variation (aCV) was calculated to establish intrapatient variability among ROI sets and interpatient variability for each ROI type. The average ratio between each intrapatient CV and the interpatient CV was calculated (aRCV). Results: Baseline primary/nodes aRCVs for different ROIs not including 95Max were, for all three MR parameters, in the range of 0.14-0.24, with Lead-In values between 0.09 and 0.2, meaning a low intrapatient vs. interpatient variation. For 95Max, intrapatient CVs approximated interpatient CVs, meaning similar data dispersion and higher aRCVs (0.6-1.27 for baseline) and 0.54-0.95 for Lead-In. Conclusion: Distinction between different patient's primary tumors and/or nodes cannot be made using 95Max ROIs. The other three strategies are viable and equivalent for using DCE-MRI to measure head and neck cancer physiology.« less

Are State-Sponsored New Radiation Therapy Facilities Economically Viable in Low- and Middle-Income Countries?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Datta, Niloy R., E-mail: nrdatta@yahoo.com; Samiei, Massoud; Bodis, Stephan

Purpose: The economic viability of establishing a state-funded radiation therapy (RT) infrastructure in low- and middle-income countries (LMICs) in accordance with the World Bank definition has been assessed through computation of a return on investment (ROI). Methods and Materials: Of the 139 LMICs, 100 were evaluated according to their RT facilities, gross national income (GNI) per capita, and employment/population ratio. The assumption was an investment of US$5 million for a basic RT center able to treat 1000 patients annually. The national breakeven points and percentage of ROI (%ROI) were calculated according to the GNI per capita and patient survival ratesmore » of 10% to 50% at 2 years. It was assumed that 50% of these patients would be of working age and that, if employed and able to work after treatment, they would contribute to the country's GNI for at least 2 years. The cumulative GNI after attaining the breakeven point until the end of the 15-year lifespan of the teletherapy unit was calculated to estimate the %ROI. The recurring and overhead costs were assumed to vary from 5.5% to 15% of the capital investment. Results: The %ROI was dependent on the GNI per capita, employment/population ratio and 2-year patient survival (all P<.001). Accordingly, none of the low-income countries would attain an ROI. If 50% of the patients survived for 2 years, the %ROI in the lower-middle and upper-middle income countries could range from 0% to 159.9% and 11.2% to 844.7%, respectively. Patient user fees to offset recurring and overhead costs could vary from “nil” to US$750, depending on state subsidies. Conclusions: Countries with a greater GNI per capita, higher employment/population ratio, and better survival could achieve a faster breakeven point, resulting in a higher %ROI. Additional factors such as user fees have also been considered. These can be tailored to the patient's ability to pay to cover the recurring costs. Certain pragmatic steps that could be undertaken to address these issues are discussed in the present study.« less

PDE based scheme for multi-modal medical image watermarking.

PubMed

Aherrahrou, N; Tairi, H

2015-11-25

This work deals with copyright protection of digital images, an issue that needs protection of intellectual property rights. It is an important issue with a large number of medical images interchanged on the Internet every day. So, it is a challenging task to ensure the integrity of received images as well as authenticity. Digital watermarking techniques have been proposed as valid solution for this problem. It is worth mentioning that the Region Of Interest (ROI)/Region Of Non Interest (RONI) selection can be seen as a significant limitation from which suffers most of ROI/RONI based watermarking schemes and that in turn affects and limit their applicability in an effective way. Generally, the ROI/RONI is defined by a radiologist or a computer-aided selection tool. And thus, this will not be efficient for an institute or health care system, where one has to process a large number of images. Therefore, developing an automatic ROI/RONI selection is a challenge task. The major aim of this work is to develop an automatic selection algorithm of embedding region based on the so called Partial Differential Equation (PDE) method. Thus avoiding ROI/RONI selection problems including: (1) computational overhead, (2) time consuming, and (3) modality dependent selection. The algorithm is evaluated in terms of imperceptibility, robustness, tamper localization and recovery using MRI, Ultrasound, CT and X-ray grey scale medical images. From experimental results that we have conducted on a database of 100 medical images of four modalities, it can be inferred that our method can achieve high imperceptibility, while showing good robustness against attacks. Furthermore, the experiment results confirm the effectiveness of the proposed algorithm in detecting and recovering the various types of tampering. The highest PSNR value reached over the 100 images is 94,746 dB, while the lowest PSNR value is 60,1272 dB, which demonstrates the higher imperceptibility nature of the proposed method. Moreover, the Normalized Correlation (NC) between the original watermark and the corresponding extracted watermark for 100 images is computed. We get a NC value greater than or equal to 0.998. This indicates that the extracted watermark is very similar to the original watermark for all modalities. The key features of our proposed method are to (1) increase the robustness of the watermark against attacks; (2) provide more transparency to the embedded watermark. (3) provide more authenticity and integrity protection of the content of medical images. (4) provide minimum ROI/RONI selection complexity.

Development and testing of texture discriminators for the analysis of trabecular bone in proximal femur radiographs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huber, M. B.; Carballido-Gamio, J.; Fritscher, K.

2009-11-15

Purpose: Texture analysis of femur radiographs may serve as a potential low cost technique to predict osteoporotic fracture risk and has received considerable attention in the past years. A further application of this technique may be the measurement of the quality of specific bone compartments to provide useful information for treatment of bone fractures. Two challenges of texture analysis are the selection of the best suitable texture measure and reproducible placement of regions of interest (ROIs). The goal of this in vitro study was to automatically place ROIs in radiographs of proximal femur specimens and to calculate correlations between variousmore » different texture analysis methods and the femurs' anchorage strength. Methods: Radiographs were obtained from 14 femoral specimens and bone mineral density (BMD) was measured in the femoral neck. Biomechanical testing was performed to assess the anchorage strength in terms of failure load, breakaway torque, and number of cycles. Images were segmented using a framework that is based on the usage of level sets and statistical in-shape models. Five ROIs were automatically placed in the head, upper and lower neck, trochanteric, and shaft compartment in an atlas subject. All other subjects were registered rigidly, affinely, and nonlinearly, and the resulting transformation was used to map the five ROIs onto the individual femora. Results: In each ROI, texture features were extracted using gray level co-occurence matrices (GLCM), third-order GLCM, morphological gradients (MGs), Minkowski dimensions (MDs), Minkowski functionals (MFs), Gaussian Markov random fields, and scaling index method (SIM). Coefficients of determination for each texture feature with parameters of anchorage strength were computed. In a stepwise multiregression analysis, the most predictive parameters were identified in different models. Texture features were highly correlated with anchorage strength estimated by the failure load of up to R{sup 2}=0.61 (MF and MG features, p<0.01) and were partially independent of BMD. The correlations were dependent on the choice of the ROI and the texture measure. The best predictive multiregression model for failure load R{sub adj}{sup 2}=0.86 (p<0.001) included a set of recently developed texture methods (MF and SIM) but excluded bone mineral density and commonly used texture measures. Conclusions: The results suggest that texture information contained in trabecular bone structure visualized on radiographs may predict whether an implant anchorage can be used and may determine the local bone quality from preoperative radiographs.« less

Composite Wavelet Filters for Enhanced Automated Target Recognition

NASA Technical Reports Server (NTRS)

Chiang, Jeffrey N.; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2012-01-01

Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low-resolution sonar and camera videos taken from unmanned vehicles. These sonar images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both sonar and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this paper.

Graphics to H.264 video encoding for 3D scene representation and interaction on mobile devices using region of interest

NASA Astrophysics Data System (ADS)

Le, Minh Tuan; Nguyen, Congdu; Yoon, Dae-Il; Jung, Eun Ku; Jia, Jie; Kim, Hae-Kwang

2007-12-01

In this paper, we propose a method of 3D graphics to video encoding and streaming that are embedded into a remote interactive 3D visualization system for rapidly representing a 3D scene on mobile devices without having to download it from the server. In particular, a 3D graphics to video framework is presented that increases the visual quality of regions of interest (ROI) of the video by performing more bit allocation to ROI during H.264 video encoding. The ROI are identified by projection 3D objects to a 2D plane during rasterization. The system offers users to navigate the 3D scene and interact with objects of interests for querying their descriptions. We developed an adaptive media streaming server that can provide an adaptive video stream in term of object-based quality to the client according to the user's preferences and the variation of network bandwidth. Results show that by doing ROI mode selection, PSNR of test sample slightly change while visual quality of objects increases evidently.

Economic value of ionophores and propylene glycol to prevent disease and treat ketosis in Canada

PubMed Central

Gohary, Khaled; Overton, Michael W.; Von Massow, Michael; LeBlanc, Stephen J.; Lissemore, Kerry D.; Duffield, Todd F.

2016-01-01

A partial budget model was developed to evaluate the economic value of Rumensin Controlled Release Capsule (CRC) boluses when administered before calving to reduce disease and increase milk production. After accounting for disease incidences in a herd and the percentage by which Rumensin CRC can reduce them, and the increase in milk production attributable to administration of Rumensin CRC, the return on investment (ROI) per lactation was 4:1. Another partial budget model was developed to estimate the economic value of propylene glycol (PG) to treat ketosis when diagnosed by 3 different cow-side tests or when administered to all cows without using any cow-side testing. After accounting for the sensitivity and specificity of each test, ROI per lactation ranged from 2:1 to 4:1. The ROI was 2:1 when no cow-side testing was used. In conclusion, prevention of diseases that occur in the postpartum period and treatment of ketosis after calving yielded a positive ROI that varies based on disease incidence and method of diagnosis. PMID:27429461

Economic value of ionophores and propylene glycol to prevent disease and treat ketosis in Canada.

PubMed

Gohary, Khaled; Overton, Michael W; Von Massow, Michael; LeBlanc, Stephen J; Lissemore, Kerry D; Duffield, Todd F

2016-07-01

A partial budget model was developed to evaluate the economic value of Rumensin Controlled Release Capsule (CRC) boluses when administered before calving to reduce disease and increase milk production. After accounting for disease incidences in a herd and the percentage by which Rumensin CRC can reduce them, and the increase in milk production attributable to administration of Rumensin CRC, the return on investment (ROI) per lactation was 4:1. Another partial budget model was developed to estimate the economic value of propylene glycol (PG) to treat ketosis when diagnosed by 3 different cow-side tests or when administered to all cows without using any cow-side testing. After accounting for the sensitivity and specificity of each test, ROI per lactation ranged from 2:1 to 4:1. The ROI was 2:1 when no cow-side testing was used. In conclusion, prevention of diseases that occur in the postpartum period and treatment of ketosis after calving yielded a positive ROI that varies based on disease incidence and method of diagnosis.

Asymmetry of cortical decline in subtypes of primary progressive aphasia

PubMed Central

Cobia, Derin; Martersteck, Adam; Rademaker, Alfred; Wieneke, Christina; Weintraub, Sandra; Mesulam, M.-Marsel

2014-01-01

Objective: The aim of this study was to provide quantitative measures of changes in cortical atrophy over a 2-year period associated with 3 subtypes of primary progressive aphasia (PPA) using whole-brain vertex-wise and region-of-interest (ROI) neuroimaging methods. The purpose was to quantitate disease progression, establish an empirical basis for clinical expectations, and provide outcome measures for therapeutic trials. Methods: Changes in cortical thickness and volume loss as well as neuropsychological performance were assessed at baseline and 2-year follow-up in 26 patients who fulfilled criteria for logopenic (8 patients), agrammatic (10 patients), and semantic (8 patients) PPA subtypes. Whole-brain vertex-wise and ROI imaging analysis were conducted using the FreeSurfer longitudinal pipeline. Results: Clinical deficits and cortical atrophy patterns showed distinct patterns of change among the subtypes over 2 years. Results confirmed that progression for each of the 3 subtypes showed left greater than right hemisphere asymmetry. An ROI analysis also revealed that progression was greater within, rather than outside, the language network. Conclusions: Preferential neurodegeneration of the left hemisphere language network is a common denominator for all 3 PPA subtypes, even as the disease progresses. Using a focal cortical language network ROI as an outcome measure of disease progression appears to be more sensitive than whole-brain or ventricular volume measures of change and may be helpful for designing future clinical trials in PPA. PMID:25165386

Practical implementation of channelized hotelling observers: effect of ROI size

NASA Astrophysics Data System (ADS)

Ferrero, Andrea; Favazza, Christopher P.; Yu, Lifeng; Leng, Shuai; McCollough, Cynthia H.

2017-03-01

Fundamental to the development and application of channelized Hotelling observer (CHO) models is the selection of the region of interest (ROI) to evaluate. For assessment of medical imaging systems, reducing the ROI size can be advantageous. Smaller ROIs enable a greater concentration of interrogable objects in a single phantom image, thereby providing more information from a set of images and reducing the overall image acquisition burden. Additionally, smaller ROIs may promote better assessment of clinical patient images as different patient anatomies present different ROI constraints. To this end, we investigated the minimum ROI size that does not compromise the performance of the CHO model. In this study, we evaluated both simulated images and phantom CT images to identify the minimum ROI size that resulted in an accurate figure of merit (FOM) of the CHO's performance. More specifically, the minimum ROI size was evaluated as a function of the following: number of channels, spatial frequency and number of rotations of the Gabor filters, size and contrast of the object, and magnitude of the image noise. Results demonstrate that a minimum ROI size exists below which the CHO's performance is grossly inaccurate. The minimum ROI size is shown to increase with number of channels and be dictated by truncation of lower frequency filters. We developed a model to estimate the minimum ROI size as a parameterized function of the number of orientations and spatial frequencies of the Gabor filters, providing a guide for investigators to appropriately select parameters for model observer studies.

Practical implementation of Channelized Hotelling Observers: Effect of ROI size.

PubMed

Ferrero, Andrea; Favazza, Christopher P; Yu, Lifeng; Leng, Shuai; McCollough, Cynthia H

2017-03-01

Fundamental to the development and application of channelized Hotelling observer (CHO) models is the selection of the region of interest (ROI) to evaluate. For assessment of medical imaging systems, reducing the ROI size can be advantageous. Smaller ROIs enable a greater concentration of interrogable objects in a single phantom image, thereby providing more information from a set of images and reducing the overall image acquisition burden. Additionally, smaller ROIs may promote better assessment of clinical patient images as different patient anatomies present different ROI constraints. To this end, we investigated the minimum ROI size that does not compromise the performance of the CHO model. In this study, we evaluated both simulated images and phantom CT images to identify the minimum ROI size that resulted in an accurate figure of merit (FOM) of the CHO's performance. More specifically, the minimum ROI size was evaluated as a function of the following: number of channels, spatial frequency and number of rotations of the Gabor filters, size and contrast of the object, and magnitude of the image noise. Results demonstrate that a minimum ROI size exists below which the CHO's performance is grossly inaccurate. The minimum ROI size is shown to increase with number of channels and be dictated by truncation of lower frequency filters. We developed a model to estimate the minimum ROI size as a parameterized function of the number of orientations and spatial frequencies of the Gabor filters, providing a guide for investigators to appropriately select parameters for model observer studies.

Liver in the analysis of body composition by dual-energy X-ray absorptiometry

PubMed Central

Bazzocchi, A; Diano, D; Albisinni, U; Marchesini, G; Battista, G

2014-01-01

Objective: To investigate the predictive value for hepatic steatosis of a new software for the quantification of visceral fat by dual-energy X-ray absorptiometry (DXA) and to design new regions of interest (ROIs). Methods: Adult volunteers were prospectively screened for hepatic steatosis by ultrasonography to obtain a well-balanced population according to the presence/absence of the disease. 90 adult patients without steatosis and 90 with steatosis (mild, 53.3%; moderate, 37.7%; and severe, 10.0%) were recruited. On the same day, all subjects were submitted to blood testing and to anthropometric and whole-body DXA for body composition evaluation. A new software for android visceral fat assessment was employed, and six new “liver-suited” ROIs as well as two modified android ROIs were designed. Their association with steatosis grade was tested by correlation analysis. Results: Fat mass (FM) of the new ROIs showed the highest correlation coefficients with steatosis grade (ρ = 0.610–0.619; p < 0.001), which was also confirmed by multivariate analysis. On the whole population, the new ROIs maintained the highest predictive role for liver steatosis, with areas under the receiver operating characteristic curve up to 0.820 ± 0.032. Inter- and intra-operator agreement for the new ROIs was excellent (k = 0.915–1.000 and k = 0.927–1.000). Conclusion: New ROIs could be designed, standardized and implemented in DXA whole-body scan to provide more specific and predictive values of hepatic lipid content. Advances in knowledge: This is the first study to investigate the predictive value for hepatic steatosis of visceral and regional FM assessed on the hepatic site by DXA in comparison with ultrasonography, anthropometry and surrogate markers derived by previously validated algorithms (fatty liver index). PMID:24919499

Defining the most probable location of the parahippocampal place area using cortex-based alignment and cross-validation.

PubMed

Weiner, Kevin S; Barnett, Michael A; Witthoft, Nathan; Golarai, Golijeh; Stigliani, Anthony; Kay, Kendrick N; Gomez, Jesse; Natu, Vaidehi S; Amunts, Katrin; Zilles, Karl; Grill-Spector, Kalanit

2018-04-15

The parahippocampal place area (PPA) is a widely studied high-level visual region in the human brain involved in place and scene processing. The goal of the present study was to identify the most probable location of place-selective voxels in medial ventral temporal cortex. To achieve this goal, we first used cortex-based alignment (CBA) to create a probabilistic place-selective region of interest (ROI) from one group of 12 participants. We then tested how well this ROI could predict place selectivity in each hemisphere within a new group of 12 participants. Our results reveal that a probabilistic ROI (pROI) generated from one group of 12 participants accurately predicts the location and functional selectivity in individual brains from a new group of 12 participants, despite between subject variability in the exact location of place-selective voxels relative to the folding of parahippocampal cortex. Additionally, the prediction accuracy of our pROI is significantly higher than that achieved by volume-based Talairach alignment. Comparing the location of the pROI of the PPA relative to published data from over 500 participants, including data from the Human Connectome Project, shows a striking convergence of the predicted location of the PPA and the cortical location of voxels exhibiting the highest place selectivity across studies using various methods and stimuli. Specifically, the most predictive anatomical location of voxels exhibiting the highest place selectivity in medial ventral temporal cortex is the junction of the collateral and anterior lingual sulci. Methodologically, we make this pROI freely available (vpnl.stanford.edu/PlaceSelectivity), which provides a means to accurately identify a functional region from anatomical MRI data when fMRI data are not available (for example, in patient populations). Theoretically, we consider different anatomical and functional factors that may contribute to the consistent anatomical location of place selectivity relative to the folding of high-level visual cortex. Copyright © 2017 Elsevier Inc. All rights reserved.

Bone age assessment by content-based image retrieval and case-based reasoning

NASA Astrophysics Data System (ADS)

Fischer, Benedikt; Welter, Petra; Grouls, Christoph; Günther, Rolf W.; Deserno, Thomas M.

2011-03-01

Skeletal maturity is assessed visually by comparing hand radiographs to a standardized reference image atlas. Most common are the methods by Greulich & Pyle and Tanner & Whitehouse. For computer-aided diagnosis (CAD), local image regions of interest (ROI) such as the epiphysis or the carpal areas are extracted and evaluated. Heuristic approaches trying to automatically extract, measure and classify bones and distances between bones suffer from the high variability of biological material and the differences in bone development resulting from age, gender and ethnic origin. Content-based image retrieval (CBIR) provides a robust solution without delineating and measuring bones. In this work, epiphyseal ROIs (eROIS) of a hand radiograph are compared to previous cases with known age, mimicking a human observer. Leaving-one-out experiments are conducted on 1,102 left hand radiographs and 15,428 metacarpal and phalangeal eROIs from the publicly available USC hand atlas. The similarity of the eROIs is assessed by a combination of cross-correlation, image distortion model, and Tamura texture features, yielding a mean error rate of 0.97 years and a variance of below 0.63 years. Furthermore, we introduce a publicly available online-demonstration system, where queries on the USC dataset as well as on uploaded radiographs are performed for instant CAD. In future, we plan to evaluate physician with CBIR-CAD against physician without CBIR-CAD rather than physician vs. CBIR-CAD.

Constructing and assessing brain templates from Chinese pediatric MRI data using SPM

NASA Astrophysics Data System (ADS)

Yin, Qingjie; Ye, Qing; Yao, Li; Chen, Kewei; Jin, Zhen; Liu, Gang; Wu, Xingchun; Wang, Tingting

2005-04-01

Spatial normalization is a very important step in the processing of magnetic resonance imaging (MRI) data. So the quality of brain templates is crucial for the accuracy of MRI analysis. In this paper, using the classical protocol and the optimized protocol plus nonlinear deformation, we constructed the T1 whole brain templates and apriori brain tissue data from 69 Chinese pediatric MRI data (age 7-16 years). Then we proposed a new assessment method to evaluate our templates. 10 pediatric subjects were chosen to do the assessment as the following steps. First, the cerebellum region, the region of interest (ROI), was located on both the pediatric volume and the template volume by an experienced neuroanatomist. Second, the pediatric whole brain was mapped to the template with affine and nonlinear deformation. Third, the parameter, derived from the second step, was used to only normalize the ROI of the child to the ROI of the template. Last, the overlapping ratio, which described the overlapping rate between the ROI of the template and the normalized ROI of the child, was calculated. The mean of overlapping ratio normalized to the classical template was 0.9687, and the mean normalized to the optimized template was 0.9713. The results show that the two Chinese pediatric brain templates are comparable and their accuracy is adequate to our studies.

GISentinel: a software platform for automatic ulcer detection on capsule endoscopy videos

NASA Astrophysics Data System (ADS)

Yi, Steven; Jiao, Heng; Meng, Fan; Leighton, Jonathon A.; Shabana, Pasha; Rentz, Lauri

2014-03-01

In this paper, we present a novel and clinically valuable software platform for automatic ulcer detection on gastrointestinal (GI) tract from Capsule Endoscopy (CE) videos. Typical CE videos take about 8 hours. They have to be reviewed manually by physicians to detect and locate diseases such as ulcers and bleedings. The process is time consuming. Moreover, because of the long-time manual review, it is easy to lead to miss-finding. Working with our collaborators, we were focusing on developing a software platform called GISentinel, which can fully automated GI tract ulcer detection and classification. This software includes 3 parts: the frequency based Log-Gabor filter regions of interest (ROI) extraction, the unique feature selection and validation method (e.g. illumination invariant feature, color independent features, and symmetrical texture features), and the cascade SVM classification for handling "ulcer vs. non-ulcer" cases. After the experiments, this SW gave descent results. In frame-wise, the ulcer detection rate is 69.65% (319/458). In instance-wise, the ulcer detection rate is 82.35%(28/34).The false alarm rate is 16.43% (34/207). This work is a part of our innovative 2D/3D based GI tract disease detection software platform. The final goal of this SW is to find and classification of major GI tract diseases intelligently, such as bleeding, ulcer, and polyp from the CE videos. This paper will mainly describe the automatic ulcer detection functional module.

Oculomotor Neurocircuitry, a Structural Connectivity Study of Infantile Nystagmus Syndrome

PubMed Central

Kashou, Nasser H.; Zampini, Angelica R.

2015-01-01

Infantile Nystagmus Syndrome (INS) is one of the leading causes of significant vision loss in children and affects about 1 in 1000 to 6000 births. In the present study, we are the first to investigate the structural pathways of patients and controls using diffusion tensor imaging (DTI). Specifically, three female INS patients from the same family were scanned, two sisters and a mother. Six regions of interest (ROIs) were created manually to analyze the number of tracks. Additionally, three ROI masks were analyzed using TBSS (Tract-Based Spatial Statistics). The number of fiber tracks was reduced in INS subjects, compared to normal subjects, by 15.9%, 13.9%, 9.2%, 18.6%, 5.3%, and 2.5% for the pons, cerebellum (right and left), brainstem, cerebrum, and thalamus. Furthermore, TBSS results indicated that the fractional anisotropy (FA) values for the patients were lower in the superior ventral aspects of the pons of the brainstem than in those of the controls. We have identified some brain regions that may be actively involved in INS. These novel findings would be beneficial to the neuroimaging clinical and research community as they will give them new direction in further pursuing neurological studies related to oculomotor function and provide a rational approach to studying INS. PMID:25860806

Vehicle speed detection based on gaussian mixture model using sequential of images

NASA Astrophysics Data System (ADS)

Setiyono, Budi; Ratna Sulistyaningrum, Dwi; Soetrisno; Fajriyah, Farah; Wahyu Wicaksono, Danang

2017-09-01

Intelligent Transportation System is one of the important components in the development of smart cities. Detection of vehicle speed on the highway is supporting the management of traffic engineering. The purpose of this study is to detect the speed of the moving vehicles using digital image processing. Our approach is as follows: The inputs are a sequence of frames, frame rate (fps) and ROI. The steps are following: First we separate foreground and background using Gaussian Mixture Model (GMM) in each frames. Then in each frame, we calculate the location of object and its centroid. Next we determine the speed by computing the movement of centroid in sequence of frames. In the calculation of speed, we only consider frames when the centroid is inside the predefined region of interest (ROI). Finally we transform the pixel displacement into a time unit of km/hour. Validation of the system is done by comparing the speed calculated manually and obtained by the system. The results of software testing can detect the speed of vehicles with the highest accuracy is 97.52% and the lowest accuracy is 77.41%. And the detection results of testing by using real video footage on the road is included with real speed of the vehicle.

Relative equilibrium plot improves graphical analysis and allows bias correction of standardized uptake value ratio in quantitative 11C-PiB PET studies.

PubMed

Zhou, Yun; Sojkova, Jitka; Resnick, Susan M; Wong, Dean F

2012-04-01

Both the standardized uptake value ratio (SUVR) and the Logan plot result in biased distribution volume ratios (DVRs) in ligand-receptor dynamic PET studies. The objective of this study was to use a recently developed relative equilibrium-based graphical (RE) plot method to improve and simplify the 2 commonly used methods for quantification of (11)C-Pittsburgh compound B ((11)C-PiB) PET. The overestimation of DVR in SUVR was analyzed theoretically using the Logan and the RE plots. A bias-corrected SUVR (bcSUVR) was derived from the RE plot. Seventy-eight (11)C-PiB dynamic PET scans (66 from controls and 12 from participants with mild cognitive impaired [MCI] from the Baltimore Longitudinal Study of Aging) were acquired over 90 min. Regions of interest (ROIs) were defined on coregistered MR images. Both the ROI and the pixelwise time-activity curves were used to evaluate the estimates of DVR. DVRs obtained using the Logan plot applied to ROI time-activity curves were used as a reference for comparison of DVR estimates. Results from the theoretic analysis were confirmed by human studies. ROI estimates from the RE plot and the bcSUVR were nearly identical to those from the Logan plot with ROI time-activity curves. In contrast, ROI estimates from DVR images in frontal, temporal, parietal, and cingulate regions and the striatum were underestimated by the Logan plot (controls, 4%-12%; MCI, 9%-16%) and overestimated by the SUVR (controls, 8%-16%; MCI, 16%-24%). This bias was higher in the MCI group than in controls (P < 0.01) but was not present when data were analyzed using either the RE plot or the bcSUVR. The RE plot improves pixelwise quantification of (11)C-PiB dynamic PET, compared with the conventional Logan plot. The bcSUVR results in lower bias and higher consistency of DVR estimates than of SUVR. The RE plot and the bcSUVR are practical quantitative approaches that improve the analysis of (11)C-PiB studies.

Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancer.

PubMed

La Macchia, Mariangela; Fellin, Francesco; Amichetti, Maurizio; Cianchetti, Marco; Gianolini, Stefano; Paola, Vitali; Lomax, Antony J; Widesott, Lamberto

2012-09-18

To validate, in the context of adaptive radiotherapy, three commercial software solutions for atlas-based segmentation. Fifteen patients, five for each group, with cancer of the Head&Neck, pleura, and prostate were enrolled in the study. In addition to the treatment planning CT (pCT) images, one replanning CT (rCT) image set was acquired for each patient during the RT course. Three experienced physicians outlined on the pCT and rCT all the volumes of interest (VOIs). We used three software solutions (VelocityAI 2.6.2 (V), MIM 5.1.1 (M) by MIMVista and ABAS 2.0 (A) by CMS-Elekta) to generate the automatic contouring on the repeated CT. All the VOIs obtained with automatic contouring (AC) were successively corrected manually. We recorded the time needed for: 1) ex novo ROIs definition on rCT; 2) generation of AC by the three software solutions; 3) manual correction of AC.To compare the quality of the volumes obtained automatically by the software and manually corrected with those drawn from scratch on rCT, we used the following indexes: overlap coefficient (DICE), sensitivity, inclusiveness index, difference in volume, and displacement differences on three axes (x, y, z) from the isocenter. The time saved by the three software solutions for all the sites, compared to the manual contouring from scratch, is statistically significant and similar for all the three software solutions. The time saved for each site are as follows: about an hour for Head&Neck, about 40 minutes for prostate, and about 20 minutes for mesothelioma. The best DICE similarity coefficient index was obtained with the manual correction for: A (contours for prostate), A and M (contours for H&N), and M (contours for mesothelioma). From a clinical point of view, the automated contouring workflow was shown to be significantly shorter than the manual contouring process, even though manual correction of the VOIs is always needed.

One-year test-retest reliability of intrinsic connectivity network fMRI in older adults

PubMed Central

Guo, Cong C.; Kurth, Florian; Zhou, Juan; Mayer, Emeran A.; Eickhoff, Simon B; Kramer, Joel H.; Seeley, William W.

2014-01-01

“Resting-state” or task-free fMRI can assess intrinsic connectivity network (ICN) integrity in health and disease, suggesting a potential for use of these methods as disease-monitoring biomarkers. Numerous analytical options are available, including model-driven ROI-based correlation analysis and model-free, independent component analysis (ICA). High test-retest reliability will be a necessary feature of a successful ICN biomarker, yet available reliability data remains limited. Here, we examined ICN fMRI test-retest reliability in 24 healthy older subjects scanned roughly one year apart. We focused on the salience network, a disease-relevant ICN not previously subjected to reliability analysis. Most ICN analytical methods proved reliable (intraclass coefficients > 0.4) and could be further improved by wavelet analysis. Seed-based ROI correlation analysis showed high map-wise reliability, whereas graph theoretical measures and temporal concatenation group ICA produced the most reliable individual unit-wise outcomes. Including global signal regression in ROI-based correlation analyses reduced reliability. Our study provides a direct comparison between the most commonly used ICN fMRI methods and potential guidelines for measuring intrinsic connectivity in aging control and patient populations over time. PMID:22446491

ImageParser: a tool for finite element generation from three-dimensional medical images

PubMed Central

Yin, HM; Sun, LZ; Wang, G; Yamada, T; Wang, J; Vannier, MW

2004-01-01

Background The finite element method (FEM) is a powerful mathematical tool to simulate and visualize the mechanical deformation of tissues and organs during medical examinations or interventions. It is yet a challenge to build up an FEM mesh directly from a volumetric image partially because the regions (or structures) of interest (ROIs) may be irregular and fuzzy. Methods A software package, ImageParser, is developed to generate an FEM mesh from 3-D tomographic medical images. This software uses a semi-automatic method to detect ROIs from the context of image including neighboring tissues and organs, completes segmentation of different tissues, and meshes the organ into elements. Results The ImageParser is shown to build up an FEM model for simulating the mechanical responses of the breast based on 3-D CT images. The breast is compressed by two plate paddles under an overall displacement as large as 20% of the initial distance between the paddles. The strain and tangential Young's modulus distributions are specified for the biomechanical analysis of breast tissues. Conclusion The ImageParser can successfully exact the geometry of ROIs from a complex medical image and generate the FEM mesh with customer-defined segmentation information. PMID:15461787

Pulmonary emphysema classification based on an improved texton learning model by sparse representation

NASA Astrophysics Data System (ADS)

Zhang, Min; Zhou, Xiangrong; Goshima, Satoshi; Chen, Huayue; Muramatsu, Chisako; Hara, Takeshi; Yokoyama, Ryujiro; Kanematsu, Masayuki; Fujita, Hiroshi

2013-03-01

In this paper, we present a texture classification method based on texton learned via sparse representation (SR) with new feature histogram maps in the classification of emphysema. First, an overcomplete dictionary of textons is learned via KSVD learning on every class image patches in the training dataset. In this stage, high-pass filter is introduced to exclude patches in smooth area to speed up the dictionary learning process. Second, 3D joint-SR coefficients and intensity histograms of the test images are used for characterizing regions of interest (ROIs) instead of conventional feature histograms constructed from SR coefficients of the test images over the dictionary. Classification is then performed using a classifier with distance as a histogram dissimilarity measure. Four hundreds and seventy annotated ROIs extracted from 14 test subjects, including 6 paraseptal emphysema (PSE) subjects, 5 centrilobular emphysema (CLE) subjects and 3 panlobular emphysema (PLE) subjects, are used to evaluate the effectiveness and robustness of the proposed method. The proposed method is tested on 167 PSE, 240 CLE and 63 PLE ROIs consisting of mild, moderate and severe pulmonary emphysema. The accuracy of the proposed system is around 74%, 88% and 89% for PSE, CLE and PLE, respectively.

Practical implementation of Channelized Hotelling Observers: Effect of ROI size

PubMed Central

Yu, Lifeng; Leng, Shuai; McCollough, Cynthia H.

2017-01-01

Fundamental to the development and application of channelized Hotelling observer (CHO) models is the selection of the region of interest (ROI) to evaluate. For assessment of medical imaging systems, reducing the ROI size can be advantageous. Smaller ROIs enable a greater concentration of interrogable objects in a single phantom image, thereby providing more information from a set of images and reducing the overall image acquisition burden. Additionally, smaller ROIs may promote better assessment of clinical patient images as different patient anatomies present different ROI constraints. To this end, we investigated the minimum ROI size that does not compromise the performance of the CHO model. In this study, we evaluated both simulated images and phantom CT images to identify the minimum ROI size that resulted in an accurate figure of merit (FOM) of the CHO’s performance. More specifically, the minimum ROI size was evaluated as a function of the following: number of channels, spatial frequency and number of rotations of the Gabor filters, size and contrast of the object, and magnitude of the image noise. Results demonstrate that a minimum ROI size exists below which the CHO’s performance is grossly inaccurate. The minimum ROI size is shown to increase with number of channels and be dictated by truncation of lower frequency filters. We developed a model to estimate the minimum ROI size as a parameterized function of the number of orientations and spatial frequencies of the Gabor filters, providing a guide for investigators to appropriately select parameters for model observer studies. PMID:28943699

Quantitative diagnostic method for biceps long head tendinitis by using ultrasound.

PubMed

Huang, Shih-Wei; Wang, Wei-Te

2013-01-01

To investigate the feasibility of grayscale quantitative diagnostic method for biceps tendinitis and determine the cut-off points of a quantitative biceps ultrasound (US) method to diagnose biceps tendinitis. Design. Prospective cross-sectional case controlled study. Outpatient rehabilitation service. A total of 336 shoulder pain patients with suspected biceps tendinitis were recruited in this prospective observational study. The grayscale pixel data of the range of interest (ROI) were obtained for both the transverse and longitudinal views of the biceps US. A total of 136 patients were classified with biceps tendinitis, and 200 patients were classified as not having biceps tendinitis based on the diagnostic criteria. Based on the Youden index, the cut-off points were determined as 26.85 for the transverse view and 21.25 for the longitudinal view of the standard deviation (StdDev) of the ROI values, respectively. When the ROI evaluation of the US surpassed the cut-off point, the sensitivity was 68% and the specificity was 90% in the StdDev of the transverse view, and the sensitivity was 81% and the specificity was 73% in the StdDev of the longitudinal view to diagnose biceps tendinitis. For equivocal cases or inexperienced sonographers, our study provides a more objective method for diagnosing biceps tendinitis in shoulder pain patients.

Defect inspection in hot slab surface: multi-source CCD imaging based fuzzy-rough sets method

NASA Astrophysics Data System (ADS)

Zhao, Liming; Zhang, Yi; Xu, Xiaodong; Xiao, Hong; Huang, Chao

2016-09-01

To provide an accurate surface defects inspection method and make the automation of robust image region of interests(ROI) delineation strategy a reality in production line, a multi-source CCD imaging based fuzzy-rough sets method is proposed for hot slab surface quality assessment. The applicability of the presented method and the devised system are mainly tied to the surface quality inspection for strip, billet and slab surface etcetera. In this work we take into account the complementary advantages in two common machine vision (MV) systems(line array CCD traditional scanning imaging (LS-imaging) and area array CCD laser three-dimensional (3D) scanning imaging (AL-imaging)), and through establishing the model of fuzzy-rough sets in the detection system the seeds for relative fuzzy connectedness(RFC) delineation for ROI can placed adaptively, which introduces the upper and lower approximation sets for RIO definition, and by which the boundary region can be delineated by RFC region competitive classification mechanism. For the first time, a Multi-source CCD imaging based fuzzy-rough sets strategy is attempted for CC-slab surface defects inspection that allows an automatic way of AI algorithms and powerful ROI delineation strategies to be applied to the MV inspection field.

DWI-associated entire-tumor histogram analysis for the differentiation of low-grade prostate cancer from intermediate-high-grade prostate cancer.

PubMed

Wu, Chen-Jiang; Wang, Qing; Li, Hai; Wang, Xiao-Ning; Liu, Xi-Sheng; Shi, Hai-Bin; Zhang, Yu-Dong

2015-10-01

To investigate diagnostic efficiency of DWI using entire-tumor histogram analysis in differentiating the low-grade (LG) prostate cancer (PCa) from intermediate-high-grade (HG) PCa in comparison with conventional ROI-based measurement. DW images (b of 0-1400 s/mm(2)) from 126 pathology-confirmed PCa (diameter >0.5 cm) in 110 patients were retrospectively collected and processed by mono-exponential model. The measurement of tumor apparent diffusion coefficients (ADCs) was performed with using histogram-based and ROI-based approach, respectively. The diagnostic ability of ADCs from two methods for differentiating LG-PCa (Gleason score, GS ≤ 6) from HG-PCa (GS > 6) was determined by ROC regression, and compared by McNemar's test. There were 49 LG-tumor and 77 HG-tumor at pathologic findings. Histogram-based ADCs (mean, median, 10th and 90th) and ROI-based ADCs (mean) showed dominant relationships with ordinal GS of Pca (ρ = -0.225 to -0.406, p < 0.05). All above imaging indices reflected significant difference between LG-PCa and HG-PCa (all p values <0.01). Histogram 10th ADCs had dominantly high Az (0.738), Youden index (0.415), and positive likelihood ratio (LR+, 2.45) in stratifying tumor GS against mean, median and 90th ADCs, and ROI-based ADCs. Histogram mean, median, and 10th ADCs showed higher specificity (65.3%-74.1% vs. 44.9%, p < 0.01), but lower sensitivity (57.1%-71.3% vs. 84.4%, p < 0.05) than ROI-based ADCs in differentiating LG-PCa from HG-PCa. DWI-associated histogram analysis had higher specificity, Az, Youden index, and LR+ for differentiation of PCa Gleason grade than ROI-based approach.

Quantitative accuracy of the closed-form least-squares solution for targeted SPECT.

PubMed

Shcherbinin, S; Celler, A

2010-10-07

The aim of this study is to investigate the quantitative accuracy of the closed-form least-squares solution (LSS) for single photon emission computed tomography (SPECT). The main limitation for employing this method in actual clinical reconstructions is the computational cost related to operations with a large-sized system matrix. However, in some clinical situations, the size of the system matrix can be decreased using targeted reconstruction. For example, some oncology SPECT studies are characterized by intense tracer uptakes that are localized in relatively small areas, while the remaining parts of the patient body have only a low activity background. Conventional procedures reconstruct the activity distribution in the whole object, which leads to relatively poor image accuracy/resolution for tumors while computer resources are wasted, trying to rebuild diagnostically useless background. In this study, we apply a concept of targeted reconstruction to SPECT phantom experiments imitating such oncology scans. Our approach includes two major components: (i) disconnection of the entire imaging system of equations and extraction of only those parts that correspond to the targets, i.e., regions of interest (ROI) encompassing active containers/tumors and (ii) generation of the closed-form LSS for each target ROI. We compared these ROI-based LSS with those reconstructed by the conventional MLEM approach. The analysis of the five processed cases from two phantom experiments demonstrated that the LSS approach outperformed MLEM in terms of the noise level inside ROI. On the other hand, MLEM better recovered total activity if the number of iterations was large enough. For the experiment without background activity, the ROI-based LSS led to noticeably better spatial activity distribution inside ROI. However, the distributions pertaining to both approaches were practically identical for the experiment with the concentration ratio 7:1 between the containers and the background.

Individual Functional ROI Optimization via Maximization of Group-wise Consistency of Structural and Functional Profiles

PubMed Central

Li, Kaiming; Guo, Lei; Zhu, Dajiang; Hu, Xintao; Han, Junwei; Liu, Tianming

2013-01-01

Studying connectivities among functional brain regions and the functional dynamics on brain networks has drawn increasing interest. A fundamental issue that affects functional connectivity and dynamics studies is how to determine the best possible functional brain regions or ROIs (regions of interest) for a group of individuals, since the connectivity measurements are heavily dependent on ROI locations. Essentially, identification of accurate, reliable and consistent corresponding ROIs is challenging due to the unclear boundaries between brain regions, variability across individuals, and nonlinearity of the ROIs. In response to these challenges, this paper presents a novel methodology to computationally optimize ROIs locations derived from task-based fMRI data for individuals so that the optimized ROIs are more consistent, reproducible and predictable across brains. Our computational strategy is to formulate the individual ROI location optimization as a group variance minimization problem, in which group-wise consistencies in functional/structural connectivity patterns and anatomic profiles are defined as optimization constraints. Our experimental results from multimodal fMRI and DTI data show that the optimized ROIs have significantly improved consistency in structural and functional profiles across individuals. These improved functional ROIs with better consistency could contribute to further study of functional interaction and dynamics in the human brain. PMID:22281931

The effect of the sample size and location on contrast ultrasound measurement of perfusion parameters.

PubMed

Leinonen, Merja R; Raekallio, Marja R; Vainio, Outi M; Ruohoniemi, Mirja O; O'Brien, Robert T

2011-01-01

Contrast-enhanced ultrasound can be used to quantify tissue perfusion based on region of interest (ROI) analysis. The effect of the location and size of the ROI on the obtained perfusion parameters has been described in phantom, ex vivo and in vivo studies. We assessed the effects of location and size of the ROI on perfusion parameters in the renal cortex of 10 healthy, anesthetized cats using Definity contrast-enhanced ultrasound to estimate the importance of the ROI on quantification of tissue perfusion with contrast-enhanced ultrasound. Three separate sets of ROIs were placed in the renal cortex, varying in location, size or depth. There was a significant inverse association between increased depth or increased size of the ROI and peak intensity (P < 0.05). There was no statistically significant difference in the peak intensity between the ROIs placed in a row in the near field cortex. There was no significant difference in the ROIs with regard to arrival time, time to peak intensity and wash-in rate. When comparing two different ROIs in a patient with focal lesions, such as suspected neoplasia or infarction, the ROIs should always be placed at same depth and be as similar in size as possible.

Two new regions of interest to evaluate separately cortical and trabecular BMD in the proximal femur using DXA.

PubMed

Prevrhal, Sven; Meta, Margarita; Genant, Harry K

2004-01-01

To differentiate changes in trabecular and cortical bone density at a skeletal site bearing body weight, the main goal of this retrospective study was to develop and characterize two new regions of interest (ROIs) for DXA at the hip, one mainly focusing on trabecular bone and another mainly focusing on cortical bone. Specific aims were to maximize the precision of the ROIs and to characterize their usefulness for monitoring age-related bone loss and discriminating controls from fracture cases in a cross-sectional study population and to compare them with earlier ROIs designed by our group. The study used populations from two different previous studies conducted in our laboratory, with one comprising cohorts of healthy premenopausal women, healthy postmenopausal women, and postmenopausal osteoporotic women with at least one spinal fracture (Spine Fx Study) and the other one comprising two cohorts of age-matched postmenopausal women, in whom cases had sustained a hip fracture (Hip Fx study). The new ROI for trabecular bone (CIRCROI) tries to improve on the earlier custom-designed Central ROI, which was also targeted at trabecular bone. CIRCROI consists of an approximate largest circle that can fit inside the femoral proximal metaphysis without touching the superior and inferior endocortical walls. The new ROI for cortical bone (CORTROI) at a site bearing body weight is defined as a horizontal rectangular box crossing the femoral shaft below the lesser trochanter. CORTROI BMD cohort means were significantly higher than all other ROIs, and CIRCROI BMD cohort means were lower than standard ROIs with the exception of Ward's ROI. CIRCROI BMD was highly correlated with total femur BMD ( r=0.94) and Central BMD ( r=0.93), whereas CORTROI BMD correlations were lower (highest with total femur BMD ( r=0.86)). Fracture discrimination odds ratios (ORs) of all ROIs were significant for the Hip Fx Study, with CIRCROI BMD having the highest, and CORTROI BMD the lowest, OR (4.83 and 2.49 per SD, respectively, compared with 3.69 for Ward's ROI as the highest OR of standard ROIs). For the Spine Fx Study, only spinal and trochanteric BMD had significant OR. The new trabecular ROI had good short-term precision, comparable to the standard ROIs at the hip, but improving on that of Ward's triangle, the only standard ROI only including the anterior and posterior cortical walls and therefore more predominantly consisting of trabecular bone than other standard ROIs. The precision of the new cortical ROI was lower than standard DXA ROIs, except for Ward's triangle, but provides unique information on purely cortical bone at a skeletal site bearing body weight.

Evaluation of fatty proportion in fatty liver using least squares method with constraints.

PubMed

Li, Xingsong; Deng, Yinhui; Yu, Jinhua; Wang, Yuanyuan; Shamdasani, Vijay

2014-01-01

Backscatter and attenuation parameters are not easily measured in clinical applications due to tissue inhomogeneity in the region of interest (ROI). A least squares method(LSM) that fits the echo signal power spectra from a ROI to a 3-parameter tissue model was used to get attenuation coefficient imaging in fatty liver. Since fat's attenuation value is higher than normal liver parenchyma, a reasonable threshold was chosen to evaluate the fatty proportion in fatty liver. Experimental results using clinical data of fatty liver illustrate that the least squares method can get accurate attenuation estimates. It is proved that the attenuation values have a positive correlation with the fatty proportion, which can be used to evaluate the syndrome of fatty liver.

Monocular Vision-Based Underwater Object Detection

PubMed Central

Zhang, Zhen; Dai, Fengzhao; Bu, Yang; Wang, Huibin

2017-01-01

In this paper, we propose an underwater object detection method using monocular vision sensors. In addition to commonly used visual features such as color and intensity, we investigate the potential of underwater object detection using light transmission information. The global contrast of various features is used to initially identify the region of interest (ROI), which is then filtered by the image segmentation method, producing the final underwater object detection results. We test the performance of our method with diverse underwater datasets. Samples of the datasets are acquired by a monocular camera with different qualities (such as resolution and focal length) and setups (viewing distance, viewing angle, and optical environment). It is demonstrated that our ROI detection method is necessary and can largely remove the background noise and significantly increase the accuracy of our underwater object detection method. PMID:28771194

Automatic detection of the macula in retinal fundus images using seeded mode tracking approach.

PubMed

Wong, Damon W K; Liu, Jiang; Tan, Ngan-Meng; Yin, Fengshou; Cheng, Xiangang; Cheng, Ching-Yu; Cheung, Gemmy C M; Wong, Tien Yin

2012-01-01

The macula is the part of the eye responsible for central high acuity vision. Detection of the macula is an important task in retinal image processing as a landmark for subsequent disease assessment, such as for age-related macula degeneration. In this paper, we have presented an approach to automatically determine the macula centre in retinal fundus images. First contextual information on the image is combined with a statistical model to obtain an approximate macula region of interest localization. Subsequently, we propose the use of a seeded mode tracking technique to locate the macula centre. The proposed approach is tested on a large dataset composed of 482 normal images and 162 glaucoma images from the ORIGA database and an additional 96 AMD images. The results show a ROI detection of 97.5%, and 90.5% correct detection of the macula within 1/3DD from a manual reference, which outperforms other current methods. The results are promising for the use of the proposed approach to locate the macula for the detection of macula diseases from retinal images.

Intellicount: High-Throughput Quantification of Fluorescent Synaptic Protein Puncta by Machine Learning

PubMed Central

Fantuzzo, J. A.; Mirabella, V. R.; Zahn, J. D.

2017-01-01

Abstract Synapse formation analyses can be performed by imaging and quantifying fluorescent signals of synaptic markers. Traditionally, these analyses are done using simple or multiple thresholding and segmentation approaches or by labor-intensive manual analysis by a human observer. Here, we describe Intellicount, a high-throughput, fully-automated synapse quantification program which applies a novel machine learning (ML)-based image processing algorithm to systematically improve region of interest (ROI) identification over simple thresholding techniques. Through processing large datasets from both human and mouse neurons, we demonstrate that this approach allows image processing to proceed independently of carefully set thresholds, thus reducing the need for human intervention. As a result, this method can efficiently and accurately process large image datasets with minimal interaction by the experimenter, making it less prone to bias and less liable to human error. Furthermore, Intellicount is integrated into an intuitive graphical user interface (GUI) that provides a set of valuable features, including automated and multifunctional figure generation, routine statistical analyses, and the ability to run full datasets through nested folders, greatly expediting the data analysis process. PMID:29218324

Testing of a Composite Wavelet Filter to Enhance Automated Target Recognition in SONAR

NASA Technical Reports Server (NTRS)

Chiang, Jeffrey N.

2011-01-01

Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low resolution SONAR and camera videos taken from Unmanned Underwater Vehicles (UUVs). These SONAR images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both SONAR and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this report.

An automatic search of Alzheimer patterns using a nonnegative matrix factorization

NASA Astrophysics Data System (ADS)

Giraldo, Diana L.; García-Arteaga, Juan D.; Romero, Eduardo

2013-11-01

This paper presents a fully automatic method that condenses relevant morphometric information from a database of magnetic resonance images (MR) labeled as either normal (NC) or Alzheimer's disease (AD). The proposed method generates class templates using Nonnegative Matrix Factorization (NMF) which will be used to develop an NC/AD classi cator. It then nds regions of interest (ROI) with discerning inter-class properties. by inspecting the di erence volume of the two class templates. From these templates local probability distribution functions associated to low level features such as intensities, orientation and edges within the found ROI are calculated. A sample brain volume can then be characterized by a similarity measure in the ROI to both the normal and the pathological templates. These characteristics feed a simple binary SVM classi er which, when tested with an experimental group extracted from a public brain MR dataset (OASIS), reveals an equal error rate measure which is better than the state-of-the-art tested on the same dataset (0:9 in the former and 0:8 in the latter).

Multi-scales region segmentation for ROI separation in digital mammograms

NASA Astrophysics Data System (ADS)

Zhang, Dapeng; Zhang, Di; Li, Yue; Wang, Wei

2017-02-01

Mammography is currently the most effective imaging modality used by radiologists for the screening of breast cancer. Segmentation is one of the key steps in the process of developing anatomical models for calculation of safe medical dose of radiation. This paper explores the potential of the statistical region merging segmentation technique for Breast segmentation in digital mammograms. First, the mammograms are pre-processing for regions enhancement, then the enhanced images are segmented using SRM with multi scales, finally these segmentations are combined for region of interest (ROI) separation and edge detection. The proposed algorithm uses multi-scales region segmentation in order to: separate breast region from background region, region edge detection and ROIs separation. The experiments are performed using a data set of mammograms from different patients, demonstrating the validity of the proposed criterion. Results show that, the statistical region merging segmentation algorithm actually can work on the segmentation of medical image and more accurate than another methods. And the outcome shows that the technique has a great potential to become a method of choice for segmentation of mammograms.

Analysis of iterative region-of-interest image reconstruction for x-ray computed tomography

PubMed Central

Sidky, Emil Y.; Kraemer, David N.; Roth, Erin G.; Ullberg, Christer; Reiser, Ingrid S.; Pan, Xiaochuan

2014-01-01

Abstract. One of the challenges for iterative image reconstruction (IIR) is that such algorithms solve an imaging model implicitly, requiring a complete representation of the scanned subject within the viewing domain of the scanner. This requirement can place a prohibitively high computational burden for IIR applied to x-ray computed tomography (CT), especially when high-resolution tomographic volumes are required. In this work, we aim to develop an IIR algorithm for direct region-of-interest (ROI) image reconstruction. The proposed class of IIR algorithms is based on an optimization problem that incorporates a data fidelity term, which compares a derivative of the estimated data with the available projection data. In order to characterize this optimization problem, we apply it to computer-simulated two-dimensional fan-beam CT data, using both ideal noiseless data and realistic data containing a level of noise comparable to that of the breast CT application. The proposed method is demonstrated for both complete field-of-view and ROI imaging. To demonstrate the potential utility of the proposed ROI imaging method, it is applied to actual CT scanner data. PMID:25685824

Analysis of iterative region-of-interest image reconstruction for x-ray computed tomography.

PubMed

Sidky, Emil Y; Kraemer, David N; Roth, Erin G; Ullberg, Christer; Reiser, Ingrid S; Pan, Xiaochuan

2014-10-03

One of the challenges for iterative image reconstruction (IIR) is that such algorithms solve an imaging model implicitly, requiring a complete representation of the scanned subject within the viewing domain of the scanner. This requirement can place a prohibitively high computational burden for IIR applied to x-ray computed tomography (CT), especially when high-resolution tomographic volumes are required. In this work, we aim to develop an IIR algorithm for direct region-of-interest (ROI) image reconstruction. The proposed class of IIR algorithms is based on an optimization problem that incorporates a data fidelity term, which compares a derivative of the estimated data with the available projection data. In order to characterize this optimization problem, we apply it to computer-simulated two-dimensional fan-beam CT data, using both ideal noiseless data and realistic data containing a level of noise comparable to that of the breast CT application. The proposed method is demonstrated for both complete field-of-view and ROI imaging. To demonstrate the potential utility of the proposed ROI imaging method, it is applied to actual CT scanner data.

Metal-induced streak artifact reduction using iterative reconstruction algorithms in x-ray computed tomography image of the dentoalveolar region.

PubMed

Dong, Jian; Hayakawa, Yoshihiko; Kannenberg, Sven; Kober, Cornelia

2013-02-01

The objective of this study was to reduce metal-induced streak artifact on oral and maxillofacial x-ray computed tomography (CT) images by developing the fast statistical image reconstruction system using iterative reconstruction algorithms. Adjacent CT images often depict similar anatomical structures in thin slices. So, first, images were reconstructed using the same projection data of an artifact-free image. Second, images were processed by the successive iterative restoration method where projection data were generated from reconstructed image in sequence. Besides the maximum likelihood-expectation maximization algorithm, the ordered subset-expectation maximization algorithm (OS-EM) was examined. Also, small region of interest (ROI) setting and reverse processing were applied for improving performance. Both algorithms reduced artifacts instead of slightly decreasing gray levels. The OS-EM and small ROI reduced the processing duration without apparent detriments. Sequential and reverse processing did not show apparent effects. Two alternatives in iterative reconstruction methods were effective for artifact reduction. The OS-EM algorithm and small ROI setting improved the performance. Copyright © 2012 Elsevier Inc. All rights reserved.

Direct evidence for tumor necrosis factor-induced mitochondrial reactive oxygen intermediates and their involvement in cytotoxicity.

PubMed Central

Goossens, V; Grooten, J; De Vos, K; Fiers, W

1995-01-01

Tumor necrosis factor (TNF) is selectively cytotoxic to some types of tumor cells in vitro and exerts antitumor activity in vivo. Reactive oxygen intermediates (ROIs) have been implicated in the direct cytotoxic activity of TNF. By using confocal microscopy, flow cytometry, and the ROI-specific probe dihydrorhodamine 123, we directly demonstrate that intracellular ROIs are formed after TNF stimulation. These ROIs are observed exclusively under conditions where cells are sensitive to the cytotoxic activity of TNF, suggesting a direct link between both phenomena. ROI scavengers, such as butylated hydroxyanisole, effectively blocked the formation of free radicals and arrested the cytotoxic response, confirming that the observed ROIs are cytocidal. The mitochondrial glutathione system scavenges the major part of the produced ROIs, an activity that could be blocked by diethyl maleate; under these conditions, TNF-induced ROIs detectable by dihydrorhodamine 123 oxidation were 5- to 20-fold higher. Images Fig. 1 Fig. 4 PMID:7667254

Database of normal human cerebral blood flow measured by SPECT: II. Quantification of I-123-IMP studies with ARG method and effects of partial volume correction.

PubMed

Inoue, Kentaro; Ito, Hiroshi; Shidahara, Miho; Goto, Ryoi; Kinomura, Shigeo; Sato, Kazunori; Taki, Yasuyuki; Okada, Ken; Kaneta, Tomohiro; Fukuda, Hiroshi

2006-02-01

The limited spatial resolution of SPECT causes a partial volume effect (PVE) and can lead to the significant underestimation of regional tracer concentration in the small structures surrounded by a low tracer concentration, such as the cortical gray matter of an atrophied brain. The aim of the present study was to determine, using 123I-IMP and SPECT, normal CBF of elderly subjects with and without PVE correction (PVC), and to determine regional differences in the effect of PVC and their association with the regional tissue fraction of the brain. Quantitative CBF SPECT using 123I-IMP was performed in 33 healthy elderly subjects (18 males, 15 females, 54-74 years old) using the autoradiographic method. We corrected CBF for PVE using segmented MR images, and analyzed quantitative CBF and regional differences in the effect of PVC using tissue fractions of gray matter (GM) and white matter (WM) in regions of interest (ROIs) placed on the cortical and subcortical GM regions and deep WM regions. The mean CBF in GM-ROIs were 31.7 +/- 6.6 and 41.0 +/- 8.1 ml/100 g/min for males and females, and in WM-ROIs, 18.2 +/- 0.7 and 22.9 +/- 0.8 ml/100 g/min for males and females, respectively. The mean CBF in GM-ROIs after PVC were 50.9 +/- 12.8 and 65.8 +/- 16.1 ml/100 g/min for males and females, respectively. There were statistically significant differences in the effect of PVC among ROIs, but not between genders. The effect of PVC was small in the cerebellum and parahippocampal gyrus, and it was large in the superior frontal gyrus, superior parietal lobule and precentral gyrus. Quantitative CBF in GM recovered significantly, but did not reach values as high as those obtained by invasive methods or in the H2(15)O PET study that used PVC. There were significant regional differences in the effect of PVC, which were considered to result from regional differences in GM tissue fraction, which is more reduced in the frontoparietal regions in the atrophied brain of the elderly.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moore, B; Yin, F; Cai, J

Purpose: To determine the variation in tumor contrast between different MRI sequences and between patients for the purpose of MRI-based treatment planning. Methods: Multiple MRI scans of 11 patients with cancer(s) in the liver were included in this IRB-approved study. Imaging sequences consisted of T1W MRI, Contrast-Enhanced T1W MRI, T2W MRI, and T2*/T1W MRI. MRI images were acquired on a 1.5T GE Signa scanner with a four-channel torso coil. We calculated the tumor-to-tissue contrast to noise ratio (CNR) for each MR sequence by contouring the tumor and a region of interest (ROI) in a homogeneous region of the liver usingmore » the Eclipse treatment planning software. CNR was calculated (I-Tum-I-ROI)/SD-ROI, where I-Tum and I-ROI are the mean values of the tumor and the ROI respectively, and SD-ROI is the standard deviation of the ROI. The same tumor and ROI structures were used in all measurements for different MR sequences. Inter-patient Coefficient of variation (CV), and inter-sequence CV was determined. In addition, mean and standard deviation of CNR were calculated and compared between different MR sequences. Results: Our preliminary results showed large inter-patient CV (range: 37.7% to 88%) and inter-sequence CV (range 5.3% to 104.9%) of liver tumor CNR, indicating great variations in tumor CNR between MR sequences and between patients. Tumor CNR was found to be largest in CE-T1W (8.5±7.5), followed by T2W (4.2±2.4), T1W (3.4±2.2), and T2*/T1W (1.7±0.6) MR scans. The inter-patient CV of tumor CNR was also the largest in CE-T1W (88%), followed by T1W (64.3%), T1W (56.2%), and T2*/T1W (37.7) MR scans. Conclusion: Large inter-sequence and inter-patient variations were observed in liver tumor CNR. CE-T1W MR images on average provided the best tumor CNR. Efforts are needed to optimize tumor contrast and its consistency for MRI-based treatment planning of cancer in the liver. This project is supported by NIH grant: 1R21CA165384.« less

Diffusion-weighted imaging of breast lesions: Region-of-interest placement and different ADC parameters influence apparent diffusion coefficient values.

PubMed

Bickel, Hubert; Pinker, Katja; Polanec, Stephan; Magometschnigg, Heinrich; Wengert, Georg; Spick, Claudio; Bogner, Wolfgang; Bago-Horvath, Zsuzsanna; Helbich, Thomas H; Baltzer, Pascal

2017-05-01

To investigate the influence of region-of-interest (ROI) placement and different apparent diffusion coefficient (ADC) parameters on ADC values, diagnostic performance, reproducibility and measurement time in breast tumours. In this IRB-approved, retrospective study, 149 histopathologically proven breast tumours (109 malignant, 40 benign) in 147 women (mean age 53.2) were investigated. Three radiologists independently measured minimum, mean and maximum ADC, each using three ROI placement approaches:1 - small 2D-ROI, 2 - large 2D-ROI and 3 - 3D-ROI covering the whole lesion. One reader performed all measurements twice. Median ADC values, diagnostic performance, reproducibility, and measurement time were calculated and compared between all combinations of ROI placement approaches and ADC parameters. Median ADC values differed significantly between the ROI placement approaches (p < .001). Minimum ADC showed the best diagnostic performance (AUC .928-.956), followed by mean ADC obtained from 2D ROIs (.926-.94). Minimum and mean ADC showed high intra- (ICC .85-.94) and inter-reader reproducibility (ICC .74-.94). Median measurement time was significantly shorter for the 2D ROIs (p < .001). ROI placement significantly influences ADC values measured in breast tumours. Minimum and mean ADC acquired from 2D-ROIs are useful for the differentiation of benign and malignant breast lesions, and are highly reproducible, with rapid measurement. • Region of interest placement significantly influences apparent diffusion coefficient of breast tumours. • Minimum and mean apparent diffusion coefficient perform best and are reproducible. • 2D regions of interest perform best and provide rapid measurement times.

Poster — Thur Eve — 09: Evaluation of electrical impedance and computed tomography fusion algorithms using an anthropomorphic phantom

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chugh, Brige Paul; Krishnan, Kalpagam; Liu, Jeff

2014-08-15

Integration of biological conductivity information provided by Electrical Impedance Tomography (EIT) with anatomical information provided by Computed Tomography (CT) imaging could improve the ability to characterize tissues in clinical applications. In this paper, we report results of our study which compared the fusion of EIT with CT using three different image fusion algorithms, namely: weighted averaging, wavelet fusion, and ROI indexing. The ROI indexing method of fusion involves segmenting the regions of interest from the CT image and replacing the pixels with the pixels of the EIT image. The three algorithms were applied to a CT and EIT image ofmore » an anthropomorphic phantom, constructed out of five acrylic contrast targets with varying diameter embedded in a base of gelatin bolus. The imaging performance was assessed using Detectability and Structural Similarity Index Measure (SSIM). Wavelet fusion and ROI-indexing resulted in lower Detectability (by 35% and 47%, respectively) yet higher SSIM (by 66% and 73%, respectively) than weighted averaging. Our results suggest that wavelet fusion and ROI-indexing yielded more consistent and optimal fusion performance than weighted averaging.« less

SU-E-J-224: Multimodality Segmentation of Head and Neck Tumors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aristophanous, M; Yang, J; Beadle, B

2014-06-01

Purpose: Develop an algorithm that is able to automatically segment tumor volume in Head and Neck cancer by integrating information from CT, PET and MR imaging simultaneously. Methods: Twenty three patients that were recruited under an adaptive radiotherapy protocol had MR, CT and PET/CT scans within 2 months prior to start of radiotherapy. The patients had unresectable disease and were treated either with chemoradiotherapy or radiation therapy alone. Using the Velocity software, the PET/CT and MR (T1 weighted+contrast) scans were registered to the planning CT using deformable and rigid registration respectively. The PET and MR images were then resampled accordingmore » to the registration to match the planning CT. The resampled images, together with the planning CT, were fed into a multi-channel segmentation algorithm, which is based on Gaussian mixture models and solved with the expectation-maximization algorithm and Markov random fields. A rectangular region of interest (ROI) was manually placed to identify the tumor area and facilitate the segmentation process. The auto-segmented tumor contours were compared with the gross tumor volume (GTV) manually defined by the physician. The volume difference and Dice similarity coefficient (DSC) between the manual and autosegmented GTV contours were calculated as the quantitative evaluation metrics. Results: The multimodality segmentation algorithm was applied to all 23 patients. The volumes of the auto-segmented GTV ranged from 18.4cc to 32.8cc. The average (range) volume difference between the manual and auto-segmented GTV was −42% (−32.8%–63.8%). The average DSC value was 0.62, ranging from 0.39 to 0.78. Conclusion: An algorithm for the automated definition of tumor volume using multiple imaging modalities simultaneously was successfully developed and implemented for Head and Neck cancer. This development along with more accurate registration algorithms can aid physicians in the efforts to interpret the multitude of imaging information available in radiotherapy today. This project was supported by a grant by Varian Medical Systems.« less

SU-F-T-92: Clinical Benefit for Breast and Chest Wall Setup in Using a Breast Board

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, S; Miyamoto, C; Serratore, D

Purpose: To validate benefit of using a breast board (BB) by analyzing the geometry and dosimetry changes of the regions of interest (ROIs) between CT scans with and without BB. Methods: Seven patients, two chest walls (CW) and five breasts, use BB at CT simulation and no BB at diagnostic CT were included. By using deformable image registration software (Velocity AI), diagnostic CT and planning CT were rigidly co-registered according to the thoracic cage at the target. The heart and the target were then deformedly matched and the contours of the planned ROIs were transferred to the diagnostic CT. Whichmore » were brought back to the planning CT data set though the initial rigid co-registration in order to keep the deformed ROIs redefined in the diagnostic CT. Anatomic shifts and volume changes of a ROI beyond the rigid translation were recorded and dosimetry changes to ROIs were compared with recalculated DVHs. Results: Patient setup without the BB had small but systematic heart shifts superiorly by ∼5 mm. Torso rotations in two cases moved the heart in opposite directions by ∼10 mm. The breast target volume, shape, and locations were significantly changed with arm extension over the head but not in cases with the arm extended laterally. Breast setup without BB could increase the mean dose to the heart and the maximal dose to the anterior ventricle wall by 1.1 and 6.7 Gy, respectively. Conclusion: A method for evaluation of breast setup technique is introduced and applied for patients. Results of systematic heart displacement without using the BB and the potential increase of heart doses encourage us to further investigate the current trend of not using a BB for easy setup and CT scans. Using a BB would likely increase patient sag during prolonged IMRT and real-time patient position monitoring is clinically desired.« less

Parameter selection with the Hotelling observer in linear iterative image reconstruction for breast tomosynthesis

NASA Astrophysics Data System (ADS)

Rose, Sean D.; Roth, Jacob; Zimmerman, Cole; Reiser, Ingrid; Sidky, Emil Y.; Pan, Xiaochuan

2018-03-01

In this work we investigate an efficient implementation of a region-of-interest (ROI) based Hotelling observer (HO) in the context of parameter optimization for detection of a rod signal at two orientations in linear iterative image reconstruction for DBT. Our preliminary results suggest that ROI-HO performance trends may be efficiently estimated by modeling only the 2D plane perpendicular to the detector and containing the X-ray source trajectory. In addition, the ROI-HO is seen to exhibit orientation dependent trends in detectability as a function of the regularization strength employed in reconstruction. To further investigate the ROI-HO performance in larger 3D system models, we present and validate an iterative methodology for calculating the ROI-HO. Lastly, we present a real data study investigating the correspondence between ROI-HO performance trends and signal conspicuity. Conspicuity of signals in real data reconstructions is seen to track well with trends in ROI-HO detectability. In particular, we observe orientation dependent conspicuity matching the orientation dependent detectability of the ROI-HO.

Prediction of venous wound healing with laser speckle imaging.

PubMed

van Vuuren, Timme Maj; Van Zandvoort, Carina; Doganci, Suat; Zwiers, Ineke; tenCate-Hoek, Arina J; Kurstjens, Ralph Lm; Wittens, Cees Ha

2017-12-01

Introduction Laser speckle imaging is used for noninvasive assessment of blood flow of cutaneous wounds. The aim of this study was to assess if laser speckle imaging can be used as a predictor of venous ulcer healing. Methods After generating the flux speckle images, three regions of interest (ROI) were identified to measure the flow. Sensitivity, specificity, negative predictive value, and positive predictive value for ulcer healing were calculated. Results In total, 17 limbs were included. A sensitivity of 92.3%, specificity of 75.0%, PPV of 80.0%, and NPV 75.0% were found in predicting wound healing based on laser speckle images. Mean flux values were lowest in the center (ROI I) and showed an increase at the wound edge (ROI II, p = 0.03). Conclusion Laser speckle imaging shows acceptable sensitivity and specificity rates in predicting venous ulcer healing. The wound edge proved to be the best probability for the prediction of wound healing.

[A quality controllable algorithm for ECG compression based on wavelet transform and ROI coding].

PubMed

Zhao, An; Wu, Baoming

2006-12-01

This paper presents an ECG compression algorithm based on wavelet transform and region of interest (ROI) coding. The algorithm has realized near-lossless coding in ROI and quality controllable lossy coding outside of ROI. After mean removal of the original signal, multi-layer orthogonal discrete wavelet transform is performed. Simultaneously,feature extraction is performed on the original signal to find the position of ROI. The coefficients related to the ROI are important coefficients and kept. Otherwise, the energy loss of the transform domain is calculated according to the goal PRDBE (Percentage Root-mean-square Difference with Baseline Eliminated), and then the threshold of the coefficients outside of ROI is determined according to the loss of energy. The important coefficients, which include the coefficients of ROI and the coefficients that are larger than the threshold outside of ROI, are put into a linear quantifier. The map, which records the positions of the important coefficients in the original wavelet coefficients vector, is compressed with a run-length encoder. Huffman coding has been applied to improve the compression ratio. ECG signals taken from the MIT/BIH arrhythmia database are tested, and satisfactory results in terms of clinical information preserving, quality and compress ratio are obtained.

TU-C-12A-12: Differentiating Bone Lesions and Degenerative Joint Disease in NaF PET/CT Scans Using Machine Learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perk, T; Bradshaw, T; Muzahir, S

2014-06-15

Purpose: [F-18]NaF PET can be used to image bone metastases; however, tracer uptake in degenerative joint disease (DJD) often appears similar to metastases. This study aims to develop and compare different machine learning algorithms to automatically identify regions of [F-18]NaF scans that correspond to DJD. Methods: 10 metastatic prostate cancer patients received whole body [F-18]NaF PET/CT scans prior to treatment. Image segmentation resulted in 852 ROIs, 69 of which were identified by a nuclear medicine physician as DJD. For all ROIs, various PET and CT textural features were computed. ROIs were divided into training and testing sets used to trainmore » eight different machine learning classifiers. Classifiers were evaluated based on receiver operating characteristics area under the curve (AUC), sensitivity, specificity, and positive predictive value (PPV). We also assessed the added value of including CT features in addition to PET features for training classifiers. Results: The training set consisted of 37 DJD ROIs with 475 non-DJD ROIs, and the testing set consisted of 32 DJD ROIs with 308 non-DJD ROIs. Of all classifiers, generalized linear models (GLM), decision forests (DF), and support vector machines (SVM) had the best performance. AUCs of GLM (0.929), DF (0.921), and SVM (0.889) were significantly higher than the other models (p<0.001). GLM and DF, overall, had the best sensitivity, specificity, and PPV, and gave a significantly better performance (p<0.01) than all other models. PET/CT GLM classifiers had higher AUC than just PET or just CT. GLMs built using PET/CT information had superior or comparable sensitivities, specificities and PPVs to just PET or just CT. Conclusion: Machine learning algorithms trained with PET/CT features were able to identify some cases of DJD. GLM outperformed the other classification algorithms. Using PET and CT information together was shown to be superior to using PET or CT features alone. Research supported by the Prostate Cancer Foundation.« less

Quantitative Lesion-to-Fat Elasticity Ratio Measured by Shear-Wave Elastography for Breast Mass: Which Area Should Be Selected as the Fat Reference?

PubMed Central

Youk, Ji Hyun; Son, Eun Ju; Gweon, Hye Mi; Han, Kyung Hwa; Kim, Jeong-Ah

2015-01-01

Objectives To investigate whether the diagnostic performance of lesion-to-fat elasticity ratio (Eratio) was affected by the location of the reference fat. Methods For 257 breast masses in 250 women who underwent shear-wave elastography before biopsy or surgery, multiple Eratios were measured with a fixed region-of-interest (ROI) in the mass along with multiple ROIs over the surrounding fat in different locations. Logistic regression analysis was used to determine that Eratio was independently associated with malignancy adjusted for the location of fat ROI (depth, laterality, and distance from lesion or skin). Mean (Emean) and maximum (Emax) elasticity values of fat were divided into four groups according to their interquartile ranges. Diagnostic performance of each group was evaluated using the area under the ROC curve (AUC). False diagnoses of Eratio were reviewed for ROIs on areas showing artifactual high or low stiffness and analyzed by logistic regression analysis to determine variables (associated palpable abnormality, lesion size, the vertical distance from fat ROI to skin, and elasticity values of lesion or fat) independently associated with false results. Results Eratio was independently associated with malignancy adjusted for the location of fat ROI (P<0.0001). Among four groups of fat elasticity values, the AUC showed no significant difference (<25th percentile, 25th percentile~median, median~75th percentile, and ≥75th percentile; 0.973, 0.982, 0.967, and 0.954 for Emean; 0.977, 0.967, 0.966, and 0.957 for Emax). Fat elasticity values were independently associated with false results of Eratio with the cut-off of 3.18 from ROC curve (P<0.0001). ROIs were set on fat showing artifactual high stiffness in 90% of 10 false negatives and on lesion showing vertical striped artifact or fat showing artifactual low stiffness in 77.5% of 71 false positives. Conclusion Eratio shows good diagnostic performance regardless of the location of reference fat, except when it is placed in areas of artifacts. PMID:26368920

Pilot Study of the Use of Hybrid Multidimensional T2-Weighted Imaging-DWI for the Diagnosis of Prostate Cancer and Evaluation of Gleason Score.

PubMed

Sadinski, Meredith; Karczmar, Gregory; Peng, Yahui; Wang, Shiyang; Jiang, Yulei; Medved, Milica; Yousuf, Ambereen; Antic, Tatjana; Oto, Aytekin

2016-09-01

The objective of our study was to evaluate the role of a hybrid T2-weighted imaging-DWI sequence for prostate cancer diagnosis and differentiation of aggressive prostate cancer from nonaggressive prostate cancer. Twenty-one patients with prostate cancer who underwent preoperative 3-T MRI and prostatectomy were included in this study. Patients underwent a hybrid T2-weighted imaging-DWI examination consisting of DW images acquired with TEs of 47, 75, and 100 ms and b values of 0 and 750 s/mm(2). The apparent diffusion coefficient (ADC) and T2 were calculated for cancer and normal prostate ROIs at each TE and b value. Changes in ADC and T2 as a function of increasing the TE and b value, respectively, were analyzed. A new metric termed "PQ4" was defined as the percentage of voxels within an ROI that has increasing T2 with increasing b value and has decreasing ADC with increasing TE. ADC values were significantly higher in normal ROIs than in cancer ROIs at all TEs (p < 0.0001). With increasing TE, the mean ADC increased 3% in cancer ROIs and increased 12% in normal ROIs. T2 was significantly higher in normal ROIs than in cancer ROIs at both b values (p ≤ 0.0002). The mean T2 decreased with increasing b value in cancer ROIs (ΔT2 = -17 ms) and normal ROIs (ΔT2 = -52 ms). PQ4 clearly differentiated normal ROIs from prostate cancer ROIs (p = 0.0004) and showed significant correlation with Gleason score (ρ = 0.508, p < 0.0001). Hybrid MRI measures the response of ADC and T2 to changing TEs and b values, respectively. This approach shows promise for detecting prostate cancer and determining its aggressiveness noninvasively.

Application of a dual-resolution voxelization scheme to compressed-sensing (CS)-based iterative reconstruction in digital tomosynthesis (DTS)

NASA Astrophysics Data System (ADS)

Park, S. Y.; Kim, G. A.; Cho, H. S.; Park, C. K.; Lee, D. Y.; Lim, H. W.; Lee, H. W.; Kim, K. S.; Kang, S. Y.; Park, J. E.; Kim, W. S.; Jeon, D. H.; Je, U. K.; Woo, T. H.; Oh, J. E.

2018-02-01

In recent digital tomosynthesis (DTS), iterative reconstruction methods are often used owing to the potential to provide multiplanar images of superior image quality to conventional filtered-backprojection (FBP)-based methods. However, they require enormous computational cost in the iterative process, which has still been an obstacle to put them to practical use. In this work, we propose a new DTS reconstruction method incorporated with a dual-resolution voxelization scheme in attempt to overcome these difficulties, in which the voxels outside a small region-of-interest (ROI) containing target diagnosis are binned by 2 × 2 × 2 while the voxels inside the ROI remain unbinned. We considered a compressed-sensing (CS)-based iterative algorithm with a dual-constraint strategy for more accurate DTS reconstruction. We implemented the proposed algorithm and performed a systematic simulation and experiment to demonstrate its viability. Our results indicate that the proposed method seems to be effective for reducing computational cost considerably in iterative DTS reconstruction, keeping the image quality inside the ROI not much degraded. A binning size of 2 × 2 × 2 required only about 31.9% computational memory and about 2.6% reconstruction time, compared to those for no binning case. The reconstruction quality was evaluated in terms of the root-mean-square error (RMSE), the contrast-to-noise ratio (CNR), and the universal-quality index (UQI).

Returns on Investment in Training. Research at a Glance.

ERIC Educational Resources Information Center

National Centre for Vocational Education Research, Leabrook (Australia).

Recent Australian research provides a solid body of evidence that training investments can yield very high levels of returns for firms across a range of sectors. It highlights these important factors about returns on investment (ROI): ROI come in many forms; immediate ROI are highest when training is highly focused; measuring ROI is not always…

Return on Investment (ROI): Calculating the Monetary Return of a Leadership Development Program

ERIC Educational Resources Information Center

Rohs, Frederick R.

2004-01-01

Measuring the Return on Investment (ROI) in training and development has consistently earned a place among the critical issues in the human resource development (HRD) field. Leadership educators may soon find that program sponsors and administrators asking for ROI information as well. This paper reports the ROI of the Southern Extension Leadership…

Tumor propagation model using generalized hidden Markov model

NASA Astrophysics Data System (ADS)

Park, Sun Young; Sargent, Dustin

2017-02-01

Tumor tracking and progression analysis using medical images is a crucial task for physicians to provide accurate and efficient treatment plans, and monitor treatment response. Tumor progression is tracked by manual measurement of tumor growth performed by radiologists. Several methods have been proposed to automate these measurements with segmentation, but many current algorithms are confounded by attached organs and vessels. To address this problem, we present a new generalized tumor propagation model considering time-series prior images and local anatomical features using a Hierarchical Hidden Markov model (HMM) for tumor tracking. First, we apply the multi-atlas segmentation technique to identify organs/sub-organs using pre-labeled atlases. Second, we apply a semi-automatic direct 3D segmentation method to label the initial boundary between the lesion and neighboring structures. Third, we detect vessels in the ROI surrounding the lesion. Finally, we apply the propagation model with the labeled organs and vessels to accurately segment and measure the target lesion. The algorithm has been designed in a general way to be applicable to various body parts and modalities. In this paper, we evaluate the proposed algorithm on lung and lung nodule segmentation and tracking. We report the algorithm's performance by comparing the longest diameter and nodule volumes using the FDA lung Phantom data and a clinical dataset.

Black-blood native T1 mapping: Blood signal suppression for reduced partial voluming in the myocardium.

PubMed

Weingärtner, Sebastian; Meßner, Nadja M; Zöllner, Frank G; Akçakaya, Mehmet; Schad, Lothar R

2017-08-01

To study the feasibility of black-blood contrast in native T 1 mapping for reduction of partial voluming at the blood-myocardium interface. A saturation pulse prepared heart-rate-independent inversion recovery (SAPPHIRE) T 1 mapping sequence was combined with motion-sensitized driven-equilibrium (MSDE) blood suppression for black-blood T 1 mapping at 3 Tesla. Phantom scans were performed to assess the T 1 time accuracy. In vivo black-blood and conventional SAPPHIRE T 1 mapping was performed in eight healthy subjects and analyzed for T 1 times, precision, and inter- and intraobserver variability. Furthermore, manually drawn regions of interest (ROIs) in all T 1 maps were dilated and eroded to analyze the dependence of septal T 1 times on the ROI thickness. Phantom results and in vivo myocardial T 1 times show comparable accuracy with black-blood compared to conventional SAPPHIRE (in vivo: black-blood: 1562 ± 56 ms vs. conventional: 1583 ± 58 ms, P = 0.20); Using black-blood SAPPHIRE precision was significantly lower (standard deviation: 133.9 ± 24.6 ms vs. 63.1 ± 6.4 ms, P < .0001), and blood T 1 time measurement was not possible. Significantly increased interobserver interclass correlation coefficient (ICC) (0.996 vs. 0.967, P = 0.011) and similar intraobserver ICC (0.979 vs. 0.939, P = 0.11) was obtained with the black-blood sequence. Conventional SAPPHIRE showed strong dependence on the ROI thickness (R 2 = 0.99). No such trend was observed using the black-blood approach (R 2 = 0.29). Black-blood SAPPHIRE successfully eliminates partial voluming at the blood pool in native myocardial T 1 mapping while providing accurate T 1 times, albeit at a reduced precision. Magn Reson Med 78:484-493, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Dual energy X-ray absorptiometry spine scans to determine abdominal fat in postmenopausal women.

PubMed

Bea, J W; Blew, R M; Going, S B; Hsu, C-H; Lee, M C; Lee, V R; Caan, B J; Kwan, M L; Lohman, T G

2016-11-01

Body composition may be a better predictor of chronic disease risk than body mass index (BMI) in older populations. We sought to validate spine fat fraction (%) from dual energy X-ray absorptiometry (DXA) spine scans as a proxy for total abdominal fat. Total body DXA scan abdominal fat regions of interest (ROI) that have been previously validated by magnetic resonance imaging were assessed among healthy, postmenopausal women who also had antero-posterior spine scans (n = 103). ROIs were (1) lumbar vertebrae L2-L4 and (2) L2-Iliac Crest (L2-IC), manually selected by two independent raters, and (3) trunk, auto-selected by DXA software. Intra-class correlation coefficients evaluated intra and inter-rater reliability on a random subset (N = 25). Linear regression models, validated by bootstrapping, assessed the relationship between spine fat fraction (%) and total abdominal fat (%) ROIs. Mean age, BMI, and total body fat were 66.1 ± 4.8 y, 25.8 ± 3.8 kg/m 2 and 40.0 ± 6.6%, respectively. There were no significant differences within or between raters. Linear regression models adjusted for several participant and scan characteristics were equivalent to using only BMI and spine fat fraction. The model predicted L2-L4 (Adj. R 2 : 0.83) and L2-IC (Adj. R 2 : 0.84) abdominal fat (%) well; the adjusted R 2 for trunk fat (%) was 0.78. Model validation demonstrated minimal over-fitting (Adj. R 2 : 0.82, 0.83, and 0.77 for L2-L4, L2-IC, and trunk fat, respectively). The strong correlation between spine fat fraction and DXA abdominal fat measures make it suitable for further development in postmenopausal chronic disease risk prediction models. Am. J. Hum. Biol. 28:918-926, 2016. © 2016Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Cortical Asymmetries during Hand Laterality Task Vary with Hand Laterality: A fMRI Study in 295 Participants

PubMed Central

Mellet, Emmanuel; Mazoyer, Bernard; Leroux, Gaelle; Joliot, Marc; Tzourio-Mazoyer, Nathalie

2016-01-01

The aim of this study was to characterize, using fMRI, the functional asymmetries of hand laterality task (HLT) in a sample of 295 participants balanced for handedness. During HLT, participants have to decide whether the displayed picture of a hand represent a right or a left hand. Pictures of hands’ back view were presented for 150 ms in the right or left hemifield. At the whole hemisphere level, we evidenced that the laterality of the hand and of the hemifield in which the picture was displayed combined their effects on the hemispheric asymmetry in an additive way. We then identified a set of 17 functional homotopic regions of interest (hROIs) including premotor, motor, somatosensory and parietal regions, whose activity and asymmetry varied with the laterality of the presented hands. When the laterality of a right hand had to be evaluated, these areas showed stronger leftward asymmetry, the hROI located in the primary motor area showing a significant larger effect than all other hROIs. In addition a subset of six parietal regions involved in visuo-motor integration together with two postcentral areas showed a variation in asymmetry with hemifield of presentation. Finally, while handedness had no effect at the hemispheric level, two regions located in the parietal operculum and intraparietal sulcus exhibited larger leftward asymmetry with right handedness independently of the hand of presentation. The present results extend those of previous works in showing a shift of asymmetries during HLT according to the hand presented in sensorimotor areas including primary motor cortex. This shift was not affected by manual preference. They also demonstrate that the coordination of visual information and handedness identification of hands relied on the coexistence of contralateral motor and visual representations in the superior parietal lobe and the postcentral gyrus. PMID:27999536

A new method for tracking organ motion on diagnostic ultrasound images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kubota, Yoshiki, E-mail: y-kubota@gunma-u.ac.jp; Matsumura, Akihiko, E-mail: matchan.akihiko@gunma-u.ac.jp; Fukahori, Mai, E-mail: fukahori@nirs.go.jp

2014-09-15

Purpose: Respiratory-gated irradiation is effective in reducing the margins of a target in the case of abdominal organs, such as the liver, that change their position as a result of respiratory motion. However, existing technologies are incapable of directly measuring organ motion in real-time during radiation beam delivery. Hence, the authors proposed a novel quantitative organ motion tracking method involving the use of diagnostic ultrasound images; it is noninvasive and does not entail radiation exposure. In the present study, the authors have prospectively evaluated this proposed method. Methods: The method involved real-time processing of clinical ultrasound imaging data rather thanmore » organ monitoring; it comprised a three-dimensional ultrasound device, a respiratory sensing system, and two PCs for data storage and analysis. The study was designed to evaluate the effectiveness of the proposed method by tracking the gallbladder in one subject and a liver vein in another subject. To track a moving target organ, the method involved the control of a region of interest (ROI) that delineated the target. A tracking algorithm was used to control the ROI, and a large number of feature points and an error correction algorithm were used to achieve long-term tracking of the target. Tracking accuracy was assessed in terms of how well the ROI matched the center of the target. Results: The effectiveness of using a large number of feature points and the error correction algorithm in the proposed method was verified by comparing it with two simple tracking methods. The ROI could capture the center of the target for about 5 min in a cross-sectional image with changing position. Indeed, using the proposed method, it was possible to accurately track a target with a center deviation of 1.54 ± 0.9 mm. The computing time for one frame image using our proposed method was 8 ms. It is expected that it would be possible to track any soft-tissue organ or tumor with large deformations and changing cross-sectional position using this method. Conclusions: The proposed method achieved real-time processing and continuous tracking of the target organ for about 5 min. It is expected that our method will enable more accurate radiation treatment than is the case using indirect observational methods, such as the respiratory sensor method, because of direct visualization of the tumor. Results show that this tracking system facilitates safe treatment in clinical practice.« less

Automatic coronary calcium scoring using noncontrast and contrast CT images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Guanyu, E-mail: yang.list@seu.edu.cn; Chen, Yang; Shu, Huazhong

Purpose: Calcium scoring is widely used to assess the risk of coronary heart disease (CHD). Accurate coronary artery calcification detection in noncontrast CT image is a prerequisite step for coronary calcium scoring. Currently, calcified lesions in the coronary arteries are manually identified by radiologists in clinical practice. Thus, in this paper, a fully automatic calcium scoring method was developed to alleviate the work load of the radiologists or cardiologists. Methods: The challenge of automatic coronary calcification detection is to discriminate the calcification in the coronary arteries from the calcification in the other tissues. Since the anatomy of coronary arteries ismore » difficult to be observed in the noncontrast CT images, the contrast CT image of the same patient is used to extract the regions of the aorta, heart, and coronary arteries. Then, a patient-specific region-of-interest (ROI) is generated in the noncontrast CT image according to the segmentation results in the contrast CT image. This patient-specific ROI focuses on the regions in the neighborhood of coronary arteries for calcification detection, which can eliminate the calcifications in the surrounding tissues. A support vector machine classifier is applied finally to refine the results by removing possible image noise. Furthermore, the calcified lesions in the noncontrast images belonging to the different main coronary arteries are identified automatically using the labeling results of the extracted coronary arteries. Results: Forty datasets from four different CT machine vendors were used to evaluate their algorithm, which were provided by the MICCAI 2014 Coronary Calcium Scoring (orCaScore) Challenge. The sensitivity and positive predictive value for the volume of detected calcifications are 0.989 and 0.948. Only one patient out of 40 patients had been assigned to the wrong risk category defined according to Agatston scores (0, 1–100, 101–300, >300) by comparing with the ground truth. Conclusions: The calcified lesions in the noncontrast CT images can be detected automatically by using the segmentation results of the aorta, heart, and coronary arteries obtained in the contrast CT images with a very high accuracy.« less

Preclinical Multimodal Molecular Imaging Using 18F-FDG PET/CT and MRI in a Phase I Study of a Knee Osteoarthritis in In Vivo Canine Model.

PubMed

Menendez, Maria I; Hettlich, Bianca; Wei, Lai; Knopp, Michael V

2017-01-01

The aim of this study was to use a multimodal molecular imaging approach to serially assess regional metabolic changes in the knee in an in vivo anterior cruciate ligament transection (ACLT) canine model of osteoarthritis (OA). Five canine underwent ACLT in one knee and the contralateral knee served as uninjured control. Prior, 3, 6, and 12 weeks post-ACLT, the dogs underwent 18 F-fluoro-d-glucose ( 18 F-FDG) positron emission tomography (PET)/computed tomography (CT) and magnetic resonance imaging (MRI). The MRI was coregistered with the PET/CT, and 3-dimensional regions of interest (ROIs) were traced manually and maximum standardized uptake values (SUV max ) were evaluated. 18 F-fluoro-d-glucose SUV max in the ACLT knee ROIs was significantly higher compared to the uninjured contralateral knees at 3, 6, and 12 weeks. Higher 18 F-FDG uptake observed in ACLT knees compared to the uninjured knees reflects greater metabolic changes in the injured knees over time. Knee 18 F-FDG uptake in an in vivo ACLT canine model using combined PET/CT and MRI demonstrated to be highly sensitive in the detection of metabolic alterations in osseous and nonosteochondral structures comprising the knee joint. 18 F-fluoro-d-glucose appeared to be a capable potential imaging biomarker for early human knee OA diagnosis, prognosis, and management.

A Modified Tri-Exponential Model for Multi-b-value Diffusion-Weighted Imaging: A Method to Detect the Strictly Diffusion-Limited Compartment in Brain

PubMed Central

Zeng, Qiang; Shi, Feina; Zhang, Jianmin; Ling, Chenhan; Dong, Fei; Jiang, Biao

2018-01-01

Purpose: To present a new modified tri-exponential model for diffusion-weighted imaging (DWI) to detect the strictly diffusion-limited compartment, and to compare it with the conventional bi- and tri-exponential models. Methods: Multi-b-value diffusion-weighted imaging (DWI) with 17 b-values up to 8,000 s/mm2 were performed on six volunteers. The corrected Akaike information criterions (AICc) and squared predicted errors (SPE) were calculated to compare these three models. Results: The mean f0 values were ranging 11.9–18.7% in white matter ROIs and 1.2–2.7% in gray matter ROIs. In all white matter ROIs: the AICcs of the modified tri-exponential model were the lowest (p < 0.05 for five ROIs), indicating the new model has the best fit among these models; the SPEs of the bi-exponential model were the highest (p < 0.05), suggesting the bi-exponential model is unable to predict the signal intensity at ultra-high b-value. The mean ADCvery−slow values were extremely low in white matter (1–7 × 10−6 mm2/s), but not in gray matter (251–445 × 10−6 mm2/s), indicating that the conventional tri-exponential model fails to represent a special compartment. Conclusions: The strictly diffusion-limited compartment may be an important component in white matter. The new model fits better than the other two models, and may provide additional information. PMID:29535599

WE-AB-BRA-08: Results of a Multi-Institutional Study for the Evaluation of Deformable Image Registration Algorithms for Structure Delineation Via Computational Phantoms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Loi, G; Fusella, M; Fiandra, C

2015-06-15

Purpose: To investigate the accuracy of various algorithms for deformable image registration (DIR), to propagate regions of interest (ROIs) in computational phantoms based on patient images using different commercial systems. This work is part of an Italian multi-institutional study to test on common datasets the accuracy, reproducibility and safety of DIR applications in Adaptive Radiotherapy. Methods: Eleven institutions with three available commercial solutions provided data to assess the agreement of DIR-propagated ROIs with automatically drown ROIs considered as ground-truth for the comparison. The DIR algorithms were tested on real patient data from three different anatomical districts: head and neck, thoraxmore » and pelvis. For every dataset two specific Deformation Vector Fields (DVFs) provided by ImSimQA software were applied to the reference data set. Three different commercial software were used in this study: RayStation, Velocity and Mirada. The DIR-mapped ROIs were then compared with the reference ROIs using the Jaccard Conformity Index (JCI). Results: More than 600 DIR-mapped ROIs were analyzed. Putting together all JCI data of all institutions for the first DVF, the mean JCI was 0.87 ± 0.7 (1 SD) while for the second DVF JCI was 0.8 ± 0.13 (1 SD). Several considerations on different structures are available from collected data: the standard deviation among different institutions on specific structure raise as the larger is the applied DVF. The higher value is 10% for bladder. Conclusion: Although the complexity of deformation of human body is very difficult to model, this work illustrates some clinical scenarios with well-known DVFs provided by specific software. CI parameter gives the inter-user variability and may put in evidence the need of improving the working protocol in order to reduce the inter-institution JCI variability.« less

Bladder cancer treatment response assessment using deep learning in CT with transfer learning

NASA Astrophysics Data System (ADS)

Cha, Kenny H.; Hadjiiski, Lubomir M.; Chan, Heang-Ping; Samala, Ravi K.; Cohan, Richard H.; Caoili, Elaine M.; Paramagul, Chintana; Alva, Ajjai; Weizer, Alon Z.

2017-03-01

We are developing a CAD system for bladder cancer treatment response assessment in CT. We compared the performance of the deep-learning convolution neural network (DL-CNN) using different network sizes, and with and without transfer learning using natural scene images or regions of interest (ROIs) inside and outside the bladder. The DL-CNN was trained to identify responders (T0 disease) and non-responders to chemotherapy. ROIs were extracted from segmented lesions in pre- and post-treatment scans of a patient and paired to generate hybrid pre-post-treatment paired ROIs. The 87 lesions from 82 patients generated 104 temporal lesion pairs and 6,700 pre-post-treatment paired ROIs. Two-fold cross-validation and receiver operating characteristic analysis were performed and the area under the curve (AUC) was calculated for the DL-CNN estimates. The AUCs for prediction of T0 disease after treatment were 0.77+/-0.08 and 0.75+/-0.08, respectively, for the two partitions using DL-CNN without transfer learning and a small network, and were 0.74+/-0.07 and 0.74+/-0.08 with a large network. The AUCs were 0.73+/-0.08 and 0.62+/-0.08 with transfer learning using a small network pre-trained with bladder ROIs. The AUC values were 0.77+/-0.08 and 0.73+/-0.07 using the large network pre-trained with the same bladder ROIs. With transfer learning using the large network pretrained with the Canadian Institute for Advanced Research (CIFAR-10) data set, the AUCs were 0.72+/-0.06 and 0.64+/-0.09, respectively, for the two partitions. None of the differences in the methods reached statistical significance. Our study demonstrated the feasibility of using DL-CNN for the estimation of treatment response in CT. Transfer learning did not improve the treatment response estimation. The DL-CNN performed better when transfer learning with bladder images was used instead of natural scene images.

Testing of Haar-Like Feature in Region of Interest Detection for Automated Target Recognition (ATR) System

NASA Technical Reports Server (NTRS)

Zhang, Yuhan; Lu, Dr. Thomas

2010-01-01

The objectives of this project were to develop a ROI (Region of Interest) detector using Haar-like feature similar to the face detection in Intel's OpenCV library, implement it in Matlab code, and test the performance of the new ROI detector against the existing ROI detector that uses Optimal Trade-off Maximum Average Correlation Height filter (OTMACH). The ROI detector included 3 parts: 1, Automated Haar-like feature selection in finding a small set of the most relevant Haar-like features for detecting ROIs that contained a target. 2, Having the small set of Haar-like features from the last step, a neural network needed to be trained to recognize ROIs with targets by taking the Haar-like features as inputs. 3, using the trained neural network from the last step, a filtering method needed to be developed to process the neural network responses into a small set of regions of interests. This needed to be coded in Matlab. All the 3 parts needed to be coded in Matlab. The parameters in the detector needed to be trained by machine learning and tested with specific datasets. Since OpenCV library and Haar-like feature were not available in Matlab, the Haar-like feature calculation needed to be implemented in Matlab. The codes for Adaptive Boosting and max/min filters in Matlab could to be found from the Internet but needed to be integrated to serve the purpose of this project. The performance of the new detector was tested by comparing the accuracy and the speed of the new detector against the existing OTMACH detector. The speed was referred as the average speed to find the regions of interests in an image. The accuracy was measured by the number of false positives (false alarms) at the same detection rate between the two detectors.

Optical 3D watermark based digital image watermarking for telemedicine

NASA Astrophysics Data System (ADS)

Li, Xiao Wei; Kim, Seok Tae

2013-12-01

Region of interest (ROI) of a medical image is an area including important diagnostic information and must be stored without any distortion. This algorithm for application of watermarking technique for non-ROI of the medical image preserving ROI. The paper presents a 3D watermark based medical image watermarking scheme. In this paper, a 3D watermark object is first decomposed into 2D elemental image array (EIA) by a lenslet array, and then the 2D elemental image array data is embedded into the host image. The watermark extraction process is an inverse process of embedding. The extracted EIA through the computational integral imaging reconstruction (CIIR) technique, the 3D watermark can be reconstructed. Because the EIA is composed of a number of elemental images possesses their own perspectives of a 3D watermark object. Even though the embedded watermark data badly damaged, the 3D virtual watermark can be successfully reconstructed. Furthermore, using CAT with various rule number parameters, it is possible to get many channels for embedding. So our method can recover the weak point having only one transform plane in traditional watermarking methods. The effectiveness of the proposed watermarking scheme is demonstrated with the aid of experimental results.

Comparison of region-of-interest-averaged and pixel-averaged analysis of DCE-MRI data based on simulations and pre-clinical experiments

NASA Astrophysics Data System (ADS)

He, Dianning; Zamora, Marta; Oto, Aytekin; Karczmar, Gregory S.; Fan, Xiaobing

2017-09-01

Differences between region-of-interest (ROI) and pixel-by-pixel analysis of dynamic contrast enhanced (DCE) MRI data were investigated in this study with computer simulations and pre-clinical experiments. ROIs were simulated with 10, 50, 100, 200, 400, and 800 different pixels. For each pixel, a contrast agent concentration as a function of time, C(t), was calculated using the Tofts DCE-MRI model with randomly generated physiological parameters (K trans and v e) and the Parker population arterial input function. The average C(t) for each ROI was calculated and then K trans and v e for the ROI was extracted. The simulations were run 100 times for each ROI with new K trans and v e generated. In addition, white Gaussian noise was added to C(t) with 3, 6, and 12 dB signal-to-noise ratios to each C(t). For pre-clinical experiments, Copenhagen rats (n  =  6) with implanted prostate tumors in the hind limb were used in this study. The DCE-MRI data were acquired with a temporal resolution of ~5 s in a 4.7 T animal scanner, before, during, and after a bolus injection (<5 s) of Gd-DTPA for a total imaging duration of ~10 min. K trans and v e were calculated in two ways: (i) by fitting C(t) for each pixel, and then averaging the pixel values over the entire ROI, and (ii) by averaging C(t) over the entire ROI, and then fitting averaged C(t) to extract K trans and v e. The simulation results showed that in heterogeneous ROIs, the pixel-by-pixel averaged K trans was ~25% to ~50% larger (p  <  0.01) than the ROI-averaged K trans. At higher noise levels, the pixel-averaged K trans was greater than the ‘true’ K trans, but the ROI-averaged K trans was lower than the ‘true’ K trans. The ROI-averaged K trans was closer to the true K trans than pixel-averaged K trans for high noise levels. In pre-clinical experiments, the pixel-by-pixel averaged K trans was ~15% larger than the ROI-averaged K trans. Overall, with the Tofts model, the extracted physiological parameters from the pixel-by-pixel averages were larger than the ROI averages. These differences were dependent on the heterogeneity of the ROI.

Motion robust remote photoplethysmography in CIELab color space

NASA Astrophysics Data System (ADS)

Yang, Yuting; Liu, Chenbin; Yu, Hui; Shao, Dangdang; Tsow, Francis; Tao, Nongjian

2016-11-01

Remote photoplethysmography (rPPG) is attractive for tracking a subject's physiological parameters without wearing a device. However, rPPG is known to be prone to body movement-induced artifacts, making it unreliable in realistic situations. Here we report a method to minimize the movement-induced artifacts. The method selects an optimal region of interest (ROI) automatically, prunes frames in which the ROI is not clearly captured (e.g., subject moves out of the view), and analyzes rPPG using an algorithm in CIELab color space, rather than the widely used RGB color space. We show that body movement primarily affects image intensity, rather than chromaticity, and separating chromaticity from intensity in CIELab color space thus helps achieve effective reduction of the movement-induced artifacts. We validate the method by performing a pilot study including 17 people with diverse skin tones.

The Heterogeneity in Retrieved Relations between the Personality Trait ‘Harm Avoidance’ and Gray Matter Volumes Due to Variations in the VBM and ROI Labeling Processing Settings

PubMed Central

Van Schuerbeek, Peter; Baeken, Chris; De Mey, Johan

2016-01-01

Concerns are raising about the large variability in reported correlations between gray matter morphology and affective personality traits as ‘Harm Avoidance’ (HA). A recent review study (Mincic 2015) stipulated that this variability could come from methodological differences between studies. In order to achieve more robust results by standardizing the data processing procedure, as a first step, we repeatedly analyzed data from healthy females while changing the processing settings (voxel-based morphology (VBM) or region-of-interest (ROI) labeling, smoothing filter width, nuisance parameters included in the regression model, brain atlas and multiple comparisons correction method). The heterogeneity in the obtained results clearly illustrate the dependency of the study outcome to the opted analysis settings. Based on our results and the existing literature, we recommended the use of VBM over ROI labeling for whole brain analyses with a small or intermediate smoothing filter (5-8mm) and a model variable selection step included in the processing procedure. Additionally, it is recommended that ROI labeling should only be used in combination with a clear hypothesis and that authors are encouraged to report their results uncorrected for multiple comparisons as supplementary material to aid review studies. PMID:27096608

Cerebro-cerebellar resting state functional connectivity in children and adolescents with autism spectrum disorder

PubMed Central

Khan, Amanda J.; Nair, Aarti; Keown, Christopher L.; Datko, Michael C.; Lincoln, Alan J.; Müller, Ralph-Axel

2017-01-01

Background The cerebellum plays important roles in both sensorimotor and supramodal cognitive functions. Cellular, volumetric, and functional abnormalities of the cerebellum have been found in autism spectrum disorders (ASD), but no comprehensive investigation of cerebro-cerebellar connectivity in ASD is available. Methods We used resting-state functional connectivity MRI in 56 children and adolescents (28 ASD, 28 typically developing [TD]) aged 8–17 years. Partial and total correlation analyses were performed for unilateral regions of interest (ROIs), distinguished in two broad domains as sensorimotor (premotor/primary motor, somatosensory, superior temporal, occipital) and supramodal (prefrontal, posterior parietal, and inferior and middle temporal). Results There were three main findings: (i) Total correlation analyses showed predominant cerebro-cerebellar functional overconnectivity in the ASD group; (ii) partial correlation analyses that emphasized domain-specificity (sensorimotor vs. supramodal) indicated a pattern of robustly increased connectivity in the ASD group (compared to the TD group) for sensorimotor ROIs, but predominantly reduced connectivity for supramodal ROIs; (iii) this atypical pattern of connectivity was supported by significantly increased non-canonical connections (between sensorimotor cerebral and supramodal cerebellar ROIs, and vice versa) in the ASD group. Conclusions Our findings indicate that sensorimotor intrinsic functional connectivity is atypically increased in ASD, at the expense of connectivity supporting cerebellar participation in supramodal cognition. PMID:25959247

Gender differences in functional connectivities between insular subdivisions and selective pain-related brain structures.

PubMed

Dai, Yu-Jie; Zhang, Xin; Yang, Yang; Nan, Hai-Yan; Yu, Ying; Sun, Qian; Yan, Lin-Feng; Hu, Bo; Zhang, Jin; Qiu, Zi-Yu; Gao, Yi; Cui, Guang-Bin; Chen, Bi-Liang; Wang, Wen

2018-03-14

The incidence of pain disorders in women is higher than in men, making gender differences in pain a research focus. The human insular cortex is an important brain hub structure for pain processing and is divided into several subdivisions, serving different functions in pain perception. Here we aimed to examine the gender differences of the functional connectivities (FCs) between the twelve insular subdivisions and selected pain-related brain structures in healthy adults. Twenty-six healthy males and 11 age-matched healthy females were recruited in this cross-sectional study. FCs between the 12 insular subdivisions (as 12 regions of interest (ROIs)) and the whole brain (ROI-whole brain level) or 64 selected pain-related brain regions (64 ROIs, ROI-ROI level) were measured between the males and females. Significant gender differences in the FCs of the insular subdivisions were revealed: (1) The FCs between the dorsal dysgranular insula (dId) and other brain regions were significantly increased in males using two different techniques (ROI-whole brain and ROI-ROI analyses); (2) Based on the ROI-whole brain analysis, the FC increases in 4 FC-pairs were observed in males, including the left dId - the right median cingulate and paracingulate/ right posterior cingulate gyrus/ right precuneus, the left dId - the right median cingulate and paracingulate, the left dId - the left angular as well as the left dId - the left middle frontal gyrus; (3) According to the ROI-ROI analysis, increased FC between the left dId and the right rostral anterior cingulate cortex was investigated in males. In summary, the gender differences in the FCs of the insular subdivisions with pain-related brain regions were revealed in the current study, offering neuroimaging evidence for gender differences in pain processing. ClinicalTrials.gov, NCT02820974 . Registered 28 June 2016.

Definition of the thermographic regions of interest in cycling by using a factor analysis

NASA Astrophysics Data System (ADS)

Priego Quesada, Jose Ignacio; Lucas-Cuevas, Angel Gabriel; Salvador Palmer, Rosario; Pérez-Soriano, Pedro; Cibrián Ortiz de Anda, Rosa M.^a.

2016-03-01

Research in exercise physiology using infrared thermography has increased in the last years. However, the definition of the Regions of Interest (ROIs) varies strongly between studies. Therefore, the aim of this study was to use a factor analysis approach to define highly correlated groups of thermographic ROIs during a cycling test. Factor analyses were performed based on the moment of measurement and on the variation of skin temperatures as a result of the cycling exercise. 19 male participants cycled during 45 min at 50% of their individual peak power output with a cadence of 90 rpm. Infrared thermography was used to measure skin temperatures in sixteen ROIs of the trunk and lower limbs at three moments: before, immediately after and 10 min after the cycling test. Factor analyses were used to identify groups of ROIs based on the skin absolute temperatures at each moment of measurement as well as on skin temperature variations between moments. All the factor analyses performed for each moment and skin temperature variation explained more than the 80% of the variance. Different groups of ROIs were obtained when the analysis was based on the moment of measurement or on the effect of exercise on the skin temperature. Furthermore, some ROIs were grouped in the same way in both analyses (e.g. the ROIs of the trunk), whereas other regions (legs and their joints) were grouped differently in each analysis. Differences between groups of ROIs are related to their tissue composition, muscular activity and capacity of sweating. In conclusion, the resultant groups of ROIs were coherent and could help researchers to define the ROIs in future thermal studies.

Nonlocal atlas-guided multi-channel forest learning for human brain labeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ma, Guangkai; Gao, Yaozong; Wu, Guorong

Purpose: It is important for many quantitative brain studies to label meaningful anatomical regions in MR brain images. However, due to high complexity of brain structures and ambiguous boundaries between different anatomical regions, the anatomical labeling of MR brain images is still quite a challenging task. In many existing label fusion methods, appearance information is widely used. However, since local anatomy in the human brain is often complex, the appearance information alone is limited in characterizing each image point, especially for identifying the same anatomical structure across different subjects. Recent progress in computer vision suggests that the context features canmore » be very useful in identifying an object from a complex scene. In light of this, the authors propose a novel learning-based label fusion method by using both low-level appearance features (computed from the target image) and high-level context features (computed from warped atlases or tentative labeling maps of the target image). Methods: In particular, the authors employ a multi-channel random forest to learn the nonlinear relationship between these hybrid features and target labels (i.e., corresponding to certain anatomical structures). Specifically, at each of the iterations, the random forest will output tentative labeling maps of the target image, from which the authors compute spatial label context features and then use in combination with original appearance features of the target image to refine the labeling. Moreover, to accommodate the high inter-subject variations, the authors further extend their learning-based label fusion to a multi-atlas scenario, i.e., they train a random forest for each atlas and then obtain the final labeling result according to the consensus of results from all atlases. Results: The authors have comprehensively evaluated their method on both public LONI-LBPA40 and IXI datasets. To quantitatively evaluate the labeling accuracy, the authors use the dice similarity coefficient to measure the overlap degree. Their method achieves average overlaps of 82.56% on 54 regions of interest (ROIs) and 79.78% on 80 ROIs, respectively, which significantly outperform the baseline method (random forests), with the average overlaps of 72.48% on 54 ROIs and 72.09% on 80 ROIs, respectively. Conclusions: The proposed methods have achieved the highest labeling accuracy, compared to several state-of-the-art methods in the literature.« less

Nonlocal atlas-guided multi-channel forest learning for human brain labeling

PubMed Central

Ma, Guangkai; Gao, Yaozong; Wu, Guorong; Wu, Ligang; Shen, Dinggang

2016-01-01

Purpose: It is important for many quantitative brain studies to label meaningful anatomical regions in MR brain images. However, due to high complexity of brain structures and ambiguous boundaries between different anatomical regions, the anatomical labeling of MR brain images is still quite a challenging task. In many existing label fusion methods, appearance information is widely used. However, since local anatomy in the human brain is often complex, the appearance information alone is limited in characterizing each image point, especially for identifying the same anatomical structure across different subjects. Recent progress in computer vision suggests that the context features can be very useful in identifying an object from a complex scene. In light of this, the authors propose a novel learning-based label fusion method by using both low-level appearance features (computed from the target image) and high-level context features (computed from warped atlases or tentative labeling maps of the target image). Methods: In particular, the authors employ a multi-channel random forest to learn the nonlinear relationship between these hybrid features and target labels (i.e., corresponding to certain anatomical structures). Specifically, at each of the iterations, the random forest will output tentative labeling maps of the target image, from which the authors compute spatial label context features and then use in combination with original appearance features of the target image to refine the labeling. Moreover, to accommodate the high inter-subject variations, the authors further extend their learning-based label fusion to a multi-atlas scenario, i.e., they train a random forest for each atlas and then obtain the final labeling result according to the consensus of results from all atlases. Results: The authors have comprehensively evaluated their method on both public LONI_LBPA40 and IXI datasets. To quantitatively evaluate the labeling accuracy, the authors use the dice similarity coefficient to measure the overlap degree. Their method achieves average overlaps of 82.56% on 54 regions of interest (ROIs) and 79.78% on 80 ROIs, respectively, which significantly outperform the baseline method (random forests), with the average overlaps of 72.48% on 54 ROIs and 72.09% on 80 ROIs, respectively. Conclusions: The proposed methods have achieved the highest labeling accuracy, compared to several state-of-the-art methods in the literature. PMID:26843260

Repeatability of Brain Volume Measurements Made with the Atlas-based Method from T1-weighted Images Acquired Using a 0.4 Tesla Low Field MR Scanner.

PubMed

Goto, Masami; Suzuki, Makoto; Mizukami, Shinya; Abe, Osamu; Aoki, Shigeki; Miyati, Tosiaki; Fukuda, Michinari; Gomi, Tsutomu; Takeda, Tohoru

2016-10-11

An understanding of the repeatability of measured results is important for both the atlas-based and voxel-based morphometry (VBM) methods of magnetic resonance (MR) brain volumetry. However, many recent studies that have investigated the repeatability of brain volume measurements have been performed using static magnetic fields of 1-4 tesla, and no study has used a low-strength static magnetic field. The aim of this study was to investigate the repeatability of measured volumes using the atlas-based method and a low-strength static magnetic field (0.4 tesla). Ten healthy volunteers participated in this study. Using a 0.4 tesla magnetic resonance imaging (MRI) scanner and a quadrature head coil, three-dimensional T 1 -weighted images (3D-T 1 WIs) were obtained from each subject, twice on the same day. VBM8 software was used to construct segmented normalized images [gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) images]. The regions-of-interest (ROIs) of GM, WM, CSF, hippocampus (HC), orbital gyrus (OG), and cerebellum posterior lobe (CPL) were generated using WFU PickAtlas. The percentage change was defined as[100 × (measured volume with first segmented image - mean volume in each subject)/(mean volume in each subject)]The average percentage change was calculated as the percentage change in the 6 ROIs of the 10 subjects. The mean of the average percentage changes for each ROI was as follows: GM, 0.556%; WM, 0.324%; CSF, 0.573%; HC, 0.645%; OG, 1.74%; and CPL, 0.471%. The average percentage change was higher for the orbital gyrus than for the other ROIs. We consider that repeatability of the atlas-based method is similar between 0.4 and 1.5 tesla MR scanners. To our knowledge, this is the first report to show that the level of repeatability with a 0.4 tesla MR scanner is adequate for the estimation of brain volume change by the atlas-based method.

Development of a dynamic quality assurance testing protocol for multisite clinical trial DCE-CT accreditation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Driscoll, B.; Keller, H.; Jaffray, D.

2013-08-15

Purpose: Credentialing can have an impact on whether or not a clinical trial produces useful quality data that is comparable between various institutions and scanners. With the recent increase of dynamic contrast enhanced-computed tomography (DCE-CT) usage as a companion biomarker in clinical trials, effective quality assurance, and control methods are required to ensure there is minimal deviation in the results between different scanners and protocols at various institutions. This paper attempts to address this problem by utilizing a dynamic flow imaging phantom to develop and evaluate a DCE-CT quality assurance (QA) protocol.Methods: A previously designed flow phantom, capable of producingmore » predictable and reproducible time concentration curves from contrast injection was fully validated and then utilized to design a DCE-CT QA protocol. The QA protocol involved a set of quantitative metrics including injected and total mass error, as well as goodness of fit comparison to the known truth concentration curves. An additional region of interest (ROI) sensitivity analysis was also developed to provide additional details on intrascanner variability and determine appropriate ROI sizes for quantitative analysis. Both the QA protocol and ROI sensitivity analysis were utilized to test variations in DCE-CT results using different imaging parameters (tube voltage and current) as well as alternate reconstruction methods and imaging techniques. The developed QA protocol and ROI sensitivity analysis was then applied at three institutions that were part of clinical trial involving DCE-CT and results were compared.Results: The inherent specificity of robustness of the phantom was determined through calculation of the total intraday variability and determined to be less than 2.2 ± 1.1% (total calculated output contrast mass error) with a goodness of fit (R{sup 2}) of greater than 0.99 ± 0.0035 (n= 10). The DCE-CT QA protocol was capable of detecting significant deviations from the expected phantom result when scanning at low mAs and low kVp in terms of quantitative metrics (Injected Mass Error 15.4%), goodness of fit (R{sup 2}) of 0.91, and ROI sensitivity (increase in minimum input function ROI radius by 146 ± 86%). These tests also confirmed that the ASIR reconstruction process was beneficial in reducing noise without substantially increasing partial volume effects and that vendor specific modes (e.g., axial shuttle) did not significantly affect the phantom results. The phantom and QA protocol were finally able to quickly (<90 min) and successfully validate the DCE-CT imaging protocol utilized at the three separate institutions of a multicenter clinical trial; thereby enhancing the confidence in the patient data collected.Conclusions: A DCE QA protocol was developed that, in combination with a dynamic multimodality flow phantom, allows the intrascanner variability to be separated from other sources of variability such as the impact of injection protocol and ROI selection. This provides a valuable resource that can be utilized at various clinical trial institutions to test conformance with imaging protocols and accuracy requirements as well as ensure that the scanners are performing as expected for dynamic scans.« less

Towards standardization of 18F-FET PET imaging: do we need a consistent method of background activity assessment?

PubMed

Unterrainer, Marcus; Vettermann, Franziska; Brendel, Matthias; Holzgreve, Adrien; Lifschitz, Michael; Zähringer, Matthias; Suchorska, Bogdana; Wenter, Vera; Illigens, Ben M; Bartenstein, Peter; Albert, Nathalie L

2017-12-01

PET with O-(2- 18 F-fluoroethyl)-L-tyrosine ( 18 F-FET) has reached increasing clinical significance for patients with brain neoplasms. For quantification of standard PET-derived parameters such as the tumor-to-background ratio, the background activity is assessed using a region of interest (ROI) or volume of interest (VOI) in unaffected brain tissue. However, there is no standardized approach regarding the assessment of the background reference. Therefore, we evaluated the intra- and inter-reader variability of commonly applied approaches for clinical 18 F-FET PET reading. The background activity of 20 18 F-FET PET scans was independently evaluated by 6 readers using a (i) simple 2D-ROI, (ii) spherical VOI with 3.0 cm diameter, and (iii) VOI consisting of crescent-shaped ROIs; each in the contralateral, non-affected hemisphere including white and gray matter in line with the European Association of Nuclear Medicine (EANM) and German guidelines. To assess intra-reader variability, each scan was evaluated 10 times by each reader. The coefficient of variation (CoV) was assessed for determination of intra- and inter-reader variability. In a second step, the best method was refined by instructions for a guided background activity assessment and validated by 10 further scans. Compared to the other approaches, the crescent-shaped VOIs revealed most stable results with the lowest intra-reader variabilities (median CoV 1.52%, spherical VOI 4.20%, 2D-ROI 3.69%; p < 0.001) and inter-reader variabilities (median CoV 2.14%, spherical VOI 4.02%, 2D-ROI 3.83%; p = 0.001). Using the guided background assessment, both intra-reader variabilities (median CoV 1.10%) and inter-reader variabilities (median CoV 1.19%) could be reduced even more. The commonly applied methods for background activity assessment show different variability which might hamper 18 F-FET PET quantification and comparability in multicenter settings. The proposed background activity assessment using a (guided) crescent-shaped VOI allows minimization of both intra- and inter-reader variability and might facilitate comprehensive methodological standardization of amino acid PET which is of interest in the light of the anticipated EANM technical guidelines.

First-Pass Angiography in Mice Using FDG-PET: A Simple Method of Deriving the Cardiovascular Transit Time Without the Need of Region-of-Interest Drawing.

PubMed

Wu, Hsiao-Ming; Kreissl, Michael C; Schelbert, Heinrich R; Ladno, Waldemar; Prins, Mayumi; Shoghi-Jadid, Kooresh; Chatziioannou, Arion; Phelps, Michael E; Huang, Sung-Cheng

2005-10-01

In this study, we developed a simple and robust semi-automatic method to measure the right ventricle to left ventricle (RV-to-LV) transit time (TT) in mice using 2-[ 18 F]fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET). The accuracy of the method was first evaluated using a 4-D digital dynamic mouse phantom. The RV-to-LV TTs of twenty-nine mouse studies were measured using the new method and compared to those obtained from the conventional ROI-drawing method. The results showed that the new method correctly separated different structures (e.g., RV, lung, and LV) in the PET images and generated corresponding time activity curve (TAC) of each structure. The RV-to-LV TTs obtained from the new method and ROI method were not statistically different (P = 0.20; r = 0.76). We expect that this fast and robust method is applicable to the pathophysiology of cardiovascular diseases using small animal models such as rats and mice.

A flower image retrieval method based on ROI feature.

PubMed

Hong, An-Xiang; Chen, Gang; Li, Jun-Li; Chi, Zhe-Ru; Zhang, Dan

2004-07-01

Flower image retrieval is a very important step for computer-aided plant species recognition. In this paper, we propose an efficient segmentation method based on color clustering and domain knowledge to extract flower regions from flower images. For flower retrieval, we use the color histogram of a flower region to characterize the color features of flower and two shape-based features sets, Centroid-Contour Distance (CCD) and Angle Code Histogram (ACH), to characterize the shape features of a flower contour. Experimental results showed that our flower region extraction method based on color clustering and domain knowledge can produce accurate flower regions. Flower retrieval results on a database of 885 flower images collected from 14 plant species showed that our Region-of-Interest (ROI) based retrieval approach using both color and shape features can perform better than a method based on the global color histogram proposed by Swain and Ballard (1991) and a method based on domain knowledge-driven segmentation and color names proposed by Das et al.(1999).

A robust pointer segmentation in biomedical images toward building a visual ontology for biomedical article retrieval

NASA Astrophysics Data System (ADS)

You, Daekeun; Simpson, Matthew; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

2013-01-01

Pointers (arrows and symbols) are frequently used in biomedical images to highlight specific image regions of interest (ROIs) that are mentioned in figure captions and/or text discussion. Detection of pointers is the first step toward extracting relevant visual features from ROIs and combining them with textual descriptions for a multimodal (text and image) biomedical article retrieval system. Recently we developed a pointer recognition algorithm based on an edge-based pointer segmentation method, and subsequently reported improvements made on our initial approach involving the use of Active Shape Models (ASM) for pointer recognition and region growing-based method for pointer segmentation. These methods contributed to improving the recall of pointer recognition but not much to the precision. The method discussed in this article is our recent effort to improve the precision rate. Evaluation performed on two datasets and compared with other pointer segmentation methods show significantly improved precision and the highest F1 score.

Estimating the Return on Investment From a Health Risk Management Program Offered to Small Colorado-Based Employers

PubMed Central

Goetzel, Ron Z.; Tabrizi, Maryam; Henke, Rachel Mosher; Benevent, Richele; Brockbank, Claire v. S.; Stinson, Kaylan; Trotter, Margo; Newman, Lee S.

2015-01-01

Objective To determine whether changes in health risks for workers in small businesses can produce medical and productivity cost savings. Methods A 1-year pre- and posttest study tracked changes in 10 modifiable health risks for 2458 workers at 121 Colorado businesses that participated in a comprehensive worksite health promotion program. Risk reductions were entered into a return-on-investment (ROI) simulation model. Results Reductions were recorded in 10 risk factors examined, including obesity (−2.0%), poor eating habits (−5.8%), poor physical activity (−6.5%), tobacco use (−1.3%), high alcohol consumption (−1.7%), high stress (−3.5%), depression (−2.3%), high blood pressure (−0.3%), high total cholesterol (−0.9%), and high blood glucose (−0.2%). The ROI model estimated medical and productivity savings of $2.03 for every $1.00 invested. Conclusions Pooled data suggest that small businesses can realize a positive ROI from effective risk reduction programs. PMID:24806569

TU-D-209-02: A Backscatter Point Spread Function for Entrance Skin Dose Determination

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vijayan, S; Xiong, Z; Shankar, A

Purpose: To determine the distribution of backscattered radiation to the skin resulting from a non-uniform distribution of primary radiation through convolution with a backscatter point spread function (PSF). Methods: A backscatter PSF is determined using Monte Carlo simulation of a 1 mm primary beam incident on a 30 × 30 cm × 20 cm thick PMMA phantom using EGSnrc software. A primary profile is similarly obtained without the phantom and the difference from the total provides the backscatter profile. This scatter PSF characterizes the backscatter spread for a “point” primary interaction and can be convolved with the entrance primary dosemore » distribution to obtain the total entrance skin dose. The backscatter PSF was integrated into the skin dose tracking system (DTS), a graphical utility for displaying the color-coded skin dose distribution on a 3D graphic of the patient during interventional fluoroscopic procedures. The backscatter convolution method was validated for the non-uniform beam resulting from the use of an ROI attenuator. The ROI attenuator is a copper sheet with about 20% primary transmission (0.7 mm thick) containing a circular aperture; this attenuator is placed in the beam to reduce dose in the periphery while maintaining full dose in the region of interest. The DTS calculated primary plus backscatter distribution is compared to that measured with GafChromic film and that calculated using EGSnrc Monte-Carlo software. Results: The PSF convolution method used in the DTS software was able to account for the spread of backscatter from the ROI region to the region under the attenuator. The skin dose distribution determined using DTS with the ROI attenuator was in good agreement with the distributions measured with Gafchromic film and determined by Monte Carlo simulation Conclusion: The PSF convolution technique provides an accurate alternative for entrance skin dose determination with non-uniform primary x-ray beams. Partial support from NIH Grant R01-EB002873 and Toshiba Medical Systems Corp.« less

Jansen-MIDAS: A multi-level photomicrograph segmentation software based on isotropic undecimated wavelets.

PubMed

de Siqueira, Alexandre Fioravante; Cabrera, Flávio Camargo; Nakasuga, Wagner Massayuki; Pagamisse, Aylton; Job, Aldo Eloizo

2018-01-01

Image segmentation, the process of separating the elements within a picture, is frequently used for obtaining information from photomicrographs. Segmentation methods should be used with reservations, since incorrect results can mislead when interpreting regions of interest (ROI). This decreases the success rate of extra procedures. Multi-Level Starlet Segmentation (MLSS) and Multi-Level Starlet Optimal Segmentation (MLSOS) were developed to be an alternative for general segmentation tools. These methods gave rise to Jansen-MIDAS, an open-source software. A scientist can use it to obtain several segmentations of hers/his photomicrographs. It is a reliable alternative to process different types of photomicrographs: previous versions of Jansen-MIDAS were used to segment ROI in photomicrographs of two different materials, with an accuracy superior to 89%. © 2017 Wiley Periodicals, Inc.

A combination of spatial and recursive temporal filtering for noise reduction when using region of interest (ROI) fluoroscopy for patient dose reduction in image guided vascular interventions with significant anatomical motion

NASA Astrophysics Data System (ADS)

Setlur Nagesh, S. V.; Khobragade, P.; Ionita, C.; Bednarek, D. R.; Rudin, S.

2015-03-01

Because x-ray based image-guided vascular interventions are minimally invasive they are currently the most preferred method of treating disorders such as stroke, arterial stenosis, and aneurysms; however, the x-ray exposure to the patient during long image-guided interventional procedures could cause harmful effects such as cancer in the long run and even tissue damage in the short term. ROI fluoroscopy reduces patient dose by differentially attenuating the incident x-rays outside the region-of-interest. To reduce the noise in the dose-reduced regions previously recursive temporal filtering was successfully demonstrated for neurovascular interventions. However, in cardiac interventions, anatomical motion is significant and excessive recursive filtering could cause blur. In this work the effects of three noise-reduction schemes, including recursive temporal filtering, spatial mean filtering, and a combination of spatial and recursive temporal filtering, were investigated in a simulated ROI dose-reduced cardiac intervention. First a model to simulate the aortic arch and its movement was built. A coronary stent was used to simulate a bioprosthetic valve used in TAVR procedures and was deployed under dose-reduced ROI fluoroscopy during the simulated heart motion. The images were then retrospectively processed for noise reduction in the periphery, using recursive temporal filtering, spatial filtering and a combination of both. Quantitative metrics for all three noise reduction schemes are calculated and are presented as results. From these it can be concluded that with significant anatomical motion, a combination of spatial and recursive temporal filtering scheme is best suited for reducing the excess quantum noise in the periphery. This new noise-reduction technique in combination with ROI fluoroscopy has the potential for substantial patient-dose savings in cardiac interventions.

Optimization of the reconstruction parameters in [123I]FP-CIT SPECT

NASA Astrophysics Data System (ADS)

Niñerola-Baizán, Aida; Gallego, Judith; Cot, Albert; Aguiar, Pablo; Lomeña, Francisco; Pavía, Javier; Ros, Domènec

2018-04-01

The aim of this work was to obtain a set of parameters to be applied in [123I]FP-CIT SPECT reconstruction in order to minimize the error between standardized and true values of the specific uptake ratio (SUR) in dopaminergic neurotransmission SPECT studies. To this end, Monte Carlo simulation was used to generate a database of 1380 projection data-sets from 23 subjects, including normal cases and a variety of pathologies. Studies were reconstructed using filtered back projection (FBP) with attenuation correction and ordered subset expectation maximization (OSEM) with correction for different degradations (attenuation, scatter and PSF). Reconstruction parameters to be optimized were the cut-off frequency of a 2D Butterworth pre-filter in FBP, and the number of iterations and the full width at Half maximum of a 3D Gaussian post-filter in OSEM. Reconstructed images were quantified using regions of interest (ROIs) derived from Magnetic Resonance scans and from the Automated Anatomical Labeling map. Results were standardized by applying a simple linear regression line obtained from the entire patient dataset. Our findings show that we can obtain a set of optimal parameters for each reconstruction strategy. The accuracy of the standardized SUR increases when the reconstruction method includes more corrections. The use of generic ROIs instead of subject-specific ROIs adds significant inaccuracies. Thus, after reconstruction with OSEM and correction for all degradations, subject-specific ROIs led to errors between standardized and true SUR values in the range [‑0.5, +0.5] in 87% and 92% of the cases for caudate and putamen, respectively. These percentages dropped to 75% and 88% when the generic ROIs were used.

Effective Diagnosis of Alzheimer's Disease by Means of Association Rules

NASA Astrophysics Data System (ADS)

Chaves, R.; Ramírez, J.; Górriz, J. M.; López, M.; Salas-Gonzalez, D.; Illán, I.; Segovia, F.; Padilla, P.

In this paper we present a novel classification method of SPECT images for the early diagnosis of the Alzheimer's disease (AD). The proposed method is based on Association Rules (ARs) aiming to discover interesting associations between attributes contained in the database. The system uses firstly voxel-as-features (VAF) and Activation Estimation (AE) to find tridimensional activated brain regions of interest (ROIs) for each patient. These ROIs act as inputs to secondly mining ARs between activated blocks for controls, with a specified minimum support and minimum confidence. ARs are mined in supervised mode, using information previously extracted from the most discriminant rules for centering interest in the relevant brain areas, reducing the computational requirement of the system. Finally classification process is performed depending on the number of previously mined rules verified by each subject, yielding an up to 95.87% classification accuracy, thus outperforming recent developed methods for AD diagnosis.

Motion robust remote photoplethysmography in CIELab color space

PubMed Central

Yang, Yuting; Liu, Chenbin; Yu, Hui; Shao, Dangdang; Tsow, Francis; Tao, Nongjian

2016-01-01

Abstract. Remote photoplethysmography (rPPG) is attractive for tracking a subject’s physiological parameters without wearing a device. However, rPPG is known to be prone to body movement-induced artifacts, making it unreliable in realistic situations. Here we report a method to minimize the movement-induced artifacts. The method selects an optimal region of interest (ROI) automatically, prunes frames in which the ROI is not clearly captured (e.g., subject moves out of the view), and analyzes rPPG using an algorithm in CIELab color space, rather than the widely used RGB color space. We show that body movement primarily affects image intensity, rather than chromaticity, and separating chromaticity from intensity in CIELab color space thus helps achieve effective reduction of the movement-induced artifacts. We validate the method by performing a pilot study including 17 people with diverse skin tones. PMID:27812695

A New Approach to Develop Computer-aided Diagnosis Scheme of Breast Mass Classification Using Deep Learning Technology

PubMed Central

Qiu, Yuchen; Yan, Shiju; Gundreddy, Rohith Reddy; Wang, Yunzhi; Cheng, Samuel; Liu, Hong; Zheng, Bin

2017-01-01

PURPOSE To develop and test a deep learning based computer-aided diagnosis (CAD) scheme of mammograms for classifying between malignant and benign masses. METHODS An image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms was used. After down-sampling each ROI from 512×512 to 64×64 pixel size, we applied an 8 layer deep learning network that involves 3 pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perceptron (MLP) classifier for feature categorization to process ROIs. The 3 pairs of convolution layers contain 20, 10, and 5 feature maps, respectively. Each convolution layer is connected with a max-pooling layer to improve the feature robustness. The output of the sixth layer is fully connected with a MLP classifier, which is composed of one hidden layer and one logistic regression layer. The network then generates a classification score to predict the likelihood of ROI depicting a malignant mass. A four-fold cross validation method was applied to train and test this deep learning network. RESULTS The results revealed that this CAD scheme yields an area under the receiver operation characteristic curve (AUC) of 0.696±0.044, 0.802±0.037, 0.836±0.036, and 0.822±0.035 for fold 1 to 4 testing datasets, respectively. The overall AUC of the entire dataset is 0.790±0.019. CONCLUSIONS This study demonstrates the feasibility of applying a deep learning based CAD scheme to classify between malignant and benign breast masses without a lesion segmentation, image feature computation and selection process. PMID:28436410

Half-Fan-Based Intensity-Weighted Region-of-Interest Imaging for Low-Dose Cone-Beam CT in Image-Guided Radiation Therapy.

PubMed

Yoo, Boyeol; Son, Kihong; Pua, Rizza; Kim, Jinsung; Solodov, Alexander; Cho, Seungryong

2016-10-01

With the increased use of computed tomography (CT) in clinics, dose reduction is the most important feature people seek when considering new CT techniques or applications. We developed an intensity-weighted region-of-interest (IWROI) imaging method in an exact half-fan geometry to reduce the imaging radiation dose to patients in cone-beam CT (CBCT) for image-guided radiation therapy (IGRT). While dose reduction is highly desirable, preserving the high-quality images of the ROI is also important for target localization in IGRT. An intensity-weighting (IW) filter made of copper was mounted in place of a bowtie filter on the X-ray tube unit of an on-board imager (OBI) system such that the filter can substantially reduce radiation exposure to the outer ROI. In addition to mounting the IW filter, the lead-blade collimation of the OBI was adjusted to produce an exact half-fan scanning geometry for a further reduction of the radiation dose. The chord-based rebinned backprojection-filtration (BPF) algorithm in circular CBCT was implemented for image reconstruction, and a humanoid pelvis phantom was used for the IWROI imaging experiment. The IWROI image of the phantom was successfully reconstructed after beam-quality correction, and it was registered to the reference image within an acceptable level of tolerance. Dosimetric measurements revealed that the dose is reduced by approximately 61% in the inner ROI and by 73% in the outer ROI compared to the conventional bowtie filter-based half-fan scan. The IWROI method substantially reduces the imaging radiation dose and provides reconstructed images with an acceptable level of quality for patient setup and target localization. The proposed half-fan-based IWROI imaging technique can add a valuable option to CBCT in IGRT applications.

Defect detection in slab surface: a novel dual Charge-coupled Device imaging-based fuzzy connectedness strategy.

PubMed

Zhao, Liming; Ouyang, Qi; Chen, Dengfu; Udupa, Jayaram K; Wang, Huiqian; Zeng, Yuebin

2014-11-01

To provide an accurate surface defects inspection system and make the automation of robust image segmentation method a reality in routine production line, a general approach is presented for continuous casting slab (CC-slab) surface defects extraction and delineation. The applicability of the system is not tied to CC-slab exclusively. We combined the line array CCD (Charge-coupled Device) traditional scanning imaging (LS-imaging) and area array CCD laser three-dimensional (3D) scanning imaging (AL-imaging) strategies in designing the system. Its aim is to suppress the respective imaging system's limitations. In the system, the images acquired from the two CCD sensors are carefully aligned in space and in time by maximum mutual information-based full-fledged registration schema. Subsequently, the image information is fused from these two subsystems such as the unbroken 2D information in LS-imaging and 3D depressed information in AL-imaging. Finally, on the basis of the established dual scanning imaging system the region of interest (ROI) localization by seed specification was designed, and the delineation for ROI by iterative relative fuzzy connectedness (IRFC) algorithm was utilized to get a precise inspection result. Our method takes into account the complementary advantages in the two common machine vision (MV) systems and it performs competitively with the state-of-the-art as seen from the comparison of experimental results. For the first time, a joint imaging scanning strategy is proposed for CC-slab surface defect inspection that allows a feasible way of powerful ROI delineation strategies to be applied to the MV inspection field. Multi-ROI delineation by using IRFC in this research field may further improve the results.

Informed actions: where to cost effectively manage multiple threats to species to maximize return on investment.

PubMed

Auerbach, Nancy A; Tulloch, Ayesha I T; Possingham, Hugh P

Conservation practitioners, faced with managing multiple threats to biodiversity and limited funding, must prioritize investment in different management actions. From an economic perspective, it is routine practice to invest where the highest rate of return is expected. This return-on-investment (ROI) thinking can also benefit species conservation, and researchers are developing sophisticated approaches to support decision-making for cost-effective conservation. However, applied use of these approaches is limited. Managers may be wary of “black-box” algorithms or complex methods that are difficult to explain to funding agencies. As an alternative, we demonstrate the use of a basic ROI analysis for determining where to invest in cost-effective management to address threats to species. This method can be applied using basic geographic information system and spreadsheet calculations. We illustrate the approach in a management action prioritization for a biodiverse region of eastern Australia. We use ROI to prioritize management actions for two threats to a suite of threatened species: habitat degradation by cattle grazing, and predation by invasive red foxes (Vulpes vulpes). We show how decisions based on cost-effective threat management depend upon how expected benefits to species are defined and how benefits and costs co-vary. By considering a combination of species richness, restricted habitats, species vulnerability, and costs of management actions, small investments can result in greater expected benefit compared with management decisions that consider only species richness. Furthermore, a landscape management strategy that implements multiple actions is more efficient than managing only for one threat, or more traditional approaches that don't consider ROI. Our approach provides transparent and logical decision support for prioritizing different actions intended to abate threats associated with multiple species; it is of use when managers need a justifiable and repeatable approach to investment.

Improving the Nulling Beamformer Using Subspace Suppression.

PubMed

Rana, Kunjan D; Hämäläinen, Matti S; Vaina, Lucia M

2018-01-01

Magnetoencephalography (MEG) captures the magnetic fields generated by neuronal current sources with sensors outside the head. In MEG analysis these current sources are estimated from the measured data to identify the locations and time courses of neural activity. Since there is no unique solution to this so-called inverse problem, multiple source estimation techniques have been developed. The nulling beamformer (NB), a modified form of the linearly constrained minimum variance (LCMV) beamformer, is specifically used in the process of inferring interregional interactions and is designed to eliminate shared signal contributions, or cross-talk, between regions of interest (ROIs) that would otherwise interfere with the connectivity analyses. The nulling beamformer applies the truncated singular value decomposition (TSVD) to remove small signal contributions from a ROI to the sensor signals. However, ROIs with strong crosstalk will have high separating power in the weaker components, which may be removed by the TSVD operation. To address this issue we propose a new method, the nulling beamformer with subspace suppression (NBSS). This method, controlled by a tuning parameter, reweights the singular values of the gain matrix mapping from source to sensor space such that components with high overlap are reduced. By doing so, we are able to measure signals between nearby source locations with limited cross-talk interference, allowing for reliable cortical connectivity analysis between them. In two simulations, we demonstrated that NBSS reduces cross-talk while retaining ROIs' signal power, and has higher separating power than both the minimum norm estimate (MNE) and the nulling beamformer without subspace suppression. We also showed that NBSS successfully localized the auditory M100 event-related field in primary auditory cortex, measured from a subject undergoing an auditory localizer task, and suppressed cross-talk in a nearby region in the superior temporal sulcus.

Automatic detection of regions of interest in mammographic images

NASA Astrophysics Data System (ADS)

Cheng, Erkang; Ling, Haibin; Bakic, Predrag R.; Maidment, Andrew D. A.; Megalooikonomou, Vasileios

2011-03-01

This work is a part of our ongoing study aimed at comparing the topology of anatomical branching structures with the underlying image texture. Detection of regions of interest (ROIs) in clinical breast images serves as the first step in development of an automated system for image analysis and breast cancer diagnosis. In this paper, we have investigated machine learning approaches for the task of identifying ROIs with visible breast ductal trees in a given galactographic image. Specifically, we have developed boosting based framework using the AdaBoost algorithm in combination with Haar wavelet features for the ROI detection. Twenty-eight clinical galactograms with expert annotated ROIs were used for training. Positive samples were generated by resampling near the annotated ROIs, and negative samples were generated randomly by image decomposition. Each detected ROI candidate was given a confidences core. Candidate ROIs with spatial overlap were merged and their confidence scores combined. We have compared three strategies for elimination of false positives. The strategies differed in their approach to combining confidence scores by summation, averaging, or selecting the maximum score.. The strategies were compared based upon the spatial overlap with annotated ROIs. Using a 4-fold cross-validation with the annotated clinical galactographic images, the summation strategy showed the best performance with 75% detection rate. When combining the top two candidates, the selection of maximum score showed the best performance with 96% detection rate.

An interactive toolkit to extract phenological time series data from digital repeat photography

NASA Astrophysics Data System (ADS)

Seyednasrollah, B.; Milliman, T. E.; Hufkens, K.; Kosmala, M.; Richardson, A. D.

2017-12-01

Near-surface remote sensing and in situ photography are powerful tools to study how climate change and climate variability influence vegetation phenology and the associated seasonal rhythms of green-up and senescence. The rapidly-growing PhenoCam network has been using in situ digital repeat photography to study phenology in almost 500 locations around the world, with an emphasis on North America. However, extracting time series data from multiple years of half-hourly imagery - while each set of images may contain several regions of interest (ROI's), corresponding to different species or vegetation types - is not always straightforward. Large volumes of data require substantial processing time, and changes (either intentional or accidental) in camera field of view requires adjustment of ROI masks. Here, we introduce and present "DrawROI" as an interactive web-based application for imagery from PhenoCam. DrawROI can also be used offline, as a fully independent toolkit that significantly facilitates extraction of phenological data from any stack of digital repeat photography images. DrawROI provides a responsive environment for phenological scientists to interactively a) delineate ROIs, b) handle field of view (FOV) shifts, and c) extract and export time series data characterizing image color (i.e. red, green and blue channel digital numbers for the defined ROI). The application utilizes artificial intelligence and advanced machine learning techniques and gives user the opportunity to redraw new ROIs every time an FOV shift occurs. DrawROI also offers a quality control flag to indicate noisy data and images with low quality due to presence of foggy weather or snow conditions. The web-based application significantly accelerates the process of creating new ROIs and modifying pre-existing ROI in the PhenoCam database. The offline toolkit is presented as an open source R-package that can be used with similar datasets with time-lapse photography to obtain more data for studying phenology for a large community of ecologists. We will illustrate the use of the toolkit using imagery from a selection of sites within the National Ecological Observatory Network (NEON).

The influence of the region of interest width on two-dimensional speckle tracking-based measurements of strain and strain rate.

PubMed

Spriestersbach, Hendrik; Oh-Icí, Darach; Schmitt, Boris; Berger, Felix; Schmitz, Lothar

2015-01-01

There are significant variations in the published normal values of two-dimensional speckle tracking-derived strain and strain rate. These occur even when authors use the same software. To measure strain, the operator creates a region of interest (ROI) to define the myocardium to be analyzed. The purpose of this study was to test the hypothesis that measurements vary significantly with the chosen ROI width. In 20 healthy subjects (11 males, mean age 17.6 ± 6.18 years) an apical four-chamber view (4CH) and parasternal short-axis view (SAX) were analyzed. Initially ROI width was set automatically by the software. Two subsequent measurements were obtained from each cine loop by choosing the ROI width one step narrower and one step wider than the automatic ROI width. The mean differences between the measurements of narrower and automatic ROI and between automatic and wider ROI were -1.8 ± 0.7% and -0.9 ± 0.5% for global longitudinal strain (SL), -2.2 ± 0.6% and -1.7 ± 0.7% for global circumferential strain (SC), -0.10 ± 0.06/sec and -0.07 ± 0.06/sec for global longitudinal strain rate (SrL), and -0.15 ± 0.09/sec and -0.12 ± 0.07/sec for global circumferential strain rate (SrC) (all P < 0.000). This corresponds to a relative difference to the mean of both measurements of -4.4 to -11.0%. Layer-specific myocardial deformation and curvature dependency lead to an inverse correlation between the chosen ROI width and strain and strain rate measurements. Just one step of ROI-width change leads to a significant bias. Precise ROI-width definition is essential but technical factors limit its feasibility. © 2014, Wiley Periodicals, Inc.

White matter changes in comatose survivors of anoxic ischemic encephalopathy and traumatic brain injury: comparative diffusion-tensor imaging study.

PubMed

van der Eerden, Anke W; Khalilzadeh, Omid; Perlbarg, Vincent; Dinkel, Julien; Sanchez, Paola; Vos, Pieter E; Luyt, Charles-Edouard; Stevens, Robert D; Menjot de Champfleur, Nicolas; Delmaire, Christine; Tollard, Eleonore; Gupta, Rajiv; Dormont, Didier; Laureys, Steven; Benali, Habib; Vanhaudenhuyse, Audrey; Galanaud, Damien; Puybasset, Louis

2014-02-01

To analyze white matter pathologic abnormalities by using diffusion-tensor (DT) imaging in a multicenter prospective cohort of comatose patients following cardiac arrest or traumatic brain injury (TBI). Institutional review board approval and informed consent from proxies and control subjects were obtained. DT imaging was performed 5-57 days after insult in 49 cardiac arrest and 40 TBI patients. To control for DT imaging-processing variability, patients' values were normalized to those of 111 control subjects. Automated segmentation software calculated normalized axial diffusivity (λ1) and radial diffusivity (λ⊥) in 19 predefined white matter regions of interest (ROIs). DT imaging variables were compared by using general linear modeling, and side-to-side Pearson correlation coefficients were calculated. P values were corrected for multiple testing (Bonferroni). In central white matter, λ1 differed from that in control subjects in six of seven TBI ROIs and five of seven cardiac arrest ROIs (all P < .01). The λ⊥ differed from that in control subjects in all ROIs in both patient groups (P < .01). In hemispheres, λ1 was decreased compared with that in control subjects in three of 12 TBI ROIs (P < .05) and nine of 12 cardiac arrest ROIs (P < .01). The λ⊥ was increased in all TBI ROIs (P < .01) and in seven of 12 cardiac arrest ROIs (P < .05). Cerebral hemisphere λ1 was lower in cardiac arrest than in TBI in six of 12 ROIs (P < .01), while λ⊥ was higher in TBI than in cardiac arrest in eight of 12 ROIs (P < .01). Diffusivity values were symmetrically distributed in cardiac arrest (P < .001 for side-to-side correlation) but not in TBI patients. DT imaging findings are consistent with the known predominance of cerebral hemisphere axonal injury in cardiac arrest and chiefly central myelin injury in TBI. This consistency supports the validity of DT imaging for differentiating axon and myelin damage in vivo in humans. © RSNA, 2013

Improvement of light penetration based silkworm gender identification with confined regions of interest

NASA Astrophysics Data System (ADS)

Kamtongdee, Chakkrit; Sumriddetchkajorn, Sarun; Sa-ngiamsak, Chiranut

2013-06-01

Based on our previous work on light penetration-based silkworm gender identification, we find that unwanted optical noises scattering from the surrounding area near the silkworm pupa and the transparent support are sometimes analyzed and misinterpreted leading to incorrect silkworm gender identification. To alleviate this issue, we place a small rectangular hole on a transparent support so that it not only helps the user precisely place the silkworm pupa but also functions as a region of interest (ROI) for blocking unwanted optical noises and for roughly locating the abdomen region in the image for ease of image processing. Apart from the external ROI, we also assign a smaller ROI inside the image in order to remove strong scattering light from all edges of the external ROI and at the same time speed up our image processing operations. With only the external ROI in function, our experiment shows a measured 86% total accuracy in identifying gender of 120 silkworm pupae with a measured average processing time of 38 ms. Combining the external ROI and the image ROI together revamps the total accuracy in identifying the silkworm gender to 95% with a measured faster 18 ms processing time.

Fast and effective characterization of 3D region of interest in medical image data

NASA Astrophysics Data System (ADS)

Kontos, Despina; Megalooikonomou, Vasileios

2004-05-01

We propose a framework for detecting, characterizing and classifying spatial Regions of Interest (ROIs) in medical images, such as tumors and lesions in MRI or activation regions in fMRI. A necessary step prior to classification is efficient extraction of discriminative features. For this purpose, we apply a characterization technique especially designed for spatial ROIs. The main idea of this technique is to extract a k-dimensional feature vector using concentric spheres in 3D (or circles in 2D) radiating out of the ROI's center of mass. These vectors form characterization signatures that can be used to represent the initial ROIs. We focus on classifying fMRI ROIs obtained from a study that explores neuroanatomical correlates of semantic processing in Alzheimer's disease (AD). We detect a ROI highly associated with AD and apply the feature extraction technique with different experimental settings. We seek to distinguish control from patient samples. We study how classification can be performed using the extracted signatures as well as how different experimental parameters affect classification accuracy. The obtained classification accuracy ranged from 82% to 87% (based on the selected ROI) suggesting that the proposed classification framework can be potentially useful in supporting medical decision-making.

Network modelling methods for FMRI.

PubMed

Smith, Stephen M; Miller, Karla L; Salimi-Khorshidi, Gholamreza; Webster, Matthew; Beckmann, Christian F; Nichols, Thomas E; Ramsey, Joseph D; Woolrich, Mark W

2011-01-15

There is great interest in estimating brain "networks" from FMRI data. This is often attempted by identifying a set of functional "nodes" (e.g., spatial ROIs or ICA maps) and then conducting a connectivity analysis between the nodes, based on the FMRI timeseries associated with the nodes. Analysis methods range from very simple measures that consider just two nodes at a time (e.g., correlation between two nodes' timeseries) to sophisticated approaches that consider all nodes simultaneously and estimate one global network model (e.g., Bayes net models). Many different methods are being used in the literature, but almost none has been carefully validated or compared for use on FMRI timeseries data. In this work we generate rich, realistic simulated FMRI data for a wide range of underlying networks, experimental protocols and problematic confounds in the data, in order to compare different connectivity estimation approaches. Our results show that in general correlation-based approaches can be quite successful, methods based on higher-order statistics are less sensitive, and lag-based approaches perform very poorly. More specifically: there are several methods that can give high sensitivity to network connection detection on good quality FMRI data, in particular, partial correlation, regularised inverse covariance estimation and several Bayes net methods; however, accurate estimation of connection directionality is more difficult to achieve, though Patel's τ can be reasonably successful. With respect to the various confounds added to the data, the most striking result was that the use of functionally inaccurate ROIs (when defining the network nodes and extracting their associated timeseries) is extremely damaging to network estimation; hence, results derived from inappropriate ROI definition (such as via structural atlases) should be regarded with great caution. Copyright © 2010 Elsevier Inc. All rights reserved.

A vessel length-based method to compute coronary fractional flow reserve from optical coherence tomography images.

PubMed

Lee, Kyung Eun; Lee, Seo Ho; Shin, Eun-Seok; Shim, Eun Bo

2017-06-26

Hemodynamic simulation for quantifying fractional flow reserve (FFR) is often performed in a patient-specific geometry of coronary arteries reconstructed from the images from various imaging modalities. Because optical coherence tomography (OCT) images can provide more precise vascular lumen geometry, regardless of stenotic severity, hemodynamic simulation based on OCT images may be effective. The aim of this study is to perform OCT-FFR simulations by coupling a 3D CFD model from geometrically correct OCT images with a LPM based on vessel lengths extracted from CAG data with clinical validations for the present method. To simulate coronary hemodynamics, we developed a fast and accurate method that combined a computational fluid dynamics (CFD) model of an OCT-based region of interest (ROI) with a lumped parameter model (LPM) of the coronary microvasculature and veins. Here, the LPM was based on vessel lengths extracted from coronary X-ray angiography (CAG) images. Based on a vessel length-based approach, we describe a theoretical formulation for the total resistance of the LPM from a three-dimensional (3D) CFD model of the ROI. To show the utility of this method, we present calculated examples of FFR from OCT images. To validate the OCT-based FFR calculation (OCT-FFR) clinically, we compared the computed OCT-FFR values for 17 vessels of 13 patients with clinically measured FFR (M-FFR) values. A novel formulation for the total resistance of LPM is introduced to accurately simulate a 3D CFD model of the ROI. The simulated FFR values compared well with clinically measured ones, showing the accuracy of the method. Moreover, the present method is fast in terms of computational time, enabling clinicians to provide solutions handled within the hospital.

Optical coherence tomography to evaluate variance in the extent of carious lesions in depth.

PubMed

Park, Kyung-Jin; Schneider, Hartmut; Ziebolz, Dirk; Krause, Felix; Haak, Rainer

2018-05-03

Evaluation of variance in the extent of carious lesions in depth at smooth surfaces within the same ICDAS code group using optical coherence tomography (OCT) in vitro and in vivo. (1) Verification/validation of OCT to assess non-cavitated caries: 13 human molars with ICDAS code 2 at smooth surfaces were imaged using OCT and light microscopy. Regions of interest (ROI) were categorized according to the depth of carious lesions. Agreement between histology and OCT was determined by unweighted Cohen's Kappa and Wilcoxon test. (2) Assessment of 133 smooth surfaces using ICDAS and OCT in vitro, 49 surfaces in vivo. ROI were categorized according to the caries extent (ICDAS: codes 0-4, OCT: scoring based on lesion depth). A frequency distribution of the OCT scores for each ICDAS code was determined. (1) Histology and OCT agreed moderately (κ = 0.54, p ≤ 0.001) with no significant difference between both methods (p = 0.25). The lesions (76.9% (10 of 13)) _were equally scored. (2) In vitro, OCT revealed caries in 42% of ROI clinically assessed as sound. OCT detected dentin-caries in 40% of ROIs visually assessed as enamel-caries. In vivo, large differences between ICDAS and OCT were observed. Carious lesions of ICDAS codes 1 and 2 vary largely in their extent in depth.

Asymmetry of cortical decline in subtypes of primary progressive aphasia.

PubMed

Rogalski, Emily; Cobia, Derin; Martersteck, Adam; Rademaker, Alfred; Wieneke, Christina; Weintraub, Sandra; Mesulam, M-Marsel

2014-09-23

The aim of this study was to provide quantitative measures of changes in cortical atrophy over a 2-year period associated with 3 subtypes of primary progressive aphasia (PPA) using whole-brain vertex-wise and region-of-interest (ROI) neuroimaging methods. The purpose was to quantitate disease progression, establish an empirical basis for clinical expectations, and provide outcome measures for therapeutic trials. Changes in cortical thickness and volume loss as well as neuropsychological performance were assessed at baseline and 2-year follow-up in 26 patients who fulfilled criteria for logopenic (8 patients), agrammatic (10 patients), and semantic (8 patients) PPA subtypes. Whole-brain vertex-wise and ROI imaging analysis were conducted using the FreeSurfer longitudinal pipeline. Clinical deficits and cortical atrophy patterns showed distinct patterns of change among the subtypes over 2 years. Results confirmed that progression for each of the 3 subtypes showed left greater than right hemisphere asymmetry. An ROI analysis also revealed that progression was greater within, rather than outside, the language network. Preferential neurodegeneration of the left hemisphere language network is a common denominator for all 3 PPA subtypes, even as the disease progresses. Using a focal cortical language network ROI as an outcome measure of disease progression appears to be more sensitive than whole-brain or ventricular volume measures of change and may be helpful for designing future clinical trials in PPA. © 2014 American Academy of Neurology.

Methionine Uptake and Required Radiation Dose to Control Glioblastoma

DOE Office of Scientific and Technical Information (OSTI.GOV)

Iuchi, Toshihiko, E-mail: tiuchi@chiba-cc.jp; Hatano, Kazuo; Uchino, Yoshio

Purpose: The purpose of this study was to retrospectively assess the feasibility of radiation therapy planning for glioblastoma multiforme (GBM) based on the use of methionine (MET) positron emission tomography (PET), and the correlation among MET uptake, radiation dose, and tumor control. Methods and Materials: Twenty-two patients with GBM who underwent MET-PET prior to radiation therapy were enrolled. MET uptake in 30 regions of interest (ROIs) from 22 GBMs, biologically effective doses (BEDs) for the ROIs and their ratios (MET uptake:BED) were compared in terms of whether the ROIs were controlled for >12 months. Results: MET uptake was significantly correlated withmore » tumor control (odds ratio [OR], 10.0; P=.005); however, there was a higher level of correlation between MET uptake:BED ratio and tumor control (OR, 40.0; P<.0001). These data indicated that the required BEDs for controlling the ROIs could be predicted in terms of MET uptake; BED could be calculated as [34.0 × MET uptake] Gy from the optimal threshold of the MET uptake:BED ratio for tumor control. Conclusions: Target delineation based on MET-PET was demonstrated to be feasible for radiation therapy treatment planning. MET-PET could not only provide precise visualization of infiltrating tumor cells but also predict the required radiation doses to control target regions.« less

Automated selection of brain regions for real-time fMRI brain-computer interfaces

NASA Astrophysics Data System (ADS)

Lührs, Michael; Sorger, Bettina; Goebel, Rainer; Esposito, Fabrizio

2017-02-01

Objective. Brain-computer interfaces (BCIs) implemented with real-time functional magnetic resonance imaging (rt-fMRI) use fMRI time-courses from predefined regions of interest (ROIs). To reach best performances, localizer experiments and on-site expert supervision are required for ROI definition. To automate this step, we developed two unsupervised computational techniques based on the general linear model (GLM) and independent component analysis (ICA) of rt-fMRI data, and compared their performances on a communication BCI. Approach. 3 T fMRI data of six volunteers were re-analyzed in simulated real-time. During a localizer run, participants performed three mental tasks following visual cues. During two communication runs, a letter-spelling display guided the subjects to freely encode letters by performing one of the mental tasks with a specific timing. GLM- and ICA-based procedures were used to decode each letter, respectively using compact ROIs and whole-brain distributed spatio-temporal patterns of fMRI activity, automatically defined from subject-specific or group-level maps. Main results. Letter-decoding performances were comparable to supervised methods. In combination with a similarity-based criterion, GLM- and ICA-based approaches successfully decoded more than 80% (average) of the letters. Subject-specific maps yielded optimal performances. Significance. Automated solutions for ROI selection may help accelerating the translation of rt-fMRI BCIs from research to clinical applications.

Sustained reduction of nicotine craving with real-time neurofeedback: exploring the role of severity of dependence.

PubMed

Canterberry, Melanie; Hanlon, Colleen A; Hartwell, Karen J; Li, Xingbao; Owens, Max; LeMatty, Todd; Prisciandaro, James J; Borckardt, Jeffrey; Saladin, Michael E; Brady, Kathleen T; George, Mark S

2013-12-01

Neurofeedback delivered via real-time functional magnetic resonance imaging (rtfMRI) is a promising therapeutic technique being explored to facilitate self-regulation of craving in nicotine-dependent cigarette smokers. The current study examined the role of nicotine-dependence severity and the efficacy of multiple visits of neurofeedback from a single region of interest (ROI) in the anterior cingulate cortex (ACC) on craving reduction. Nine nicotine-dependent cigarette smokers participated in three rtfMRI visits that examined cue-induced craving and brain activation. Severity of nicotine dependence was assessed with the Fagerström Test for Nicotine Dependence. When viewing smoking-related images with instructions to "crave," patient-tailored ROIs were generated in the vicinity of the ACC. Activity levels from the ROI were fed back while participants viewed smoking cues with the instruction to reduce craving. Neurofeedback from a single ROI in the ACC led to consistent decreases in self-reported craving and activation in the ACC across the three visits. Dependence severity predicted response to neurofeedback at Visit 3. This study builds upon previous rtfMRI studies on the regulation of nicotine craving in demonstrating that feedback from the ACC can reduce activation to smoking cues across three separate visits. Individuals with lower nicotine-dependence severity were more successful in reducing ACC activation over time. These data highlight the need to consider dependence severity in developing more individualized neurofeedback methods.

Detection of soft tissue densities from digital breast tomosynthesis: comparison of conventional and deep learning approaches

NASA Astrophysics Data System (ADS)

Fotin, Sergei V.; Yin, Yin; Haldankar, Hrishikesh; Hoffmeister, Jeffrey W.; Periaswamy, Senthil

2016-03-01

Computer-aided detection (CAD) has been used in screening mammography for many years and is likely to be utilized for digital breast tomosynthesis (DBT). Higher detection performance is desirable as it may have an impact on radiologist's decisions and clinical outcomes. Recently the algorithms based on deep convolutional architectures have been shown to achieve state of the art performance in object classification and detection. Similarly, we trained a deep convolutional neural network directly on patches sampled from two-dimensional mammography and reconstructed DBT volumes and compared its performance to a conventional CAD algorithm that is based on computation and classification of hand-engineered features. The detection performance was evaluated on the independent test set of 344 DBT reconstructions (GE SenoClaire 3D, iterative reconstruction algorithm) containing 328 suspicious and 115 malignant soft tissue densities including masses and architectural distortions. Detection sensitivity was measured on a region of interest (ROI) basis at the rate of five detection marks per volume. Moving from conventional to deep learning approach resulted in increase of ROI sensitivity from 0:832 +/- 0:040 to 0:893 +/- 0:033 for suspicious ROIs; and from 0:852 +/- 0:065 to 0:930 +/- 0:046 for malignant ROIs. These results indicate the high utility of deep feature learning in the analysis of DBT data and high potential of the method for broader medical image analysis tasks.

Visual words for lip-reading

NASA Astrophysics Data System (ADS)

Hassanat, Ahmad B. A.; Jassim, Sabah

2010-04-01

In this paper, the automatic lip reading problem is investigated, and an innovative approach to providing solutions to this problem has been proposed. This new VSR approach is dependent on the signature of the word itself, which is obtained from a hybrid feature extraction method dependent on geometric, appearance, and image transform features. The proposed VSR approach is termed "visual words". The visual words approach consists of two main parts, 1) Feature extraction/selection, and 2) Visual speech feature recognition. After localizing face and lips, several visual features for the lips where extracted. Such as the height and width of the mouth, mutual information and the quality measurement between the DWT of the current ROI and the DWT of the previous ROI, the ratio of vertical to horizontal features taken from DWT of ROI, The ratio of vertical edges to horizontal edges of ROI, the appearance of the tongue and the appearance of teeth. Each spoken word is represented by 8 signals, one of each feature. Those signals maintain the dynamic of the spoken word, which contains a good portion of information. The system is then trained on these features using the KNN and DTW. This approach has been evaluated using a large database for different people, and large experiment sets. The evaluation has proved the visual words efficiency, and shown that the VSR is a speaker dependent problem.

Psychometric properties of the Irish Management Standards Indicator Tool.

PubMed

Boyd, S; Kerr, R; Murray, P

2016-12-01

Work Positive is Ireland's national policy initiative to control work-related stress. Since the introduction of the UK Health and Safety Executive's Management Standards (MS) in 2004, a number of studies have been undertaken to assess the potential adaptation of the MS framework within Ireland. To investigate the dimensionality, reliability and validity of the Irish version of the MS Indicator Tool (ROI-MSIT). Between February 2011 and June 2014, we collected data from a wide range of public and private sector organizations that used the ROI-MSIT. In addition to the ROI-MSIT, respondents completed the WHO-Five Well-being Index (WHO-5). Exploratory factor analysis (EFA) was used to determine whether the ROI-MSIT maintained the structure of the UK instrument. The internal consistency of the ROI-MSIT was also assessed to determine its reliability, while its criterion-related validity was explored through correlation analysis with the WHO-5. Data were collected from 7377 participants. The factor structure of the ROI-MSIT consisted of six factors; the Demands, Control, Peer Support, Relationships and Role factors were equivalent to the original UK factors. Like the Italian version, a principal factor emerged that combined the Manager Support and Change domains. Cronbach's alpha scores ranged from 0.75 to 0.91. Finally, the ROI-MSIT's subscales and WHO-5 were positively correlated (r = 0.42-0.59, P < 0.001). The ROI-MSIT is reliable and valid, with a factor structure similar to the original UK instrument and the Italian MSIT. Further psychometric evaluation of the ROI-MSIT is recommended. © The Author 2016. Published by Oxford University Press on behalf of the Society of Occupational Medicine. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Evaluation of the reproducibility of a protocol for the pharmacokinetic study of breast tumors by dynamic magnetic resonance imaging.

PubMed

Etxano, J; García-Lallana Valbuena, A; Antón Ibáñez, I; Elizalde, A; Pina, L; García-Foncillas, J; Boni, V

2015-01-01

To evaluate the reproducibility of a protocol for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the pharmacokinetic study of breast tumors. We carried out this prospective study from October 2009 through December 2009. We studied 12 patients with stage ii-iii invasive breast cancer without prior treatment. Our center's research ethics committee approved the study. The 12 patients underwent on two consecutive days DCE-MRI with a high temporal resolution protocol (21 acquisitions/minute). The data obtained in an ROI traced around the largest diameter of the tumor (ROI 1) and in another ROI traced around the area of the lesion's highest K(trans) intensity (ROI 2) were analyzed separately. We used parametric and nonparametric statistical tests to study the reproducibility and concordance of the principal pharmacokinetic variables (K(trans), Kep, Ve and AUC90). The correlations were very high (r>.80; P<.01) for all the variables for ROI 1 and high (r=.70-.80; P<.01) for all the variables for ROI 2, with the exception of Ve both in ROI 1 (r=.44; P=.07) and in ROI 2 (r=.13; P=.235). There were no statistically significant differences between the two studies in the values obtained for K(trans), Kep and AUC90 (P>.05 for each), but there was a statistically significant difference between the two studies in the values obtained for Ve in ROI 2 (P=.008). The high temporal resolution protocol for DCE-MRI used at out center is very reproducible for the principal pharmacokinetic constants of breast. Copyright © 2012 SERAM. Published by Elsevier España, S.L.U. All rights reserved.

Rapid computation of single PET scan rest-stress myocardial blood flow parametric images by table look up.

PubMed

Guehl, Nicolas J; Normandin, Marc D; Wooten, Dustin W; Rozen, Guy; Ruskin, Jeremy N; Shoup, Timothy M; Woo, Jonghye; Ptaszek, Leon M; Fakhri, Georges El; Alpert, Nathaniel M

2017-09-01

We have recently reported a method for measuring rest-stress myocardial blood flow (MBF) using a single, relatively short, PET scan session. The method requires two IV tracer injections, one to initiate rest imaging and one at peak stress. We previously validated absolute flow quantitation in ml/min/cc for standard bull's eye, segmental analysis. In this work, we extend the method for fast computation of rest-stress MBF parametric images. We provide an analytic solution to the single-scan rest-stress flow model which is then solved using a two-dimensional table lookup method (LM). Simulations were performed to compare the accuracy and precision of the lookup method with the original nonlinear method (NLM). Then the method was applied to 16 single scan rest/stress measurements made in 12 pigs: seven studied after infarction of the left anterior descending artery (LAD) territory, and nine imaged in the native state. Parametric maps of rest and stress MBF as well as maps of left (f LV ) and right (f RV ) ventricular spill-over fractions were generated. Regions of interest (ROIs) for 17 myocardial segments were defined in bull's eye fashion on the parametric maps. The mean of each ROI was then compared to the rest (K 1r ) and stress (K 1s ) MBF estimates obtained from fitting the 17 regional TACs with the NLM. In simulation, the LM performed as well as the NLM in terms of precision and accuracy. The simulation did not show that bias was introduced by the use of a predefined two-dimensional lookup table. In experimental data, parametric maps demonstrated good statistical quality and the LM was computationally much more efficient than the original NLM. Very good agreement was obtained between the mean MBF calculated on the parametric maps for each of the 17 ROIs and the regional MBF values estimated by the NLM (K 1map LM  = 1.019 × K 1 ROI NLM  + 0.019, R 2  = 0.986; mean difference = 0.034 ± 0.036 mL/min/cc). We developed a table lookup method for fast computation of parametric imaging of rest and stress MBF. Our results show the feasibility of obtaining good quality MBF maps using modest computational resources, thus demonstrating that the method can be applied in a clinical environment to obtain full quantitative MBF information. © 2017 American Association of Physicists in Medicine.

A comparison of methods to evaluate gray scale response of tomosynthesis systems using a software breast phantom

NASA Astrophysics Data System (ADS)

Sousa, Maria A. Z.; Bakic, Predrag R.; Schiabel, Homero; Maidment, Andrew D. A.

2017-03-01

Digital breast tomosynthesis (DBT) has been shown to be an effective imaging tool for breast cancer diagnosis as it provides three-dimensional images of the breast with minimal tissue overlap. The quality of the reconstructed image depends on many factors that can be assessed using uniform or realistic phantoms. In this paper, we created four models of phantoms using an anthropomorphic software breast phantom and compared four methods to evaluate the gray scale response in terms of the contrast, noise and detectability of adipose and glandular tissues binarized according to phantom ground truth. For each method, circular regions of interest (ROIs) were selected with various sizes, quantity and positions inside a square area in the phantom. We also estimated the percent density of the simulated breast and the capability of distinguishing both tissues by receiver operating characteristic (ROC) analysis. Results shows a sensitivity of the methods to the ROI size, placement and to the slices considered.

Finger vein verification system based on sparse representation.

PubMed

Xin, Yang; Liu, Zhi; Zhang, Haixia; Zhang, Hong

2012-09-01

Finger vein verification is a promising biometric pattern for personal identification in terms of security and convenience. The recognition performance of this technology heavily relies on the quality of finger vein images and on the recognition algorithm. To achieve efficient recognition performance, a special finger vein imaging device is developed, and a finger vein recognition method based on sparse representation is proposed. The motivation for the proposed method is that finger vein images exhibit a sparse property. In the proposed system, the regions of interest (ROIs) in the finger vein images are segmented and enhanced. Sparse representation and sparsity preserving projection on ROIs are performed to obtain the features. Finally, the features are measured for recognition. An equal error rate of 0.017% was achieved based on the finger vein image database, which contains images that were captured by using the near-IR imaging device that was developed in this study. The experimental results demonstrate that the proposed method is faster and more robust than previous methods.

A multiresolution prostate representation for automatic segmentation in magnetic resonance images.

PubMed

Alvarez, Charlens; Martínez, Fabio; Romero, Eduardo

2017-04-01

Accurate prostate delineation is necessary in radiotherapy processes for concentrating the dose onto the prostate and reducing side effects in neighboring organs. Currently, manual delineation is performed over magnetic resonance imaging (MRI) taking advantage of its high soft tissue contrast property. Nevertheless, as human intervention is a consuming task with high intra- and interobserver variability rates, (semi)-automatic organ delineation tools have emerged to cope with these challenges, reducing the time spent for these tasks. This work presents a multiresolution representation that defines a novel metric and allows to segment a new prostate by combining a set of most similar prostates in a dataset. The proposed method starts by selecting the set of most similar prostates with respect to a new one using the proposed multiresolution representation. This representation characterizes the prostate through a set of salient points, extracted from a region of interest (ROI) that encloses the organ and refined using structural information, allowing to capture main relevant features of the organ boundary. Afterward, the new prostate is automatically segmented by combining the nonrigidly registered expert delineations associated to the previous selected similar prostates using a weighted patch-based strategy. Finally, the prostate contour is smoothed based on morphological operations. The proposed approach was evaluated with respect to the expert manual segmentation under a leave-one-out scheme using two public datasets, obtaining averaged Dice coefficients of 82% ± 0.07 and 83% ± 0.06, and demonstrating a competitive performance with respect to atlas-based state-of-the-art methods. The proposed multiresolution representation provides a feature space that follows a local salient point criteria and a global rule of the spatial configuration among these points to find out the most similar prostates. This strategy suggests an easy adaptation in the clinical routine, as supporting tool for annotation. © 2017 American Association of Physicists in Medicine.

Differential diagnosis of benign and malignant breast masses using diffusion-weighted magnetic resonance imaging.

PubMed

Min, Qinghua; Shao, Kangwei; Zhai, Lulan; Liu, Wei; Zhu, Caisong; Yuan, Lixin; Yang, Jun

2015-02-07

Diffusion-weighted magnetic resonance imaging (DW-MRI) is different from conventional diagnostic methods and has the potential to delineate the microscopic anatomy of a target tissue or organ. The purpose of our study was to evaluate the value of DW-MRI in the diagnosis of benign and malignant breast masses, which would help the clinical surgeon to decide the scope and pattern of operation. A total of 52 female patients with palpable solid breast masses received breast MRI scans using routine sequences, dynamic contrast-enhanced imaging, and diffusion-weighted echo-planar imaging at b values of 400, 600, and 800 s/mm(2), respectively. Two regions of interest (ROIs) were plotted, with a smaller ROI for the highest signal and a larger ROI for the overall lesion. Apparent diffusion coefficient (ADC) values were calculated at three different b values for all detectable lesions and from two different ROIs. The sensitivity, specificity, positive predictive value, and positive likelihood ratio of DW-MRI were determined for comparison with histological results. A total of 49 (49/52, 94.2%) lesions were detected using DW-MRI, including 20 benign lesions (two lesions detected in the same patient) and 29 malignant lesions. Benign lesion had a higher mean ADC value than their malignant counterparts, regardless of b value. According to the receiver operating characteristic (ROC) curve, the smaller-range ROI was more effective in differentiation between benign and malignant lesions. The area under the ROC curve was the largest at a b value of 800 s/mm(2). With a threshold ADC value at 1.23 × 10(-3) mm(2)/s, DW-MRI achieved a sensitivity of 82.8%, specificity of 90.0%, positive predictive value of 92.3%, and positive likelihood ratio of 8.3 for differentiating benign and malignant lesions. DW-MRI is an accurate diagnostic tool for differentiation between benign and malignant breast lesions, with an optimal b value of 800 s/mm(2). A smaller-range ROI focusing on the highest signal has a better differential value.

Quantitative assessment of colorectal cancer tumor vascular parameters by using perfusion CT: influence of tumor region of interest.

PubMed

Goh, Vicky; Halligan, Steve; Gharpuray, Anita; Wellsted, David; Sundin, Josefin; Bartram, Clive I

2008-06-01

To prospectively determine whether position and size of tumor region of interest (ROI) influence estimates of colorectal cancer vascular parameters at computed tomography (CT). After institutional review board approval and informed consent, 25 men and 22 women (mean age, 65.8 years) with colorectal adenocarcinoma underwent 65-second CT perfusion study. Blood volume, blood flow, and permeability-surface area product were determined for 40- or 120-mm(2) circular ROIs placed at the tumor edge and center and around (outlining) visible tumor. ROI analysis was repeated by two observers in different subsets of patients to assess intra- and interobserver variation. Measurements were compared by using analysis of variance; a difference with P = .002 was significant. Blood volume, blood flow, and permeability-surface area product measurements were substantially higher at the edge than at the center for both 40- and 120-mm(2) ROIs. For 40-mm(2) ROI, means of the three measurements were 6.9 mL/100 g (standard deviation [SD], 1.4), 108.7 mL/100 g per minute (SD, 39.2), and 16.9 mL/100 g per minute (SD, 4.2), respectively, at the edge versus 5.1 mL/100 g (SD, 1.5), 56.3 mL/100 g per minute (SD, 33.1), and 13.9 mL/100 g per minute (SD, 4.6), respectively, at the center. For 120-mm(2) ROI, means of the three measurements were 6.6 mL/100 g (SD, 1.3), 96.7 mL/100 g per minute (SD, 42.5), and 16.3 mL/100 g per minute (SD, 5.6), respectively, at the edge versus 5.1 mL/100 g (SD, 1.4), 58.3 mL/100 g per minute (SD, 32.5), and 13.4 mL/100 g per minute (SD, 4.3) at the center (P < .0001). Measurements varied substantially depending on the ROI size; values for the ROI for outlined tumor were intermediate between those at the tumor edge and center. Inter- and intraobserver agreement was poor for both 40- and 120-mm(2) ROIs. Position and size of tumor ROI and observer variation substantially influence ultimate perfusion values. ROI for outlined entire tumor is more reliable for perfusion measurements and more appropriate clinically than use of arbitrarily determined smaller ROIs. (c) RSNA, 2008.

Automated segmentation of murine lung tumors in x-ray micro-CT images

NASA Astrophysics Data System (ADS)

Swee, Joshua K. Y.; Sheridan, Clare; de Bruin, Elza; Downward, Julian; Lassailly, Francois; Pizarro, Luis

2014-03-01

Recent years have seen micro-CT emerge as a means of providing imaging analysis in pre-clinical study, with in-vivo micro-CT having been shown to be particularly applicable to the examination of murine lung tumors. Despite this, existing studies have involved substantial human intervention during the image analysis process, with the use of fully-automated aids found to be almost non-existent. We present a new approach to automate the segmentation of murine lung tumors designed specifically for in-vivo micro-CT-based pre-clinical lung cancer studies that addresses the specific requirements of such study, as well as the limitations human-centric segmentation approaches experience when applied to such micro-CT data. Our approach consists of three distinct stages, and begins by utilizing edge enhancing and vessel enhancing non-linear anisotropic diffusion filters to extract anatomy masks (lung/vessel structure) in a pre-processing stage. Initial candidate detection is then performed through ROI reduction utilizing obtained masks and a two-step automated segmentation approach that aims to extract all disconnected objects within the ROI, and consists of Otsu thresholding, mathematical morphology and marker-driven watershed. False positive reduction is finally performed on initial candidates through random-forest-driven classification using the shape, intensity, and spatial features of candidates. We provide validation of our approach using data from an associated lung cancer study, showing favorable results both in terms of detection (sensitivity=86%, specificity=89%) and structural recovery (Dice Similarity=0.88) when compared against manual specialist annotation.

Computer-aided diagnosis of malignant mammograms using Zernike moments and SVM.

PubMed

Sharma, Shubhi; Khanna, Pritee

2015-02-01

This work is directed toward the development of a computer-aided diagnosis (CAD) system to detect abnormalities or suspicious areas in digital mammograms and classify them as malignant or nonmalignant. Original mammogram is preprocessed to separate the breast region from its background. To work on the suspicious area of the breast, region of interest (ROI) patches of a fixed size of 128×128 are extracted from the original large-sized digital mammograms. For training, patches are extracted manually from a preprocessed mammogram. For testing, patches are extracted from a highly dense area identified by clustering technique. For all extracted patches corresponding to a mammogram, Zernike moments of different orders are computed and stored as a feature vector. A support vector machine (SVM) is used to classify extracted ROI patches. The experimental study shows that the use of Zernike moments with order 20 and SVM classifier gives better results among other studies. The proposed system is tested on Image Retrieval In Medical Application (IRMA) reference dataset and Digital Database for Screening Mammography (DDSM) mammogram database. On IRMA reference dataset, it attains 99% sensitivity and 99% specificity, and on DDSM mammogram database, it obtained 97% sensitivity and 96% specificity. To verify the applicability of Zernike moments as a fitting texture descriptor, the performance of the proposed CAD system is compared with the other well-known texture descriptors namely gray-level co-occurrence matrix (GLCM) and discrete cosine transform (DCT).

Kinetic Modeling of PET Data Without Blood Sampling

NASA Astrophysics Data System (ADS)

Bentourkia, M.

2005-06-01

In positron emission tomography (PET) imaging, application of kinetic modeling always requires an input curve (IC) together with the PET data. The IC can be obtained by means of external blood sampling or, in the case of cardiac studies, by means of a region-of-interest (ROI) drawn on the blood pool. It is, however, very unsuitable to withdraw and to analyze blood samples, and in small animals, these operations become difficult, while ICs determined from ROIs are generally contaminated by emissions from neighboring sites, or they are underestimated because of partial volume effect. In this paper, we report a new method to extract kinetic parameters from dynamic PET studies without a priori knowledge of the IC. The method is applied in human brain data measured with fluorodeoxyglucose (FDG) human-brain and in cardiac-rat perfusion studies with /sup 13/N-ammonia and /sup 11/C-acetate. The tissue blood volume (TBV), usually fitted together with the rate constants, is extracted simultaneously with the tissue time activity curves for cardiac studies, while for brain gray matter, TBV is known to be about 4% to 7%. The shape of IC is obtained by means of factor analysis from an ROI drawn around a cardiac tissue or a brain artery. The results show a good correlation (p<0.05) between the cerebral metabolic rate of glucose, myocardial blood flow, and oxygen consumption obtained with the new method in comparison to the usual method. In conclusion, it is possible to apply kinetic modeling without any blood sampling, which significantly simplifies PET acquisition and data analysis.

Segmentation of prostate biopsy needles in transrectal ultrasound images

NASA Astrophysics Data System (ADS)

Krefting, Dagmar; Haupt, Barbara; Tolxdorff, Thomas; Kempkensteffen, Carsten; Miller, Kurt

2007-03-01

Prostate cancer is the most common cancer in men. Tissue extraction at different locations (biopsy) is the gold-standard for diagnosis of prostate cancer. These biopsies are commonly guided by transrectal ultrasound imaging (TRUS). Exact location of the extracted tissue within the gland is desired for more specific diagnosis and provides better therapy planning. While the orientation and the position of the needle within clinical TRUS image are limited, the appearing length and visibility of the needle varies strongly. Marker lines are present and tissue inhomogeneities and deflection artefacts may appear. Simple intensity, gradient oder edge-detecting based segmentation methods fail. Therefore a multivariate statistical classificator is implemented. The independent feature model is built by supervised learning using a set of manually segmented needles. The feature space is spanned by common binary object features as size and eccentricity as well as imaging-system dependent features like distance and orientation relative to the marker line. The object extraction is done by multi-step binarization of the region of interest. The ROI is automatically determined at the beginning of the segmentation and marker lines are removed from the images. The segmentation itself is realized by scale-invariant classification using maximum likelihood estimation and Mahalanobis distance as discriminator. The technique presented here could be successfully applied in 94% of 1835 TRUS images from 30 tissue extractions. It provides a robust method for biopsy needle localization in clinical prostate biopsy TRUS images.

Implementing an ROI Measurement Process at Dell Computer.

ERIC Educational Resources Information Center

Tesoro, Ferdinand

1998-01-01

This return-on-investment (ROI) evaluation study determined the business impact of the sales negotiation training course to Dell Computer Corporation. A five-step ROI measurement process was used: Plan-Develop-Analyze-Communicate-Leverage. The corporate sales information database was used to compare pre- and post-training metrics for both training…

Local ROI Reconstruction via Generalized FBP and BPF Algorithms along More Flexible Curves.

PubMed

Yu, Hengyong; Ye, Yangbo; Zhao, Shiying; Wang, Ge

2006-01-01

We study the local region-of-interest (ROI) reconstruction problem, also referred to as the local CT problem. Our scheme includes two steps: (a) the local truncated normal-dose projections are extended to global dataset by combining a few global low-dose projections; (b) the ROI are reconstructed by either the generalized filtered backprojection (FBP) or backprojection-filtration (BPF) algorithms. The simulation results show that both the FBP and BPF algorithms can reconstruct satisfactory results with image quality in the ROI comparable to that of the corresponding global CT reconstruction.

Comparison of two Medication Therapy Management Practice Models on Return on Investment.

PubMed

Gazda, Nicholas P; Berenbrok, Lucas A; Ferreri, Stefanie P

2017-06-01

To compare the return on investment (ROI) of an integrated practice model versus a "hub and spoke" practice model of pharmacist provided medication therapy management (MTM). A cohort retrospective analysis of MTM claims billed in 76 pharmacies in North Carolina in the 2010 hub and spoke practice model and the 2012 "integrated" practice model were analyzed to calculate the ROI. In 2010, 4089 patients received an MTM resulting in 8757 claims in the hub and spoke model. In 2012, 4896 patients received an MTM resulting in 13 730 claims in the integrated model. In 2010, US$165 897.26 was invested in pharmacist salary and $173 498.00 was received in reimbursement, resulting in an ROI of +US$7600.74 (+4.6%). In 2012, US$280 890.09 was invested in pharmacist salary and US$302 963 was received in reimbursement, resulting in an ROI of +US$22 072.91 or (+7.9%). The integrated model of MTM showed an increase in number of claims submitted and in number of patients receiving MTM services, ultimately resulting in a higher ROI. While a higher ROI was evident in the integrated model, both models resulted in positive ROI (1:12-1:21), highlighting that MTM programs can be cost effective with different strategies of execution.

Influence of region of interest size and ultrasound lesion size on the performance of 2D shear wave elastography (SWE) in solid breast masses.

PubMed

Skerl, K; Vinnicombe, S; Giannotti, E; Thomson, K; Evans, A

2015-12-01

To evaluate the influence of the region of interest (ROI) size and lesion diameter on the diagnostic performance of 2D shear wave elastography (SWE) of solid breast lesions. A study group of 206 consecutive patients (age range 21-92 years) with 210 solid breast lesions (70 benign, 140 malignant) who underwent core biopsy or surgical excision was evaluated. Lesions were divided into small (diameter <15 mm, n=112) and large lesions (diameter ≥15 mm, n=98). An ROI with a diameter of 1, 2, and 3 mm was positioned over the stiffest part of the lesion. The maximum elasticity (Emax), mean elasticity (Emean) and standard deviation (SD) for each ROI size were compared to the pathological outcome. Statistical analysis was undertaken using the chi-square test and receiver operating characteristic (ROC) analysis. The ROI size used has a significant impact on the performance of Emean and SD but not on Emax. Youden's indices show a correlation with the ROI size and lesion size: generally, the benign/malignant threshold is lower with increasing ROI size but higher with increasing lesion size. No single SWE parameter has superior performance. Lesion size and ROI size influence diagnostic performance. Copyright © 2015. Published by Elsevier Ltd.

The relationship between return on investment and quality of study methodology in workplace health promotion programs.

PubMed

Baxter, Siyan; Sanderson, Kristy; Venn, Alison J; Blizzard, C Leigh; Palmer, Andrew J

2014-01-01

To determine the relationship between return on investment (ROI) and quality of study methodology in workplace health promotion programs. Data were obtained through a systematic literature search of National Health Service Economic Evaluation Database (NHS EED), Database of Abstracts of Reviews of Effects (DARE), Health Technology Database (HTA), Cost Effectiveness Analysis (CEA) Registry, EconLit, PubMed, Embase, Wiley, and Scopus. Included were articles written in English or German reporting cost(s) and benefit(s) and single or multicomponent health promotion programs on working adults. Return-to-work and workplace injury prevention studies were excluded. Methodological quality was graded using British Medical Journal Economic Evaluation Working Party checklist. Economic outcomes were presented as ROI. ROI was calculated as ROI = (benefits - costs of program)/costs of program. Results were weighted by study size and combined using meta-analysis techniques. Sensitivity analysis was performed using two additional methodological quality checklists. The influences of quality score and important study characteristics on ROI were explored. Fifty-one studies (61 intervention arms) published between 1984 and 2012 included 261,901 participants and 122,242 controls from nine industry types across 12 countries. Methodological quality scores were highly correlated between checklists (r = .84-.93). Methodological quality improved over time. Overall weighted ROI [mean ± standard deviation (confidence interval)] was 1.38 ± 1.97 (1.38-1.39), which indicated a 138% return on investment. When accounting for methodological quality, an inverse relationship to ROI was found. High-quality studies (n = 18) had a smaller mean ROI, 0.26 ± 1.74 (.23-.30), compared to moderate (n = 16) 0.90 ± 1.25 (.90-.91) and low-quality (n = 27) 2.32 ± 2.14 (2.30-2.33) studies. Randomized control trials (RCTs) (n = 12) exhibited negative ROI, -0.22 ± 2.41(-.27 to -.16). Financial returns become increasingly positive across quasi-experimental, nonexperimental, and modeled studies: 1.12 ± 2.16 (1.11-1.14), 1.61 ± 0.91 (1.56-1.65), and 2.05 ± 0.88 (2.04-2.06), respectively. Overall, mean weighted ROI in workplace health promotion demonstrated a positive ROI. Higher methodological quality studies provided evidence of smaller financial returns. Methodological quality and study design are important determinants.

A symmetric multivariate leakage correction for MEG connectomes

PubMed Central

Colclough, G.L.; Brookes, M.J.; Smith, S.M.; Woolrich, M.W.

2015-01-01

Ambiguities in the source reconstruction of magnetoencephalographic (MEG) measurements can cause spurious correlations between estimated source time-courses. In this paper, we propose a symmetric orthogonalisation method to correct for these artificial correlations between a set of multiple regions of interest (ROIs). This process enables the straightforward application of network modelling methods, including partial correlation or multivariate autoregressive modelling, to infer connectomes, or functional networks, from the corrected ROIs. Here, we apply the correction to simulated MEG recordings of simple networks and to a resting-state dataset collected from eight subjects, before computing the partial correlations between power envelopes of the corrected ROItime-courses. We show accurate reconstruction of our simulated networks, and in the analysis of real MEGresting-state connectivity, we find dense bilateral connections within the motor and visual networks, together with longer-range direct fronto-parietal connections. PMID:25862259

Natural scene logo recognition by joint boosting feature selection in salient regions

NASA Astrophysics Data System (ADS)

Fan, Wei; Sun, Jun; Naoi, Satoshi; Minagawa, Akihiro; Hotta, Yoshinobu

2011-01-01

Logos are considered valuable intellectual properties and a key component of the goodwill of a business. In this paper, we propose a natural scene logo recognition method which is segmentation-free and capable of processing images extremely rapidly and achieving high recognition rates. The classifiers for each logo are trained jointly, rather than independently. In this way, common features can be shared across multiple classes for better generalization. To deal with large range of aspect ratio of different logos, a set of salient regions of interest (ROI) are extracted to describe each class. We ensure the selected ROIs to be both individually informative and two-by-two weakly dependant by a Class Conditional Entropy Maximization criteria. Experimental results on a large logo database demonstrate the effectiveness and efficiency of our proposed method.

Assessing vertebral fracture risk on volumetric quantitative computed tomography by geometric characterization of trabecular bone structure

NASA Astrophysics Data System (ADS)

Checefsky, Walter A.; Abidin, Anas Z.; Nagarajan, Mahesh B.; Bauer, Jan S.; Baum, Thomas; Wismüller, Axel

2016-03-01

The current clinical standard for measuring Bone Mineral Density (BMD) is dual X-ray absorptiometry, however more recently BMD derived from volumetric quantitative computed tomography has been shown to demonstrate a high association with spinal fracture susceptibility. In this study, we propose a method of fracture risk assessment using structural properties of trabecular bone in spinal vertebrae. Experimental data was acquired via axial multi-detector CT (MDCT) from 12 spinal vertebrae specimens using a whole-body 256-row CT scanner with a dedicated calibration phantom. Common image processing methods were used to annotate the trabecular compartment in the vertebral slices creating a circular region of interest (ROI) that excluded cortical bone for each slice. The pixels inside the ROI were converted to values indicative of BMD. High dimensional geometrical features were derived using the scaling index method (SIM) at different radii and scaling factors (SF). The mean BMD values within the ROI were then extracted and used in conjunction with a support vector machine to predict the failure load of the specimens. Prediction performance was measured using the root-mean-square error (RMSE) metric and determined that SIM combined with mean BMD features (RMSE = 0.82 +/- 0.37) outperformed MDCT-measured mean BMD (RMSE = 1.11 +/- 0.33) (p < 10-4). These results demonstrate that biomechanical strength prediction in vertebrae can be significantly improved through the use of SIM-derived texture features from trabecular bone.

Markerless attenuation correction for carotid MRI surface receiver coils in combined PET/MR imaging

NASA Astrophysics Data System (ADS)

Eldib, Mootaz; Bini, Jason; Robson, Philip M.; Calcagno, Claudia; Faul, David D.; Tsoumpas, Charalampos; Fayad, Zahi A.

2015-06-01

The purpose of the study was to evaluate the effect of attenuation of MR coils on quantitative carotid PET/MR exams. Additionally, an automated attenuation correction method for flexible carotid MR coils was developed and evaluated. The attenuation of the carotid coil was measured by imaging a uniform water phantom injected with 37 MBq of 18F-FDG in a combined PET/MR scanner for 24 min with and without the coil. In the same session, an ultra-short echo time (UTE) image of the coil on top of the phantom was acquired. Using a combination of rigid and non-rigid registration, a CT-based attenuation map was registered to the UTE image of the coil for attenuation and scatter correction. After phantom validation, the effect of the carotid coil attenuation and the attenuation correction method were evaluated in five subjects. Phantom studies indicated that the overall loss of PET counts due to the coil was 6.3% with local region-of-interest (ROI) errors reaching up to 18.8%. Our registration method to correct for attenuation from the coil decreased the global error and local error (ROI) to 0.8% and 3.8%, respectively. The proposed registration method accurately captured the location and shape of the coil with a maximum spatial error of 2.6 mm. Quantitative analysis in human studies correlated with the phantom findings, but was dependent on the size of the ROI used in the analysis. MR coils result in significant error in PET quantification and thus attenuation correction is needed. The proposed strategy provides an operator-free method for attenuation and scatter correction for a flexible MRI carotid surface coil for routine clinical use.

Generating region proposals for histopathological whole slide image retrieval.

PubMed

Ma, Yibing; Jiang, Zhiguo; Zhang, Haopeng; Xie, Fengying; Zheng, Yushan; Shi, Huaqiang; Zhao, Yu; Shi, Jun

2018-06-01

Content-based image retrieval is an effective method for histopathological image analysis. However, given a database of huge whole slide images (WSIs), acquiring appropriate region-of-interests (ROIs) for training is significant and difficult. Moreover, histopathological images can only be annotated by pathologists, resulting in the lack of labeling information. Therefore, it is an important and challenging task to generate ROIs from WSI and retrieve image with few labels. This paper presents a novel unsupervised region proposing method for histopathological WSI based on Selective Search. Specifically, the WSI is over-segmented into regions which are hierarchically merged until the WSI becomes a single region. Nucleus-oriented similarity measures for region mergence and Nucleus-Cytoplasm color space for histopathological image are specially defined to generate accurate region proposals. Additionally, we propose a new semi-supervised hashing method for image retrieval. The semantic features of images are extracted with Latent Dirichlet Allocation and transformed into binary hashing codes with Supervised Hashing. The methods are tested on a large-scale multi-class database of breast histopathological WSIs. The results demonstrate that for one WSI, our region proposing method can generate 7.3 thousand contoured regions which fit well with 95.8% of the ROIs annotated by pathologists. The proposed hashing method can retrieve a query image among 136 thousand images in 0.29 s and reach precision of 91% with only 10% of images labeled. The unsupervised region proposing method can generate regions as predictions of lesions in histopathological WSI. The region proposals can also serve as the training samples to train machine-learning models for image retrieval. The proposed hashing method can achieve fast and precise image retrieval with small amount of labels. Furthermore, the proposed methods can be potentially applied in online computer-aided-diagnosis systems. Copyright © 2018 Elsevier B.V. All rights reserved.

Land-use change and managed aquifer recharge effects on the hydrogeochemistry of two contrasting atoll island aquifers, Roi-Namur Island, Republic of the Marshall Islands

USGS Publications Warehouse

Hejazian, Mehrdad; Gurdak, Jason J.; Swarzenski, Peter W.; Odigie, Kingsley; Storlazzi, Curt

2017-01-01

Freshwater resources on low-lying atoll islands are highly vulnerable to climate change and sea-level rise. In addition to rainwater catchment, groundwater in the freshwater lens is a critically important water resource on many atoll islands, especially during drought. Although many atolls have high annual rainfall rates, dense natural vegetation and high evapotranspiration rates can limit recharge to the freshwater lens. Here we evaluate the effects of land-use/land-cover change and managed aquifer recharge on the hydrogeochemistry and supply of groundwater on Roi-Namur Island, Republic of the Marshall Islands. Roi-Namur is an artificially conjoined island that has similar hydrogeology on the Roi and Namur lobes, but has contrasting land-use/land-cover and managed aquifer recharge only on Roi. Vegetation removal and managed aquifer recharge operations have resulted in an estimated 8.6 x 105 m3 of potable groundwater in the freshwater lens on Roi, compared to only 1.6 x 104 m3 on Namur. We use groundwater samples from a suite of 33 vertically nested monitoring wells, statistical testing, and geochemical modeling using PHREEQC to show that the differences in land-use/land-cover and managed aquifer recharge on Roi and Namur have a statistically significant effect on several groundwater-quality parameters and the controlling geochemical processes. Results also indicate a seven-fold reduction in the dissolution of carbonate rock in the freshwater lens and overlying vadose zone of Roi compared to Namur. Mixing of seawater and the freshwater lens is a more dominant hydrogeochemical process on Roi because of the greater recharge and flushing of the aquifer with freshwater as compared to Namur. In contrast, equilibrium processes and dissolution-precipitation non-equilibrium reactions are more dominant on Namur because of the longer residence times relative to the rate of geochemical reactions. Findings from Roi-Namur Island support selective land-use/land-cover change and managed aquifer recharge as a promising management approach for communities on other low-lying atoll islands to increase the resilience of their groundwater supplies and help them adapt to future climate change related stresses.

Genetic and environmental influences on the size of specific brain regions in midlife: the VETSA MRI study.

PubMed

Kremen, William S; Prom-Wormley, Elizabeth; Panizzon, Matthew S; Eyler, Lisa T; Fischl, Bruce; Neale, Michael C; Franz, Carol E; Lyons, Michael J; Pacheco, Jennifer; Perry, Michele E; Stevens, Allison; Schmitt, J Eric; Grant, Michael D; Seidman, Larry J; Thermenos, Heidi W; Tsuang, Ming T; Eisen, Seth A; Dale, Anders M; Fennema-Notestine, Christine

2010-01-15

The impact of genetic and environmental factors on human brain structure is of great importance for understanding normative cognitive and brain aging as well as neuropsychiatric disorders. However, most studies of genetic and environmental influences on human brain structure have either focused on global measures or have had samples that were too small for reliable estimates. Using the classical twin design, we assessed genetic, shared environmental, and individual-specific environmental influences on individual differences in the size of 96 brain regions of interest (ROIs). Participants were 474 middle-aged male twins (202 pairs; 70 unpaired) in the Vietnam Era Twin Study of Aging (VETSA). They were 51-59 years old, and were similar to U.S. men in their age range in terms of sociodemographic and health characteristics. We measured thickness of cortical ROIs and volume of other ROIs. On average, genetic influences accounted for approximately 70% of the variance in the volume of global, subcortical, and ventricular ROIs and approximately 45% of the variance in the thickness of cortical ROIs. There was greater variability in the heritability of cortical ROIs (0.00-0.75) as compared with subcortical and ventricular ROIs (0.48-0.85). The results did not indicate lateralized heritability differences or greater genetic influences on the size of regions underlying higher cognitive functions. The findings provide key information for imaging genetic studies and other studies of brain phenotypes and endophenotypes. Longitudinal analysis will be needed to determine whether the degree of genetic and environmental influences changes for different ROIs from midlife to later life.

AICHA: An atlas of intrinsic connectivity of homotopic areas.

PubMed

Joliot, Marc; Jobard, Gaël; Naveau, Mikaël; Delcroix, Nicolas; Petit, Laurent; Zago, Laure; Crivello, Fabrice; Mellet, Emmanuel; Mazoyer, Bernard; Tzourio-Mazoyer, Nathalie

2015-10-30

Atlases of brain anatomical ROIs are widely used for functional MRI data analysis. Recently, it was proposed that an atlas of ROIs derived from a functional brain parcellation could be advantageous, in particular for understanding how different regions share information. However, functional atlases so far proposed do not account for a crucial aspect of cerebral organization, namely homotopy, i.e. that each region in one hemisphere has a homologue in the other hemisphere. We present AICHA (for Atlas of Intrinsic Connectivity of Homotopic Areas), a functional brain ROIs atlas based on resting-state fMRI data acquired in 281 individuals. AICHA ROIs cover the whole cerebrum, each having 1-homogeneity of its constituting voxels intrinsic activity, and 2-a unique homotopic contralateral counterpart with which it has maximal intrinsic connectivity. AICHA was built in 4 steps: (1) estimation of resting-state networks (RSNs) using individual resting-state fMRI independent components, (2) k-means clustering of voxel-wise group level profiles of connectivity, (3) homotopic regional grouping based on maximal inter-hemispheric functional correlation, and (4) ROI labeling. AICHA includes 192 homotopic region pairs (122 gyral, 50 sulcal, and 20 gray nuclei). As an application, we report inter-hemispheric (homotopic and heterotopic) and intra-hemispheric connectivity patterns at different sparsities. ROI functional homogeneity was higher for AICHA than for anatomical ROI atlases, but slightly lower than for another functional ROI atlas not accounting for homotopy. AICHA is ideally suited for intrinsic/effective connectivity analyses, as well as for investigating brain hemispheric specialization. Copyright © 2015 Elsevier B.V. All rights reserved.

The Profitability Analysis of PT. Garuda Indonesia (Persero) Tbk. Before and After Privatization

NASA Astrophysics Data System (ADS)

Nurasiah, I.; Anggara

2017-03-01

This study purposes to determine differences in the profitability of PT. Garuda Indonesia (Persero) Tbk. before and after privatization using Net Profit Margin (NPM), Return on Investmen (ROI) and Return on Equity (ROE). This research used a case study method with a qualitative approach. The data used are secondary data from official financial statements of PT. Garuda Indonesia (Persero) Tbk. periode 2008-2013, 3 years before privatization and 3 years after privatization. Data analysis was performed by reviewing the financial statement data, calculate & determine the value of profitability ratios before and after privatization, and determine the amount of the average difference before and after privatization. The result proved that the average ratio of profitability calculated by applying NPM, ROI and ROE in every year shows a decrease that caused imbalance components forming of NPM, ROI, ROE, where profit is getting down while the selling, total assets and equity increase more and more from the previous period. The implication for the next research is a research that focus on determine how long a company can emerged from the crisis by privatization decision.

Comparison of glass slides and various digital-slide modalities for cytopathology screening and interpretation.

PubMed

Hanna, Matthew G; Monaco, Sara E; Cuda, Jacqueline; Xing, Juan; Ahmed, Ishtiaque; Pantanowitz, Liron

2017-09-01

Whole-slide imaging in cytology is limited when glass slides are digitized without z-stacks for focusing. Different vendors have started to provide z-stacking solutions to overcome this limitation. The Panoptiq imaging system allows users to create digital files combining low-magnification panoramic images with regions of interest (ROIs) that are imaged with high-magnification z-stacks. The aim of this study was to compare such panoramic images with conventional whole-slide images and glass slides for the tasks of screening and interpretation in cytopathology. Thirty glass slides, including 10 ThinPrep Papanicolaou tests and 20 nongynecologic cytology cases, were digitized with an Olympus BX45 integrated microscope with an attached Prosilica GT camera. ViewsIQ software was used for image acquisition and viewing. These glass slides were also scanned on an Aperio ScanScope XT at ×40 (0.25 μm/pixel) with 1 z-plane and were viewed with ImageScope software. Digital and glass sides were screened and dotted/annotated by a cytotechnologist and were subsequently reviewed by 3 cytopathologists. For panoramic images, the cytotechnologist manually created digital maps and selected representative ROIs to generate z-stacks at a higher magnification. After 3-week washout periods, panoramic images were compared with Aperio digital slides and glass slides. The Panoptiq system permitted fine focusing of thick smears and cell clusters. In comparison with glass slides, the average screening times were 5.5 and 1.8 times longer with Panoptiq and Aperio images, respectively, but this improved with user experience. There was no statistical difference in diagnostic concordance between all 3 modalities. Users' diagnostic confidence was also similar for all modalities. The Aperio whole-slide scanner with 1 z-plane scanning and the Panoptiq imaging system with z-stacking are both suitable for cytopathology screening and interpretation. However, ROI z-stacks do offer a superior mechanism for overcoming focusing problems commonly encountered with digital cytology slides. Unlike whole-slide imaging, the acquisition of representative z-stack images with the Panoptiq system requires a trained cytologist to create digital files. Cancer Cytopathol 2017;125:701-9. © 2017 American Cancer Society. © 2017 American Cancer Society.

Local ROI Reconstruction via Generalized FBP and BPF Algorithms along More Flexible Curves

PubMed Central

Ye, Yangbo; Zhao, Shiying; Wang, Ge

2006-01-01

We study the local region-of-interest (ROI) reconstruction problem, also referred to as the local CT problem. Our scheme includes two steps: (a) the local truncated normal-dose projections are extended to global dataset by combining a few global low-dose projections; (b) the ROI are reconstructed by either the generalized filtered backprojection (FBP) or backprojection-filtration (BPF) algorithms. The simulation results show that both the FBP and BPF algorithms can reconstruct satisfactory results with image quality in the ROI comparable to that of the corresponding global CT reconstruction. PMID:23165018

Automated detection of periventricular veins on 7 T brain MRI

NASA Astrophysics Data System (ADS)

Kuijf, Hugo J.; Bouvy, Willem H.; Zwanenburg, Jaco J. M.; Viergever, Max A.; Biessels, Geert Jan; Vincken, Koen L.

2015-03-01

Cerebral small vessel disease is common in elderly persons and a leading cause of cognitive decline, dementia, and acute stroke. With the introduction of ultra-high field strength 7.0T MRI, it is possible to visualize small vessels in the brain. In this work, a proof-of-principle study is conducted to assess the feasibility of automatically detecting periventricular veins. Periventricular veins are organized in a fan-pattern and drain venous blood from the brain towards the caudate vein of Schlesinger, which is situated along the lateral ventricles. Just outside this vein, a region-of- interest (ROI) through which all periventricular veins must cross is defined. Within this ROI, a combination of the vesselness filter, tubular tracking, and hysteresis thresholding is applied to locate periventricular veins. All detected locations were evaluated by an expert human observer. The results showed a positive predictive value of 88% and a sensitivity of 95% for detecting periventricular veins. The proposed method shows good results in detecting periventricular veins in the brain on 7.0T MR images. Compared to previous works, that only use a 1D or 2D ROI and limited image processing, our work presents a more comprehensive definition of the ROI, advanced image processing techniques to detect periventricular veins, and a quantitative analysis of the performance. The results of this proof-of-principle study are promising and will be used to assess periventricular veins on 7.0T brain MRI.

Segmentation of the Canine Corpus Callosum using Diffusion Tensor Imaging Tractography

PubMed Central

Pierce, T.T.; Calabrese, E.; White, L.E.; Chen, S.D.; Platt, S.R.; Provenzale, J.M.

2014-01-01

Background We set out to determine functional white matter (WM) connections passing through the canine corpus callosum useful for subsequent studies of canine brains that serve as models for human WM pathway disease. Based on prior studies, we anticipated that the anterior corpus callosum would send projections to the anterior cerebral cortex while progressively posterior segments would send projections to more posterior cortex. Methods A post mortem canine brain was imaged using a 7T MRI producing 100 micron isotropic resolution DTI analyzed by tractography. Using ROIs within cortical locations, which were confirmed by a Nissl stain that identified distinct cortical architecture, we successfully identified 6 important WM pathways. We also compared fractional anisotropy (FA), apparent diffusion coefficient (ADC), radial diffusivity (RD), and axial diffusivity (AD) in tracts passing through the genu and splenium. Results Callosal fibers were organized based upon cortical destination, i.e. fibers from the genu project to the frontal cortex. Histologic results identified the motor cortex based on cytoarchitectonic criteria that allowed placement of ROIs to discriminate between frontal and parietal lobes. We also identified cytoarchitecture typical of the orbital frontal, anterior frontal, and occipital regions and placed ROIs accordingly. FA, ADC, RD and AD values were all higher in posterior corpus callosum fiber tracts. Conclusions Using 6 cortical ROIs, we identified 6 major white matter tracts that reflect major functional divisions of the cerebral hemispheres and we derived quantitative values that can be used for study of canine models of human WM pathological states. PMID:24370161

Dose reduction in fluoroscopic interventions using a combination of a region of interest (ROI) x-ray attenuator and spatially different, temporally variable temporal filtering

NASA Astrophysics Data System (ADS)

Swetadri Vasan, S. N.; Pope, Liza; Ionita, Ciprian N.; Titus, A. H.; Bednarek, D. R.; Rudin, S.

2013-03-01

A novel dose reduction technique for fluoroscopic interventions involving a combination of a material x-ray region of interest (ROI) attenuator and spatially different, temporally variable ROI temporal recursive filter, was used to guide the catheter to the ROI in three live animal studies, two involving rabbits and one involving a sheep. In the two rabbit studies presented , a catheter was guided to the entrance of the carotid artery. With the added ROI attenuator the image under the high attenuation region is very noisy. By using temporal filtering with a filter weight of 0.6 on previous frames, the noise is reduced. In the sheep study the catheter was guided to the descending aorta of the animal. The sheep offered a relatively higher attenuation to the incident x-rays and thus a higher temporal filter weight of 0.8 on previous frames was used during the procedure to reduce the noise to levels acceptable by the interventionalist. The image sequences from both studies show that significant dose reduction of 5-6 times can be achieved with acceptable image quality outside the ROI by using the above mentioned technique. Even though the temporal filter weighting outside the ROI is higher, the consequent lag does not prevent perception of catheter movement.

Monitoring worksite clinic performance using a cost-benefit tool.

PubMed

Tao, Xuguang; Chenoweth, David; Alfriend, Amy S; Baron, David M; Kirkland, Tracie W; Scherb, Jill; Bernacki, Edward J

2009-10-01

The purpose of this study was to explore the usefulness of continuously assessing the return on investment (ROI) of worksite medical clinics as a means of evaluating clinic performance. Visit data from January 1, 2007, to December 31, 2008, were collected from all the on-site clinics operated for the Pepsi Bottling Group. An average system-wide ROI was calculated from the time of each clinic's opening and throughout the study period. A multivariate linear regression model was used to determine the association of average ROI with penetration/utilization rate and plant size. A total of 26 on-site clinics were actively running as of December 2008. The average ROI at the time of start up was 0.4, which increased to 1.2 at approximately 4 months and 1.6 at the end of the first year of operation. Overall, it seems that the cost of operating a clinic becomes equal to the cost of similar care purchased in the community (ROI = 1) at approximately 3 months after a clinic's opening and flattens out at the end of the first year. The magnitude of the ROI was closely related to the number of visits (a function of the penetration/utilization rate) and the size of the plant population served. Serial monitoring of ROIs is a useful metric in assessing on-site clinic performance and quantifying the effect of new initiatives aimed at increasing a clinic's cost effectiveness.

Insight into the messenger role of reactive oxygen intermediates in immunostimulated hemocytes from the scallop Argopecten purpuratus.

PubMed

Oyanedel, Daniel; Gonzalez, Roxana; Brokordt, Katherina; Schmitt, Paulina; Mercado, Luis

2016-12-01

Reactive oxygen intermediates (ROI) are metabolites produced by aerobic cells which have been linked to oxidative stress. Evidence reported in vertebrates indicates that ROI can also act as messengers in a variety of cellular signaling pathways, including those involved in innate immunity. In a recent study, an inhibitor of NF-kB transcription factors was identified in the scallop Argopecten purpuratus, and its functional characterization suggested that it may regulate the expression of the big defensin antimicrobial peptide ApBD1. In order to give new insights into the messenger role of ROI in the immune response of bivalve mollusks, the effect of ROI production on gene transcription of ApBD1 was assessed in A. purpuratus. The results showed that 48 h-cultured hemocytes were able to display phagocytic activity and ROI production in response to the β-glucan zymosan. The immune stimulation also induced the transcription of ApBD1, which was upregulated in cultured hemocytes. After neutralizing the ROI produced by the stimulated hemocytes with the antioxidant trolox, the transcription of ApBD1 was reduced near to base levels. The results suggest a potential messenger role of intracellular ROI on the regulation of ApBD1 transcription during the immune response of scallops. Copyright © 2016 Elsevier Ltd. All rights reserved.

Implementing a Process to Measure Return on Investment for Nursing Professional Development.

PubMed

Garrison, Elisabeth; Beverage, Jodie

Return on investment (ROI) is one way to quantify the value that nursing professional development brings to the organization. This article describes a process to begin tracking ROI for nursing professional development. Implementing a process of tracking nursing professional development practitioners' ROI increased awareness of the financial impact and effectiveness of the department.

Return on Investment: A Placebo for the Chief Financial Officer... and Other Paradoxes

ERIC Educational Resources Information Center

Andru, Peter; Botchkarev, Alexei

2011-01-01

Background: Return on investment (ROI) is one of the most popular evaluation metrics. ROI analysis (when applied correctly) is a powerful tool of evaluating existing information systems and making informed decisions on the acquisitions. However, practical use of the ROI is complicated by a number of uncertainties and controversies. The article…

RFID Benefits; Looking Beyond ROI

DTIC Science & Technology

2005-12-01

NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA MBA PROFESSIONAL REPORT RFID Benefits ; Looking Beyond ROI By: Shane... Benefits ; Looking Beyond ROI 6. AUTHOR(S) Shane Guilford and Mark Kutis 5. FUNDING NUMBERS 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Naval... benefits are being realized in RFID initiatives that are not being captured by traditional Return on Investment analysis. Utilizing the Naval Supply

SU-G-206-01: A Fully Automated CT Tool to Facilitate Phantom Image QA for Quantitative Imaging in Clinical Trials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wahi-Anwar, M; Lo, P; Kim, H

Purpose: The use of Quantitative Imaging (QI) methods in Clinical Trials requires both verification of adherence to a specified protocol and an assessment of scanner performance under that protocol, which are currently accomplished manually. This work introduces automated phantom identification and image QA measure extraction towards a fully-automated CT phantom QA system to perform these functions and facilitate the use of Quantitative Imaging methods in clinical trials. Methods: This study used a retrospective cohort of CT phantom scans from existing clinical trial protocols - totaling 84 phantoms, across 3 phantom types using various scanners and protocols. The QA system identifiesmore » the input phantom scan through an ensemble of threshold-based classifiers. Each classifier - corresponding to a phantom type - contains a template slice, which is compared to the input scan on a slice-by-slice basis, resulting in slice-wise similarity metric values for each slice compared. Pre-trained thresholds (established from a training set of phantom images matching the template type) are used to filter the similarity distribution, and the slice with the most optimal local mean similarity, with local neighboring slices meeting the threshold requirement, is chosen as the classifier’s matched slice (if it existed). The classifier with the matched slice possessing the most optimal local mean similarity is then chosen as the ensemble’s best matching slice. If the best matching slice exists, image QA algorithm and ROIs corresponding to the matching classifier extracted the image QA measures. Results: Automated phantom identification performed with 84.5% accuracy and 88.8% sensitivity on 84 phantoms. Automated image quality measurements (following standard protocol) on identified water phantoms (n=35) matched user QA decisions with 100% accuracy. Conclusion: We provide a fullyautomated CT phantom QA system consistent with manual QA performance. Further work will include parallel component to automatically verify image acquisition parameters and automated adherence to specifications. Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics; NIH Grant support from: U01 CA181156.« less

3D Reconstruction of Coronary Artery Vascular Smooth Muscle Cells

PubMed Central

Luo, Tong; Chen, Huan; Kassab, Ghassan S.

2016-01-01

Aims The 3D geometry of individual vascular smooth muscle cells (VSMCs), which are essential for understanding the mechanical function of blood vessels, are currently not available. This paper introduces a new 3D segmentation algorithm to determine VSMC morphology and orientation. Methods and Results A total of 112 VSMCs from six porcine coronary arteries were used in the analysis. A 3D semi-automatic segmentation method was developed to reconstruct individual VSMCs from cell clumps as well as to extract the 3D geometry of VSMCs. A new edge blocking model was introduced to recognize cell boundary while an edge growing was developed for optimal interpolation and edge verification. The proposed methods were designed based on Region of Interest (ROI) selected by user and interactive responses of limited key edges. Enhanced cell boundary features were used to construct the cell’s initial boundary for further edge growing. A unified framework of morphological parameters (dimensions and orientations) was proposed for the 3D volume data. Virtual phantom was designed to validate the tilt angle measurements, while other parameters extracted from 3D segmentations were compared with manual measurements to assess the accuracy of the algorithm. The length, width and thickness of VSMCs were 62.9±14.9μm, 4.6±0.6μm and 6.2±1.8μm (mean±SD). In longitudinal-circumferential plane of blood vessel, VSMCs align off the circumferential direction with two mean angles of -19.4±9.3° and 10.9±4.7°, while an out-of-plane angle (i.e., radial tilt angle) was found to be 8±7.6° with median as 5.7°. Conclusions A 3D segmentation algorithm was developed to reconstruct individual VSMCs of blood vessel walls based on optical image stacks. The results were validated by a virtual phantom and manual measurement. The obtained 3D geometries can be utilized in mathematical models and leads a better understanding of vascular mechanical properties and function. PMID:26882342

Modelling probabilities of heavy precipitation by regional approaches

NASA Astrophysics Data System (ADS)

Gaal, L.; Kysely, J.

2009-09-01

Extreme precipitation events are associated with large negative consequences for human society, mainly as they may trigger floods and landslides. The recent series of flash floods in central Europe (affecting several isolated areas) on June 24-28, 2009, the worst one over several decades in the Czech Republic as to the number of persons killed and the extent of damage to buildings and infrastructure, is an example. Estimates of growth curves and design values (corresponding e.g. to 50-yr and 100-yr return periods) of precipitation amounts, together with their uncertainty, are important in hydrological modelling and other applications. The interest in high quantiles of precipitation distributions is also related to possible climate change effects, as climate model simulations tend to project increased severity of precipitation extremes in a warmer climate. The present study compares - in terms of Monte Carlo simulation experiments - several methods to modelling probabilities of precipitation extremes that make use of ‘regional approaches’: the estimation of distributions of extremes takes into account data in a ‘region’ (‘pooling group’), in which one may assume that the distributions at individual sites are identical apart from a site-specific scaling factor (the condition is referred to as ‘regional homogeneity’). In other words, all data in a region - often weighted in some way - are taken into account when estimating the probability distribution of extremes at a given site. The advantage is that sampling variations in the estimates of model parameters and high quantiles are to a large extent reduced compared to the single-site analysis. We focus on the ‘region-of-influence’ (ROI) method which is based on the identification of unique pooling groups (forming the database for the estimation) for each site under study. The similarity of sites is evaluated in terms of a set of site attributes related to the distributions of extremes. The issue of the size of the region is linked with a built-in test on regional homogeneity of data. Once a pooling group is delineated, weights based on a dissimilarity measure are assigned to individual sites involved in a pooling group, and all (weighted) data are employed in the estimation of model parameters and high quantiles at a given location. The ROI method is compared with the Hosking-Wallis (HW) regional frequency analysis, which is based on delineating fixed regions (instead of flexible pooling groups) and assigning unit weights to all sites in a region. The comparison of the performance of the individual regional models makes use of data on annual maxima of 1-day precipitation amounts at 209 stations covering the Czech Republic, with altitudes ranging from 150 to 1490 m a.s.l. We conclude that the ROI methodology is superior to the HW analysis, particularly for very high quantiles (100-yr return values). Another advantage of the ROI approach is that subjective decisions - unavoidable when fixed regions in the HW analysis are formed - may efficiently be suppressed, and almost all settings of the ROI method may be justified by results of the simulation experiments. The differences between (any) regional method and single-site analysis are very pronounced and suggest that the at-site estimation is highly unreliable. The ROI method is then applied to estimate high quantiles of precipitation amounts at individual sites. The estimates and their uncertainty are compared with those from a single-site analysis. We focus on the eastern part of the Czech Republic, i.e. an area with complex orography and a particularly pronounced role of Mediterranean cyclones in producing precipitation extremes. The design values are compared with precipitation amounts recorded during the recent heavy precipitation events, including the one associated with the flash flood on June 24, 2009. We also show that the ROI methodology may easily be transferred to the analysis of precipitation extremes in climate model outputs. It efficiently reduces (random) variations in the estimates of parameters of the extreme value distributions in individual gridboxes that result from large spatial variability of heavy precipitation, and represents a straightforward tool for ‘weighting’ data from neighbouring gridboxes within the estimation procedure. The study is supported by the Grant Agency of AS CR under project B300420801.

Laser line scan underwater imaging by complementary metal-oxide-semiconductor camera

NASA Astrophysics Data System (ADS)

He, Zhiyi; Luo, Meixing; Song, Xiyu; Wang, Dundong; He, Ning

2017-12-01

This work employs the complementary metal-oxide-semiconductor (CMOS) camera to acquire images in a scanning manner for laser line scan (LLS) underwater imaging to alleviate backscatter impact of seawater. Two operating features of the CMOS camera, namely the region of interest (ROI) and rolling shutter, can be utilized to perform image scan without the difficulty of translating the receiver above the target as the traditional LLS imaging systems have. By the dynamically reconfigurable ROI of an industrial CMOS camera, we evenly divided the image into five subareas along the pixel rows and then scanned them by changing the ROI region automatically under the synchronous illumination by the fun beams of the lasers. Another scanning method was explored by the rolling shutter operation of the CMOS camera. The fun beam lasers were turned on/off to illuminate the narrow zones on the target in a good correspondence to the exposure lines during the rolling procedure of the camera's electronic shutter. The frame synchronization between the image scan and the laser beam sweep may be achieved by either the strobe lighting output pulse or the external triggering pulse of the industrial camera. Comparison between the scanning and nonscanning images shows that contrast of the underwater image can be improved by our LLS imaging techniques, with higher stability and feasibility than the mechanically controlled scanning method.

Intracranial electrical impedance tomography: a method of continuous monitoring in an animal model of head trauma.

PubMed

Manwaring, Preston K; Moodie, Karen L; Hartov, Alexander; Manwaring, Kim H; Halter, Ryan J

2013-10-01

Electrical impedance tomography (EIT) is a method that can render continuous graphical cross-sectional images of the brain's electrical properties. Because these properties can be altered by variations in water content, shifts in sodium concentration, bleeding, and mass deformation, EIT has promise as a sensitive instrument for head injury monitoring to improve early recognition of deterioration and to observe the benefits of therapeutic intervention. This study presents a swine model of head injury used to determine the detection capabilities of an inexpensive bedside EIT monitoring system with a novel intracranial pressure (ICP)/EIT electrode combination sensor on induced intraparenchymal mass effect, intraparenchymal hemorrhage, and cessation of brain blood flow. Conductivity difference images are shown in conjunction with ICP data, confirming the effects. Eight domestic piglets (3-4 weeks of age, mean 10 kg), under general anesthesia, were subjected to 4 injuries: induced intraparenchymal mass effect using an inflated, and later, deflated 0.15-mL Fogarty catheter; hemorrhage by intraparenchymal injection of 1-mL arterial blood; and ischemia/infarction by euthanasia. EIT and ICP data were recorded 10 minutes before inducing the injury until 10 minutes after injury. Continuous EIT and ICP monitoring were facilitated by a ring of circumferentially disposed cranial Ag/AgCl electrodes and 1 intraparenchymal ICP/EIT sensor electrode combination. Data were recorded at 100 Hz. Two-dimensional tomographic conductivity difference (Δσ) images, rendered using data before and after an injury, were displayed in real time on an axial circular mesh. Regions of interest (ROI) within the images were automatically selected as the upper or lower 5% of conductivity data depending on the nature of the injury. Mean Δσ within the ROIs and background were statistically analyzed. ROI Δσ was compared with the background Δσ after an injury event using an unpaired, unequal variance t test. Conductivity change within an ROI after injury was likewise compared with the same ROI before the injury making use of unpaired t tests with unequal variance. Eight animal subjects were studied, each undergoing 4 injury events including euthanasia. Changes in conductivity due to injury showed expected pathophysiologic effects in an ROI identified within the middle of the left hemisphere; this localization is reasonable given the actual site of injury (left hemisphere) and spatial warping associated with estimating a 3-dimensional conductivity distribution in 2-dimensional space. Results are shown as mean ± 1 SD. When averaged across all 8 animals, balloon inflation caused the mean Δσ within the ROI to shift by -11.4 ± 10.9 mS/m; balloon deflation by +9.4 ± 8.8 mS/m; blood injection by +19.5 ± 11.5 mS/m; death by -12.6 ± 13.2 mS/m. All induced injuries were detectable to statistical significance (P < 0.0001). This study confirms that the bedside EIT system with ICP/EIT combination sensor can detect induced trauma. Such a technique may hold promise for further research in the monitoring and management of traumatically brain-injured individuals.

Impact of the definition of peak standardized uptake value on quantification of treatment response.

PubMed

Vanderhoek, Matt; Perlman, Scott B; Jeraj, Robert

2012-01-01

PET-based treatment response assessment typically measures the change in maximum standardized uptake value (SUV(max)), which is adversely affected by noise. Peak SUV (SUV(peak)) has been recommended as a more robust alternative, but its associated region of interest (ROI(peak)) is not uniquely defined. We investigated the impact of different ROI(peak) definitions on quantification of SUV(peak) and tumor response. Seventeen patients with solid malignancies were treated with a multitargeted receptor tyrosine kinase inhibitor resulting in a variety of responses. Using the cellular proliferation marker 3'-deoxy-3'-(18)F-fluorothymidine ((18)F-FLT), whole-body PET/CT scans were acquired at baseline and during treatment. (18)F-FLT-avid lesions (∼2/patient) were segmented on PET images, and tumor response was assessed via the relative change in SUV(peak). For each tumor, 24 different SUV(peaks) were determined by changing ROI(peak) shape (circles vs. spheres), size (7.5-20 mm), and location (centered on SUV(max) vs. placed in highest-uptake region), encompassing different definitions from the literature. Within each tumor, variations in the 24 SUV(peaks) and tumor responses were measured using coefficient of variation (CV), standardized deviation (SD), and range. For each ROI(peak) definition, a population average SUV(peak) and tumor response were determined over all tumors. A substantial variation in both SUV(peak) and tumor response resulted from changing the ROI(peak) definition. The variable ROI(peak) definition led to an intratumor SUV(peak) variation ranging from 49% above to 46% below the mean (CV, 17%) and an intratumor SUV(peak) response variation ranging from 49% above to 35% below the mean (SD, 9%). The variable ROI(peak) definition led to a population average SUV(peak) variation ranging from 24% above to 28% below the mean (CV, 14%) and a population average SUV(peak) response variation ranging from only 3% above to 3% below the mean (SD, 2%). The size of ROI(peak) caused more variation in intratumor response than did the location or shape of ROI(peak). Population average tumor response was independent of size, shape, and location of ROI(peak). Quantification of individual tumor response using SUV(peak) is highly sensitive to the ROI(peak) definition, which can significantly affect the use of SUV(peak) for assessment of treatment response. Clinical trials are necessary to compare the efficacy of SUV(peak) and SUV(max) for quantification of response to therapy.

Return-on-Investment (ROI) Analyses of an Inpatient Lay Health Worker Model on 30-Day Readmission Rates in a Rural Community Hospital.

PubMed

Cardarelli, Roberto; Bausch, Gregory; Murdock, Joan; Chyatte, Michelle Renee

2017-07-07

The purpose of the study was to assess the return-on-investment (ROI) of an inpatient lay health worker (LHW) model in a rural Appalachian community hospital impacting 30-day readmission rates. The Bridges to Home (BTH) study completed an evaluation in 2015 of an inpatient LHW model in a rural Kentucky hospital that demonstrated a reduction in 30-day readmission rates by 47.7% compared to a baseline period. Using the hospital's utilization and financial data, a validated ROI calculator specific to care transition programs was used to assess the ROI of the BTH model comparing 3 types of payment models including Diagnosis Related Group (DRG)-only payments, pay-for-performance (P4P) contracts, and accountable care organizations (ACOs). The BTH program had a -$0.67 ROI if the hospital had only a DRG-based payment model. If the hospital had P4P contracts with payers and 0.1% of its annual operating revenue was at risk, the ROI increased to $7.03 for every $1 spent on the BTH program. However, if the hospital was an ACO as was the case for this study's community hospital, the ROI significantly increased to $38.48 for every $1 spent on the BTH program. The BTH model showed a viable ROI to be considered by community hospitals that are part of an ACO or P4P program. A LHW care transition model may be a cost-effective alternative for impacting excess 30-day readmissions and avoiding associated penalties for hospital systems with a value-based payment model. © 2017 National Rural Health Association.

Effect of Positioning of the ROI on BMD of the Forearm and Its Subregions.

PubMed

Rosen, Elizabeth O; McNamara, Elizabeth A; Whittaker, LaTarsha G; Malabanan, Alan O; Rosen, Harold N

2018-03-21

Inconsistent positioning of patients and region of interest (ROI) is known to influence the precision of bone mineral density (BMD) measurements in the spine and hip. However, it is unknown whether minor shifts in the positioning of the ROI along the shaft of the radius affect the measurement of forearm BMD and its subregions. The ultradistal (UD-), mid-, one-third, and total radius BMDs of 50 consecutive clinical densitometry patients were acquired. At baseline the distal end of the ROI was placed at the tip of the ulnar styloid as usual, and then the forearm was reanalyzed 10 more times, each time shifting the ROI 1 mm proximally. No corrections for multiple comparisons were necessary since the differences that were significant were significant at p < 0.001. The UD-radius BMD increased as the ROI was shifted proximally; the increase was significant when shifted even 1 mm proximally (p < 0.001). These same findings held true for the mid- and total radius bone density, though the percent increase with moving proximally was significantly greater for the UD radius than for the other subregions. However, there was no significant change in the one-third radius BMD when shifted proximally 1-10 mm. Minor proximal shifts of the forearm ROI substantially affect the BMD of the UD-, mid- and total radius, while having no effect on the one-third radius BMD. Since the one-third radius is the only forearm region usually reported, minor proximal shifts of the ROI should not influence forearm BMD results significantly. Copyright © 2018 The International Society for Clinical Densitometry. Published by Elsevier Inc. All rights reserved.

Return on Investment of a Work-Family Intervention: Evidence From the Work, Family, and Health Network.

PubMed

Barbosa, Carolina; Bray, Jeremy W; Dowd, William N; Mills, Michael J; Moen, Phyllis; Wipfli, Brad; Olson, Ryan; Kelly, Erin L

2015-09-01

To estimate the return on investment (ROI) of a workplace initiative to reduce work-family conflict in a group-randomized 18-month field experiment in an information technology firm in the United States. Intervention resources were micro-costed; benefits included medical costs, productivity (presenteeism), and turnover. Regression models were used to estimate the ROI, and cluster-robust bootstrap was used to calculate its confidence interval. For each participant, model-adjusted costs of the intervention were $690 and company savings were $1850 (2011 prices). The ROI was 1.68 (95% confidence interval, -8.85 to 9.47) and was robust in sensitivity analyses. The positive ROI indicates that employers' investment in an intervention to reduce work-family conflict can enhance their business. Although this was the first study to present a confidence interval for the ROI, results are comparable with the literature.

Estimating Return on Investment in Translational Research: Methods and Protocols

PubMed Central

Trochim, William; Dilts, David M.; Kirk, Rosalind

2014-01-01

Assessing the value of clinical and translational research funding on accelerating the translation of scientific knowledge is a fundamental issue faced by the National Institutes of Health and its Clinical and Translational Awards (CTSA). To address this issue, the authors propose a model for measuring the return on investment (ROI) of one key CTSA program, the clinical research unit (CRU). By estimating the economic and social inputs and outputs of this program, this model produces multiple levels of ROI: investigator, program and institutional estimates. A methodology, or evaluation protocol, is proposed to assess the value of this CTSA function, with specific objectives, methods, descriptions of the data to be collected, and how data are to be filtered, analyzed, and evaluated. This paper provides an approach CTSAs could use to assess the economic and social returns on NIH and institutional investments in these critical activities. PMID:23925706

Estimating return on investment in translational research: methods and protocols.

PubMed

Grazier, Kyle L; Trochim, William M; Dilts, David M; Kirk, Rosalind

2013-12-01

Assessing the value of clinical and translational research funding on accelerating the translation of scientific knowledge is a fundamental issue faced by the National Institutes of Health (NIH) and its Clinical and Translational Awards (CTSAs). To address this issue, the authors propose a model for measuring the return on investment (ROI) of one key CTSA program, the clinical research unit (CRU). By estimating the economic and social inputs and outputs of this program, this model produces multiple levels of ROI: investigator, program, and institutional estimates. A methodology, or evaluation protocol, is proposed to assess the value of this CTSA function, with specific objectives, methods, descriptions of the data to be collected, and how data are to be filtered, analyzed, and evaluated. This article provides an approach CTSAs could use to assess the economic and social returns on NIH and institutional investments in these critical activities.

Atypical white-matter microstructure in congenitally deaf adults: A region of interest and tractography study using diffusion-tensor imaging.

PubMed

Karns, Christina M; Stevens, Courtney; Dow, Mark W; Schorr, Emily M; Neville, Helen J

2017-01-01

Considerable research documents the cross-modal reorganization of auditory cortices as a consequence of congenital deafness, with remapped functions that include visual and somatosensory processing of both linguistic and nonlinguistic information. Structural changes accompany this cross-modal neuroplasticity, but precisely which specific structural changes accompany congenital and early deafness and whether there are group differences in hemispheric asymmetries remain to be established. Here, we used diffusion tensor imaging (DTI) to examine microstructural white matter changes accompanying cross-modal reorganization in 23 deaf adults who were genetically, profoundly, and congenitally deaf, having learned sign language from infancy with 26 hearing controls who participated in our previous fMRI studies of cross-modal neuroplasticity. In contrast to prior literature using a whole-brain approach, we introduce a semiautomatic method for demarcating auditory regions in which regions of interest (ROIs) are defined on the normalized white matter skeleton for all participants, projected into each participants native space, and manually constrained to anatomical boundaries. White-matter ROIs were left and right Heschl's gyrus (HG), left and right anterior superior temporal gyrus (aSTG), left and right posterior superior temporal gyrus (pSTG), as well as one tractography-defined region in the splenium of the corpus callosum connecting homologous left and right superior temporal regions (pCC). Within these regions, we measured fractional anisotropy (FA), radial diffusivity (RD), axial diffusivity (AD), and white-matter volume. Congenitally deaf adults had reduced FA and volume in white matter structures underlying bilateral HG, aSTG, pSTG, and reduced FA in pCC. In HG and pCC, this reduction in FA corresponded with increased RD, but differences in aSTG and pSTG could not be localized to alterations in RD or AD. Direct statistical tests of hemispheric asymmetries in these differences indicated the most prominent effects in pSTG, where the largest differences between groups occurred in the right hemisphere. Other regions did not show significant hemispheric asymmetries in group differences. Taken together, these results indicate that atypical white matter microstructure and reduced volume underlies regions of superior temporal primary and association auditory cortex and introduce a robust method for quantifying volumetric and white matter microstructural differences that can be applied to future studies of special populations. Published by Elsevier B.V.

A voxelwise approach to determine consensus regions-of-interest for the study of brain network plasticity.

PubMed

Rajtmajer, Sarah M; Roy, Arnab; Albert, Reka; Molenaar, Peter C M; Hillary, Frank G

2015-01-01

Despite exciting advances in the functional imaging of the brain, it remains a challenge to define regions of interest (ROIs) that do not require investigator supervision and permit examination of change in networks over time (or plasticity). Plasticity is most readily examined by maintaining ROIs constant via seed-based and anatomical-atlas based techniques, but these approaches are not data-driven, requiring definition based on prior experience (e.g., choice of seed-region, anatomical landmarks). These approaches are limiting especially when functional connectivity may evolve over time in areas that are finer than known anatomical landmarks or in areas outside predetermined seeded regions. An ideal method would permit investigators to study network plasticity due to learning, maturation effects, or clinical recovery via multiple time point data that can be compared to one another in the same ROI while also preserving the voxel-level data in those ROIs at each time point. Data-driven approaches (e.g., whole-brain voxelwise approaches) ameliorate concerns regarding investigator bias, but the fundamental problem of comparing the results between distinct data sets remains. In this paper we propose an approach, aggregate-initialized label propagation (AILP), which allows for data at separate time points to be compared for examining developmental processes resulting in network change (plasticity). To do so, we use a whole-brain modularity approach to parcellate the brain into anatomically constrained functional modules at separate time points and then apply the AILP algorithm to form a consensus set of ROIs for examining change over time. To demonstrate its utility, we make use of a known dataset of individuals with traumatic brain injury sampled at two time points during the first year of recovery and show how the AILP procedure can be applied to select regions of interest to be used in a graph theoretical analysis of plasticity.

Intraoral radiographs texture analysis for dental implant planning.

PubMed

Mundim, Mayara B V; Dias, Danilo R; Costa, Ronaldo M; Leles, Cláudio R; Azevedo-Marques, Paulo M; Ribeiro-Rotta, Rejane F

2016-11-01

Computer vision extracts features or attributes from images improving diagnosis accuracy and aiding in clinical decisions. This study aims to investigate the feasibility of using texture analysis of periapical radiograph images as a tool for dental implant treatment planning. Periapical radiograph images of 127 jawbone sites were obtained before and after implant placement. From the superimposition of the pre- and post-implant images, four regions of interest (ROI) were delineated on the pre-implant images for each implant site: mesial, distal and apical peri-implant areas and a central area. Each ROI was analysed using Matlab® software and seven image attributes were extracted: mean grey level (MGL), standard deviation of grey levels (SDGL), coefficient of variation (CV), entropy (En), contrast, correlation (Cor) and angular second moment (ASM). Images were grouped by bone types-Lekholm and Zarb classification (1,2,3,4). Peak insertion torque (PIT) and resonance frequency analysis (RFA) were recorded during implant placement. Differences among groups were tested for each image attribute. Agreement between measurements of the peri-implant ROIs and overall ROI (peri-implant + central area) was tested, as well as the association between primary stability measures (PIT and RFA) and texture attributes. Differences among bone type groups were found for MGL (p = 0.035), SDGL (p = 0.024), CV (p < 0.001) and En (p < 0.001). The apical ROI showed a significant difference from the other regions for all attributes, except Cor. Concordance correlation coefficients were all almost perfect (ρ > 0.93), except for ASM (ρ = 0.62). Texture attributes were significantly associated with the implant stability measures. Texture analysis of periapical radiographs may be a reliable non-invasive quantitative method for the assessment of jawbone and prediction of implant stability, with potential clinical applications. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

EQUIPT: protocol of a comparative effectiveness research study evaluating cross-context transferability of economic evidence on tobacco control

PubMed Central

Pokhrel, Subhash; Evers, Silvia; Leidl, Reiner; Trapero-Bertran, Marta; Kalo, Zoltan; de Vries, Hein; Crossfield, Andrea; Andrews, Fiona; Rutter, Ailsa; Coyle, Kathryn; Lester-George, Adam; West, Robert; Owen, Lesley; Jones, Teresa; Vogl, Matthias; Radu-Loghin, Cornel; Voko, Zoltan; Huic, Mirjana; Coyle, Doug

2014-01-01

Introduction Tobacco smoking claims 700 000 lives every year in Europe and the cost of tobacco smoking in the EU is estimated between €98 and €130 billion annually; direct medical care costs and indirect costs such as workday losses each represent half of this amount. Policymakers all across Europe are in need of bespoke information on the economic and wider returns of investing in evidence-based tobacco control, including smoking cessation agendas. EQUIPT is designed to test the transferability of one such economic evidence base—the English Tobacco Return on Investment (ROI) tool—to other EU member states. Methods and analysis EQUIPT is a multicentre, interdisciplinary comparative effectiveness research study in public health. The Tobacco ROI tool already developed in England by the National Institute for Health and Care Excellence (NICE) will be adapted to meet the needs of European decision-makers, following transferability criteria. Stakeholders' needs and intention to use ROI tools in sample countries (Germany, Hungary, Spain and the Netherlands) will be analysed through interviews and surveys and complemented by secondary analysis of the contextual and other factors. Informed by this contextual analysis, the next phase will develop country-specific ROI tools in sample countries using a mix of economic modelling and Visual Basic programming. The results from the country-specific ROI models will then be compared to derive policy proposals that are transferable to other EU states, from which a centralised web tool will be developed. This will then be made available to stakeholders to cater for different decision-making contexts across Europe. Ethics and dissemination The Brunel University Ethics Committee and relevant authorities in each of the participating countries approved the protocol. EQUIPT has a dedicated work package on dissemination, focusing on stakeholders’ communication needs. Results will be disseminated via peer-reviewed publications, e-learning resources and policy briefs. PMID:25421342

Quantification of dental prostheses on cone‐beam CT images by the Taguchi method

PubMed Central

Kuo, Rong‐Fu; Fang, Kwang‐Ming; TY, Wong

2016-01-01

The gray values accuracy of dental cone‐beam computed tomography (CBCT) is affected by dental metal prostheses. The distortion of dental CBCT gray values could lead to inaccuracies of orthodontic and implant treatment. The aim of this study was to quantify the effect of scanning parameters and dental metal prostheses on the accuracy of dental cone‐beam computed tomography (CBCT) gray values using the Taguchi method. Eight dental model casts of an upper jaw including prostheses, and a ninth prosthesis‐free dental model cast, were scanned by two dental CBCT devices. The mean gray value of the selected circular regions of interest (ROIs) were measured using dental CBCT images of eight dental model casts and were compared with those measured from CBCT images of the prosthesis‐free dental model cast. For each image set, four consecutive slices of gingiva were selected. The seven factors (CBCTs, occlusal plane canting, implant connection, prosthesis position, coping material, coping thickness, and types of dental restoration) were used to evaluate scanning parameter and dental prostheses effects. Statistical methods of signal to noise ratio (S/N) and analysis of variance (ANOVA) with 95% confidence were applied to quantify the effects of scanning parameters and dental prostheses on dental CBCT gray values accuracy. For ROIs surrounding dental prostheses, the accuracy of CBCT gray values were affected primarily by implant connection (42%), followed by type of restoration (29%), prostheses position (19%), coping material (4%), and coping thickness (4%). For a single crown prosthesis (without support of implants) placed in dental model casts, gray value differences for ROIs 1–9 were below 12% and gray value differences for ROIs 13–18 away from prostheses were below 10%. We found the gray value differences set to be between 7% and 8% for regions next to a single implant‐supported titanium prosthesis, and between 46% and 59% for regions between double implant‐supported, nickel‐chromium alloys (Ni‐Cr) prostheses. Quantification of the effect of prostheses and scanning parameters on dental CBCT gray values was assessed. PACS numbers: 87.59.bd, 87.57Q PMID:26894354

Return on Investment: A Cost-Effectiveness Measure for the Texas' Workforce System.

ERIC Educational Resources Information Center

Norris, Davy N.; King, Christopher T.

Texas is developing a return-on-investment (ROI) measure to assess the cost-effectiveness of work force programs. Emphasis is on issues related to conducting cross-program ROI analysis at the level of the Local Workforce Development Board (LWDB) in career or one-stop centers. To make the most appropriate and effective use of ROI, the following…

Topology optimization based design of unilateral NMR for generating a remote homogeneous field.

PubMed

Wang, Qi; Gao, Renjing; Liu, Shutian

2017-06-01

This paper presents a topology optimization based design method for the design of unilateral nuclear magnetic resonance (NMR), with which a remote homogeneous field can be obtained. The topology optimization is actualized by seeking out the optimal layout of ferromagnetic materials within a given design domain. The design objective is defined as generating a sensitive magnetic field with optimal homogeneity and maximal field strength within a required region of interest (ROI). The sensitivity of the objective function with respect to the design variables is derived and the method for solving the optimization problem is presented. A design example is provided to illustrate the utility of the design method, specifically the ability to improve the quality of the magnetic field over the required ROI by determining the optimal structural topology for the ferromagnetic poles. Both in simulations and experiments, the sensitive region of the magnetic field achieves about 2 times larger than that of the reference design, validating validates the feasibility of the design method. Copyright © 2017. Published by Elsevier Inc.

Research and implementation of finger-vein recognition algorithm

NASA Astrophysics Data System (ADS)

Pang, Zengyao; Yang, Jie; Chen, Yilei; Liu, Yin

2017-06-01

In finger vein image preprocessing, finger angle correction and ROI extraction are important parts of the system. In this paper, we propose an angle correction algorithm based on the centroid of the vein image, and extract the ROI region according to the bidirectional gray projection method. Inspired by the fact that features in those vein areas have similar appearance as valleys, a novel method was proposed to extract center and width of palm vein based on multi-directional gradients, which is easy-computing, quick and stable. On this basis, an encoding method was designed to determine the gray value distribution of texture image. This algorithm could effectively overcome the edge of the texture extraction error. Finally, the system was equipped with higher robustness and recognition accuracy by utilizing fuzzy threshold determination and global gray value matching algorithm. Experimental results on pairs of matched palm images show that, the proposed method has a EER with 3.21% extracts features at the speed of 27ms per image. It can be concluded that the proposed algorithm has obvious advantages in grain extraction efficiency, matching accuracy and algorithm efficiency.

The heritability of the functional connectome is robust to common nonlinear registration methods

NASA Astrophysics Data System (ADS)

Hafzalla, George W.; Prasad, Gautam; Baboyan, Vatche G.; Faskowitz, Joshua; Jahanshad, Neda; McMahon, Katie L.; de Zubicaray, Greig I.; Wright, Margaret J.; Braskie, Meredith N.; Thompson, Paul M.

2016-03-01

Nonlinear registration algorithms are routinely used in brain imaging, to align data for inter-subject and group comparisons, and for voxelwise statistical analyses. To understand how the choice of registration method affects maps of functional brain connectivity in a sample of 611 twins, we evaluated three popular nonlinear registration methods: Advanced Normalization Tools (ANTs), Automatic Registration Toolbox (ART), and FMRIB's Nonlinear Image Registration Tool (FNIRT). Using both structural and functional MRI, we used each of the three methods to align the MNI152 brain template, and 80 regions of interest (ROIs), to each subject's T1-weighted (T1w) anatomical image. We then transformed each subject's ROIs onto the associated resting state functional MRI (rs-fMRI) scans and computed a connectivity network or functional connectome for each subject. Given the different degrees of genetic similarity between pairs of monozygotic (MZ) and same-sex dizygotic (DZ) twins, we used structural equation modeling to estimate the additive genetic influences on the elements of the function networks, or their heritability. The functional connectome and derived statistics were relatively robust to nonlinear registration effects.

Bone age assessment meets SIFT

NASA Astrophysics Data System (ADS)

Kashif, Muhammad; Jonas, Stephan; Haak, Daniel; Deserno, Thomas M.

2015-03-01

Bone age assessment (BAA) is a method of determining the skeletal maturity and finding the growth disorder in the skeleton of a person. BAA is frequently used in pediatric medicine but also a time-consuming and cumbersome task for a radiologist. Conventionally, the Greulich and Pyle and the Tanner and Whitehouse methods are used for bone age assessment, which are based on visual comparison of left hand radiographs with a standard atlas. We present a novel approach for automated bone age assessment, combining scale invariant feature transform (SIFT) features and support vector machine (SVM) classification. In this approach, (i) data is grouped into 30 classes to represent the age range of 0- 18 years, (ii) 14 epiphyseal ROIs are extracted from left hand radiographs, (iii) multi-level image thresholding, using Otsu method, is applied to specify key points on bone and osseous tissues of eROIs, (iv) SIFT features are extracted for specified key points for each eROI of hand radiograph, and (v) classification is performed using a multi-class extension of SVM. A total of 1101 radiographs of University of Southern California are used in training and testing phases using 5- fold cross-validation. Evaluation is performed for two age ranges (0-18 years and 2-17 years) for comparison with previous work and the commercial product BoneXpert, respectively. Results were improved significantly, where the mean errors of 0.67 years and 0.68 years for the age ranges 0-18 years and 2-17 years, respectively, were obtained. Accuracy of 98.09 %, within the range of two years was achieved.

Histogram-based adaptive gray level scaling for texture feature classification of colorectal polyps

NASA Astrophysics Data System (ADS)

Pomeroy, Marc; Lu, Hongbing; Pickhardt, Perry J.; Liang, Zhengrong

2018-02-01

Texture features have played an ever increasing role in computer aided detection (CADe) and diagnosis (CADx) methods since their inception. Texture features are often used as a method of false positive reduction for CADe packages, especially for detecting colorectal polyps and distinguishing them from falsely tagged residual stool and healthy colon wall folds. While texture features have shown great success there, the performance of texture features for CADx have lagged behind primarily because of the more similar features among different polyps types. In this paper, we present an adaptive gray level scaling and compare it to the conventional equal-spacing of gray level bins. We use a dataset taken from computed tomography colonography patients, with 392 polyp regions of interest (ROIs) identified and have a confirmed diagnosis through pathology. Using the histogram information from the entire ROI dataset, we generate the gray level bins such that each bin contains roughly the same number of voxels Each image ROI is the scaled down to two different numbers of gray levels, using both an equal spacing of Hounsfield units for each bin, and our adaptive method. We compute a set of texture features from the scaled images including 30 gray level co-occurrence matrix (GLCM) features and 11 gray level run length matrix (GLRLM) features. Using a random forest classifier to distinguish between hyperplastic polyps and all others (adenomas and adenocarcinomas), we find that the adaptive gray level scaling can improve performance based on the area under the receiver operating characteristic curve by up to 4.6%.

Improved explanation of human intelligence using cortical features with second order moments and regression.

PubMed

Park, Hyunjin; Yang, Jin-ju; Seo, Jongbum; Choi, Yu-yong; Lee, Kun-ho; Lee, Jong-min

2014-04-01

Cortical features derived from magnetic resonance imaging (MRI) provide important information to account for human intelligence. Cortical thickness, surface area, sulcal depth, and mean curvature were considered to explain human intelligence. One region of interest (ROI) of a cortical structure consisting of thousands of vertices contained thousands of measurements, and typically, one mean value (first order moment), was used to represent a chosen ROI, which led to a potentially significant loss of information. We proposed a technological improvement to account for human intelligence in which a second moment (variance) in addition to the mean value was adopted to represent a chosen ROI, so that the loss of information would be less severe. Two computed moments for the chosen ROIs were analyzed with partial least squares regression (PLSR). Cortical features for 78 adults were measured and analyzed in conjunction with the full-scale intelligence quotient (FSIQ). Our results showed that 45% of the variance of the FSIQ could be explained using the combination of four cortical features using two moments per chosen ROI. Our results showed improvement over using a mean value for each ROI, which explained 37% of the variance of FSIQ using the same set of cortical measurements. Our results suggest that using additional second order moments is potentially better than using mean values of chosen ROIs for regression analysis to account for human intelligence. Copyright © 2014 Elsevier Ltd. All rights reserved.

Systematic review on the financial return of worksite health promotion programmes aimed at improving nutrition and/or increasing physical activity.

PubMed

van Dongen, J M; Proper, K I; van Wier, M F; van der Beek, A J; Bongers, P M; van Mechelen, W; van Tulder, M W

2011-12-01

This systematic review summarizes the current evidence on the financial return of worksite health promotion programmes aimed at improving nutrition and/or increasing physical activity. Data on study characteristics and results were extracted from 18 studies published up to 14 January 2011. Two reviewers independently assessed the risk of bias of included studies. Three metrics were (re-)calculated per study: the net benefits, benefit cost ratio (BCR) and return on investment (ROI). Metrics were averaged, and a post hoc subgroup analysis was performed to compare financial return estimates between study designs. Four randomized controlled trials (RCTs), 13 non-randomized studies (NRSs) and one modelling study were included. Average financial return estimates in terms of absenteeism benefits (NRS: ROI 325%, BCR 4.25; RCT: ROI -49%, BCR 0.51), medical benefits (NRS: ROI 95%, BCR 1.95; RCT: ROI -112%, BCR -0.12) or both (NRS: ROI 387%, BCR 4.87; RCT: ROI -92%, BCR 0.08) were positive in NRSs, but negative in RCTs. Worksite health promotion programmes aimed at improving nutrition and/or increasing physical activity generate financial savings in terms of reduced absenteeism costs, medical costs or both according to NRSs, whereas they do not according to RCTs. Since these programmes are associated with additional types of benefits, conclusions about their overall profitability cannot be made. © 2011 The Authors. obesity reviews © 2011 International Association for the Study of Obesity.

A cost-benefit analysis of three older adult fall prevention interventions.

PubMed

Carande-Kulis, Vilma; Stevens, Judy A; Florence, Curtis S; Beattie, Bonita L; Arias, Ileana

2015-02-01

One out of three persons aged 65 and older falls annually and 20% to 30% of falls result in injury. The purpose of this cost-benefit analysis was to identify community-based fall interventions that were feasible, effective, and provided a positive return on investment (ROI). A third-party payer perspective was used to determine the costs and benefits of three effective fall interventions. Intervention effectiveness was based on randomized controlled trial results. National data were used to estimate the average annual benefits from averting the direct medical costs of a fall. The net benefit and ROI were estimated for each of the interventions. For the Otago Exercise Program delivered to persons aged 65 and older, the net benefit was $121.85 per participant and the ROI was 36% for each dollar invested. For Otago delivered to persons aged 80 and older, the net benefit was $429.18 and the ROI was 127%. Tai chi: Moving for Better Balance had a net benefit of $529.86 and an ROI of 509% and Stepping On had a net benefit of $134.37 and an ROI of 64%. All three fall interventions provided positive net benefits. The ROIs showed that the benefits not only covered the implementation costs but also exceeded the expected direct program delivery costs. These results can help health care funders and other community organizations select appropriate and effective fall interventions that also can provide positive returns on investment. Published by Elsevier Ltd.

Improving cervical region of interest by eliminating vaginal walls and cotton-swabs for automated image analysis

NASA Astrophysics Data System (ADS)

Venkataraman, Sankar; Li, Wenjing

2008-03-01

Image analysis for automated diagnosis of cervical cancer has attained high prominence in the last decade. Automated image analysis at all levels requires a basic segmentation of the region of interest (ROI) within a given image. The precision of the diagnosis is often reflected by the precision in detecting the initial region of interest, especially when some features outside the ROI mimic the ones within the same. Work described here discusses algorithms that are used to improve the cervical region of interest as a part of automated cervical image diagnosis. A vital visual aid in diagnosing cervical cancer is the aceto-whitening of the cervix after the application of acetic acid. Color and texture are used to segment acetowhite regions within the cervical ROI. Vaginal walls along with cottonswabs sometimes mimic these essential features leading to several false positives. Work presented here is focused towards detecting in-focus vaginal wall boundaries and then extrapolating them to exclude vaginal walls from the cervical ROI. In addition, discussed here is a marker-controlled watershed segmentation that is used to detect cottonswabs from the cervical ROI. A dataset comprising 50 high resolution images of the cervix acquired after 60 seconds of acetic acid application were used to test the algorithm. Out of the 50 images, 27 benefited from a new cervical ROI. Significant improvement in overall diagnosis was observed in these images as false positives caused by features outside the actual ROI mimicking acetowhite region were eliminated.

SU-F-J-29: Dosimetric Effect of Image Registration ROI Size and Focus in Automated CBCT Registration for Spine SBRT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Magnelli, A; Smith, A; Chao, S

2016-06-15

Purpose: Spinal stereotactic body radiotherapy (SBRT) involves highly conformal dose distributions and steep dose gradients due to the proximity of the spinal cord to the treatment volume. To achieve the planning goals while limiting the spinal cord dose, patients are setup using kV cone-beam CT (kV-CBCT) with 6 degree corrections. The kV-CBCT registration with the reference CT is dependent on a user selected region of interest (ROI). The objective of this work is to determine the dosimetric impact of ROI selection. Methods: Twenty patients were selected for this study. For each patient, the kV-CBCT was registered to the reference CTmore » using three ROIs including: 1) the external body, 2) a large anatomic region, and 3) a small region focused in the target volume. Following each registration, the aligned CBCTs and contours were input to the treatment planning system for dose evaluation. The minimum dose, dose to 99% and 90% of the tumor volume (D99%, D90%), dose to 0.03cc and the dose to 10% of the spinal cord subvolume (V10Gy) were compared to the planned values. Results: The average deviations in the tumor minimum dose were 2.68%±1.7%, 4.6%±4.0%, 14.82%±9.9% for small, large and the external ROIs, respectively. The average deviations in tumor D99% were 1.15%±0.7%, 3.18%±1.7%, 10.0%±6.6%, respectively. The average deviations in tumor D90% were 1.00%±0.96%, 1.14%±1.05%, 3.19%±4.77% respectively. The average deviations in the maximum dose to the spinal cord were 2.80%±2.56%, 7.58%±8.28%, 13.35%±13.14%, respectively. The average deviation in V10Gy to the spinal cord were 1.69%±0.88%, 1.98%±2.79%, 2.71%±5.63%. Conclusion: When using automated registration algorithms for CBCT-Reference alignment, a small target-focused ROI results in the least dosimetric deviation from the plan. It is recommended to focus narrowly on the target volume to keep the spinal cord dose below tolerance.« less

SU-D-303-03: Impact of Uncertainty in T1 Measurements On Quantification of Dynamic Contrast Enhanced MRI

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aryal, M; Cao, Y

2015-06-15

Purpose: Quantification of dynamic contrast enhanced (DCE) MRI requires native longitudinal relaxation time (T1) measurement. This study aimed to assess uncertainty in T1 measurements using two different methods. Methods and Materials: Brain MRI scans were performed on a 3T scanner in 9 patients who had low grade/benign tumors and partial brain radiotherapy without chemotherapy at pre-RT, week-3 during RT (wk-3), end-RT, and 1, 6 and 18 months after RT. T1-weighted images were acquired using gradient echo sequences with 1) 2 different flip angles (50 and 150), and 2) 5 variable TRs (100–2000ms). After creating quantitative T1 maps, average T1 wasmore » calculated in regions of interest (ROI), which were distant from tumors and received a total of accumulated radiation doses < 5 Gy at wk-3. ROIs included left and right normal Putamen and Thalamus (gray matter: GM), and frontal and parietal white matter (WM). Since there were no significant or even a trend of T1 changes from pre-RT to wk-3 in these ROIs, a relative repeatability coefficient (RC) of T1 as a measure of uncertainty was estimated in each ROI using the data pre-RT and at wk-3. The individual T1 changes at later time points were evaluated compared to the estimated RCs. Results: The 2-flip angle method produced small RCs in GM (9.7–11.7%) but large RCs in WM (12.2–13.6%) compared to the saturation-recovery (SR) method (11.0–17.7% for GM and 7.5–11.2% for WM). More than 81% of individual T1 changes were within T1 uncertainty ranges defined by RCs. Conclusion: Our study suggests that the impact of T1 uncertainty on physiological parameters derived from DCE MRI is not negligible. A short scan with 2 flip angles is able to achieve repeatability of T1 estimates similar to a long scan with 5 different TRs, and is desirable to be integrated in the DCE protocol. Present study was supported by National Institute of Health (NIH) under grant numbers; UO1 CA183848 and RO1 NS064973.« less

Returns to Investment in Ontario University Education, 1960-1990, and Implications for Tuition Fee Policy. Discussion Series, Issue 5.

ERIC Educational Resources Information Center

Stager, David A. A.

This analysis of Ontario's returns to investment and implications for tuition fee policy updates a 1989 publication titled "Focus on Fees." The paper examines: data on public and private return on investment (ROI) from university education, pattern of ROI rates over time, and impact of tuition fee levels on estimated ROI for various…

Supplemental Analysis on Compressed Sensing Based Interior Tomography

PubMed Central

Yu, Hengyong; Yang, Jiansheng; Jiang, Ming; Wang, Ge

2010-01-01

Recently, in the compressed sensing framework we proved that an interior ROI can be exactly reconstructed via the total variation minimization if the ROI is piecewise constant. In the proofs, we implicitly utilized the property that if an artifact image assumes a constant value within the ROI then this constant must be zero. Here we prove this property in the space of square integrable functions. PMID:19717891

Perfusion CT to assess angiogenesis in colon cancer: technical limitations and practical challenges.

PubMed

Dighe, S; Castellano, E; Blake, H; Jeyadevan, N; Koh, M U; Orten, M; Swift, I; Brown, G

2012-10-01

Perfusion CT may have the potential to quantify the degree of angiogenesis of solid tumours in vivo. This study aims to identify the practical and technical challenges inherent to the technique, and evaluate its feasibility in colorectal tumours. 51 patients from 2 institutions prospectively underwent a single perfusion CT on 2 different multidetector scanners. The patients were advised to breath-hold as long as possible, followed by shallow breathing, and were given intravenous buscopan to reduce movement. Numerous steps were explored to identify the challenges. 43 patients successfully completed the perfusion CT as per protocol. Inability to detect the tumour (n=3), misplacement of dynamic sequence co-ordinates (n=2), failure of contrast injection (n=2) and displacement of tumour (n=1) were the reasons for failure. In 14 cases excessive respiratory motion displaced the tumour out of the scanning field along the temporal sequence, leading to erroneous data capture. In nine patients, minor displacements of the tumour were corrected by repositioning the region of interest (ROI) to its original position after reviewing each dynamic sequence slice. In 20 patients the tumour was stable, and data captured from the ROI were representative, and could have been analysed by commercially available Body Tumor Perfusion 3.0® software (GE Healthcare, Waukesha, WI). Hence all data were manually analysed by MATLAB® processing software (MathWorks, Cambridge, UK). Perfusion CT in tumours susceptible to motion during acquisition makes accurate data capture challenging and requires meticulous attention to detail. Motion correction software is essential if perfusion CT is to be used routinely in colorectal cancer.

Identifying minimal hepatic encephalopathy in cirrhotic patients by measuring spontaneous brain activity.

PubMed

Chen, Hua-Jun; Zhang, Ling; Jiang, Long-Feng; Chen, Qiu-Feng; Li, Jun; Shi, Hai-Bin

2016-08-01

It has been demonstrated that minimal hepatic encephalopathy (MHE) is associated with aberrant regional intrinsic brain activity in cirrhotic patients. However, few studies have investigated whether altered intrinsic brain activity can be used as a biomarker of MHE among cirrhotic patients. In this study, 36 cirrhotic patients (with MHE, n = 16; without MHE [NHE], n = 20) underwent resting-state functional magnetic resonance imaging (fMRI). Spontaneous brain activity was measured by examining the amplitude of low-frequency fluctuations (ALFF) in the fMRI signal. MHE was diagnosed based on the Psychometric Hepatic Encephalopathy Score (PHES). A two-sample t-test was used to determine the regions of interest (ROIs) in which ALFF differed significantly between the two groups; then, ALFF values within ROIs were selected as classification features. A linear discriminative analysis was used to differentiate MHE patients from NHE patients. The leave-one-out cross-validation method was used to estimate the performance of the classifier. The classification analysis was 80.6 % accurate (81.3 % sensitivity and 80.0 % specificity) in terms of distinguishing between the two groups. Six ROIs were identified as the most discriminative features, including the bilateral medial frontal cortex/anterior cingulate cortex, posterior cingulate cortex/precuneus, left precentral and postcentral gyrus, right lingual gyrus, middle frontal gyrus, and inferior/superior parietal lobule. The ALFF values within ROIs were correlated with PHES in cirrhotic patients. Our findings suggest that altered regional brain spontaneous activity is a useful biomarker for MHE detection among cirrhotic patients.

Automatic intrinsic cardiac and respiratory gating from cone-beam CT scans of the thorax region

NASA Astrophysics Data System (ADS)

Hahn, Andreas; Sauppe, Sebastian; Lell, Michael; Kachelrieß, Marc

2016-03-01

We present a new algorithm that allows for raw data-based automated cardiac and respiratory intrinsic gating in cone-beam CT scans. It can be summarized in three steps: First, a median filter is applied to an initially reconstructed volume. The forward projection of this volume contains less motion information and is subtracted from the original projections. This results in new raw data that contain only moving and not static anatomy like bones, that would otherwise impede the cardiac or respiratory signal acquisition. All further steps are applied to these modified raw data. Second, the raw data are cropped to a region of interest (ROI). The ROI in the raw data is determined by the forward projection of a binary volume of interest (VOI) that includes the diaphragm for respiratory gating and most of the edge of the heart for cardiac gating. Third, the mean gray value in this ROI is calculated for every projection and the respiratory/cardiac signal is acquired using a bandpass filter. Steps two and three are carried out simultaneously for 64 or 1440 overlapping VOI inside the body for the respiratory or cardiac signal respectively. The signals acquired from each ROI are compared and the most consistent one is chosen as the desired cardiac or respiratory motion signal. Consistency is assessed by the standard deviation of the time between two maxima. The robustness and efficiency of the method is evaluated using simulated and measured patient data by computing the standard deviation of the mean signal difference between the ground truth and the intrinsic signal.

Initial Validation for the Estimation of Resting-State fMRI Effective Connectivity by a Generalization of the Correlation Approach.

PubMed

Xu, Nan; Spreng, R Nathan; Doerschuk, Peter C

2017-01-01

Resting-state functional MRI (rs-fMRI) is widely used to noninvasively study human brain networks. Network functional connectivity is often estimated by calculating the timeseries correlation between blood-oxygen-level dependent (BOLD) signal from different regions of interest (ROIs). However, standard correlation cannot characterize the direction of information flow between regions. In this paper, we introduce and test a new concept, prediction correlation, to estimate effective connectivity in functional brain networks from rs-fMRI. In this approach, the correlation between two BOLD signals is replaced by a correlation between one BOLD signal and a prediction of this signal via a causal system driven by another BOLD signal. Three validations are described: (1) Prediction correlation performed well on simulated data where the ground truth was known, and outperformed four other methods. (2) On simulated data designed to display the "common driver" problem, prediction correlation did not introduce false connections between non-interacting driven ROIs. (3) On experimental data, prediction correlation recovered the previously identified network organization of human brain. Prediction correlation scales well to work with hundreds of ROIs, enabling it to assess whole brain interregional connectivity at the single subject level. These results provide an initial validation that prediction correlation can capture the direction of information flow and estimate the duration of extended temporal delays in information flow between regions of interest ROIs based on BOLD signal. This approach not only maintains the high sensitivity to network connectivity provided by the correlation analysis, but also performs well in the estimation of causal information flow in the brain.

Rare earth nanoparticles prevent retinal degeneration induced by intracellular peroxides:

NASA Astrophysics Data System (ADS)

Chen, Junping; Patil, Swanand; Seal, Sudipta; McGinnis, James F.

2006-11-01

Photoreceptor cells are incessantly bombarded with photons of light, which, along with the cells' high rate of oxygen metabolism, continuously exposes them to elevated levels of toxic reactive oxygen intermediates (ROIs). Vacancy-engineered mixed-valence-state cerium oxide nanoparticles (nanoceria particles) scavenge ROIs. Our data show that nanoceria particles prevent increases in the intracellular concentrations of ROIs in primary cell cultures of rat retina and, in vivo, prevent loss of vision due to light-induced degeneration of photoreceptor cells. These data indicate that the nanoceria particles may be effective in inhibiting the progression of ROI-induced cell death, which is thought to be involved in macular degeneration, retinitis pigmentosa and other blinding diseases, as well as the ROI-induced death of other cell types in diabetes, Alzheimer's disease, atherosclerosis, stroke and so on. The use of nanoceria particles as a direct therapy for multiple diseases represents a novel strategy and suggests that they may represent a unique platform technology.

Visual analytics of brain networks.

PubMed

Li, Kaiming; Guo, Lei; Faraco, Carlos; Zhu, Dajiang; Chen, Hanbo; Yuan, Yixuan; Lv, Jinglei; Deng, Fan; Jiang, Xi; Zhang, Tuo; Hu, Xintao; Zhang, Degang; Miller, L Stephen; Liu, Tianming

2012-05-15

Identification of regions of interest (ROIs) is a fundamental issue in brain network construction and analysis. Recent studies demonstrate that multimodal neuroimaging approaches and joint analysis strategies are crucial for accurate, reliable and individualized identification of brain ROIs. In this paper, we present a novel approach of visual analytics and its open-source software for ROI definition and brain network construction. By combining neuroscience knowledge and computational intelligence capabilities, visual analytics can generate accurate, reliable and individualized ROIs for brain networks via joint modeling of multimodal neuroimaging data and an intuitive and real-time visual analytics interface. Furthermore, it can be used as a functional ROI optimization and prediction solution when fMRI data is unavailable or inadequate. We have applied this approach to an operation span working memory fMRI/DTI dataset, a schizophrenia DTI/resting state fMRI (R-fMRI) dataset, and a mild cognitive impairment DTI/R-fMRI dataset, in order to demonstrate the effectiveness of visual analytics. Our experimental results are encouraging. Copyright © 2012 Elsevier Inc. All rights reserved.

CBF measured by Xe-CT: Approach to analysis and normal values

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yonas, H.; Darby, J.M.; Marks, E.C.

1991-09-01

Normal reference values and a practical approach to CBF analysis are needed for routine clinical analysis and interpretation of xenon-enhanced computed tomography (CT) CBF studies. The authors measured CBF in 67 normal individuals with the GE 9800 CT scanner adapted for CBF imaging with stable Xe. CBF values for vascular territories were systematically analyzed using the clustering of contiguous 2-cm circular regions of interest (ROIs) placed within the cortical mantle and basal ganglia. Mixed cortical flows averaged 51 {plus minus} 10ml.100g-1.min-1. High and low flow compartments, sampled by placing 5-mm circular ROIs in regions containing the highest and lowest flowmore » values in each hemisphere, averaged 84 {plus minus} 14 and 20 {plus minus} 5 ml.100 g-1.min-1, respectively. Mixed cortical flow values as well as values within the high flow compartment demonstrated significant decline with age; however, there were no significant age-related changes in the low flow compartment. The clustering of systematically placed cortical and subcortical ROIs has provided a normative data base for Xe-CT CBF and a flexible and uncomplicated method for the analysis of CBF maps generated by Xe-enhanced CT.« less

Data analysis in emission tomography using emission-count posteriors

NASA Astrophysics Data System (ADS)

Sitek, Arkadiusz

2012-11-01

A novel approach to the analysis of emission tomography data using the posterior probability of the number of emissions per voxel (emission count) conditioned on acquired tomographic data is explored. The posterior is derived from the prior and the Poisson likelihood of the emission-count data by marginalizing voxel activities. Based on emission-count posteriors, examples of Bayesian analysis including estimation and classification tasks in emission tomography are provided. The application of the method to computer simulations of 2D tomography is demonstrated. In particular, the minimum-mean-square-error point estimator of the emission count is demonstrated. The process of finding this estimator can be considered as a tomographic image reconstruction technique since the estimates of the number of emissions per voxel divided by voxel sensitivities and acquisition time are the estimates of the voxel activities. As an example of a classification task, a hypothesis stating that some region of interest (ROI) emitted at least or at most r-times the number of events in some other ROI is tested. The ROIs are specified by the user. The analysis described in this work provides new quantitative statistical measures that can be used in decision making in diagnostic imaging using emission tomography.

Intercomparison of methods for image quality characterization. II. Noise power spectrum

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dobbins, James T. III; Samei, Ehsan; Ranger, Nicole T.

Second in a two-part series comparing measurement techniques for the assessment of basic image quality metrics in digital radiography, in this paper we focus on the measurement of the image noise power spectrum (NPS). Three methods were considered: (1) a method published by Dobbins et al. [Med. Phys. 22, 1581-1593 (1995)] (2) a method published by Samei et al. [Med. Phys. 30, 608-622 (2003)], and (3) a new method sanctioned by the International Electrotechnical Commission (IEC 62220-1, 2003), developed as part of an international standard for the measurement of detective quantum efficiency. In addition to an overall comparison of themore » estimated NPS between the three techniques, the following factors were also evaluated for their effect on the measured NPS: horizontal versus vertical directional dependence, the use of beam-limiting apertures, beam spectrum, and computational methods of NPS analysis, including the region-of-interest (ROI) size and the method of ROI normalization. Of these factors, none was found to demonstrate a substantial impact on the amplitude of the NPS estimates ({<=}3.1% relative difference in NPS averaged over frequency, for each factor considered separately). Overall, the three methods agreed to within 1.6%{+-}0.8% when averaged over frequencies >0.15 mm{sup -1}.« less

Company Reinvesting and Corporate ROI

DTIC Science & Technology

2011-08-01

Company Reinvesting and Corporate ROI Rob Holder HP-DOJ/DOS Account Executive Report Documentation Page Form ApprovedOMB No. 0704-0188 Public...reporting burden for the collection of information is estimated to average 1 hour per response , including the time for reviewing instructions, searching...4. TITLE AND SUBTITLE Company Reinvesting and Corporate ROI 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d

A Novel Azimuth Super-Resolution Method by Synthesizing Azimuth Bandwidth of Multiple Tracks of Airborne Stripmap SAR Data

PubMed Central

Wang, Yan; Li, Jingwen; Sun, Bing; Yang, Jian

2016-01-01

Azimuth resolution of airborne stripmap synthetic aperture radar (SAR) is restricted by the azimuth antenna size. Conventionally, a higher azimuth resolution should be achieved by employing alternate modes that steer the beam in azimuth to enlarge the synthetic antenna aperture. However, if a data set of a certain region, consisting of multiple tracks of airborne stripmap SAR data, is available, the azimuth resolution of specific small region of interest (ROI) can be conveniently improved by a novel azimuth super-resolution method as introduced by this paper. The proposed azimuth super-resolution method synthesize the azimuth bandwidth of the data selected from multiple discontinuous tracks and contributes to a magnifier-like function with which the ROI can be further zoomed in with a higher azimuth resolution than that of the original stripmap images. Detailed derivation of the azimuth super-resolution method, including the steps of two-dimensional dechirping, residual video phase (RVP) removal, data stitching and data correction, is provided. The restrictions of the proposed method are also discussed. Lastly, the presented approach is evaluated via both the single- and multi-target computer simulations. PMID:27304959

Localized-atlas-based segmentation of breast MRI in a decision-making framework.

PubMed

Fooladivanda, Aida; Shokouhi, Shahriar B; Ahmadinejad, Nasrin

2017-03-01

Breast-region segmentation is an important step for density estimation and Computer-Aided Diagnosis (CAD) systems in Magnetic Resonance Imaging (MRI). Detection of breast-chest wall boundary is often a difficult task due to similarity between gray-level values of fibroglandular tissue and pectoral muscle. This paper proposes a robust breast-region segmentation method which is applicable for both complex cases with fibroglandular tissue connected to the pectoral muscle, and simple cases with high contrast boundaries. We present a decision-making framework based on geometric features and support vector machine (SVM) to classify breasts in two main groups, complex and simple. For complex cases, breast segmentation is done using a combination of intensity-based and atlas-based techniques; however, only intensity-based operation is employed for simple cases. A novel atlas-based method, that is called localized-atlas, accomplishes the processes of atlas construction and registration based on the region of interest (ROI). Atlas-based segmentation is performed by relying on the chest wall template. Our approach is validated using a dataset of 210 cases. Based on similarity between automatic and manual segmentation results, the proposed method achieves Dice similarity coefficient, Jaccard coefficient, total overlap, false negative, and false positive values of 96.3, 92.9, 97.4, 2.61 and 4.77%, respectively. The localization error of the breast-chest wall boundary is 1.97 mm, in terms of averaged deviation distance. The achieved results prove that the suggested framework performs the breast segmentation with negligible errors and efficient computational time for different breasts from the viewpoints of size, shape, and density pattern.

Can state-of-the-art HVS-based objective image quality criteria be used for image reconstruction techniques based on ROI analysis?

NASA Astrophysics Data System (ADS)

Dostal, P.; Krasula, L.; Klima, M.

2012-06-01

Various image processing techniques in multimedia technology are optimized using visual attention feature of the human visual system. Spatial non-uniformity causes that different locations in an image are of different importance in terms of perception of the image. In other words, the perceived image quality depends mainly on the quality of important locations known as regions of interest. The performance of such techniques is measured by subjective evaluation or objective image quality criteria. Many state-of-the-art objective metrics are based on HVS properties; SSIM, MS-SSIM based on image structural information, VIF based on the information that human brain can ideally gain from the reference image or FSIM utilizing the low-level features to assign the different importance to each location in the image. But still none of these objective metrics utilize the analysis of regions of interest. We solve the question if these objective metrics can be used for effective evaluation of images reconstructed by processing techniques based on ROI analysis utilizing high-level features. In this paper authors show that the state-of-the-art objective metrics do not correlate well with subjective evaluation while the demosaicing based on ROI analysis is used for reconstruction. The ROI were computed from "ground truth" visual attention data. The algorithm combining two known demosaicing techniques on the basis of ROI location is proposed to reconstruct the ROI in fine quality while the rest of image is reconstructed with low quality. The color image reconstructed by this ROI approach was compared with selected demosaicing techniques by objective criteria and subjective testing. The qualitative comparison of the objective and subjective results indicates that the state-of-the-art objective metrics are still not suitable for evaluation image processing techniques based on ROI analysis and new criteria is demanded.

68 Ga-PSMA-PET/CT for the evaluation of pulmonary metastases and opacities in patients with prostate cancer.

PubMed

Damjanovic, Jonathan; Janssen, Jan-Carlo; Furth, Christian; Diederichs, Gerd; Walter, Thula; Amthauer, Holger; Makowski, Marcus R

2018-05-16

The purpose of this study was to investigate the imaging properties of pulmonary metastases and benign opacities in 68 Ga-PSMA positron emission tomography (PET) in patients with prostate cancer (PC). 68 Ga-PSMA-PET/CT scans of 739 PC patients available in our database were evaluated retrospectively for lung metastases and non-solid focal pulmonary opacities. Maximum standardized uptake values (SUV max ) were assessed by two- and three-dimensional regions of interest (2D/3D ROI). Additionally CT features of the lesions, such as location, morphology and size were identified. Ninety-one pulmonary metastases and fourteen opacities were identified in 34 PC patients. In total, 66 PSMA-positive (72.5%) and 25 PSMA-negative (27.5%) metastases were identified. The mean SUV max of pulmonary opacities was 2.2±0.7 in 2D ROI and 2.4±0.8 in 3D ROI. The mean SUV max of PSMA-positive pulmonary metastases was 4.5±2.7 in 2D ROI and in 4.7±2.9 in 3D ROI; this was significantly higher than the SUV max of pulmonary opacities in both 2D and 3D ROI (p<0.001). The mean SUV max of PSMA-negative metastases was 1.0±0.5 in 2D ROI and 1.0±0.4 in 3D ROI, and significantly lower than that of the pulmonary opacities (p<0.001). A significant (p<0.05) weak linear correlation between size and 3D SUV max in lung metastases (ρ Spearman =0.207) was found. Based on the SUV max in 68 Ga-PSMA-PET alone, it was not possible to differentiate between pulmonary metastases and pulmonary opacities. The majority of lung metastases highly overexpressed PSMA, while a relevant number of metastases were PSMA-negative. Pulmonary opacities demonstrated a moderate tracer uptake, significantly lower than PSMA-positive lung metastases, yet significantly higher than PSMA-negative metastases.

Forward collision warning based on kernelized correlation filters

NASA Astrophysics Data System (ADS)

Pu, Jinchuan; Liu, Jun; Zhao, Yong

2017-07-01

A vehicle detection and tracking system is one of the indispensable methods to reduce the occurrence of traffic accidents. The nearest vehicle is the most likely to cause harm to us. So, this paper will do more research on about the nearest vehicle in the region of interest (ROI). For this system, high accuracy, real-time and intelligence are the basic requirement. In this paper, we set up a system that combines the advanced KCF tracking algorithm with the HaarAdaBoost detection algorithm. The KCF algorithm reduces computation time and increase the speed through the cyclic shift and diagonalization. This algorithm satisfies the real-time requirement. At the same time, Haar features also have the same advantage of simple operation and high speed for detection. The combination of this two algorithm contribute to an obvious improvement of the system running rate comparing with previous works. The detection result of the HaarAdaBoost classifier provides the initial value for the KCF algorithm. This fact optimizes KCF algorithm flaws that manual car marking in the initial phase, which is more scientific and more intelligent. Haar detection and KCF tracking with Histogram of Oriented Gradient (HOG) ensures the accuracy of the system. We evaluate the performance of framework on dataset that were self-collected. The experimental results demonstrate that the proposed method is robust and real-time. The algorithm can effectively adapt to illumination variation, even in the night it can meet the detection and tracking requirements, which is an improvement compared with the previous work.

Oriented Markov random field based dendritic spine segmentation for fluorescence microscopy images.

PubMed

Cheng, Jie; Zhou, Xiaobo; Miller, Eric L; Alvarez, Veronica A; Sabatini, Bernardo L; Wong, Stephen T C

2010-10-01

Dendritic spines have been shown to be closely related to various functional properties of the neuron. Usually dendritic spines are manually labeled to analyze their morphological changes, which is very time-consuming and susceptible to operator bias, even with the assistance of computers. To deal with these issues, several methods have been recently proposed to automatically detect and measure the dendritic spines with little human interaction. However, problems such as degraded detection performance for images with larger pixel size (e.g. 0.125 μm/pixel instead of 0.08 μm/pixel) still exist in these methods. Moreover, the shapes of detected spines are also distorted. For example, the "necks" of some spines are missed. Here we present an oriented Markov random field (OMRF) based algorithm which improves spine detection as well as their geometric characterization. We begin with the identification of a region of interest (ROI) containing all the dendrites and spines to be analyzed. For this purpose, we introduce an adaptive procedure for identifying the image background. Next, the OMRF model is discussed within a statistical framework and the segmentation is solved as a maximum a posteriori estimation (MAP) problem, whose optimal solution is found by a knowledge-guided iterative conditional mode (KICM) algorithm. Compared with the existing algorithms, the proposed algorithm not only provides a more accurate representation of the spine shape, but also improves the detection performance by more than 50% with regard to reducing both the misses and false detection.

Computerized Classification of Pneumoconiosis on Digital Chest Radiography Artificial Neural Network with Three Stages.

PubMed

Okumura, Eiichiro; Kawashita, Ikuo; Ishida, Takayuki

2017-08-01

It is difficult for radiologists to classify pneumoconiosis from category 0 to category 3 on chest radiographs. Therefore, we have developed a computer-aided diagnosis (CAD) system based on a three-stage artificial neural network (ANN) method for classification based on four texture features. The image database consists of 36 chest radiographs classified as category 0 to category 3. Regions of interest (ROIs) with a matrix size of 32 × 32 were selected from chest radiographs. We obtained a gray-level histogram, histogram of gray-level difference, gray-level run-length matrix (GLRLM) feature image, and gray-level co-occurrence matrix (GLCOM) feature image in each ROI. For ROI-based classification, the first ANN was trained with each texture feature. Next, the second ANN was trained with output patterns obtained from the first ANN. Finally, we obtained a case-based classification for distinguishing among four categories with the third ANN method. We determined the performance of the third ANN by receiver operating characteristic (ROC) analysis. The areas under the ROC curve (AUC) of the highest category (severe pneumoconiosis) case and the lowest category (early pneumoconiosis) case were 0.89 ± 0.09 and 0.84 ± 0.12, respectively. The three-stage ANN with four texture features showed the highest performance for classification among the four categories. Our CAD system would be useful for assisting radiologists in classification of pneumoconiosis from category 0 to category 3.

Linear associations between clinically assessed upper motor neuron disease and diffusion tensor imaging metrics in amyotrophic lateral sclerosis.

PubMed

Woo, John H; Wang, Sumei; Melhem, Elias R; Gee, James C; Cucchiara, Andrew; McCluskey, Leo; Elman, Lauren

2014-01-01

To assess the relationship between clinically assessed Upper Motor Neuron (UMN) disease in Amyotrophic Lateral Sclerosis (ALS) and local diffusion alterations measured in the brain corticospinal tract (CST) by a tractography-driven template-space region-of-interest (ROI) analysis of Diffusion Tensor Imaging (DTI). This cross-sectional study included 34 patients with ALS, on whom DTI was performed. Clinical measures were separately obtained including the Penn UMN Score, a summary metric based upon standard clinical methods. After normalizing all DTI data to a population-specific template, tractography was performed to determine a region-of-interest (ROI) outlining the CST, in which average Mean Diffusivity (MD) and Fractional Anisotropy (FA) were estimated. Linear regression analyses were used to investigate associations of DTI metrics (MD, FA) with clinical measures (Penn UMN Score, ALSFRS-R, duration-of-disease), along with age, sex, handedness, and El Escorial category as covariates. For MD, the regression model was significant (p = 0.02), and the only significant predictors were the Penn UMN Score (p = 0.005) and age (p = 0.03). The FA regression model was also significant (p = 0.02); the only significant predictor was the Penn UMN Score (p = 0.003). Measured by the template-space ROI method, both MD and FA were linearly associated with the Penn UMN Score, supporting the hypothesis that DTI alterations reflect UMN pathology as assessed by the clinical examination.

[Methodological aspects related to the determination of the relative renal function using 99mTC MAG3].

PubMed

Ladrón De Guevara Hernández, D; Ham, H; Franken, P; Piepsz, A; Lobo Sotomayor, G

2002-01-01

The aim of the study was to evaluate three different methods for calculating the split renal function in patients with only one functioning kidney, keeping in mind that the split function should be zero on the side of the non-functioning kidney. We retrospectively selected 28 99mTc MAG3 renograms performed in children, 12 with unilateral nephrectomy, 4 with unilateral agenesis and 12 with a non-functioning kidney. A renal and perirenal region of interest (ROI) were delineated around the functioning kidney. The ROIs around the empty kidney were drawn symmetrically to the contralateral side. The split renal function was calculated using three different methods, the integral method, the slope method and the Patlak-Rutland algorithm. For the whole group of 28 kidneys as well as for the three categories of patients, the three methods provided a split function on the side of the non-functioning kidney close to the zero value, regardless of whether the empty kidney was the left or the right one. We recommend the use of the integral method for the whole range of split renal function with 99mTc MAG3. No significant improvement was obtained by means of the more sophisticated Patlak-Rutland method.

3D quantitative breast ultrasound analysis for differentiating fibroadenomas and carcinomas smaller than 1cm.

PubMed

Meel-van den Abeelen, A S S; Weijers, G; van Zelst, J C M; Thijssen, J M; Mann, R M; de Korte, C L

2017-03-01

In (3D) ultrasound, accurate discrimination of small solid masses is difficult, resulting in a high frequency of biopsies for benign lesions. In this study, we investigate whether 3D quantitative breast ultrasound (3DQBUS) analysis can be used for improving non-invasive discrimination between benign and malignant lesions. 3D US studies of 112 biopsied solid breast lesions (size <1cm), were included (34 fibroadenomas and 78 invasive ductal carcinomas). The lesions were manually delineated and, based on sonographic criteria used by radiologists, 3 regions of interest were defined in 3D for analysis: ROI (ellipsoid covering the inside of the lesion), PER (peritumoural surrounding: 0.5mm around the lesion), and POS (posterior-tumoural acoustic phenomena: region below the lesion with the same size as delineated for the lesion). After automatic gain correction (AGC), the mean and standard deviation of the echo level within the regions were calculated. For the ROI and POS also the residual attenuation coefficient was estimated in decibel per cm [dB/cm]. The resulting eight features were used for classification of the lesions by a logistic regression analysis. The classification accuracy was evaluated by leave-one-out cross-validation. Receiver operating characteristic (ROC) curves were constructed to assess the performance of the classification. All lesions were delineated by two readers and results were compared to assess the effect of the manual delineation. The area under the ROC curve was 0.86 for both readers. At 100% sensitivity, a specificity of 26% and 50% was achieved for reader 1 and 2, respectively. Inter-reader variability in lesion delineation was marginal and did not affect the accuracy of the technique. The area under the ROC curve of 0.86 was reached for the second reader when the results of the first reader were used as training set yielding a sensitivity of 100% and a specificity of 40%. Consequently, 3DQBUS would have achieved a 40% reduction in biopsies for benign lesions for reader 2, without a decrease in sensitivity. This study shows that 3DQBUS is a promising technique to classify suspicious breast lesions as benign, potentially preventing unnecessary biopsies. Copyright © 2017 Elsevier B.V. All rights reserved.

Cortical Thickness Estimations of FreeSurfer and the CAT12 Toolbox in Patients with Alzheimer's Disease and Healthy Controls.

PubMed

Seiger, Rene; Ganger, Sebastian; Kranz, Georg S; Hahn, Andreas; Lanzenberger, Rupert

2018-05-15

Automated cortical thickness (CT) measurements are often used to assess gray matter changes in the healthy and diseased human brain. The FreeSurfer software is frequently applied for this type of analysis. The computational anatomy toolbox (CAT12) for SPM, which offers a fast and easy-to-use alternative approach, was recently made available. In this study, we compared region of interest (ROI)-wise CT estimations of the surface-based FreeSurfer 6 (FS6) software and the volume-based CAT12 toolbox for SPM using 44 elderly healthy female control subjects (HC). In addition, these 44 HCs from the cross-sectional analysis and 34 age- and sex-matched patients with Alzheimer's disease (AD) were used to assess the potential of detecting group differences for each method. Finally, a test-retest analysis was conducted using 19 HC subjects. All data were taken from the OASIS database and MRI scans were recorded at 1.5 Tesla. A strong correlation was observed between both methods in terms of ROI mean CT estimates (R 2 = .83). However, CAT12 delivered significantly higher CT estimations in 32 of the 34 ROIs, indicating a systematic difference between both approaches. Furthermore, both methods were able to reliably detect atrophic brain areas in AD subjects, with the highest decreases in temporal areas. Finally, FS6 as well as CAT12 showed excellent test-retest variability scores. Although CT estimations were systematically higher for CAT12, this study provides evidence that this new toolbox delivers accurate and robust CT estimates and can be considered a fast and reliable alternative to FreeSurfer. © 2018 The Authors. Journal of Neuroimaging published by Wiley Periodicals, Inc. on behalf of American Society of Neuroimaging.

Monitoring of bone regeneration process by means of texture analysis

NASA Astrophysics Data System (ADS)

Kokkinou, E.; Boniatis, I.; Costaridou, L.; Saridis, A.; Panagiotopoulos, E.; Panayiotakis, G.

2009-09-01

An image analysis method is proposed for the monitoring of the regeneration of the tibial bone. For this purpose, 130 digitized radiographs of 13 patients, who had undergone tibial lengthening by the Ilizarov method, were studied. For each patient, 10 radiographs, taken at an equal number of postoperative successive time moments, were available. Employing available software, 3 Regions Of Interest (ROIs), corresponding to the: (a) upper, (b) central, and (c) lower aspect of the gap, where bone regeneration was expected to occur, were determined on each radiograph. Employing custom developed algorithms: (i) a number of textural features were generated from each of the ROIs, and (ii) a texture-feature based regression model was designed for the quantitative monitoring of the bone regeneration process. Statistically significant differences (p < 0.05) were derived for the initial and the final textural features values, generated from the first and the last postoperatively obtained radiographs, respectively. A quadratic polynomial regression equation fitted data adequately (r2 = 0.9, p < 0.001). The suggested method may contribute to the monitoring of the tibial bone regeneration process.

Health Information Exchange Readiness for Demonstrating Return on Investment and Quality of Care.

PubMed

Khurshid, Anjum; Diana, Mark L; Jain, Rahul

2015-01-01

To study the extent to which community health information exchanges (HIEs) deliver and measure return on investment (ROI) and improvements in the quality of care. We surveyed operational HIEs for their characteristics, information domains, impact on quality of care, and ROI. A 60 percent response rate was achieved. Two-thirds of respondents agreed that community HIEs demonstrated a positive ROI, while one-third had no opinion or disagreed. One-fourth or fewer respondents reported using various metrics to calculate ROI. Most respondents agreed that HIEs improve the quality of care, though several were not sure and were awaiting further evidence. Most respondents indicated that they did not deliver reports on quality measures (76 percent) and that data were not being used to measure quality performance of participating providers (73 percent). Respondents from most HIEs believe that the HIEs are demonstrating a positive ROI; however, a minority of them indicated they had used or will use specific metrics to calculate ROI. HIE representatives overwhelmingly reported that they believe the HIE activities improve the quality of healthcare delivered, but only a few are using data to evaluate provider performance or generate reports on quality measures. This study demonstrates the challenge faced by policy makers and healthcare organizations that are investing millions of dollars in HIEs that are believed to improve health outcomes and increase efficiency, but still need more time to develop the evidence to confirm that belief. Our study shows that calculating ROI for HIEs or their impact on quality of care remains a secondary priority for most HIEs. This finding raises serious questions for the sustained support of HIEs, both financially and as a policy lever, given the end of Health Information Technology for Economic and Clinical Health (HITECH) Act funding.

A hybrid correlation analysis with application to imaging genetics

NASA Astrophysics Data System (ADS)

Hu, Wenxing; Fang, Jian; Calhoun, Vince D.; Wang, Yu-Ping

2018-03-01

Investigating the association between brain regions and genes continues to be a challenging topic in imaging genetics. Current brain region of interest (ROI)-gene association studies normally reduce data dimension by averaging the value of voxels in each ROI. This averaging may lead to a loss of information due to the existence of functional sub-regions. Pearson correlation is widely used for association analysis. However, it only detects linear correlation whereas nonlinear correlation may exist among ROIs. In this work, we introduced distance correlation to ROI-gene association analysis, which can detect both linear and nonlinear correlations and overcome the limitation of averaging operations by taking advantage of the information at each voxel. Nevertheless, distance correlation usually has a much lower value than Pearson correlation. To address this problem, we proposed a hybrid correlation analysis approach, by applying canonical correlation analysis (CCA) to the distance covariance matrix instead of directly computing distance correlation. Incorporating CCA into distance correlation approach may be more suitable for complex disease study because it can detect highly associated pairs of ROI and gene groups, and may improve the distance correlation level and statistical power. In addition, we developed a novel nonlinear CCA, called distance kernel CCA, which seeks the optimal combination of features with the most significant dependence. This approach was applied to imaging genetic data from the Philadelphia Neurodevelopmental Cohort (PNC). Experiments showed that our hybrid approach produced more consistent results than conventional CCA across resampling and both the correlation and statistical significance were increased compared to distance correlation analysis. Further gene enrichment analysis and region of interest (ROI) analysis confirmed the associations of the identified genes with brain ROIs. Therefore, our approach provides a powerful tool for finding the correlation between brain imaging and genomic data.

An MRI-Based Atlas for Correlation of Imaging and Pathologic Findings in Alzheimer's Disease.

PubMed

Raman, Mekala R; Schwarz, Christopher G; Murray, Melissa E; Lowe, Val J; Dickson, Dennis W; Jack, Clifford R; Kantarci, Kejal

2016-05-01

Pathologic diagnosis is the gold standard in evaluating imaging measures developed as biomarkers for pathologically defined disorders. A brain MRI atlas representing autopsy-sampled tissue can be used to directly compare imaging and pathology findings. Our objective was to develop a brain MRI atlas representing the cortical regions that are routinely sampled at autopsy for the diagnosis of Alzheimer's disease (AD). Subjects (n = 22; ages at death = 70-95) with a range of pathologies and antemortem 3T MRI were included. Histology slides from 8 cortical regions sampled from the left hemisphere at autopsy guided the localization of the atlas regions of interest (ROIs) on each subject's antemortem 3D T1 -weighted MRI. These ROIs were then registered to a common template and combined to form one ROI representing the volume of tissue that was sampled by the pathologists. A subset of the subjects (n = 4; ages at death = 79-95) had amyloid PET imaging. Density of β-amyloid immunostain was quantified from the autopsy-sampled regions in the 4 subjects using a custom-designed ImageScope algorithm. Median uptake values were calculated in each ROI on the amyloid-PET images. We found an association between β-amyloid plaque density in 8 ROIs of the 4 subjects (total ROI n = 32) and median PiB SUVR (r(2) = .64; P < .0001). In an atlas developed for imaging and pathologic correlation studies, we demonstrated that antemortem amyloid burden measured in the atlas ROIs on amyloid PET is strongly correlated with β-amyloid density measured on histology. This atlas can be used in imaging and pathologic correlation studies. © 2016 The Authors. Journal of Neuroimaging published by Wiley Periodicals, Inc. on behalf of American Society of Neuroimaging.

Antioxidative and myocardial protective effects of L-arginine in oxygen radical-induced injury of isolated perfused rat hearts.

PubMed

Suessenbacher, Astrid; Lass, Achim; Mayer, Bernd; Brunner, Friedrich

2002-04-01

Oxygen-derived free radicals and oxidants (reactive oxygen intermediates, ROI) have been implicated in cardiovascular diseases. The protective role of nitric oxide (NO) against ROI-mediated tissue injury is not resolved. We tested the effects of exogenous NO, L- and D-arginine and a NO synthase inhibitor on electrolysis-induced cardiac injury and the generation of ROI by electrolysis. Superoxide dismutase (SOD) and catalase were used for comparison. Hearts ( n=7) from male rats (350+/-30 g) were perfused in vitro at 10 ml min(-1) g(-1), ROI generated by electrolysis of the perfusion medium (15 mA, 10 s), and cardiac function and the level of isoluminol-derived chemiluminescence in electrolysed perfusion medium documented for 15 min ( n=4). The ROI-induced maximal reduction of left ventricular developed pressure to 55+/-5% of baseline, and a 2.2+/-0.1-fold rise in coronary perfusion pressure 3 min after electrolysis, were prevented by SOD (50 U ml(-1)), catalase (100 U ml(-1)), S-nitroso- N-acetyl- D,L-penicillamine (SNAP, 100 nmol l(-1)); L-arginine (1 mmol l(-1)), N(G)-nitro- L-arginine (L-NNA, 200 micromol l(-1)) or D-arginine (1 mmol l(-1)). The effect of L-arginine was concentration dependent. In all cases, the beneficial effects were closely matched by a near-total reduction of ROI in the perfusion medium.We conclude that, besides mimicking or enhancing NO activity, L-arginine and donor-derived exogenous NO are cardioprotective by reducing ROI-mediated tissue injury. The protective effect of L-NNA and D-arginine implies that the protection results from a direct chemical interaction between the drug and the oxidizing species.

Work-related ill-health: Republic of Ireland, Northern Ireland, Great Britain 2005-2012.

PubMed

Money, A; Carder, M; Noone, P; Bourke, J; Hayes, J; Turner, S; Agius, R

2015-01-01

Data on work-related ill-health (WRIH) in the Republic of Ireland is inconsistent. To compare the incidence of WRIH in the Republic of Ireland (ROI), Northern Ireland (NI) and Great Britain (GB) reported by clinical specialists in skin and respiratory medicine and by specialist occupational physicians (OPs). Analysis of data reported to three surveillance schemes in The Health and Occupation Research (THOR) network in ROI and corresponding UK schemes. Contact dermatitis was the most frequently reported skin disease in the three areas. Asthma was the most frequently-reported respiratory disease in the ROI, while asbestos-related cases predominate in GB and NI. Mental health disorders, followed by musculoskeletal disorders were reported most frequently by OPs. Annual average incidence rates for skin disease were 2 per 100000 employed (95% confidence interval [CI] 1.9-2.8) in the ROI and 7 per 100000 for GB (95% CI 4.8-9.4). Unadjusted incidence rates for respiratory disease were 1 (95% CI 0.3-1) and 8 (95% CI 6.1-10.7) per 100000 in the ROI and GB, respectively; adjusted for reporter non-response, these figures increased to 15 (95% CI 11.3-19.6) and 32 (95% CI 28.4-35.6) per 100000 respectively. This is the first paper to include THOR data on WRIH from the ROI, NI and GB. Consistent and dedicated data collection in the ROI via the THOR schemes is viable and important in the light of a deficit of occupational ill-health data. Sustained efforts to improve participation are underway. © The Author 2014. Published by Oxford University Press on behalf of the Society of Occupational Medicine. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Human Auditory and Adjacent Nonauditory Cerebral Cortices Are Hypermetabolic in Tinnitus as Measured by Functional Near-Infrared Spectroscopy (fNIRS)

PubMed Central

Issa, Mohamad; Bisconti, Silvia; Kovelman, Ioulia; Kileny, Paul

2016-01-01

Tinnitus is the phantom perception of sound in the absence of an acoustic stimulus. To date, the purported neural correlates of tinnitus from animal models have not been adequately characterized with translational technology in the human brain. The aim of the present study was to measure changes in oxy-hemoglobin concentration from regions of interest (ROI; auditory cortex) and non-ROI (adjacent nonauditory cortices) during auditory stimulation and silence in participants with subjective tinnitus appreciated equally in both ears and in nontinnitus controls using functional near-infrared spectroscopy (fNIRS). Control and tinnitus participants with normal/near-normal hearing were tested during a passive auditory task. Hemodynamic activity was monitored over ROI and non-ROI under episodic periods of auditory stimulation with 750 or 8000 Hz tones, broadband noise, and silence. During periods of silence, tinnitus participants maintained increased hemodynamic responses in ROI, while a significant deactivation was seen in controls. Interestingly, non-ROI activity was also increased in the tinnitus group as compared to controls during silence. The present results demonstrate that both auditory and select nonauditory cortices have elevated hemodynamic activity in participants with tinnitus in the absence of an external auditory stimulus, a finding that may reflect basic science neural correlates of tinnitus that ultimately contribute to phantom sound perception. PMID:27042360

Functional near-infrared spectroscopy (fNIRS) brain imaging of multi-sensory integration during computerized dynamic posturography in middle-aged and older adults.

PubMed

Lin, Chia-Cheng; Barker, Jeffrey W; Sparto, Patrick J; Furman, Joseph M; Huppert, Theodore J

2017-04-01

Studies suggest that aging affects the sensory re-weighting process, but the neuroimaging evidence is minimal. Functional Near-Infrared Spectroscopy (fNIRS) is a novel neuroimaging tool that can detect brain activities during dynamic movement condition. In this study, fNIRS was used to investigate the hemodynamic changes in the frontal-lateral, temporal-parietal, and occipital regions of interest (ROIs) during four sensory integration conditions that manipulated visual and somatosensory feedback in 15 middle-aged and 15 older adults. The results showed that the temporal-parietal ROI was activated more when somatosensory and visual information were absent in both groups, which indicated the sole use of vestibular input for maintaining balance. While both older adults and middle-aged adults had greater activity in most brain ROIs during changes in the sensory conditions, the older adults had greater increases in the occipital ROI and frontal-lateral ROIs. These findings suggest a cortical component to sensory re-weighting that is more distributed and requires greater attention in older adults.

Computer-aided diagnostic system for diffuse liver diseases with ultrasonography by neural networks

NASA Astrophysics Data System (ADS)

Ogawa, K.; Fukushima, M.; Kubota, K.; Hisa, N.

1998-12-01

The aim of the study is to establish a computer-aided diagnostic system for diffuse liver diseases such as chronic active hepatitis (CAH) and liver cirrhosis (LC). The authors introduced an artificial neural network in the classification of these diseases. In this system the neural network was trained by feature parameters extracted from B-mode ultrasonic images of normal liver (NL), CAH and LC. For input data the authors used six parameters calculated by a region of interest (ROI) and a parameter calculated by five ROIs in each image. They were variance of pixel values, coefficient of variation, annular Fourier power spectrum, longitudinal Fourier power spectrum which were calculated for the ROI, and variation of the means of the five ROIs. In addition, the authors used two more parameters calculated from a co-occurrence matrix of pixel values in the ROI. The results showed that the neural network classifier was 83.8% in sensitivity for LC, 90.0% in sensitivity for CAH and 93.6% in specificity, and the system was considered to be helpful for clinical and educational use.

Fully convolutional networks with double-label for esophageal cancer image segmentation by self-transfer learning

NASA Astrophysics Data System (ADS)

Xue, Di-Xiu; Zhang, Rong; Zhao, Yuan-Yuan; Xu, Jian-Ming; Wang, Ya-Lei

2017-07-01

Cancer recognition is the prerequisite to determine appropriate treatment. This paper focuses on the semantic segmentation task of microvascular morphological types on narrowband images to aid clinical examination of esophageal cancer. The most challenge for semantic segmentation is incomplete-labeling. Our key insight is to build fully convolutional networks (FCNs) with double-label to make pixel-wise predictions. The roi-label indicating ROIs (region of interest) is introduced as extra constraint to guild feature learning. Trained end-to-end, the FCN model with two target jointly optimizes both segmentation of sem-label (semantic label) and segmentation of roi-label within the framework of self-transfer learning based on multi-task learning theory. The learning representation ability of shared convolutional networks for sem-label is improved with support of roi-label via achieving a better understanding of information outside the ROIs. Our best FCN model gives satisfactory segmentation result with mean IU up to 77.8% (pixel accuracy > 90%). The results show that the proposed approach is able to assist clinical diagnosis to a certain extent.

Infrared thermography in the evaluation of meibomian gland dysfunction.

PubMed

Su, Tai-Yuan; Ho, Wei-Ting; Chiang, Shu-Chiung; Lu, Chien-Yi; Chiang, Huihua Kenny; Chang, Shu-Wen

2017-07-01

To evaluate meibomian gland dysfunction (MGD) by infrared thermography. An observational study was conducted at the Department of Ophthalmology, Far Eastern Memorial Hospital, New Taipei City, Taiwan. Participants included 89 MGD patients (30 in Grade 1, 49 in Grade 2, and 10 in Grade 3) and 65 controls. The close-eye thermographic images of the eyelid were obtained noninvasively by infrared thermography. Temperatures at 8 regions of interest (ROIs) of the eyelid margin and a reference temperature at the center of the upper eyelid were measured. The temperature ratio was defined as the temperature of ROI divided by the reference temperature. Eyelid margin temperature measured by infrared thermography increased from temporal side (ROI 1) to the nasal side (ROI 8) of the eye in both MGD patients and control groups. The temperature ratios were significantly higher in MGD participants than in controls, especially at ROI 8. The eyelid margin temperature measured by infrared thermography was higher in MGD participants. Further development of this infrared thermography system may become a rapid and non-invasive tool for MGD screening. Copyright © 2016. Published by Elsevier B.V.

Towards continuous monitoring of pulse rate in neonatal intensive care unit with a webcam.

PubMed

Mestha, Lalit K; Kyal, Survi; Xu, Beilei; Lewis, Leslie Edward; Kumar, Vijay

2014-01-01

We describe a novel method to monitor pulse rate (PR) on a continuous basis of patients in a neonatal intensive care unit (NICU) using videos taken from a high definition (HD) webcam. We describe algorithms that determine PR from videoplethysmographic (VPG) signals extracted from multiple regions of interest (ROI) simultaneously available within the field of view of the camera where cardiac signal is registered. We detect motion from video images and compensate for motion artifacts from each ROI. Preliminary clinical results are presented on 8 neonates each with 30 minutes of uninterrupted video. Comparisons to hospital equipment indicate that the proposed technology can meet medical industry standards and give improved patient comfort and ease of use for practitioners when instrumented with proper hardware.

Roi Detection and Vessel Segmentation in Retinal Image

NASA Astrophysics Data System (ADS)

Sabaz, F.; Atila, U.

2017-11-01

Diabetes disrupts work by affecting the structure of the eye and afterwards leads to loss of vision. Depending on the stage of disease that called diabetic retinopathy, there are sudden loss of vision and blurred vision problems. Automated detection of vessels in retinal images is a useful study to diagnose eye diseases, disease classification and other clinical trials. The shape and structure of the vessels give information about the severity of the disease and the stage of the disease. Automatic and fast detection of vessels allows for a quick diagnosis of the disease and the treatment process to start shortly. ROI detection and vessel extraction methods for retinal image are mentioned in this study. It is shown that the Frangi filter used in image processing can be successfully used in detection and extraction of vessels.

Introduction of the ASGARD Code

NASA Technical Reports Server (NTRS)

Bethge, Christian; Winebarger, Amy; Tiwari, Sanjiv; Fayock, Brian

2017-01-01

ASGARD stands for 'Automated Selection and Grouping of events in AIA Regional Data'. The code is a refinement of the event detection method in Ugarte-Urra & Warren (2014). It is intended to automatically detect and group brightenings ('events') in the AIA EUV channels, to record event parameters, and to find related events over multiple channels. Ultimately, the goal is to automatically determine heating and cooling timescales in the corona and to significantly increase statistics in this respect. The code is written in IDL and requires the SolarSoft library. It is parallelized and can run with multiple CPUs. Input files are regions of interest (ROIs) in time series of AIA images from the JSOC cutout service (http://jsoc.stanford.edu/ajax/exportdata.html). The ROIs need to be tracked, co-registered, and limited in time (typically 12 hours).

Curriculum-Based Measurement of Reading: An Evaluation of Frequentist and Bayesian Methods to Model Progress Monitoring Data

ERIC Educational Resources Information Center

Christ, Theodore J.; Desjardins, Christopher David

2018-01-01

Curriculum-Based Measurement of Oral Reading (CBM-R) is often used to monitor student progress and guide educational decisions. Ordinary least squares regression (OLSR) is the most widely used method to estimate the slope, or rate of improvement (ROI), even though published research demonstrates OLSR's lack of validity and reliability, and…

A Novel Segmentation Approach Combining Region- and Edge-Based Information for Ultrasound Images

PubMed Central

Luo, Yaozhong; Liu, Longzhong; Li, Xuelong

2017-01-01

Ultrasound imaging has become one of the most popular medical imaging modalities with numerous diagnostic applications. However, ultrasound (US) image segmentation, which is the essential process for further analysis, is a challenging task due to the poor image quality. In this paper, we propose a new segmentation scheme to combine both region- and edge-based information into the robust graph-based (RGB) segmentation method. The only interaction required is to select two diagonal points to determine a region of interest (ROI) on the original image. The ROI image is smoothed by a bilateral filter and then contrast-enhanced by histogram equalization. Then, the enhanced image is filtered by pyramid mean shift to improve homogeneity. With the optimization of particle swarm optimization (PSO) algorithm, the RGB segmentation method is performed to segment the filtered image. The segmentation results of our method have been compared with the corresponding results obtained by three existing approaches, and four metrics have been used to measure the segmentation performance. The experimental results show that the method achieves the best overall performance and gets the lowest ARE (10.77%), the second highest TPVF (85.34%), and the second lowest FPVF (4.48%). PMID:28536703

TU-C-12A-11: Comparisons Between Cu-ATSM PET and DCE-CT Kinetic Parameters in Canine Sinonasal Tumors

DOE Office of Scientific and Technical Information (OSTI.GOV)

La Fontaine, M; Bradshaw, T; Kubicek, L

2014-06-15

Purpose: Regions of poor perfusion within tumors may be associated with higher hypoxic levels. This study aimed to test this hypothesis by comparing measurements of hypoxia from Cu-ATSM PET to vasculature kinetic parameters from DCE-CT kinetic analysis. Methods: Ten canine patients with sinonasal tumors received one Cu-ATSM PET/CT scan and three DCE-CT scans prior to treatment. Cu-ATSM PET/CT and DCE-CT scans were registered and resampled to matching voxel dimensions. Kinetic analysis was performed on DCE-CT scans and for each patient, the resulting kinetic parameter values from the three DCE-CT scans were averaged together. Cu-ATSM SUVs were spatially correlated (r{sub spatial})more » on a voxel-to-voxel basis against the following DCE-CT kinetic parameters: transit time (t{sub 1}), blood flow (F), vasculature fraction (v{sub 1}), and permeability (PS). In addition, whole-tumor comparisons were performed by correlating (r{sub ROI}) the mean Cu-ATSM SUV (SUV{sub mean}) with median kinetic parameter values. Results: The spatial correlations (r{sub spatial}) were poor and ranged from -0.04 to 0.21 for all kinetic parameters. These low spatial correlations may be due to high variability in the DCE-CT kinetic parameter voxel values between scans. In our hypothesis, t{sub 1} was expected to have a positive correlation, while F was expected to have a negative correlation to hypoxia. However, in wholetumor analysis the opposite was found for both t{sub 1} (r{sub ROI} = -0.25) and F (r{sub ROI} = 0.56). PS and v{sub 1} may depict angiogenic responses to hypoxia and found positive correlations to Cu-ATSM SUV for PS (r{sub ROI} = 0.41), and v{sub 1} (r{sub ROI} = 0.57). Conclusion: Low spatial correlations were found between Cu-ATSM uptake and DCE-CT vasculature parameters, implying that poor perfusion is not associated with higher hypoxic regions. Across patients, the most hypoxic tumors tended to have higher blood flow values, which is contrary to our initial hypothesis. Funding: R01 CA136927.« less

Determination of multiple sclerosis plaque size with diffusion-tensor MR Imaging: comparison study with healthy volunteers.

PubMed

Kealey, Susan M; Kim, Youngjoo; Whiting, Wythe L; Madden, David J; Provenzale, James M

2005-08-01

To use diffusion-tensor magnetic resonance (MR) imaging to measure involvement of normal-appearing white matter (WM) immediately adjacent to multiple sclerosis (MS) plaques and thus redefine actual plaque size on diffusion-tensor images through comparison with T2-weighted images of equivalent areas in healthy volunteers. Informed consent was not required given the retrospective nature of the study on an anonymized database. The study complied with requirements of the Health Insurance Portability and Accountability Act. Twelve patients with MS (four men, eight women; mean age, 35 years) and 14 healthy volunteers (six men, eight women; mean age, 25 years) were studied. The authors obtained fractional anisotropy (FA) values in MS plaques and in the adjacent normal-appearing WM in patients with MS and in equivalent areas in healthy volunteers. They placed regions of interest (ROIs) around the periphery of plaques and defined the total ROIs (ie, plaques plus peripheral ROIs) as abnormal if their mean FA values were at least 2 standard deviations below those of equivalent ROIs within equivalent regions in healthy volunteers. The combined area of the plaque and the peripheral ROI was compared with the area of the plaque seen on T2-weighted MR images by means of a Student paired t test (P = .05). The mean plaque size on T2-weighted images was 72 mm2 +/- 21 (standard deviation). The mean plaque FA value was 0.285 +/- 0.088 (0.447 +/- 0.069 in healthy volunteers [P < .001]; mean percentage reduction in FA in MS plaques, 37%). The mean plaque size on FA maps was 91 mm2 +/- 35, a mean increase of 127% compared with the size of the original plaque on T2-weighted images (P = .03). A significant increase in plaque size was seen when normal-appearing WM was interrogated with diffusion-tensor MR imaging. This imaging technique may represent a more sensitive method of assessing disease burden and may have a future role in determining disease burden and activity.

SU-F-J-58: Evaluation of RayStation Hybrid Deformable Image Registration for Accurate Contour Propagation in Adaptive Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rong, Y; Rao, S; Daly, M

Purpose: Adaptive radiotherapy requires complete new sets of regions of interests (ROIs) delineation on the mid-treatment CT images. This work aims at evaluating the accuracy of the RayStation hybrid deformable image registration (DIR) algorithm for its overall integrity and accuracy in contour propagation for adaptive planning. Methods: The hybrid DIR is based on the combination of intensity-based algorithm and anatomical information provided by contours. Patients who received mid-treatment CT scans were identified for the study, including six lung patients (two mid-treatment CTs) and six head-and-neck (HN) patients (one mid-treatment CT). DIRpropagated ROIs were compared with physician-drawn ROIs for 8 ITVsmore » and 7 critical organs (lungs, heart, esophagus, and etc.) for the lung patients, as well as 14 GTVs and 20 critical organs (mandible, eyes, parotids, and etc.) for the HN patients. Volume difference, center of mass (COM) difference, and Dice index were used for evaluation. Clinical-relevance of each propagated ROI was scored by two physicians, and correlated with the Dice index. Results: For critical organs, good agreement (Dice>0.9) were seen on all 7 for lung patients and 13 out of 20 for HN patients, with the rest requiring minimal edits. For targets, COM differences were within 5 mm on average for all patients. For Lung, 5 out of 8 ITVs required minimal edits (Dice 0.8–0.9), with the rest 2 needed re-drawn due to their small volumes (<10 cc). However, the propagated HN GTVs resulted in relatively low Dice values (0.5–0.8) due to their small volumes (3–40 cc) and high variability, among which 2 required re-drawn due to new nodal target identified on the mid-treatment CT scans. Conclusion: The hybrid DIR algorithm was found to be clinically useful and efficient for lung and HN patients, especially for propagated critical organ ROIs. It has potential to significantly improve the workflow in adaptive planning.« less

SU-E-J-113: Effects of Deformable Registration On First-Order Texture Maps Calculated From Thoracic Lung CT Scans

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smith, C; Cunliffe, A; Al-Hallaq, H

Purpose: To determine the stability of eight first-order texture features following the deformable registration of serial computed tomography (CT) scans. Methods: CT scans at two different time points from 10 patients deemed to have no lung abnormalities by a radiologist were collected. Following lung segmentation using an in-house program, texture maps were calculated from 32×32-pixel regions of interest centered at every pixel in the lungs. The texture feature value of the ROI was assigned to the center pixel of the ROI in the corresponding location of the texture map. Pixels in the square ROI not contained within the segmented lungmore » were not included in the calculation. To quantify the agreement between ROI texture features in corresponding pixels of the baseline and follow-up texture maps, the Fraunhofer MEVIS EMPIRE10 deformable registration algorithm was used to register the baseline and follow-up scans. Bland-Altman analysis was used to compare registered scan pairs by computing normalized bias (nBias), defined as the feature value change normalized to the mean feature value, and normalized range of agreement (nRoA), defined as the range spanned by the 95% limits of agreement normalized to the mean feature value. Results: Each patient’s scans contained between 6.8–15.4 million ROIs. All of the first-order features investigated were found to have an nBias value less than 0.04% and an nRoA less than 19%, indicating that the variability introduced by deformable registration was low. Conclusion: The eight first-order features investigated were found to be registration stable. Changes in CT texture maps could allow for temporal-spatial evaluation of the evolution of lung abnormalities relating to a variety of diseases on a patient-by-patient basis. SGA and HA receives royalties and licensing fees through the University of Chicago for computer-aided diagnosis technology. Research reported in this publication was supported by the National Institute Of General Medical Sciences of the National Institutes of Health under Award Number R25GM109439.« less

TU-H-CAMPUS-IeP3-04: Evaluation of Changes in Quantitative Ultrasound Parameters During Prostate Radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Najafi, M; El Kaffas, A; Han, B

Purpose: Clarity Autoscan ultrasound monitoring system allows acquisition of raw radiofrequency (RF) ultrasound data prior and during radiotherapy. This enables the computation of 3D Quantitative Ultrasound (QUS) tissue parametric maps from. We aim to evaluate whether QUS parameters undergo changes with radiotherapy and thus potentially be used as early predictors and/or markers of treatment response in prostate cancer patients. Methods: In-vivo evaluation was performed under IRB protocol to allow data collection in prostate patients treated with VMAT whereby prostate was imaged through the acoustic window of the perineum. QUS spectroscopy analysis was carried out by computing a tissue power spectrummore » normalized to the power spectrum obtained from a quartz to remove system transfer function effects. A ROI was selected within the 3D image volume of the prostate. Because longitudinal registration was optimal, the same features could be used to select ROIs at roughly the same location in images acquired on different days. Parametric maps were generated within the rectangular ROIs with window sizes that were approximately 8 times the wavelength of the ultrasound. The mid-band fit (MBF), spectral slope (SS) and spectral intercept (SI) QUS parameters were computed for each window within the ROI and displayed as parametric maps. Quantitative parameters were obtained by averaging each of the spectral parameters over the whole ROI. Results: Data was acquired for over 21 treatment fractions. Preliminary results show changes in the parametric maps. MBF values decreased from −33.9 dB to −38.7 dB from pre-treatment to the last day of treatment. The spectral slope increased from −1.1 a.u. to −0.5 a.u., and spectral intercept decreased from −28.2 dB to −36.3 dB over the 21 treatment regimen. Conclusion: QUS parametric maps change over the course of treatment which warrants further investigation in their potential use for treatment planning and predicting treatment outcomes. Research was supported by Elekta.« less

Focal Hemodynamic Responses in the Stimulated Hemisphere During High-Definition Transcranial Direct Current Stimulation.

PubMed

Muthalib, Makii; Besson, Pierre; Rothwell, John; Perrey, Stéphane

2017-07-17

High-definition transcranial direct current stimulation (HD-tDCS) using a 4 × 1 electrode montage has been previously shown using modeling and physiological studies to constrain the electric field within the spatial extent of the electrodes. The aim of this proof-of-concept study was to determine if functional near-infrared spectroscopy (fNIRS) neuroimaging can be used to determine a hemodynamic correlate of this 4 × 1 HD-tDCS electric field on the brain. In a three session cross-over study design, 13 healthy males received one sham (2 mA, 30 sec) and two real (HD-tDCS-1 and HD-tDCS-2, 2 mA, 10 min) anodal HD-tDCS targeting the left M1 via a 4 × 1 electrode montage (anode on C3 and 4 return electrodes 3.5 cm from anode). The two real HD-tDCS sessions afforded a within-subject replication of the findings. fNIRS was used to measure changes in brain hemodynamics (oxygenated hemoglobin integral-O 2 Hb int ) during each 10 min session from two regions of interest (ROIs) in the stimulated left hemisphere that corresponded to "within" (L in ) and "outside" (L out ) the spatial extent of the 4 × 1 electrode montage, and two corresponding ROIs (R in and R out ) in the right hemisphere. The ANOVA showed that both real anodal HD-tDCS compared to sham induced a significantly greater O 2 Hb int in the L in than L out ROIs of the stimulated left hemisphere; while there were no significant differences between the real and sham sessions for the right hemisphere ROIs. Intra-class correlation coefficients showed "fair-to-good" reproducibility for the left stimulated hemisphere ROIs. The greater O 2 Hb int "within" than "outside" the spatial extent of the 4 × 1 electrode montage represents a hemodynamic correlate of the electrical field distribution, and thus provides a prospective reliable method to determine the dose of stimulation that is necessary to optimize HD-tDCS parameters in various applications. © 2017 International Neuromodulation Society.

Computationally optimized ECoG stimulation with local safety constraints.

PubMed

Guler, Seyhmus; Dannhauer, Moritz; Roig-Solvas, Biel; Gkogkidis, Alexis; Macleod, Rob; Ball, Tonio; Ojemann, Jeffrey G; Brooks, Dana H

2018-06-01

Direct stimulation of the cortical surface is used clinically for cortical mapping and modulation of local activity. Future applications of cortical modulation and brain-computer interfaces may also use cortical stimulation methods. One common method to deliver current is through electrocorticography (ECoG) stimulation in which a dense array of electrodes are placed subdurally or epidurally to stimulate the cortex. However, proximity to cortical tissue limits the amount of current that can be delivered safely. It may be desirable to deliver higher current to a specific local region of interest (ROI) while limiting current to other local areas more stringently than is guaranteed by global safety limits. Two commonly used global safety constraints bound the total injected current and individual electrode currents. However, these two sets of constraints may not be sufficient to prevent high current density locally (hot-spots). In this work, we propose an efficient approach that prevents current density hot-spots in the entire brain while optimizing ECoG stimulus patterns for targeted stimulation. Specifically, we maximize the current along a particular desired directional field in the ROI while respecting three safety constraints: one on the total injected current, one on individual electrode currents, and the third on the local current density magnitude in the brain. This third set of constraints creates a computational barrier due to the huge number of constraints needed to bound the current density at every point in the entire brain. We overcome this barrier by adopting an efficient two-step approach. In the first step, the proposed method identifies the safe brain region, which cannot contain any hot-spots solely based on the global bounds on total injected current and individual electrode currents. In the second step, the proposed algorithm iteratively adjusts the stimulus pattern to arrive at a solution that exhibits no hot-spots in the remaining brain. We report on simulations on a realistic finite element (FE) head model with five anatomical ROIs and two desired directional fields. We also report on the effect of ROI depth and desired directional field on the focality of the stimulation. Finally, we provide an analysis of optimization runtime as a function of different safety and modeling parameters. Our results suggest that optimized stimulus patterns tend to differ from those used in clinical practice. Copyright © 2018 Elsevier Inc. All rights reserved.

An Ensemble Method for Classifying Regional Disease Patterns of Diffuse Interstitial Lung Disease Using HRCT Images from Different Vendors.

PubMed

Jun, Sanghoon; Kim, Namkug; Seo, Joon Beom; Lee, Young Kyung; Lynch, David A

2017-12-01

We propose the use of ensemble classifiers to overcome inter-scanner variations in the differentiation of regional disease patterns in high-resolution computed tomography (HRCT) images of diffuse interstitial lung disease patients obtained from different scanners. A total of 600 rectangular 20 × 20-pixel regions of interest (ROIs) on HRCT images obtained from two different scanners (GE and Siemens) and the whole lung area of 92 HRCT images were classified as one of six regional pulmonary disease patterns by two expert radiologists. Textual and shape features were extracted from each ROI and the whole lung parenchyma. For automatic classification, individual and ensemble classifiers were trained and tested with the ROI dataset. We designed the following three experimental sets: an intra-scanner study in which the training and test sets were from the same scanner, an integrated scanner study in which the data from the two scanners were merged, and an inter-scanner study in which the training and test sets were acquired from different scanners. In the ROI-based classification, the ensemble classifiers showed better (p < 0.001) accuracy (89.73%, SD = 0.43) than the individual classifiers (88.38%, SD = 0.31) in the integrated scanner test. The ensemble classifiers also showed partial improvements in the intra- and inter-scanner tests. In the whole lung classification experiment, the quantification accuracies of the ensemble classifiers with integrated training (49.57%) were higher (p < 0.001) than the individual classifiers (48.19%). Furthermore, the ensemble classifiers also showed better performance in both the intra- and inter-scanner experiments. We concluded that the ensemble classifiers provide better performance when using integrated scanner images.