Acoustic radiation force impulse (ARFI) imaging: Characterizing the mechanical properties of tissues using their transient response to localized force

NASA Astrophysics Data System (ADS)

Nightingale, Kathryn R.; Palmeri, Mark L.; Congdon, Amy N.; Frinkely, Kristin D.; Trahey, Gregg E.

2004-05-01

Acoustic radiation force impulse (ARFI) imaging utilizes brief, high energy, focused acoustic pulses to generate radiation force in tissue, and conventional diagnostic ultrasound methods to detect the resulting tissue displacements in order to image the relative mechanical properties of tissue. The magnitude and spatial extent of the applied force is dependent upon the transmit beam parameters and the tissue attenuation. Forcing volumes are on the order of 5 mm3, pulse durations are less than 1 ms, and tissue displacements are typically several microns. Images of tissue displacement reflect local tissue stiffness, with softer tissues (e.g., fat) displacing farther than stiffer tissues (e.g., muscle). Parametric images of maximum displacement, time to peak displacement, and recovery time provide information about tissue material properties and structure. In both in vivo and ex vivo data, structures shown in matched B-mode images are in good agreement with those shown in ARFI images, with comparable resolution. Potential clinical applications under investigation include soft tissue lesion characterization, assessment of focal atherosclerosis, and imaging of thermal lesion formation during tissue ablation procedures. Results from ongoing studies will be presented. [Work supported by NIH Grant R01 EB002132-03, and the Whitaker Foundation. System support from Siemens Medical Solutions USA, Inc.

MR PROSTATE SEGMENTATION VIA DISTRIBUTED DISCRIMINATIVE DICTIONARY (DDD) LEARNING.

PubMed

Guo, Yanrong; Zhan, Yiqiang; Gao, Yaozong; Jiang, Jianguo; Shen, Dinggang

2013-01-01

Segmenting prostate from MR images is important yet challenging. Due to non-Gaussian distribution of prostate appearances in MR images, the popular active appearance model (AAM) has its limited performance. Although the newly developed sparse dictionary learning method[1, 2] can model the image appearance in a non-parametric fashion, the learned dictionaries still lack the discriminative power between prostate and non-prostate tissues, which is critical for accurate prostate segmentation. In this paper, we propose to integrate deformable model with a novel learning scheme, namely the Distributed Discriminative Dictionary ( DDD ) learning, which can capture image appearance in a non-parametric and discriminative fashion. In particular, three strategies are designed to boost the tissue discriminative power of DDD. First , minimum Redundancy Maximum Relevance (mRMR) feature selection is performed to constrain the dictionary learning in a discriminative feature space. Second , linear discriminant analysis (LDA) is employed to assemble residuals from different dictionaries for optimal separation between prostate and non-prostate tissues. Third , instead of learning the global dictionaries, we learn a set of local dictionaries for the local regions (each with small appearance variations) along prostate boundary, thus achieving better tissue differentiation locally. In the application stage, DDDs will provide the appearance cues to robustly drive the deformable model onto the prostate boundary. Experiments on 50 MR prostate images show that our method can yield a Dice Ratio of 88% compared to the manual segmentations, and have 7% improvement over the conventional AAM.

Radiofrequency Ablation, MR Thermometry, and High-Spatial-Resolution MR Parametric Imaging with a Single, Minimally Invasive Device.

PubMed

Ertürk, M Arcan; Sathyanarayana Hegde, Shashank; Bottomley, Paul A

2016-12-01

Purpose To develop and demonstrate in vitro and in vivo a single interventional magnetic resonance (MR)-active device that integrates the functions of precise identification of a tissue site with the delivery of radiofrequency (RF) energy for ablation, high-spatial-resolution thermal mapping to monitor thermal dose, and quantitative MR imaging relaxometry to document ablation-induced tissue changes for characterizing ablated tissue. Materials and Methods All animal studies were approved by the institutional animal care and use committee. A loopless MR imaging antenna composed of a tuned microcable either 0.8 or 2.2 mm in diameter with an extended central conductor was switched between a 3-T MR imaging unit and an RF power source to monitor and perform RF ablation in bovine muscle and human artery samples in vitro and in rabbits in vivo. High-spatial-resolution (250-300-μm) proton resonance frequency shift MR thermometry was interleaved with ablations. Quantitative spin-lattice (T1) and spin-spin (T2) relaxation time MR imaging mapping was performed before and after ablation. These maps were compared with findings from gross tissue examination of the region of ablated tissue after MR imaging. Results High-spatial-resolution MR imaging afforded temperature mapping in less than 8 seconds for monitoring ablation temperatures in excess of 85°C delivered by the same device. This produced irreversible thermal injury and necrosis. Quantitative MR imaging relaxation time maps demonstrated up to a twofold variation in mean regional T1 and T2 after ablation versus before ablation. Conclusion A simple, integrated, minimally invasive interventional probe that provides image-guided therapy delivery, thermal mapping of dose, and detection of ablation-associated MR imaging parametric changes was developed and demonstrated. With this single-device approach, coupling-related safety concerns associated with multiple conductor approaches were avoided. © RSNA, 2016 Online supplemental material is available for this article.

Method for Separation of Blood Vessels on the Three-Color Images of Biological Tissues

NASA Astrophysics Data System (ADS)

Lisenko, S. A.

2017-07-01

A new technology was developed to improve the visibility of blood vessels on images of tissues of hollow human organs(the alimentary tract and respiratory system) based on the relation between the color components of the image, the scattering properties of the tissue, and its hemoglobin content. A statistical operator was presented to convert the three-color image of the tissue into a parametric map objectively characterizing the concentration of hemoglobin in the tissue regardless of the illumination and shooting conditions. An algorithm for obtaining conversion parameters for image systems with known spectral characteristics was presented. An image of a multilayer multiple-scattering medium modeling bronchial tissue was synthesized and was used to evaluate the efficiency of the proposed conversion system. It was shown that the conversion made it possible to increase the contrast of the blood vessels by almost two orders of magnitude, to significantly improve the clarity of the display of their borders, and to eliminate almost completely the influence of background and nonuniform illumination of the medium in comparison with the original image.

Improved estimation of parametric images of cerebral glucose metabolic rate from dynamic FDG-PET using volume-wise principle component analysis

NASA Astrophysics Data System (ADS)

Dai, Xiaoqian; Tian, Jie; Chen, Zhe

2010-03-01

Parametric images can represent both spatial distribution and quantification of the biological and physiological parameters of tracer kinetics. The linear least square (LLS) method is a well-estimated linear regression method for generating parametric images by fitting compartment models with good computational efficiency. However, bias exists in LLS-based parameter estimates, owing to the noise present in tissue time activity curves (TTACs) that propagates as correlated error in the LLS linearized equations. To address this problem, a volume-wise principal component analysis (PCA) based method is proposed. In this method, firstly dynamic PET data are properly pre-transformed to standardize noise variance as PCA is a data driven technique and can not itself separate signals from noise. Secondly, the volume-wise PCA is applied on PET data. The signals can be mostly represented by the first few principle components (PC) and the noise is left in the subsequent PCs. Then the noise-reduced data are obtained using the first few PCs by applying 'inverse PCA'. It should also be transformed back according to the pre-transformation method used in the first step to maintain the scale of the original data set. Finally, the obtained new data set is used to generate parametric images using the linear least squares (LLS) estimation method. Compared with other noise-removal method, the proposed method can achieve high statistical reliability in the generated parametric images. The effectiveness of the method is demonstrated both with computer simulation and with clinical dynamic FDG PET study.

Multi-parametric monitoring and assessment of high-intensity focused ultrasound (HIFU) boiling by harmonic motion imaging for focused ultrasound (HMIFU): an ex vivo feasibility study

NASA Astrophysics Data System (ADS)

Hou, Gary Y.; Marquet, Fabrice; Wang, Shutao; Konofagou, Elisa E.

2014-03-01

Harmonic motion imaging for focused ultrasound (HMIFU) is a recently developed high-intensity focused ultrasound (HIFU) treatment monitoring method with feasibilities demonstrated in vitro and in vivo. Here, a multi-parametric study is performed to investigate both elastic and acoustics-independent viscoelastic tissue changes using the Harmonic Motion Imaging (HMI) displacement, axial compressive strain and change in relative phase shift during high energy HIFU treatment with tissue boiling. Forty three (n = 43) thermal lesions were formed in ex vivo canine liver specimens (n = 28). Two-dimensional (2D) transverse HMI displacement maps were also obtained before and after lesion formation. The same method was repeated in 10 s, 20 s and 30 s HIFU durations at three different acoustic powers of 8, 10, and 11 W, which were selected and verified as treatment parameters capable of inducing boiling using both thermocouple and passive cavitation detection (PCD) measurements. Although a steady decrease in the displacement, compressive strain, and relative change in the focal phase shift (Δϕ) were obtained in numerous cases, indicating an overall increase in relative stiffness, the study outcomes also showed that during boiling, a reverse lesion-to-background displacement contrast was detected, indicating potential change in tissue absorption, geometrical change and/or, mechanical gelatification or pulverization. Following treatment, corresponding 2D HMI displacement images of the thermal lesions also mapped consistent discrepancy in the lesion-to-background displacement contrast. Despite the expectedly chaotic changes in acoustic properties with boiling, the relative change in phase shift showed a consistent decrease, indicating its robustness to monitor biomechanical properties independent of the acoustic property changes throughout the HIFU treatment. In addition, the 2D HMI displacement images confirmed and indicated the increase in the thermal lesion size with treatment duration, which was validated against pathology. In conclusion, multi-parametric HMIFU was shown capable of monitoring and mapping tissue viscoelastic response changes during and after HIFU boiling, some of which were independent of the acoustic parameter changes.

Multi-parametric monitoring and assessment of High Intensity Focused Ultrasound (HIFU) boiling by Harmonic Motion Imaging for Focused Ultrasound (HMIFU): An ex vivo feasibility study

PubMed Central

Hou, Gary Y.; Marquet, Fabrice; Wang, Shutao; Konofagou, Elisa E.

2014-01-01

Harmonic Motion Imaging for Focused Ultrasound (HMIFU) is a recently developed high-intensity focused ultrasound (HIFU) treatment monitoring method with feasibilities demonstrated in vitro and in vivo. Here, a multi-parametric study is performed to investigate both elastic and acoustics-independent viscoelastic tissue changes using the Harmonic Motion Imaging (HMI) displacement, axial compressive strain and change in relative phase-shift during high energy HIFU treatment with tissue boiling. Forty three (n=43) thermal lesions were formed in ex vivo canine liver specimens (n=28). Two dimensional (2D) transverse HMI displacement maps were also obtained before and after lesion formation. The same method was repeated in 10-s, 20-s and 30-s HIFU durations at three different acoustic powers of 8, 10, and 11W, which were selected and verified as treatment parameters capable of inducing boiling using both thermocouple and Passive Cavitation Detection (PCD) measurements. Although a steady decrease in the displacement, compressive strain, and relative change in the focal phase shift (Δφ) were obtained in numerous cases, indicating an overall increase in relative stiffness, the study outcomes also showed that during boiling, a reverse lesion-to-background displacement contrast was detected, indicating potential change in tissue absorption, geometrical change and/or, mechanical gelatification or pulverization. Following treatment, corresponding 2D HMI displacement images of the thermal lesions also mapped consistent discrepancy in the lesion-to-background displacement contrast. Despite unpredictable changes in acoustic properties with boiling, the relative change in phase shift showed a consistent decrease, indicating its robustness to monitor biomechanical properties independent of the acoustic property change throughout the HIFU treatment. In addition, the 2D HMI displacement images confirmed and indicated the increase in the thermal lesion size with treatment duration, which was validated against pathology. In conclusion, multi-parametric HMIFU was shown capable of monitoring and mapping tissue viscoelastic response changes during and after HIFU boiling, some of which were independent of the acoustic parameter changes. PMID:24556974

Multi-parametric monitoring and assessment of high-intensity focused ultrasound (HIFU) boiling by harmonic motion imaging for focused ultrasound (HMIFU): an ex vivo feasibility study.

PubMed

Hou, Gary Y; Marquet, Fabrice; Wang, Shutao; Konofagou, Elisa E

2014-03-07

Harmonic motion imaging for focused ultrasound (HMIFU) is a recently developed high-intensity focused ultrasound (HIFU) treatment monitoring method with feasibilities demonstrated in vitro and in vivo. Here, a multi-parametric study is performed to investigate both elastic and acoustics-independent viscoelastic tissue changes using the Harmonic Motion Imaging (HMI) displacement, axial compressive strain and change in relative phase shift during high energy HIFU treatment with tissue boiling. Forty three (n = 43) thermal lesions were formed in ex vivo canine liver specimens (n = 28). Two-dimensional (2D) transverse HMI displacement maps were also obtained before and after lesion formation. The same method was repeated in 10 s, 20 s and 30 s HIFU durations at three different acoustic powers of 8, 10, and 11 W, which were selected and verified as treatment parameters capable of inducing boiling using both thermocouple and passive cavitation detection (PCD) measurements. Although a steady decrease in the displacement, compressive strain, and relative change in the focal phase shift (Δϕ) were obtained in numerous cases, indicating an overall increase in relative stiffness, the study outcomes also showed that during boiling, a reverse lesion-to-background displacement contrast was detected, indicating potential change in tissue absorption, geometrical change and/or, mechanical gelatification or pulverization. Following treatment, corresponding 2D HMI displacement images of the thermal lesions also mapped consistent discrepancy in the lesion-to-background displacement contrast. Despite the expectedly chaotic changes in acoustic properties with boiling, the relative change in phase shift showed a consistent decrease, indicating its robustness to monitor biomechanical properties independent of the acoustic property changes throughout the HIFU treatment. In addition, the 2D HMI displacement images confirmed and indicated the increase in the thermal lesion size with treatment duration, which was validated against pathology. In conclusion, multi-parametric HMIFU was shown capable of monitoring and mapping tissue viscoelastic response changes during and after HIFU boiling, some of which were independent of the acoustic parameter changes.

Direct reconstruction of cardiac PET kinetic parametric images using a preconditioned conjugate gradient approach

PubMed Central

Rakvongthai, Yothin; Ouyang, Jinsong; Guerin, Bastien; Li, Quanzheng; Alpert, Nathaniel M.; El Fakhri, Georges

2013-01-01

Purpose: Our research goal is to develop an algorithm to reconstruct cardiac positron emission tomography (PET) kinetic parametric images directly from sinograms and compare its performance with the conventional indirect approach. Methods: Time activity curves of a NCAT phantom were computed according to a one-tissue compartmental kinetic model with realistic kinetic parameters. The sinograms at each time frame were simulated using the activity distribution for the time frame. The authors reconstructed the parametric images directly from the sinograms by optimizing a cost function, which included the Poisson log-likelihood and a spatial regularization terms, using the preconditioned conjugate gradient (PCG) algorithm with the proposed preconditioner. The proposed preconditioner is a diagonal matrix whose diagonal entries are the ratio of the parameter and the sensitivity of the radioactivity associated with parameter. The authors compared the reconstructed parametric images using the direct approach with those reconstructed using the conventional indirect approach. Results: At the same bias, the direct approach yielded significant relative reduction in standard deviation by 12%–29% and 32%–70% for 50 × 106 and 10 × 106 detected coincidences counts, respectively. Also, the PCG method effectively reached a constant value after only 10 iterations (with numerical convergence achieved after 40–50 iterations), while more than 500 iterations were needed for CG. Conclusions: The authors have developed a novel approach based on the PCG algorithm to directly reconstruct cardiac PET parametric images from sinograms, and yield better estimation of kinetic parameters than the conventional indirect approach, i.e., curve fitting of reconstructed images. The PCG method increases the convergence rate of reconstruction significantly as compared to the conventional CG method. PMID:24089922

Direct reconstruction of cardiac PET kinetic parametric images using a preconditioned conjugate gradient approach.

PubMed

Rakvongthai, Yothin; Ouyang, Jinsong; Guerin, Bastien; Li, Quanzheng; Alpert, Nathaniel M; El Fakhri, Georges

2013-10-01

Our research goal is to develop an algorithm to reconstruct cardiac positron emission tomography (PET) kinetic parametric images directly from sinograms and compare its performance with the conventional indirect approach. Time activity curves of a NCAT phantom were computed according to a one-tissue compartmental kinetic model with realistic kinetic parameters. The sinograms at each time frame were simulated using the activity distribution for the time frame. The authors reconstructed the parametric images directly from the sinograms by optimizing a cost function, which included the Poisson log-likelihood and a spatial regularization terms, using the preconditioned conjugate gradient (PCG) algorithm with the proposed preconditioner. The proposed preconditioner is a diagonal matrix whose diagonal entries are the ratio of the parameter and the sensitivity of the radioactivity associated with parameter. The authors compared the reconstructed parametric images using the direct approach with those reconstructed using the conventional indirect approach. At the same bias, the direct approach yielded significant relative reduction in standard deviation by 12%-29% and 32%-70% for 50 × 10(6) and 10 × 10(6) detected coincidences counts, respectively. Also, the PCG method effectively reached a constant value after only 10 iterations (with numerical convergence achieved after 40-50 iterations), while more than 500 iterations were needed for CG. The authors have developed a novel approach based on the PCG algorithm to directly reconstruct cardiac PET parametric images from sinograms, and yield better estimation of kinetic parameters than the conventional indirect approach, i.e., curve fitting of reconstructed images. The PCG method increases the convergence rate of reconstruction significantly as compared to the conventional CG method.

Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI.

PubMed

Sauwen, N; Acou, M; Van Cauter, S; Sima, D M; Veraart, J; Maes, F; Himmelreich, U; Achten, E; Van Huffel, S

2016-01-01

Tumor segmentation is a particularly challenging task in high-grade gliomas (HGGs), as they are among the most heterogeneous tumors in oncology. An accurate delineation of the lesion and its main subcomponents contributes to optimal treatment planning, prognosis and follow-up. Conventional MRI (cMRI) is the imaging modality of choice for manual segmentation, and is also considered in the vast majority of automated segmentation studies. Advanced MRI modalities such as perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and magnetic resonance spectroscopic imaging (MRSI) have already shown their added value in tumor tissue characterization, hence there have been recent suggestions of combining different MRI modalities into a multi-parametric MRI (MP-MRI) approach for brain tumor segmentation. In this paper, we compare the performance of several unsupervised classification methods for HGG segmentation based on MP-MRI data including cMRI, DWI, MRSI and PWI. Two independent MP-MRI datasets with a different acquisition protocol were available from different hospitals. We demonstrate that a hierarchical non-negative matrix factorization variant which was previously introduced for MP-MRI tumor segmentation gives the best performance in terms of mean Dice-scores for the pathologic tissue classes on both datasets.

Estimation of viscoelastic parameters in Prony series from shear wave propagation

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

Jung, Jae-Wook; Hong, Jung-Wuk, E-mail: j.hong@kaist.ac.kr, E-mail: jwhong@alum.mit.edu; Lee, Hyoung-Ki

2016-06-21

When acquiring accurate ultrasonic images, we must precisely estimate the mechanical properties of the soft tissue. This study investigates and estimates the viscoelastic properties of the tissue by analyzing shear waves generated through an acoustic radiation force. The shear waves are sourced from a localized pushing force acting for a certain duration, and the generated waves travel horizontally. The wave velocities depend on the mechanical properties of the tissue such as the shear modulus and viscoelastic properties; therefore, we can inversely calculate the properties of the tissue through parametric studies.

Direct reconstruction of parametric images for brain PET with event-by-event motion correction: evaluation in two tracers across count levels

NASA Astrophysics Data System (ADS)

Germino, Mary; Gallezot, Jean-Dominque; Yan, Jianhua; Carson, Richard E.

2017-07-01

Parametric images for dynamic positron emission tomography (PET) are typically generated by an indirect method, i.e. reconstructing a time series of emission images, then fitting a kinetic model to each voxel time activity curve. Alternatively, ‘direct reconstruction’, incorporates the kinetic model into the reconstruction algorithm itself, directly producing parametric images from projection data. Direct reconstruction has been shown to achieve parametric images with lower standard error than the indirect method. Here, we present direct reconstruction for brain PET using event-by-event motion correction of list-mode data, applied to two tracers. Event-by-event motion correction was implemented for direct reconstruction in the Parametric Motion-compensation OSEM List-mode Algorithm for Resolution-recovery reconstruction. The direct implementation was tested on simulated and human datasets with tracers [11C]AFM (serotonin transporter) and [11C]UCB-J (synaptic density), which follow the 1-tissue compartment model. Rigid head motion was tracked with the Vicra system. Parametric images of K 1 and distribution volume (V T  =  K 1/k 2) were compared to those generated by the indirect method by regional coefficient of variation (CoV). Performance across count levels was assessed using sub-sampled datasets. For simulated and real datasets at high counts, the two methods estimated K 1 and V T with comparable accuracy. At lower count levels, the direct method was substantially more robust to outliers than the indirect method. Compared to the indirect method, direct reconstruction reduced regional K 1 CoV by 35-48% (simulated dataset), 39-43% ([11C]AFM dataset) and 30-36% ([11C]UCB-J dataset) across count levels (averaged over regions at matched iteration); V T CoV was reduced by 51-58%, 54-60% and 30-46%, respectively. Motion correction played an important role in the dataset with larger motion: correction increased regional V T by 51% on average in the [11C]UCB-J dataset. Direct reconstruction of dynamic brain PET with event-by-event motion correction is achievable and dramatically more robust to noise in V T images than the indirect method.

Multiparametric, Longitudinal Optical Coherence Tomography Imaging Reveals Acute Injury and Chronic Recovery in Experimental Ischemic Stroke

PubMed Central

Srinivasan, Vivek J.; Mandeville, Emiri T.; Can, Anil; Blasi, Francesco; Climov, Mihail; Daneshmand, Ali; Lee, Jeong Hyun; Yu, Esther; Radhakrishnan, Harsha; Lo, Eng H.; Sakadžić, Sava; Eikermann-Haerter, Katharina; Ayata, Cenk

2013-01-01

Progress in experimental stroke and translational medicine could be accelerated by high-resolution in vivo imaging of disease progression in the mouse cortex. Here, we introduce optical microscopic methods that monitor brain injury progression using intrinsic optical scattering properties of cortical tissue. A multi-parametric Optical Coherence Tomography (OCT) platform for longitudinal imaging of ischemic stroke in mice, through thinned-skull, reinforced cranial window surgical preparations, is described. In the acute stages, the spatiotemporal interplay between hemodynamics and cell viability, a key determinant of pathogenesis, was imaged. In acute stroke, microscopic biomarkers for eventual infarction, including capillary non-perfusion, cerebral blood flow deficiency, altered cellular scattering, and impaired autoregulation of cerebral blood flow, were quantified and correlated with histology. Additionally, longitudinal microscopy revealed remodeling and flow recovery after one week of chronic stroke. Intrinsic scattering properties serve as reporters of acute cellular and vascular injury and recovery in experimental stroke. Multi-parametric OCT represents a robust in vivo imaging platform to comprehensively investigate these properties. PMID:23940761

sfDM: Open-Source Software for Temporal Analysis and Visualization of Brain Tumor Diffusion MR Using Serial Functional Diffusion Mapping.

PubMed

Ceschin, Rafael; Panigrahy, Ashok; Gopalakrishnan, Vanathi

2015-01-01

A major challenge in the diagnosis and treatment of brain tumors is tissue heterogeneity leading to mixed treatment response. Additionally, they are often difficult or at very high risk for biopsy, further hindering the clinical management process. To overcome this, novel advanced imaging methods are increasingly being adapted clinically to identify useful noninvasive biomarkers capable of disease stage characterization and treatment response prediction. One promising technique is called functional diffusion mapping (fDM), which uses diffusion-weighted imaging (DWI) to generate parametric maps between two imaging time points in order to identify significant voxel-wise changes in water diffusion within the tumor tissue. Here we introduce serial functional diffusion mapping (sfDM), an extension of existing fDM methods, to analyze the entire tumor diffusion profile along the temporal course of the disease. sfDM provides the tools necessary to analyze a tumor data set in the context of spatiotemporal parametric mapping: the image registration pipeline, biomarker extraction, and visualization tools. We present the general workflow of the pipeline, along with a typical use case for the software. sfDM is written in Python and is freely available as an open-source package under the Berkley Software Distribution (BSD) license to promote transparency and reproducibility.

Sci-Thur PM - Colourful Interactions: Highlights 04: A Fast Quantitative MRI Acquisition and Processing Pipeline for Radiation Treatment Planning and Simulation

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

Jutras, Jean-David

MRI-only Radiation Treatment Planning (RTP) is becoming increasingly popular because of a simplified work-flow, and less inconvenience to the patient who avoids multiple scans. The advantages of MRI-based RTP over traditional CT-based RTP lie in its superior soft-tissue contrast, and absence of ionizing radiation dose. The lack of electron-density information in MRI can be addressed by automatic tissue classification. To distinguish bone from air, which both appear dark in MRI, an ultra-short echo time (UTE) pulse sequence may be used. Quantitative MRI parametric maps can provide improved tissue segmentation/classification and better sensitivity in monitoring disease progression and treatment outcome thanmore » standard weighted images. Superior tumor contrast can be achieved on pure T{sub 1} images compared to conventional T{sub 1}-weighted images acquired in the same scan duration and voxel resolution. In this study, we have developed a robust and fast quantitative MRI acquisition and post-processing work-flow that integrates these latest advances into the MRI-based RTP of brain lesions. Using 3D multi-echo FLASH images at two different optimized flip angles (both acquired in under 9 min, and 1mm isotropic resolution), parametric maps of T{sub 1}, proton-density (M{sub 0}), and T{sub 2}{sup *} are obtained with high contrast-to-noise ratio, and negligible geometrical distortions, water-fat shifts and susceptibility effects. An additional 3D UTE MRI dataset is acquired (in under 4 min) and post-processed to classify tissues for dose simulation. The pipeline was tested on four healthy volunteers and a clinical trial on brain cancer patients is underway.« less

18F-FLT uptake kinetics in head and neck squamous cell carcinoma: a PET imaging study.

PubMed

Liu, Dan; Chalkidou, Anastasia; Landau, David B; Marsden, Paul K; Fenwick, John D

2014-04-01

To analyze the kinetics of 3(')-deoxy-3(')-[F-18]-fluorothymidine (18F-FLT) uptake by head and neck squamous cell carcinomas and involved nodes imaged using positron emission tomography (PET). Two- and three-tissue compartment models were fitted to 12 tumor time-activity-curves (TACs) obtained for 6 structures (tumors or involved nodes) imaged in ten dynamic PET studies of 1 h duration, carried out for five patients. The ability of the models to describe the data was assessed using a runs test, the Akaike information criterion (AIC) and leave-one-out cross-validation. To generate parametric maps the models were also fitted to TACs of individual voxels. Correlations between maps of different parameters were characterized using Pearson'sr coefficient; in particular the phosphorylation rate-constants k3-2tiss and k5 of the two- and three-tissue models were studied alongside the flux parameters KFLT- 2tiss and KFLT of these models, and standardized uptake values (SUV). A methodology based on expectation-maximization clustering and the Bayesian information criterion ("EM-BIC clustering") was used to distil the information from noisy parametric images. Fits of two-tissue models 2C3K and 2C4K and three-tissue models 3C5K and 3C6K comprising three, four, five, and six rate-constants, respectively, pass the runs test for 4, 8, 10, and 11 of 12 tumor TACs. The three-tissue models have lower AIC and cross-validation scores for nine of the 12 tumors. Overall the 3C6K model has the lowest AIC and cross-validation scores and its fitted parameter values are of the same orders of magnitude as literature estimates. Maps of KFLT and KFLT- 2tiss are strongly correlated (r = 0.85) and also correlate closely with SUV maps (r = 0.72 for KFLT- 2tiss, 0.64 for KFLT). Phosphorylation rate-constant maps are moderately correlated with flux maps (r = 0.48 for k3-2tiss vs KFLT- 2tiss and r = 0.68 for k5 vs KFLT); however, neither phosphorylation rate-constant correlates significantly with SUV. EM-BIC clustering reduces the parametric maps to a small number of levels--on average 5.8, 3.5, 3.4, and 1.4 for KFLT- 2tiss, KFLT, k3-2tiss, and k5. This large simplification is potentially useful for radiotherapy dose-painting, but demonstrates the high noise in some maps. Statistical simulations show that voxel level noise degrades TACs generated from the 3C6K model sufficiently that the average AIC score, parameter bias, and total uncertainty of 2C4K model fits are similar to those of 3C6K fits, whereas at the whole tumor level the scores are lower for 3C6K fits. For the patients studied here, whole tumor FLT uptake time-courses are represented better overall by a three-tissue than by a two-tissue model. EM-BIC clustering simplifies noisy parametric maps, providing the best description of the underlying information they contain and is potentially useful for radiotherapy dose-painting. However, the clustering highlights the large degree of noise present in maps of the phosphorylation rate-constantsk5 and k3-2tiss, which are conceptually tightly linked to cellular proliferation. Methods must be found to make these maps more robust-either by constraining other model parameters or modifying dynamic imaging protocols. © 2014 American Association of Physicists in Medicine.

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

Tracer Kinetic Analysis of (S)-¹⁸F-THK5117 as a PET Tracer for Assessing Tau Pathology.

PubMed

Jonasson, My; Wall, Anders; Chiotis, Konstantinos; Saint-Aubert, Laure; Wilking, Helena; Sprycha, Margareta; Borg, Beatrice; Thibblin, Alf; Eriksson, Jonas; Sörensen, Jens; Antoni, Gunnar; Nordberg, Agneta; Lubberink, Mark

2016-04-01

Because a correlation between tau pathology and the clinical symptoms of Alzheimer disease (AD) has been hypothesized, there is increasing interest in developing PET tracers that bind specifically to tau protein. The aim of this study was to evaluate tracer kinetic models for quantitative analysis and generation of parametric images for the novel tau ligand (S)-(18)F-THK5117. Nine subjects (5 with AD, 4 with mild cognitive impairment) received a 90-min dynamic (S)-(18)F-THK5117 PET scan. Arterial blood was sampled for measurement of blood radioactivity and metabolite analysis. Volume-of-interest (VOI)-based analysis was performed using plasma-input models; single-tissue and 2-tissue (2TCM) compartment models and plasma-input Logan and reference tissue models; and simplified reference tissue model (SRTM), reference Logan, and SUV ratio (SUVr). Cerebellum gray matter was used as the reference region. Voxel-level analysis was performed using basis function implementations of SRTM, reference Logan, and SUVr. Regionally averaged voxel values were compared with VOI-based values from the optimal reference tissue model, and simulations were made to assess accuracy and precision. In addition to 90 min, initial 40- and 60-min data were analyzed. Plasma-input Logan distribution volume ratio (DVR)-1 values agreed well with 2TCM DVR-1 values (R(2)= 0.99, slope = 0.96). SRTM binding potential (BP(ND)) and reference Logan DVR-1 values were highly correlated with plasma-input Logan DVR-1 (R(2)= 1.00, slope ≈ 1.00) whereas SUVr(70-90)-1 values correlated less well and overestimated binding. Agreement between parametric methods and SRTM was best for reference Logan (R(2)= 0.99, slope = 1.03). SUVr(70-90)-1 values were almost 3 times higher than BP(ND) values in white matter and 1.5 times higher in gray matter. Simulations showed poorer accuracy and precision for SUVr(70-90)-1 values than for the other reference methods. SRTM BP(ND) and reference Logan DVR-1 values were not affected by a shorter scan duration of 60 min. SRTM BP(ND) and reference Logan DVR-1 values were highly correlated with plasma-input Logan DVR-1 values. VOI-based data analyses indicated robust results for scan durations of 60 min. Reference Logan generated quantitative (S)-(18)F-THK5117 DVR-1 parametric images with the greatest accuracy and precision and with a much lower white-matter signal than seen with SUVr(70-90)-1 images. © 2016 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Characterization of tissue response to radiofrequency ablation using 3D model-based analysis of interventional MR images

NASA Astrophysics Data System (ADS)

Weinberg, Brent D.; Lazebnik, Roee S.; Breen, Michael S.; Lewin, Jonathan S.; Wilson, David L.

2003-05-01

Using magnetic resonance imaging (MRI), real-time guidance is feasible for radiofrequency (RF) current ablation of pathologic tissue. Lesions have a characteristic two-zone appearance: an inner core (Zone I) surrounded by a hyper-intense rim (Zone II). A better understanding of both the immediate (hyper-acute) and delayed (sub-acute) physiological response of the target tissue will aid development of minimally invasive tumor treatment strategies. We performed in vivo RF ablations in a rabbit thigh model and characterized the tissue response to treatment through contrast enhanced (CE) T1 and T2 weighted MR images at two time points. We measured zonal grayscale changes as well as zone volume changes using a 3D computationally fitted globally deformable parametric model. Comparison over time demonstrated an increase in the volume of both the inner necrotic core (mean 56.5% increase) and outer rim (mean 16.8% increase) of the lesion. Additionally, T2 images of the lesion exhibited contrast greater than or equal to CE T1 (mean 35% improvement). This work establishes a foundation for the clinical use of T2 MR images coupled with a geometric model of the ablation for noninvasive lesion monitoring and characterization.

Pixel-based parametric source depth map for Cerenkov luminescence imaging

NASA Astrophysics Data System (ADS)

Altabella, L.; Boschi, F.; Spinelli, A. E.

2016-01-01

Optical tomography represents a challenging problem in optical imaging because of the intrinsically ill-posed inverse problem due to photon diffusion. Cerenkov luminescence tomography (CLT) for optical photons produced in tissues by several radionuclides (i.e.: 32P, 18F, 90Y), has been investigated using both 3D multispectral approach and multiviews methods. Difficult in convergence of 3D algorithms can discourage to use this technique to have information of depth and intensity of source. For these reasons, we developed a faster 2D corrected approach based on multispectral acquisitions, to obtain source depth and its intensity using a pixel-based fitting of source intensity. Monte Carlo simulations and experimental data were used to develop and validate the method to obtain the parametric map of source depth. With this approach we obtain parametric source depth maps with a precision between 3% and 7% for MC simulation and 5-6% for experimental data. Using this method we are able to obtain reliable information about the source depth of Cerenkov luminescence with a simple and flexible procedure.

Ultrasound Imaging of DNA-Damage Effects in Live Cultured Cells and in Brain Tissue.

PubMed

Tadayyon, Hadi; Gangeh, Mehrdad J; Vlad, Roxana; Kolios, Michael C; Czarnota, Gregory J

2017-01-01

High-frequency ultrasound (>20 MHz) spectroscopy can be used to detect noninvasively DNA damage in cell samples in vitro, and in live tissue both ex vivo and in vivo. This chapter focuses on the former two aspects. Experimental evidence suggests that morphological changes that occur in cells undergoing apoptosis result in changes in frequency-dependent ultrasound backscatter. With advances in research, ultrasound spectroscopy is advancing the boundaries of fast, label-free, noninvasive DNA damage detection technology with potential use in personalized medicine and early therapy response monitoring. Depending on the desired resolution, parametric ultrasound images can be computed and displayed within minutes to hours after ultrasound examination for cell death.

Brain tissue segmentation based on DTI data

PubMed Central

Liu, Tianming; Li, Hai; Wong, Kelvin; Tarokh, Ashley; Guo, Lei; Wong, Stephen T.C.

2008-01-01

We present a method for automated brain tissue segmentation based on the multi-channel fusion of diffusion tensor imaging (DTI) data. The method is motivated by the evidence that independent tissue segmentation based on DTI parametric images provides complementary information of tissue contrast to the tissue segmentation based on structural MRI data. This has important applications in defining accurate tissue maps when fusing structural data with diffusion data. In the absence of structural data, tissue segmentation based on DTI data provides an alternative means to obtain brain tissue segmentation. Our approach to the tissue segmentation based on DTI data is to classify the brain into two compartments by utilizing the tissue contrast existing in a single channel. Specifically, because the apparent diffusion coefficient (ADC) values in the cerebrospinal fluid (CSF) are more than twice that of gray matter (GM) and white matter (WM), we use ADC images to distinguish CSF and non-CSF tissues. Additionally, fractional anisotropy (FA) images are used to separate WM from non-WM tissues, as highly directional white matter structures have much larger fractional anisotropy values. Moreover, other channels to separate tissue are explored, such as eigenvalues of the tensor, relative anisotropy (RA), and volume ratio (VR). We developed an approach based on the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm that combines these two-class maps to obtain a complete tissue segmentation map of CSF, GM, and WM. Evaluations are provided to demonstrate the performance of our approach. Experimental results of applying this approach to brain tissue segmentation and deformable registration of DTI data and spoiled gradient-echo (SPGR) data are also provided. PMID:17804258

Acceleration of the direct reconstruction of linear parametric images using nested algorithms.

PubMed

Wang, Guobao; Qi, Jinyi

2010-03-07

Parametric imaging using dynamic positron emission tomography (PET) provides important information for biological research and clinical diagnosis. Indirect and direct methods have been developed for reconstructing linear parametric images from dynamic PET data. Indirect methods are relatively simple and easy to implement because the image reconstruction and kinetic modeling are performed in two separate steps. Direct methods estimate parametric images directly from raw PET data and are statistically more efficient. However, the convergence rate of direct algorithms can be slow due to the coupling between the reconstruction and kinetic modeling. Here we present two fast gradient-type algorithms for direct reconstruction of linear parametric images. The new algorithms decouple the reconstruction and linear parametric modeling at each iteration by employing the principle of optimization transfer. Convergence speed is accelerated by running more sub-iterations of linear parametric estimation because the computation cost of the linear parametric modeling is much less than that of the image reconstruction. Computer simulation studies demonstrated that the new algorithms converge much faster than the traditional expectation maximization (EM) and the preconditioned conjugate gradient algorithms for dynamic PET.

Registration of parametric dynamic F-18-FDG PET/CT breast images with parametric dynamic Gd-DTPA breast images

NASA Astrophysics Data System (ADS)

Magri, Alphonso; Krol, Andrzej; Lipson, Edward; Mandel, James; McGraw, Wendy; Lee, Wei; Tillapaugh-Fay, Gwen; Feiglin, David

2009-02-01

This study was undertaken to register 3D parametric breast images derived from Gd-DTPA MR and F-18-FDG PET/CT dynamic image series. Nonlinear curve fitting (Levenburg-Marquardt algorithm) based on realistic two-compartment models was performed voxel-by-voxel separately for MR (Brix) and PET (Patlak). PET dynamic series consists of 50 frames of 1-minute duration. Each consecutive PET image was nonrigidly registered to the first frame using a finite element method and fiducial skin markers. The 12 post-contrast MR images were nonrigidly registered to the precontrast frame using a free-form deformation (FFD) method. Parametric MR images were registered to parametric PET images via CT using FFD because the first PET time frame was acquired immediately after the CT image on a PET/CT scanner and is considered registered to the CT image. We conclude that nonrigid registration of PET and MR parametric images using CT data acquired during PET/CT scan and the FFD method resulted in their improved spatial coregistration. The success of this procedure was limited due to relatively large target registration error, TRE = 15.1+/-7.7 mm, as compared to spatial resolution of PET (6-7 mm), and swirling image artifacts created in MR parametric images by the FFD. Further refinement of nonrigid registration of PET and MR parametric images is necessary to enhance visualization and integration of complex diagnostic information provided by both modalities that will lead to improved diagnostic performance.

Integrated transrectal probe for translational ultrasound-photoacoustic imaging

NASA Astrophysics Data System (ADS)

Bell, Kevan L.; Harrison, Tyler; Usmani, Nawaid; Zemp, Roger J.

2016-03-01

A compact photoacoustic transrectal probe is constructed for improved imaging in brachytherapy treatment. A 192 element 5 MHz linear transducer array is mounted inside a small 3D printed casing along with an array of optical fibers. The device is fed by a pump laser and tunable NIR-optical parametric oscillator with data collected by a Verasonics ultrasound platform. This assembly demonstrates improved imaging of brachytherapy seeds in phantoms with depths up to 5 cm. The tuneable excitation in combination with standard US integration provides adjustable contrast between the brachytherapy seeds, blood filled tubes and background tissue.

Polarization image segmentation of radiofrequency ablated porcine myocardial tissue

PubMed Central

Ahmad, Iftikhar; Gribble, Adam; Murtza, Iqbal; Ikram, Masroor; Pop, Mihaela; Vitkin, Alex

2017-01-01

Optical polarimetry has previously imaged the spatial extent of a typical radiofrequency ablated (RFA) lesion in myocardial tissue, exhibiting significantly lower total depolarization at the necrotic core compared to healthy tissue, and intermediate values at the RFA rim region. Here, total depolarization in ablated myocardium was used to segment the total depolarization image into three (core, rim and healthy) zones. A local fuzzy thresholding algorithm was used for this multi-region segmentation, and then compared with a ground truth segmentation obtained from manual demarcation of RFA core and rim regions on the histopathology image. Quantitative comparison of the algorithm segmentation results was performed with evaluation metrics such as dice similarity coefficient (DSC = 0.78 ± 0.02 and 0.80 ± 0.02), sensitivity (Sn = 0.83 ± 0.10 and 0.91 ± 0.08), specificity (Sp = 0.76 ± 0.17 and 0.72 ± 0.17) and accuracy (Acc = 0.81 ± 0.09 and 0.71 ± 0.10) for RFA core and rim regions, respectively. This automatic segmentation of parametric depolarization images suggests a novel application of optical polarimetry, namely its use in objective RFA image quantification. PMID:28380013

Noninvasive Quantitative Imaging of Collagen Microstructure in Three-Dimensional Hydrogels Using High-Frequency Ultrasound

PubMed Central

Mercado, Karla P.; Helguera, María; Hocking, Denise C.

2015-01-01

Collagen I is widely used as a natural component of biomaterials for both tissue engineering and regenerative medicine applications. The physical and biological properties of fibrillar collagens are strongly tied to variations in collagen fiber microstructure. The goal of this study was to develop the use of high-frequency quantitative ultrasound to assess collagen microstructure within three-dimensional (3D) hydrogels noninvasively and nondestructively. The integrated backscatter coefficient (IBC) was employed as a quantitative ultrasound parameter to detect, image, and quantify spatial variations in collagen fiber density and diameter. Collagen fiber microstructure was varied by fabricating hydrogels with different collagen concentrations or polymerization temperatures. IBC values were computed from measurements of the backscattered radio-frequency ultrasound signals collected using a single-element transducer (38-MHz center frequency, 13–47 MHz bandwidth). The IBC increased linearly with increasing collagen concentration and decreasing polymerization temperature. Parametric 3D images of the IBC were generated to visualize and quantify regional variations in collagen microstructure throughout the volume of hydrogels fabricated in standard tissue culture plates. IBC parametric images of corresponding cell-embedded collagen gels showed cell accumulation within regions having elevated collagen IBC values. The capability of this ultrasound technique to noninvasively detect and quantify spatial differences in collagen microstructure offers a valuable tool to monitor the structural properties of collagen scaffolds during fabrication, to detect functional differences in collagen microstructure, and to guide fundamental research on the interactions of cells and collagen matrices. PMID:25517512

Noninvasive Quantitative Imaging of Collagen Microstructure in Three-Dimensional Hydrogels Using High-Frequency Ultrasound.

PubMed

Mercado, Karla P; Helguera, María; Hocking, Denise C; Dalecki, Diane

2015-07-01

Collagen I is widely used as a natural component of biomaterials for both tissue engineering and regenerative medicine applications. The physical and biological properties of fibrillar collagens are strongly tied to variations in collagen fiber microstructure. The goal of this study was to develop the use of high-frequency quantitative ultrasound to assess collagen microstructure within three-dimensional (3D) hydrogels noninvasively and nondestructively. The integrated backscatter coefficient (IBC) was employed as a quantitative ultrasound parameter to detect, image, and quantify spatial variations in collagen fiber density and diameter. Collagen fiber microstructure was varied by fabricating hydrogels with different collagen concentrations or polymerization temperatures. IBC values were computed from measurements of the backscattered radio-frequency ultrasound signals collected using a single-element transducer (38-MHz center frequency, 13-47 MHz bandwidth). The IBC increased linearly with increasing collagen concentration and decreasing polymerization temperature. Parametric 3D images of the IBC were generated to visualize and quantify regional variations in collagen microstructure throughout the volume of hydrogels fabricated in standard tissue culture plates. IBC parametric images of corresponding cell-embedded collagen gels showed cell accumulation within regions having elevated collagen IBC values. The capability of this ultrasound technique to noninvasively detect and quantify spatial differences in collagen microstructure offers a valuable tool to monitor the structural properties of collagen scaffolds during fabrication, to detect functional differences in collagen microstructure, and to guide fundamental research on the interactions of cells and collagen matrices.

Assessment of Renal Hemodynamics and Oxygenation by Simultaneous Magnetic Resonance Imaging (MRI) and Quantitative Invasive Physiological Measurements.

PubMed

Cantow, Kathleen; Arakelyan, Karen; Seeliger, Erdmann; Niendorf, Thoralf; Pohlmann, Andreas

2016-01-01

In vivo assessment of renal perfusion and oxygenation under (patho)physiological conditions by means of noninvasive diagnostic imaging is conceptually appealing. Blood oxygen level-dependent (BOLD) magnetic resonance imaging (MRI) and quantitative parametric mapping of the magnetic resonance (MR) relaxation times T 2* and T 2 are thought to provide surrogates of renal tissue oxygenation. The validity and efficacy of this technique for quantitative characterization of local tissue oxygenation and its changes under different functional conditions have not been systematically examined yet and remain to be established. For this purpose, the development of an integrative multimodality approaches is essential. Here we describe an integrated hybrid approach (MR-PHYSIOL) that combines established quantitative physiological measurements with T 2* (T 2) mapping and MR-based kidney size measurements. Standardized reversible (patho)physiologically relevant interventions, such as brief periods of aortic occlusion, hypoxia, and hyperoxia, are used for detailing the relation between the MR-PHYSIOL parameters, in particular between renal T 2* and tissue oxygenation.

Optical-thermal light-tissue interactions during photoacoustic imaging

NASA Astrophysics Data System (ADS)

Gould, Taylor; Wang, Quanzeng; Pfefer, T. Joshua

2014-03-01

Photoacoustic imaging (PAI) has grown rapidly as a biomedical imaging technique in recent years, with key applications in cancer diagnosis and oximetry. In spite of these advances, the literature provides little insight into thermal tissue interactions involved in PAI. To elucidate these basic phenomena, we have developed, validated, and implemented a three-dimensional numerical model of tissue photothermal (PT) response to repetitive laser pulses. The model calculates energy deposition, fluence distributions, transient temperature and damage profiles in breast tissue with blood vessels and generalized perfusion. A parametric evaluation of these outputs vs. vessel diameter and depth, optical beam diameter, wavelength, and irradiance, was performed. For a constant radiant exposure level, increasing beam diameter led to a significant increase in subsurface heat generation rate. Increasing vessel diameter resulted in two competing effects - reduced mean energy deposition in the vessel due to light attenuation and greater thermal superpositioning due to reduced thermal relaxation. Maximum temperatures occurred either at the surface or in subsurface regions of the dermis, depending on vessel geometry and position. Results are discussed in terms of established exposure limits and levels used in prior studies. While additional experimental and numerical study is needed, numerical modeling represents a powerful tool for elucidating the effect of PA imaging devices on biological tissue.

{sup 18}F-FLT uptake kinetics in head and neck squamous cell carcinoma: A PET imaging study

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

Liu, Dan, E-mail: dan.liu@oncology.ox.ac.uk; Fenwick, John D.; Chalkidou, Anastasia

2014-04-15

Purpose: To analyze the kinetics of 3{sup ′}-deoxy-3{sup ′}-[F-18]-fluorothymidine (18F-FLT) uptake by head and neck squamous cell carcinomas and involved nodes imaged using positron emission tomography (PET). Methods: Two- and three-tissue compartment models were fitted to 12 tumor time-activity-curves (TACs) obtained for 6 structures (tumors or involved nodes) imaged in ten dynamic PET studies of 1 h duration, carried out for five patients. The ability of the models to describe the data was assessed using a runs test, the Akaike information criterion (AIC) and leave-one-out cross-validation. To generate parametric maps the models were also fitted to TACs of individual voxels.more » Correlations between maps of different parameters were characterized using Pearson'sr coefficient; in particular the phosphorylation rate-constants k{sub 3-2tiss} and k{sub 5} of the two- and three-tissue models were studied alongside the flux parameters K{sub FLT-2tiss} and K{sub FLT} of these models, and standardized uptake values (SUV). A methodology based on expectation-maximization clustering and the Bayesian information criterion (“EM-BIC clustering”) was used to distil the information from noisy parametric images. Results: Fits of two-tissue models 2C3K and 2C4K and three-tissue models 3C5K and 3C6K comprising three, four, five, and six rate-constants, respectively, pass the runs test for 4, 8, 10, and 11 of 12 tumor TACs. The three-tissue models have lower AIC and cross-validation scores for nine of the 12 tumors. Overall the 3C6K model has the lowest AIC and cross-validation scores and its fitted parameter values are of the same orders of magnitude as literature estimates. Maps ofK{sub FLT} and K{sub FLT-2tiss} are strongly correlated (r = 0.85) and also correlate closely with SUV maps (r = 0.72 for K{sub FLT-2tiss}, 0.64 for K{sub FLT}). Phosphorylation rate-constant maps are moderately correlated with flux maps (r = 0.48 for k{sub 3-2tiss} vs K{sub FLT-2tiss} and r = 0.68 for k{sub 5} vs K{sub FLT}); however, neither phosphorylation rate-constant correlates significantly with SUV. EM-BIC clustering reduces the parametric maps to a small number of levels—on average 5.8, 3.5, 3.4, and 1.4 for K{sub FLT-2tiss}, K{sub FLT}, k{sub 3-2tiss}, and k{sub 5.} This large simplification is potentially useful for radiotherapy dose-painting, but demonstrates the high noise in some maps. Statistical simulations show that voxel level noise degrades TACs generated from the 3C6K model sufficiently that the average AIC score, parameter bias, and total uncertainty of 2C4K model fits are similar to those of 3C6K fits, whereas at the whole tumor level the scores are lower for 3C6K fits. Conclusions: For the patients studied here, whole tumor FLT uptake time-courses are represented better overall by a three-tissue than by a two-tissue model. EM-BIC clustering simplifies noisy parametric maps, providing the best description of the underlying information they contain and is potentially useful for radiotherapy dose-painting. However, the clustering highlights the large degree of noise present in maps of the phosphorylation rate-constantsk{sub 5} and k{sub 3-2tiss}, which are conceptually tightly linked to cellular proliferation. Methods must be found to make these maps more robust—either by constraining other model parameters or modifying dynamic imaging protocols.« less

Optimized statistical parametric mapping for partial-volume-corrected amyloid positron emission tomography in patients with Alzheimer's disease and Lewy body dementia

NASA Astrophysics Data System (ADS)

Oh, Jungsu S.; Kim, Jae Seung; Chae, Sun Young; Oh, Minyoung; Oh, Seung Jun; Cha, Seung Nam; Chang, Ho-Jong; Lee, Chong Sik; Lee, Jae Hong

2017-03-01

We present an optimized voxelwise statistical parametric mapping (SPM) of partial-volume (PV)-corrected positron emission tomography (PET) of 11C Pittsburgh Compound B (PiB), incorporating the anatomical precision of magnetic resonance image (MRI) and amyloid β (A β) burden-specificity of PiB PET. First, we applied region-based partial-volume correction (PVC), termed the geometric transfer matrix (GTM) method, to PiB PET, creating MRI-based lobar parcels filled with mean PiB uptakes. Then, we conducted a voxelwise PVC by multiplying the original PET by the ratio of a GTM-based PV-corrected PET to a 6-mm-smoothed PV-corrected PET. Finally, we conducted spatial normalizations of the PV-corrected PETs onto the study-specific template. As such, we increased the accuracy of the SPM normalization and the tissue specificity of SPM results. Moreover, lobar smoothing (instead of whole-brain smoothing) was applied to increase the signal-to-noise ratio in the image without degrading the tissue specificity. Thereby, we could optimize a voxelwise group comparison between subjects with high and normal A β burdens (from 10 patients with Alzheimer's disease, 30 patients with Lewy body dementia, and 9 normal controls). Our SPM framework outperformed than the conventional one in terms of the accuracy of the spatial normalization (85% of maximum likelihood tissue classification volume) and the tissue specificity (larger gray matter, and smaller cerebrospinal fluid volume fraction from the SPM results). Our SPM framework optimized the SPM of a PV-corrected A β PET in terms of anatomical precision, normalization accuracy, and tissue specificity, resulting in better detection and localization of A β burdens in patients with Alzheimer's disease and Lewy body dementia.

Noninvasive parametric blood flow imaging of head and neck tumours using [15O]H2O and PET/CT.

PubMed

Komar, Gaber; Oikonen, Vesa; Sipilä, Hannu; Seppänen, Marko; Minn, Heikki

2012-11-01

The aim of this study was to develop a simple noninvasive method for measuring blood flow using [15O]H2O PET/CT for the head and neck area applicable in daily clinical practice. Fifteen dynamic [15O]H2O PET emission scans with simultaneous online radioactivity measurements of radial arterial blood [Blood-input functions (IFs)] were performed. Two noninvasively obtained population-based input functions were calculated by averaging all Blood-IF curves corrected for patients' body mass and injected dose [standardized uptake value (SUV)-IF] and for body surface area (BSA-IF) and injected dose. Parametric perfusion images were calculated for each set of IFs using a linearized two-compartment model, and values for several tissues were compared using Blood-IF as the gold standard. On comparing all tissues, the correlation between blood flow obtained with the invasive Blood-IF and both SUV-IF and BSA-IF was significant (R2=0.785 with P<0.001 and R2=0.813 with P<0.001, respectively). In individual tissues, the performance of the two noninvasive methods was most reliable in resting muscle and slightly less reliable in tumour and cerebellar regions. In these two tissues, only BSA-IF showed a significant correlation with Blood-IF (R2=0.307 with P=0.032 in tumours and R2=0.398 with P<0.007 in the cerebellum). The BSA-based noninvasive method enables clinically relevant delineation between areas of low and high blood flow in tumours. The blood flow of low-perfusion tissues can be reliably quantified using either of the evaluated noninvasive methods.

A combined positron emission tomography (PET)-electron paramagnetic resonance imaging (EPRI) system: initial evaluation of a prototype scanner

NASA Astrophysics Data System (ADS)

Tseytlin, Mark; Stolin, Alexander V.; Guggilapu, Priyaankadevi; Bobko, Andrey A.; Khramtsov, Valery V.; Tseytlin, Oxana; Raylman, Raymond R.

2018-05-01

The advent of hybrid scanners, combining complementary modalities, has revolutionized the application of advanced imaging technology to clinical practice and biomedical research. In this project, we investigated the melding of two complementary, functional imaging methods: positron emission tomography (PET) and electron paramagnetic resonance imaging (EPRI). PET radiotracers can provide important information about cellular parameters, such as glucose metabolism. While EPR probes can provide assessment of tissue microenvironment, measuring oxygenation and pH, for example. Therefore, a combined PET/EPRI scanner promises to provide new insights not attainable with current imagers by simultaneous acquisition of multiple components of tissue microenvironments. To explore the simultaneous acquisition of PET and EPR images, a prototype system was created by combining two existing scanners. Specifically, a silicon photomultiplier (SiPM)-based PET scanner ring designed as a portable scanner was combined with an EPRI scanner designed for the imaging of small animals. The ability of the system to obtain simultaneous images was assessed with a small phantom consisting of four cylinders containing both a PET tracer and EPR spin probe. The resulting images demonstrated the ability to obtain contemporaneous PET and EPR images without cross-modality interference. Given the promising results from this initial investigation, the next step in this project is the construction of the next generation pre-clinical PET/EPRI scanner for multi-parametric assessment of physiologically-important parameters of tissue microenvironments.

Noise and analyzer-crystal angular position analysis for analyzer-based phase-contrast imaging

NASA Astrophysics Data System (ADS)

Majidi, Keivan; Li, Jun; Muehleman, Carol; Brankov, Jovan G.

2014-04-01

The analyzer-based phase-contrast x-ray imaging (ABI) method is emerging as a potential alternative to conventional radiography. Like many of the modern imaging techniques, ABI is a computed imaging method (meaning that images are calculated from raw data). ABI can simultaneously generate a number of planar parametric images containing information about absorption, refraction, and scattering properties of an object. These images are estimated from raw data acquired by measuring (sampling) the angular intensity profile of the x-ray beam passed through the object at different angular positions of the analyzer crystal. The noise in the estimated ABI parametric images depends upon imaging conditions like the source intensity (flux), measurements angular positions, object properties, and the estimation method. In this paper, we use the Cramér-Rao lower bound (CRLB) to quantify the noise properties in parametric images and to investigate the effect of source intensity, different analyzer-crystal angular positions and object properties on this bound, assuming a fixed radiation dose delivered to an object. The CRLB is the minimum bound for the variance of an unbiased estimator and defines the best noise performance that one can obtain regardless of which estimation method is used to estimate ABI parametric images. The main result of this paper is that the variance (hence the noise) in parametric images is directly proportional to the source intensity and only a limited number of analyzer-crystal angular measurements (eleven for uniform and three for optimal non-uniform) are required to get the best parametric images. The following angular measurements only spread the total dose to the measurements without improving or worsening CRLB, but the added measurements may improve parametric images by reducing estimation bias. Next, using CRLB we evaluate the multiple-image radiography, diffraction enhanced imaging and scatter diffraction enhanced imaging estimation techniques, though the proposed methodology can be used to evaluate any other ABI parametric image estimation technique.

Noise and Analyzer-Crystal Angular Position Analysis for Analyzer-Based Phase-Contrast Imaging

PubMed Central

Majidi, Keivan; Li, Jun; Muehleman, Carol; Brankov, Jovan G.

2014-01-01

The analyzer-based phase-contrast X-ray imaging (ABI) method is emerging as a potential alternative to conventional radiography. Like many of the modern imaging techniques, ABI is a computed imaging method (meaning that images are calculated from raw data). ABI can simultaneously generate a number of planar parametric images containing information about absorption, refraction, and scattering properties of an object. These images are estimated from raw data acquired by measuring (sampling) the angular intensity profile (AIP) of the X-ray beam passed through the object at different angular positions of the analyzer crystal. The noise in the estimated ABI parametric images depends upon imaging conditions like the source intensity (flux), measurements angular positions, object properties, and the estimation method. In this paper, we use the Cramér-Rao lower bound (CRLB) to quantify the noise properties in parametric images and to investigate the effect of source intensity, different analyzer-crystal angular positions and object properties on this bound, assuming a fixed radiation dose delivered to an object. The CRLB is the minimum bound for the variance of an unbiased estimator and defines the best noise performance that one can obtain regardless of which estimation method is used to estimate ABI parametric images. The main result of this manuscript is that the variance (hence the noise) in parametric images is directly proportional to the source intensity and only a limited number of analyzer-crystal angular measurements (eleven for uniform and three for optimal non-uniform) are required to get the best parametric images. The following angular measurements only spread the total dose to the measurements without improving or worsening CRLB, but the added measurements may improve parametric images by reducing estimation bias. Next, using CRLB we evaluate the Multiple-Image Radiography (MIR), Diffraction Enhanced Imaging (DEI) and Scatter Diffraction Enhanced Imaging (S-DEI) estimation techniques, though the proposed methodology can be used to evaluate any other ABI parametric image estimation technique. PMID:24651402

Direct Estimation of Kinetic Parametric Images for Dynamic PET

PubMed Central

Wang, Guobao; Qi, Jinyi

2013-01-01

Dynamic positron emission tomography (PET) can monitor spatiotemporal distribution of radiotracer in vivo. The spatiotemporal information can be used to estimate parametric images of radiotracer kinetics that are of physiological and biochemical interests. Direct estimation of parametric images from raw projection data allows accurate noise modeling and has been shown to offer better image quality than conventional indirect methods, which reconstruct a sequence of PET images first and then perform tracer kinetic modeling pixel-by-pixel. Direct reconstruction of parametric images has gained increasing interests with the advances in computing hardware. Many direct reconstruction algorithms have been developed for different kinetic models. In this paper we review the recent progress in the development of direct reconstruction algorithms for parametric image estimation. Algorithms for linear and nonlinear kinetic models are described and their properties are discussed. PMID:24396500

Ex vivo tissue imaging of human glioblastoma using a small bore 7T MRI and correlation with digital pathology and proteomics profiling

NASA Astrophysics Data System (ADS)

Matsuda, Kant M.; Lopes-Calcas, Ana; Magyar, Thalia; O'Brien-Moran, Zoe; Buist, Richard; Martin, Melanie

2017-03-01

Recent advancement in MRI established multi-parametric imaging for in vivo characterization of pathologic changes in brain cancer, which is expected to play a role in imaging biomarker development. Diffusion Tensor Imaging (DTI) is a prime example, which has been deployed for assessment of therapeutic response via analysis of apparent diffusion coefficient (ADC) / mean diffusivity (MD) values. They have been speculated to reflect apoptosis/necrosis. As newer medical imaging emerges, it is essential to verify that apparent abnormal features in imaging correlate with histopathology. Furthermore, the feasibility of imaging correlation with molecular profile should be explored in order to enhance the potential of biomedical imaging as a reliable biomarker. We focus on glioblastoma, which is an aggressive brain cancer. Despite the increased number of studies involving DTI in glioblastoma; however, little has been explored to bridge the gap between the molecular biomarkers and DTI data. Due to spatial heterogeneity in, MRI signals, pathologic change and protein expression, precise correlation is required between DTI, pathology and proteomics data in a histoanatomically identical manner. The challenge is obtaining an identical plane from in vivo imaging data that exactly matches with histopathology section. Thus, we propose to incorporate ex vivo tissue imaging to bridge between in vivo imaging data and histopathology. With ex vivo scan of removed tissue, it is feasible to use high-field 7T MRI scanner, which can achieve microscopic resolution. Once histology section showing the identical plane, it is feasible to correlate protein expression by a unique technology, "multiplex tissue immunoblotting".

Transmural ultrasound imaging of thermal lesion and action potential changes in perfused canine cardiac wedge preparations by high intensity focused ultrasound ablation.

PubMed

Wu, Ziqi; Gudur, Madhu S R; Deng, Cheri X

2013-01-01

Intra-procedural imaging is important for guiding cardiac arrhythmia ablation. It is difficult to obtain intra-procedural correlation of thermal lesion formation with action potential (AP) changes in the transmural plane during ablation. This study tested parametric ultrasound imaging for transmural imaging of lesion and AP changes in high intensity focused ultrasound (HIFU) ablation using coronary perfused canine ventricular wedge preparations (n = 13). The preparations were paced from epi/endocardial surfaces and subjected to HIFU application (3.5 MHz, 11 Hz pulse-repetition-frequency, 70% duty cycle, duration 4 s, 3500 W/cm(2)), during which simultaneous optical mapping (1 kframes/s) using di-4-ANEPPS and ultrasound imaging (30 MHz) of the same transmural surface of the wedge were performed. Spatiotemporally correlated AP measurements and ultrasound imaging allowed quantification of the reduction of AP amplitude (APA), shortening of AP duration at 50% repolarization, AP triangulation, decrease of optical AP rise, and change of conduction velocity along tissue depth direction within and surrounding HIFU lesions. The threshold of irreversible change in APA correlating to lesions was determined to be 43 ± 1% with a receiver operating characteristic (ROC) area under curve (AUC) of 0.96 ± 0.01 (n = 13). Ultrasound imaging parameters such as integrated backscatter, Rayleigh (α) and log-normal (σ) parameters, cumulative extrema of σ were tested, with the cumulative extrema of σ performing the best in detecting lesion (ROC AUC 0.89 ± 0.01, n = 13) and change of APA (ROC AUC 0.79 ± 0.03, n = 13). In conclusion, characteristic tissue and AP changes in HIFU ablation were identified and spatiotemporally correlated using optical mapping and ultrasound imaging. Parametric ultrasound imaging using cumulative extrema of σ can detect HIFU lesion and APA reduction.

Transmural Ultrasound Imaging of Thermal Lesion and Action Potential Changes in Perfused Canine Cardiac Wedge Preparations by High Intensity Focused Ultrasound Ablation

PubMed Central

Wu, Ziqi; Gudur, Madhu S. R.; Deng, Cheri X.

2013-01-01

Intra-procedural imaging is important for guiding cardiac arrhythmia ablation. It is difficult to obtain intra-procedural correlation of thermal lesion formation with action potential (AP) changes in the transmural plane during ablation. This study tested parametric ultrasound imaging for transmural imaging of lesion and AP changes in high intensity focused ultrasound (HIFU) ablation using coronary perfused canine ventricular wedge preparations (n = 13). The preparations were paced from epi/endocardial surfaces and subjected to HIFU application (3.5 MHz, 11 Hz pulse-repetition-frequency, 70% duty cycle, duration 4 s, 3500 W/cm2), during which simultaneous optical mapping (1 kframes/s) using di-4-ANEPPS and ultrasound imaging (30 MHz) of the same transmural surface of the wedge were performed. Spatiotemporally correlated AP measurements and ultrasound imaging allowed quantification of the reduction of AP amplitude (APA), shortening of AP duration at 50% repolarization, AP triangulation, decrease of optical AP rise, and change of conduction velocity along tissue depth direction within and surrounding HIFU lesions. The threshold of irreversible change in APA correlating to lesions was determined to be 43±1% with a receiver operating characteristic (ROC) area under curve (AUC) of 0.96±0.01 (n = 13). Ultrasound imaging parameters such as integrated backscatter, Rayleigh (α) and log-normal (σ) parameters, cumulative extrema of σ were tested, with the cumulative extrema of σ performing the best in detecting lesion (ROC AUC 0.89±0.01, n = 13) and change of APA (ROC AUC 0.79±0.03, n = 13). In conclusion, characteristic tissue and AP changes in HIFU ablation were identified and spatiotemporally correlated using optical mapping and ultrasound imaging. Parametric ultrasound imaging using cumulative extrema of σ can detect HIFU lesion and APA reduction. PMID:24349337

Detailing the relation between renal T2* and renal tissue pO2 using an integrated approach of parametric magnetic resonance imaging and invasive physiological measurements.

PubMed

Pohlmann, Andreas; Arakelyan, Karen; Hentschel, Jan; Cantow, Kathleen; Flemming, Bert; Ladwig, Mechthild; Waiczies, Sonia; Seeliger, Erdmann; Niendorf, Thoralf

2014-08-01

This study was designed to detail the relation between renal T2* and renal tissue pO2 using an integrated approach that combines parametric magnetic resonance imaging (MRI) and quantitative physiological measurements (MR-PHYSIOL). Experiments were performed in 21 male Wistar rats. In vivo modulation of renal hemodynamics and oxygenation was achieved by brief periods of aortic occlusion, hypoxia, and hyperoxia. Renal perfusion pressure (RPP), renal blood flow (RBF), local cortical and medullary tissue pO2, and blood flux were simultaneously recorded together with T2*, T2 mapping, and magnetic resonance-based kidney size measurements (MR-PHYSIOL). Magnetic resonance imaging was carried out on a 9.4-T small-animal magnetic resonance system. Relative changes in the invasive quantitative parameters were correlated with relative changes in the parameters derived from MRI using Spearman analysis and Pearson analysis. Changes in T2* qualitatively reflected tissue pO2 changes induced by the interventions. T2* versus pO2 Spearman rank correlations were significant for all interventions, yet quantitative translation of T2*/pO2 correlations obtained for one intervention to another intervention proved not appropriate. The closest T2*/pO2 correlation was found for hypoxia and recovery. The interlayer comparison revealed closest T2*/pO2 correlations for the outer medulla and showed that extrapolation of results obtained for one renal layer to other renal layers must be made with due caution. For T2* to RBF relation, significant Spearman correlations were deduced for all renal layers and for all interventions. T2*/RBF correlations for the cortex and outer medulla were even superior to those between T2* and tissue pO2. The closest T2*/RBF correlation occurred during hypoxia and recovery. Close correlations were observed between T2* and kidney size during hypoxia and recovery and for occlusion and recovery. In both cases, kidney size correlated well with renal vascular conductance, as did renal vascular conductance with T2*. Our findings indicate that changes in T2* qualitatively mirror changes in renal tissue pO2 but are also associated with confounding factors including vascular volume fraction and tubular volume fraction. Our results demonstrate that MR-PHYSIOL is instrumental to detail the link between renal tissue pO2 and T2* in vivo. Unravelling the link between regional renal T2* and tissue pO2, including the role of the T2* confounding parameters vascular and tubular volume fraction and oxy-hemoglobin dissociation curve, requires further research. These explorations are essential before the quantitative capabilities of parametric MRI can be translated from experimental research to improved clinical understanding of hemodynamics/oxygenation in kidney disorders.

Choice of reconstructed tissue properties affects interpretation of lung EIT images.

PubMed

Grychtol, Bartłomiej; Adler, Andy

2014-06-01

Electrical impedance tomography (EIT) estimates an image of change in electrical properties within a body from stimulations and measurements at surface electrodes. There is significant interest in EIT as a tool to monitor and guide ventilation therapy in mechanically ventilated patients. In lung EIT, the EIT inverse problem is commonly linearized and only changes in electrical properties are reconstructed. Early algorithms reconstructed changes in resistivity, while most recent work using the finite element method reconstructs conductivity. Recently, we demonstrated that EIT images of ventilation can be misleading if the electrical contrasts within the thorax are not taken into account during the image reconstruction process. In this paper, we explore the effect of the choice of the reconstructed electrical properties (resistivity or conductivity) on the resulting EIT images. We show in simulation and experimental data that EIT images reconstructed with the same algorithm but with different parametrizations lead to large and clinically significant differences in the resulting images, which persist even after attempts to eliminate the impact of the parameter choice by recovering volume changes from the EIT images. Since there is no consensus among the most popular reconstruction algorithms and devices regarding the parametrization, this finding has implications for potential clinical use of EIT. We propose a program of research to develop reconstruction techniques that account for both the relationship between air volume and electrical properties of the lung and artefacts introduced by the linearization.

Empirical validation of statistical parametric mapping for group imaging of fast neural activity using electrical impedance tomography.

PubMed

Packham, B; Barnes, G; Dos Santos, G Sato; Aristovich, K; Gilad, O; Ghosh, A; Oh, T; Holder, D

2016-06-01

Electrical impedance tomography (EIT) allows for the reconstruction of internal conductivity from surface measurements. A change in conductivity occurs as ion channels open during neural activity, making EIT a potential tool for functional brain imaging. EIT images can have  >10 000 voxels, which means statistical analysis of such images presents a substantial multiple testing problem. One way to optimally correct for these issues and still maintain the flexibility of complicated experimental designs is to use random field theory. This parametric method estimates the distribution of peaks one would expect by chance in a smooth random field of a given size. Random field theory has been used in several other neuroimaging techniques but never validated for EIT images of fast neural activity, such validation can be achieved using non-parametric techniques. Both parametric and non-parametric techniques were used to analyze a set of 22 images collected from 8 rats. Significant group activations were detected using both techniques (corrected p  <  0.05). Both parametric and non-parametric analyses yielded similar results, although the latter was less conservative. These results demonstrate the first statistical analysis of such an image set and indicate that such an analysis is an approach for EIT images of neural activity.

Empirical validation of statistical parametric mapping for group imaging of fast neural activity using electrical impedance tomography

PubMed Central

Packham, B; Barnes, G; dos Santos, G Sato; Aristovich, K; Gilad, O; Ghosh, A; Oh, T; Holder, D

2016-01-01

Abstract Electrical impedance tomography (EIT) allows for the reconstruction of internal conductivity from surface measurements. A change in conductivity occurs as ion channels open during neural activity, making EIT a potential tool for functional brain imaging. EIT images can have  >10 000 voxels, which means statistical analysis of such images presents a substantial multiple testing problem. One way to optimally correct for these issues and still maintain the flexibility of complicated experimental designs is to use random field theory. This parametric method estimates the distribution of peaks one would expect by chance in a smooth random field of a given size. Random field theory has been used in several other neuroimaging techniques but never validated for EIT images of fast neural activity, such validation can be achieved using non-parametric techniques. Both parametric and non-parametric techniques were used to analyze a set of 22 images collected from 8 rats. Significant group activations were detected using both techniques (corrected p  <  0.05). Both parametric and non-parametric analyses yielded similar results, although the latter was less conservative. These results demonstrate the first statistical analysis of such an image set and indicate that such an analysis is an approach for EIT images of neural activity. PMID:27203477

Whole-body diffusion kurtosis imaging: initial experience on non-Gaussian diffusion in various organs.

PubMed

Filli, Lukas; Wurnig, Moritz; Nanz, Daniel; Luechinger, Roger; Kenkel, David; Boss, Andreas

2014-12-01

Diffusion kurtosis imaging (DKI) is based on a non-Gaussian diffusion model that should inherently better account for restricted water diffusion within the complex microstructure of most tissues than the conventional diffusion-weighted imaging (DWI), which presumes Gaussian distributed water molecule displacement probability. The aim of this investigation was to test the technical feasibility of in vivo whole-body DKI, probe for organ-specific differences, and compare whole-body DKI and DWI results. Eight healthy subjects underwent whole-body DWI on a clinical 3.0 T magnetic resonance imaging system. Echo-planar images in the axial orientation were acquired at b-values of 0, 150, 300, 500, and 800 mm²/s. Parametrical whole-body maps of the diffusion coefficient (D), the kurtosis (K), and the traditional apparent diffusion coefficient (ADC) were generated. Goodness of fit was compared between DKI and DWI fits using the sums of squared residuals. Data groups were tested for significant differences of the mean by paired Student t tests. Good-quality parametrical whole-body maps of D, K, and ADC could be computed. Compared with ADC values, D values were significantly higher in the cerebral gray matter (by 30%) and white matter (27%), renal cortex (23%) and medulla (21%), spleen (101%), as well as erector spinae muscle (34%) (each P value <0.001). No significant differences between D and ADC were found in the cerebrospinal fluid (P = 0.08) and in the liver (P = 0.13). Curves of DKI fitted the measurement points significantly better than DWI curves did in most organs. Whole-body DKI is technically feasible and may reflect tissue microstructure more meaningfully than whole-body DWI.

Parametric PET/MR Fusion Imaging to Differentiate Aggressive from Indolent Primary Prostate Cancer with Application for Image-Guided Prostate Cancer Biopsies

DTIC Science & Technology

2013-10-01

AD_________________ Award Number: W81XWH-12-1-0597 TITLE: Parametric PET /MR Fusion Imaging to...Parametric PET /MR Fusion Imaging to Differentiate Aggressive from Indolent Primary Prostate Cancer with Application for Image-Guided Prostate Cancer Biopsies...The study investigates whether fusion PET /MRI imaging with 18F-choline PET /CT and diffusion-weighted MRI can be successfully applied to target prostate

Association between pathology and texture features of multi parametric MRI of the prostate

NASA Astrophysics Data System (ADS)

Kuess, Peter; Andrzejewski, Piotr; Nilsson, David; Georg, Petra; Knoth, Johannes; Susani, Martin; Trygg, Johan; Helbich, Thomas H.; Polanec, Stephan H.; Georg, Dietmar; Nyholm, Tufve

2017-10-01

The role of multi-parametric (mp)MRI in the diagnosis and treatment of prostate cancer has increased considerably. An alternative to visual inspection of mpMRI is the evaluation using histogram-based (first order statistics) parameters and textural features (second order statistics). The aims of the present work were to investigate the relationship between benign and malignant sub-volumes of the prostate and textures obtained from mpMR images. The performance of tumor prediction was investigated based on the combination of histogram-based and textural parameters. Subsequently, the relative importance of mpMR images was assessed and the benefit of additional imaging analyzed. Finally, sub-structures based on the PI-RADS classification were investigated as potential regions to automatically detect maligned lesions. Twenty-five patients who received mpMRI prior to radical prostatectomy were included in the study. The imaging protocol included T2, DWI, and DCE. Delineation of tumor regions was performed based on pathological information. First and second order statistics were derived from each structure and for all image modalities. The resulting data were processed with multivariate analysis, using PCA (principal component analysis) and OPLS-DA (orthogonal partial least squares discriminant analysis) for separation of malignant and healthy tissue. PCA showed a clear difference between tumor and healthy regions in the peripheral zone for all investigated images. The predictive ability of the OPLS-DA models increased for all image modalities when first and second order statistics were combined. The predictive value reached a plateau after adding ADC and T2, and did not increase further with the addition of other image information. The present study indicates a distinct difference in the signatures between malign and benign prostate tissue. This is an absolute prerequisite for automatic tumor segmentation, but only the first step in that direction. For the specific identified signature, DCE did not add complementary information to T2 and ADC maps.

Multi-atlas-based CT synthesis from conventional MRI with patch-based refinement for MRI-based radiotherapy planning

NASA Astrophysics Data System (ADS)

Lee, Junghoon; Carass, Aaron; Jog, Amod; Zhao, Can; Prince, Jerry L.

2017-02-01

Accurate CT synthesis, sometimes called electron density estimation, from MRI is crucial for successful MRI-based radiotherapy planning and dose computation. Existing CT synthesis methods are able to synthesize normal tissues but are unable to accurately synthesize abnormal tissues (i.e., tumor), thus providing a suboptimal solution. We propose a multiatlas- based hybrid synthesis approach that combines multi-atlas registration and patch-based synthesis to accurately synthesize both normal and abnormal tissues. Multi-parametric atlas MR images are registered to the target MR images by multi-channel deformable registration, from which the atlas CT images are deformed and fused by locally-weighted averaging using a structural similarity measure (SSIM). Synthetic MR images are also computed from the registered atlas MRIs by using the same weights used for the CT synthesis; these are compared to the target patient MRIs allowing for the assessment of the CT synthesis fidelity. Poor synthesis regions are automatically detected based on the fidelity measure and refined by a patch-based synthesis. The proposed approach was tested on brain cancer patient data, and showed a noticeable improvement for the tumor region.

Multi-atlas-based CT synthesis from conventional MRI with patch-based refinement for MRI-based radiotherapy planning.

PubMed

Lee, Junghoon; Carass, Aaron; Jog, Amod; Zhao, Can; Prince, Jerry L

2017-02-01

Accurate CT synthesis, sometimes called electron density estimation, from MRI is crucial for successful MRI-based radiotherapy planning and dose computation. Existing CT synthesis methods are able to synthesize normal tissues but are unable to accurately synthesize abnormal tissues (i.e., tumor), thus providing a suboptimal solution. We propose a multi-atlas-based hybrid synthesis approach that combines multi-atlas registration and patch-based synthesis to accurately synthesize both normal and abnormal tissues. Multi-parametric atlas MR images are registered to the target MR images by multi-channel deformable registration, from which the atlas CT images are deformed and fused by locally-weighted averaging using a structural similarity measure (SSIM). Synthetic MR images are also computed from the registered atlas MRIs by using the same weights used for the CT synthesis; these are compared to the target patient MRIs allowing for the assessment of the CT synthesis fidelity. Poor synthesis regions are automatically detected based on the fidelity measure and refined by a patch-based synthesis. The proposed approach was tested on brain cancer patient data, and showed a noticeable improvement for the tumor region.

Formation of parametric images using mixed-effects models: a feasibility study.

PubMed

Huang, Husan-Ming; Shih, Yi-Yu; Lin, Chieh

2016-03-01

Mixed-effects models have been widely used in the analysis of longitudinal data. By presenting the parameters as a combination of fixed effects and random effects, mixed-effects models incorporating both within- and between-subject variations are capable of improving parameter estimation. In this work, we demonstrate the feasibility of using a non-linear mixed-effects (NLME) approach for generating parametric images from medical imaging data of a single study. By assuming that all voxels in the image are independent, we used simulation and animal data to evaluate whether NLME can improve the voxel-wise parameter estimation. For testing purposes, intravoxel incoherent motion (IVIM) diffusion parameters including perfusion fraction, pseudo-diffusion coefficient and true diffusion coefficient were estimated using diffusion-weighted MR images and NLME through fitting the IVIM model. The conventional method of non-linear least squares (NLLS) was used as the standard approach for comparison of the resulted parametric images. In the simulated data, NLME provides more accurate and precise estimates of diffusion parameters compared with NLLS. Similarly, we found that NLME has the ability to improve the signal-to-noise ratio of parametric images obtained from rat brain data. These data have shown that it is feasible to apply NLME in parametric image generation, and the parametric image quality can be accordingly improved with the use of NLME. With the flexibility to be adapted to other models or modalities, NLME may become a useful tool to improve the parametric image quality in the future. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Towards an acoustic model-based poroelastic imaging method: II. experimental investigation.

PubMed

Berry, Gearóid P; Bamber, Jeffrey C; Miller, Naomi R; Barbone, Paul E; Bush, Nigel L; Armstrong, Cecil G

2006-12-01

Soft biological tissue contains mobile fluid. The volume fraction of this fluid and the ease with which it may be displaced through the tissue could be of diagnostic significance and may also have consequences for the validity with which strain images can be interpreted according to the traditional idealizations of elastography. In a previous paper, under the assumption of frictionless boundary conditions, the spatio-temporal behavior of the strain field inside a compressed cylindrical poroelastic sample was predicted (Berry et al. 2006). In this current paper, experimental evidence is provided to confirm these predictions. Finite element modeling was first used to extend the previous predictions to allow for the existence of contact friction between the sample and the compressor plates. Elastographic techniques were then applied to image the time-evolution of the strain inside cylindrical samples of tofu (a suitable poroelastic material) during sustained unconfined compression. The observed experimental strain behavior was found to be consistent with the theoretical predictions. In particular, every sample studied confirmed that reduced values of radial strain advance with time from the curved cylindrical surface inwards towards the axis of symmetry. Furthermore, by fitting the predictions of an analytical model to a time sequence of strain images, parametric images of two quantities, each related to one or more of three poroelastic material constants were produced. The two parametric images depicted the Poisson's ratio (nu(s)) of the solid matrix and the product of the aggregate modulus (H(A)) of the solid matrix with the permeability (k) of the solid matrix to the pore fluid. The means of the pixel values in these images, nu(s) = 0.088 (standard deviation 0.023) and H(A)k = 1.449 (standard deviation 0.269) x 10(-7) m(2) s(-1), were in agreement with values derived from previously published data for tofu (Righetti et al. 2005). The results provide the first experimental detection of the fluid-flow-induced characteristic diffusion-like behavior of the strain in a compressed poroelastic material and allow parameters related to the above material constants to be determined. We conclude that it may eventually be possible to use strain data to detect and measure characteristics of diffusely distributed mobile fluid in tissue spaces that are too small to be imaged directly.

Parametric methods for characterizing myocardial tissue by magnetic resonance imaging (part 2): T2 mapping.

PubMed

Perea Palazón, R J; Solé Arqués, M; Prat González, S; de Caralt Robira, T M; Cibeira López, M T; Ortiz Pérez, J T

2015-01-01

Cardiac magnetic resonance imaging is considered the reference technique for characterizing myocardial tissue; for example, T2-weighted sequences make it possible to evaluate areas of edema or myocardial inflammation. However, traditional sequences have many limitations and provide only qualitative information. Moreover, traditional sequences depend on the reference to remote myocardium or skeletal muscle, which limits their ability to detect and quantify diffuse myocardial damage. Recently developed magnetic resonance myocardial mapping techniques enable quantitative assessment of parameters indicative of edema. These techniques have proven better than traditional sequences both in acute cardiomyopathy and in acute ischemic heart disease. This article synthesizes current developments in T2 mapping as well as their clinical applications and limitations. Copyright © 2014 SERAM. Published by Elsevier España, S.L.U. All rights reserved.

Preclinical Biokinetic Modelling of Tc-99m Radiophamaceuticals Obtained from Semi-Automatic Image Processing.

PubMed

Cornejo-Aragón, Luz G; Santos-Cuevas, Clara L; Ocampo-García, Blanca E; Chairez-Oria, Isaac; Diaz-Nieto, Lorenza; García-Quiroz, Janice

2017-01-01

The aim of this study was to develop a semi automatic image processing algorithm (AIPA) based on the simultaneous information provided by X-ray and radioisotopic images to determine the biokinetic models of Tc-99m radiopharmaceuticals from quantification of image radiation activity in murine models. These radioisotopic images were obtained by a CCD (charge couple device) camera coupled to an ultrathin phosphorous screen in a preclinical multimodal imaging system (Xtreme, Bruker). The AIPA consisted of different image processing methods for background, scattering and attenuation correction on the activity quantification. A set of parametric identification algorithms was used to obtain the biokinetic models that characterize the interaction between different tissues and the radiopharmaceuticals considered in the study. The set of biokinetic models corresponded to the Tc-99m biodistribution observed in different ex vivo studies. This fact confirmed the contribution of the semi-automatic image processing technique developed in this study.

Automated prediction of tissue outcome after acute ischemic stroke in computed tomography perfusion images

NASA Astrophysics Data System (ADS)

Vos, Pieter C.; Bennink, Edwin; de Jong, Hugo; Velthuis, Birgitta K.; Viergever, Max A.; Dankbaar, Jan Willem

2015-03-01

Assessment of the extent of cerebral damage on admission in patients with acute ischemic stroke could play an important role in treatment decision making. Computed tomography perfusion (CTP) imaging can be used to determine the extent of damage. However, clinical application is hindered by differences among vendors and used methodology. As a result, threshold based methods and visual assessment of CTP images has not yet shown to be useful in treatment decision making and predicting clinical outcome. Preliminary results in MR studies have shown the benefit of using supervised classifiers for predicting tissue outcome, but this has not been demonstrated for CTP. We present a novel method for the automatic prediction of tissue outcome by combining multi-parametric CTP images into a tissue outcome probability map. A supervised classification scheme was developed to extract absolute and relative perfusion values from processed CTP images that are summarized by a trained classifier into a likelihood of infarction. Training was performed using follow-up CT scans of 20 acute stroke patients with complete recanalization of the vessel that was occluded on admission. Infarcted regions were annotated by expert neuroradiologists. Multiple classifiers were evaluated in a leave-one-patient-out strategy for their discriminating performance using receiver operating characteristic (ROC) statistics. Results showed that a RandomForest classifier performed optimally with an area under the ROC of 0.90 for discriminating infarct tissue. The obtained results are an improvement over existing thresholding methods and are in line with results found in literature where MR perfusion was used.

Parametric Imaging Of Digital Subtraction Angiography Studies For Renal Transplant Evaluation

NASA Astrophysics Data System (ADS)

Gallagher, Joe H.; Meaney, Thomas F.; Flechner, Stuart M.; Novick, Andrew C.; Buonocore, Edward

1981-11-01

A noninvasive method for diagnosing acute tubular necrosis and rejection would be an important tool for the management of renal transplant patients. From a sequence of digital subtraction angiographic images acquired after an intravenous injection of radiographic contrast material, the parametric images of the maximum contrast, the time when the maximum contrast is reached, and two times the time at which one half of the maximum contrast is reached are computed. The parametric images of the time when the maximum is reached clearly distinguish normal from abnormal renal function. However, it is the parametric image of two times the time when one half of the maximum is reached which provides some assistance in differentiating acute tubular necrosis from rejection.

Towards precision medicine: from quantitative imaging to radiomics

PubMed Central

Acharya, U. Rajendra; Hagiwara, Yuki; Sudarshan, Vidya K.; Chan, Wai Yee; Ng, Kwan Hoong

2018-01-01

Radiology (imaging) and imaging-guided interventions, which provide multi-parametric morphologic and functional information, are playing an increasingly significant role in precision medicine. Radiologists are trained to understand the imaging phenotypes, transcribe those observations (phenotypes) to correlate with underlying diseases and to characterize the images. However, in order to understand and characterize the molecular phenotype (to obtain genomic information) of solid heterogeneous tumours, the advanced sequencing of those tissues using biopsy is required. Thus, radiologists image the tissues from various views and angles in order to have the complete image phenotypes, thereby acquiring a huge amount of data. Deriving meaningful details from all these radiological data becomes challenging and raises the big data issues. Therefore, interest in the application of radiomics has been growing in recent years as it has the potential to provide significant interpretive and predictive information for decision support. Radiomics is a combination of conventional computer-aided diagnosis, deep learning methods, and human skills, and thus can be used for quantitative characterization of tumour phenotypes. This paper discusses the overview of radiomics workflow, the results of various radiomics-based studies conducted using various radiological images such as computed tomography (CT), magnetic resonance imaging (MRI), and positron-emission tomography (PET), the challenges we are facing, and the potential contribution of radiomics towards precision medicine. PMID:29308604

Ghost imaging via optical parametric amplification

NASA Astrophysics Data System (ADS)

Li, Hong-Guo; Zhang, De-Jian; Xu, De-Qin; Zhao, Qiu-Li; Wang, Sen; Wang, Hai-Bo; Xiong, Jun; Wang, Kaige

2015-10-01

We investigate theoretically and experimentally thermal light ghost imaging where the light transmitted through the object as the seed light is amplified by an optical parametric amplifier (OPA). In conventional lens imaging systems with OPA, the spectral bandwidth of OPA dominates the image resolution. Theoretically, we prove that in ghost imaging via optical parametric amplification (GIOPA) the bandwidth of OPA will not affect the image resolution. The experimental results show that for weak seed light the image quality in GIOPA is better than that of conventional ghost imaging. Our work may be valuable in remote sensing with ghost imaging technique, where the light passed through the object is weak after a long-distance propagation.

In vivo multiphoton imaging of a diverse array of fluorophores to investigate deep neurovascular structure

PubMed Central

Miller, David R.; Hassan, Ahmed M.; Jarrett, Jeremy W.; Medina, Flor A.; Perillo, Evan P.; Hagan, Kristen; Shams Kazmi, S. M.; Clark, Taylor A.; Sullender, Colin T.; Jones, Theresa A.; Zemelman, Boris V.; Dunn, Andrew K.

2017-01-01

We perform high-resolution, non-invasive, in vivo deep-tissue imaging of the mouse neocortex using multiphoton microscopy with a high repetition rate optical parametric amplifier laser source tunable between λ=1,100 and 1,400 nm. By combining the high repetition rate (511 kHz) and high pulse energy (400 nJ) of our amplifier laser system, we demonstrate imaging of vasculature labeled with Texas Red and Indocyanine Green, and neurons expressing tdTomato and yellow fluorescent protein. We measure the blood flow speed of a single capillary at a depth of 1.2 mm, and image vasculature to a depth of 1.53 mm with fine axial steps (5 μm) and reasonable acquisition times. The high image quality enabled analysis of vascular morphology at depths to 1.45 mm. PMID:28717582

Parametric techniques for characterizing myocardial tissue by magnetic resonance imaging (part 1): T1 mapping.

PubMed

Perea Palazón, R J; Ortiz Pérez, J T; Prat González, S; de Caralt Robira, T M; Cibeira López, M T; Solé Arqués, M

2016-01-01

The development of myocardial fibrosis is a common process in the appearance of ventricular dysfunction in many heart diseases. Magnetic resonance imaging makes it possible to accurately evaluate the structure and function of the heart, and its role in the macroscopic characterization of myocardial fibrosis by late enhancement techniques has been widely validated clinically. Recent studies have demonstrated that T1-mapping techniques can quantify diffuse myocardial fibrosis and the expansion of the myocardial extracellular space in absolute terms. However, further studies are necessary to validate the usefulness of this technique in the early detection of tissue remodeling at a time when implementing early treatment would improve a patient's prognosis. This article reviews the state of the art for T1 mapping of the myocardium, its clinical applications, and its limitations. Copyright © 2016 SERAM. Published by Elsevier España, S.L.U. All rights reserved.

Coherent white light amplification

DOEpatents

Jovanovic, Igor; Barty, Christopher P.

2004-05-25

A system for coherent simultaneous amplification of a broad spectral range of light that includes an optical parametric amplifier and a source of a seed pulse is described. A first angular dispersive element is operatively connected to the source of a seed pulse. A first imaging telescope is operatively connected to the first angular dispersive element and operatively connected to the optical parametric amplifier. A source of a pump pulse is operatively connected to the optical parametric amplifier. A second imaging telescope is operatively connected to the optical parametric amplifier and a second angular dispersive element is operatively connected to the second imaging telescope.

Direct 4D reconstruction of parametric images incorporating anato-functional joint entropy.

PubMed

Tang, Jing; Kuwabara, Hiroto; Wong, Dean F; Rahmim, Arman

2010-08-07

We developed an anatomy-guided 4D closed-form algorithm to directly reconstruct parametric images from projection data for (nearly) irreversible tracers. Conventional methods consist of individually reconstructing 2D/3D PET data, followed by graphical analysis on the sequence of reconstructed image frames. The proposed direct reconstruction approach maintains the simplicity and accuracy of the expectation-maximization (EM) algorithm by extending the system matrix to include the relation between the parametric images and the measured data. A closed-form solution was achieved using a different hidden complete-data formulation within the EM framework. Furthermore, the proposed method was extended to maximum a posterior reconstruction via incorporation of MR image information, taking the joint entropy between MR and parametric PET features as the prior. Using realistic simulated noisy [(11)C]-naltrindole PET and MR brain images/data, the quantitative performance of the proposed methods was investigated. Significant improvements in terms of noise versus bias performance were demonstrated when performing direct parametric reconstruction, and additionally upon extending the algorithm to its Bayesian counterpart using the MR-PET joint entropy measure.

Full-direct method for imaging pharmacokinetic parameters in dynamic fluorescence molecular tomography

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

Zhang, Guanglei, E-mail: guangleizhang@bjtu.edu.cn; Department of Biomedical Engineering, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044; Pu, Huangsheng

2015-02-23

Images of pharmacokinetic parameters (also known as parametric images) in dynamic fluorescence molecular tomography (FMT) can provide three-dimensional metabolic information for biological studies and drug development. However, the ill-posed nature of FMT and the high temporal variation of fluorophore concentration together make it difficult to obtain accurate parametric images in small animals in vivo. In this letter, we present a method to directly reconstruct the parametric images from the boundary measurements based on hybrid FMT/X-ray computed tomography (XCT) system. This method can not only utilize structural priors obtained from the XCT system to mitigate the ill-posedness of FMT but alsomore » make full use of the temporal correlations of boundary measurements to model the high temporal variation of fluorophore concentration. The results of numerical simulation and mouse experiment demonstrate that the proposed method leads to significant improvements in the reconstruction quality of parametric images.« less

Rapid Parametric Mapping of the Longitudinal Relaxation Time T1 Using Two-Dimensional Variable Flip Angle Magnetic Resonance Imaging at 1.5 Tesla, 3 Tesla, and 7 Tesla

PubMed Central

Dieringer, Matthias A.; Deimling, Michael; Santoro, Davide; Wuerfel, Jens; Madai, Vince I.; Sobesky, Jan; von Knobelsdorff-Brenkenhoff, Florian; Schulz-Menger, Jeanette; Niendorf, Thoralf

2014-01-01

Introduction Visual but subjective reading of longitudinal relaxation time (T1) weighted magnetic resonance images is commonly used for the detection of brain pathologies. For this non-quantitative measure, diagnostic quality depends on hardware configuration, imaging parameters, radio frequency transmission field (B1+) uniformity, as well as observer experience. Parametric quantification of the tissue T1 relaxation parameter offsets the propensity for these effects, but is typically time consuming. For this reason, this study examines the feasibility of rapid 2D T1 quantification using a variable flip angles (VFA) approach at magnetic field strengths of 1.5 Tesla, 3 Tesla, and 7 Tesla. These efforts include validation in phantom experiments and application for brain T1 mapping. Methods T1 quantification included simulations of the Bloch equations to correct for slice profile imperfections, and a correction for B1+. Fast gradient echo acquisitions were conducted using three adjusted flip angles for the proposed T1 quantification approach that was benchmarked against slice profile uncorrected 2D VFA and an inversion-recovery spin-echo based reference method. Brain T1 mapping was performed in six healthy subjects, one multiple sclerosis patient, and one stroke patient. Results Phantom experiments showed a mean T1 estimation error of (-63±1.5)% for slice profile uncorrected 2D VFA and (0.2±1.4)% for the proposed approach compared to the reference method. Scan time for single slice T1 mapping including B1+ mapping could be reduced to 5 seconds using an in-plane resolution of (2×2) mm2, which equals a scan time reduction of more than 99% compared to the reference method. Conclusion Our results demonstrate that rapid 2D T1 quantification using a variable flip angle approach is feasible at 1.5T/3T/7T. It represents a valuable alternative for rapid T1 mapping due to the gain in speed versus conventional approaches. This progress may serve to enhance the capabilities of parametric MR based lesion detection and brain tissue characterization. PMID:24621588

Rapid parametric mapping of the longitudinal relaxation time T1 using two-dimensional variable flip angle magnetic resonance imaging at 1.5 Tesla, 3 Tesla, and 7 Tesla.

PubMed

Dieringer, Matthias A; Deimling, Michael; Santoro, Davide; Wuerfel, Jens; Madai, Vince I; Sobesky, Jan; von Knobelsdorff-Brenkenhoff, Florian; Schulz-Menger, Jeanette; Niendorf, Thoralf

2014-01-01

Visual but subjective reading of longitudinal relaxation time (T1) weighted magnetic resonance images is commonly used for the detection of brain pathologies. For this non-quantitative measure, diagnostic quality depends on hardware configuration, imaging parameters, radio frequency transmission field (B1+) uniformity, as well as observer experience. Parametric quantification of the tissue T1 relaxation parameter offsets the propensity for these effects, but is typically time consuming. For this reason, this study examines the feasibility of rapid 2D T1 quantification using a variable flip angles (VFA) approach at magnetic field strengths of 1.5 Tesla, 3 Tesla, and 7 Tesla. These efforts include validation in phantom experiments and application for brain T1 mapping. T1 quantification included simulations of the Bloch equations to correct for slice profile imperfections, and a correction for B1+. Fast gradient echo acquisitions were conducted using three adjusted flip angles for the proposed T1 quantification approach that was benchmarked against slice profile uncorrected 2D VFA and an inversion-recovery spin-echo based reference method. Brain T1 mapping was performed in six healthy subjects, one multiple sclerosis patient, and one stroke patient. Phantom experiments showed a mean T1 estimation error of (-63±1.5)% for slice profile uncorrected 2D VFA and (0.2±1.4)% for the proposed approach compared to the reference method. Scan time for single slice T1 mapping including B1+ mapping could be reduced to 5 seconds using an in-plane resolution of (2×2) mm2, which equals a scan time reduction of more than 99% compared to the reference method. Our results demonstrate that rapid 2D T1 quantification using a variable flip angle approach is feasible at 1.5T/3T/7T. It represents a valuable alternative for rapid T1 mapping due to the gain in speed versus conventional approaches. This progress may serve to enhance the capabilities of parametric MR based lesion detection and brain tissue characterization.

Outcome of temporal lobe epilepsy surgery predicted by statistical parametric PET imaging.

PubMed

Wong, C Y; Geller, E B; Chen, E Q; MacIntyre, W J; Morris, H H; Raja, S; Saha, G B; Lüders, H O; Cook, S A; Go, R T

1996-07-01

PET is useful in the presurgical evaluation of temporal lobe epilepsy. The purpose of this retrospective study is to assess the clinical use of statistical parametric imaging in predicting surgical outcome. Interictal 18FDG-PET scans in 17 patients with surgically-treated temporal lobe epilepsy (Group A-13 seizure-free, group B = 4 not seizure-free at 6 mo) were transformed into statistical parametric imaging, with each pixel representing a z-score value by using the mean and s.d. of count distribution in each individual patient, for both visual and quantitative analysis. Mean z-scores were significantly more negative in anterolateral (AL) and mesial (M) regions on the operated side than the nonoperated side in group A (AL: p < 0.00005, M: p = 0.0097), but not in group B (AL: p = 0.46, M: p = 0.08). Statistical parametric imaging correctly lateralized 16 out of 17 patients. Only the AL region, however, was significant in predicting surgical outcome (F = 29.03, p < 0.00005). Using a cut-off z-score value of -1.5, statistical parametric imaging correctly classified 92% of temporal lobes from group A and 88% of those from Group B. The preliminary results indicate that statistical parametric imaging provides both clinically useful information for lateralization in temporal lobe epilepsy and a reliable predictive indicator of clinical outcome following surgical treatment.

Separation of β-amyloid binding and white matter uptake of 18F-flutemetamol using spectral analysis

PubMed Central

Heurling, Kerstin; Buckley, Christopher; Vandenberghe, Rik; Laere, Koen Van; Lubberink, Mark

2015-01-01

The kinetic components of the β-amyloid ligand 18F-flutemetamol binding in grey and white matter were investigated through spectral analysis, and a method developed for creation of parametric images separating grey and white matter uptake. Tracer uptake in grey and white matter and cerebellar cortex was analyzed through spectral analysis in six subjects, with (n=4) or without (n=2) apparent β-amyloid deposition, having undergone dynamic 18F-flutemetamol scanning with arterial blood sampling. The spectra were divided into three components: slow, intermediate and fast basis function rates. The contribution of each of the components to total volume of distribution (VT) was assessed for different tissue types. The slow component dominated in white matter (average 90%), had a higher contribution to grey matter VT in subjects with β-amyloid deposition (average 44%) than without (average 6%) and was absent in cerebellar cortex, attributing the slow component of 18F-flutemetamol uptake in grey matter to β-amyloid binding. Parametric images of voxel-based spectral analysis were created for VT, the slow component and images segmented based on the slow component contribution; confirming that grey matter and white matter uptake can be discriminated on voxel-level using a threshold for the contribution from the slow component to VT. PMID:26550542

Spectral Clustering Predicts Tumor Tissue Heterogeneity Using Dynamic 18F-FDG PET: A Complement to the Standard Compartmental Modeling Approach.

PubMed

Katiyar, Prateek; Divine, Mathew R; Kohlhofer, Ursula; Quintanilla-Martinez, Leticia; Schölkopf, Bernhard; Pichler, Bernd J; Disselhorst, Jonathan A

2017-04-01

In this study, we described and validated an unsupervised segmentation algorithm for the assessment of tumor heterogeneity using dynamic 18 F-FDG PET. The aim of our study was to objectively evaluate the proposed method and make comparisons with compartmental modeling parametric maps and SUV segmentations using simulations of clinically relevant tumor tissue types. Methods: An irreversible 2-tissue-compartmental model was implemented to simulate clinical and preclinical 18 F-FDG PET time-activity curves using population-based arterial input functions (80 clinical and 12 preclinical) and the kinetic parameter values of 3 tumor tissue types. The simulated time-activity curves were corrupted with different levels of noise and used to calculate the tissue-type misclassification errors of spectral clustering (SC), parametric maps, and SUV segmentation. The utility of the inverse noise variance- and Laplacian score-derived frame weighting schemes before SC was also investigated. Finally, the SC scheme with the best results was tested on a dynamic 18 F-FDG measurement of a mouse bearing subcutaneous colon cancer and validated using histology. Results: In the preclinical setup, the inverse noise variance-weighted SC exhibited the lowest misclassification errors (8.09%-28.53%) at all noise levels in contrast to the Laplacian score-weighted SC (16.12%-31.23%), unweighted SC (25.73%-40.03%), parametric maps (28.02%-61.45%), and SUV (45.49%-45.63%) segmentation. The classification efficacy of both weighted SC schemes in the clinical case was comparable to the unweighted SC. When applied to the dynamic 18 F-FDG measurement of colon cancer, the proposed algorithm accurately identified densely vascularized regions from the rest of the tumor. In addition, the segmented regions and clusterwise average time-activity curves showed excellent correlation with the tumor histology. Conclusion: The promising results of SC mark its position as a robust tool for quantification of tumor heterogeneity using dynamic PET studies. Because SC tumor segmentation is based on the intrinsic structure of the underlying data, it can be easily applied to other cancer types as well. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Direct parametric reconstruction in dynamic PET myocardial perfusion imaging: in vivo studies.

PubMed

Petibon, Yoann; Rakvongthai, Yothin; El Fakhri, Georges; Ouyang, Jinsong

2017-05-07

Dynamic PET myocardial perfusion imaging (MPI) used in conjunction with tracer kinetic modeling enables the quantification of absolute myocardial blood flow (MBF). However, MBF maps computed using the traditional indirect method (i.e. post-reconstruction voxel-wise fitting of kinetic model to PET time-activity-curves-TACs) suffer from poor signal-to-noise ratio (SNR). Direct reconstruction of kinetic parameters from raw PET projection data has been shown to offer parametric images with higher SNR compared to the indirect method. The aim of this study was to extend and evaluate the performance of a direct parametric reconstruction method using in vivo dynamic PET MPI data for the purpose of quantifying MBF. Dynamic PET MPI studies were performed on two healthy pigs using a Siemens Biograph mMR scanner. List-mode PET data for each animal were acquired following a bolus injection of ~7-8 mCi of 18 F-flurpiridaz, a myocardial perfusion agent. Fully-3D dynamic PET sinograms were obtained by sorting the coincidence events into 16 temporal frames covering ~5 min after radiotracer administration. Additionally, eight independent noise realizations of both scans-each containing 1/8th of the total number of events-were generated from the original list-mode data. Dynamic sinograms were then used to compute parametric maps using the conventional indirect method and the proposed direct method. For both methods, a one-tissue compartment model accounting for spillover from the left and right ventricle blood-pools was used to describe the kinetics of 18 F-flurpiridaz. An image-derived arterial input function obtained from a TAC taken in the left ventricle cavity was used for tracer kinetic analysis. For the indirect method, frame-by-frame images were estimated using two fully-3D reconstruction techniques: the standard ordered subset expectation maximization (OSEM) reconstruction algorithm on one side, and the one-step late maximum a posteriori (OSL-MAP) algorithm on the other side, which incorporates a quadratic penalty function. The parametric images were then calculated using voxel-wise weighted least-square fitting of the reconstructed myocardial PET TACs. For the direct method, parametric images were estimated directly from the dynamic PET sinograms using a maximum a posteriori (MAP) parametric reconstruction algorithm which optimizes an objective function comprised of the Poisson log-likelihood term, the kinetic model and a quadratic penalty function. Maximization of the objective function with respect to each set of parameters was achieved using a preconditioned conjugate gradient algorithm with a specifically developed pre-conditioner. The performance of the direct method was evaluated by comparing voxel- and segment-wise estimates of [Formula: see text], the tracer transport rate (ml · min -1 · ml -1 ), to those obtained using the indirect method applied to both OSEM and OSL-MAP dynamic reconstructions. The proposed direct reconstruction method produced [Formula: see text] maps with visibly lower noise than the indirect method based on OSEM and OSL-MAP reconstructions. At normal count levels, the direct method was shown to outperform the indirect method based on OSL-MAP in the sense that at matched level of bias, reduced regional noise levels were obtained. At lower count levels, the direct method produced [Formula: see text] estimates with significantly lower standard deviation across noise realizations than the indirect method based on OSL-MAP at matched bias level. In all cases, the direct method yielded lower noise and standard deviation than the indirect method based on OSEM. Overall, the proposed direct reconstruction offered a better bias-variance tradeoff than the indirect method applied to either OSEM and OSL-MAP. Direct parametric reconstruction as applied to in vivo dynamic PET MPI data is therefore a promising method for producing MBF maps with lower variance.

Direct parametric reconstruction in dynamic PET myocardial perfusion imaging: in-vivo studies

PubMed Central

Petibon, Yoann; Rakvongthai, Yothin; Fakhri, Georges El; Ouyang, Jinsong

2017-01-01

Dynamic PET myocardial perfusion imaging (MPI) used in conjunction with tracer kinetic modeling enables the quantification of absolute myocardial blood flow (MBF). However, MBF maps computed using the traditional indirect method (i.e. post-reconstruction voxel-wise fitting of kinetic model to PET time-activity-curves -TACs) suffer from poor signal-to-noise ratio (SNR). Direct reconstruction of kinetic parameters from raw PET projection data has been shown to offer parametric images with higher SNR compared to the indirect method. The aim of this study was to extend and evaluate the performance of a direct parametric reconstruction method using in-vivo dynamic PET MPI data for the purpose of quantifying MBF. Dynamic PET MPI studies were performed on two healthy pigs using a Siemens Biograph mMR scanner. List-mode PET data for each animal were acquired following a bolus injection of ~7-8 mCi of 18F-flurpiridaz, a myocardial perfusion agent. Fully-3D dynamic PET sinograms were obtained by sorting the coincidence events into 16 temporal frames covering ~5 min after radiotracer administration. Additionally, eight independent noise realizations of both scans - each containing 1/8th of the total number of events - were generated from the original list-mode data. Dynamic sinograms were then used to compute parametric maps using the conventional indirect method and the proposed direct method. For both methods, a one-tissue compartment model accounting for spillover from the left and right ventricle blood-pools was used to describe the kinetics of 18F-flurpiridaz. An image-derived arterial input function obtained from a TAC taken in the left ventricle cavity was used for tracer kinetic analysis. For the indirect method, frame-by-frame images were estimated using two fully-3D reconstruction techniques: the standard Ordered Subset Expectation Maximization (OSEM) reconstruction algorithm on one side, and the One-Step Late Maximum a Posteriori (OSL-MAP) algorithm on the other side, which incorporates a quadratic penalty function. The parametric images were then calculated using voxel-wise weighted least-square fitting of the reconstructed myocardial PET TACs. For the direct method, parametric images were estimated directly from the dynamic PET sinograms using a maximum a posteriori (MAP) parametric reconstruction algorithm which optimizes an objective function comprised of the Poisson log-likelihood term, the kinetic model and a quadratic penalty function. Maximization of the objective function with respect to each set of parameters was achieved using a preconditioned conjugate gradient algorithm with a specifically developed pre-conditioner. The performance of the direct method was evaluated by comparing voxel- and segment-wise estimates of K1, the tracer transport rate (mL.min−1.mL−1), to those obtained using the indirect method applied to both OSEM and OSL-MAP dynamic reconstructions. The proposed direct reconstruction method produced K1 maps with visibly lower noise than the indirect method based on OSEM and OSL-MAP reconstructions. At normal count levels, the direct method was shown to outperform the indirect method based on OSL-MAP in the sense that at matched level of bias, reduced regional noise levels were obtained. At lower count levels, the direct method produced K1 estimates with significantly lower standard deviation across noise realizations than the indirect method based on OSL-MAP at matched bias level. In all cases, the direct method yielded lower noise and standard deviation than the indirect method based on OSEM. Overall, the proposed direct reconstruction offered a better bias-variance tradeoff than the indirect method applied to either OSEM and OSL-MAP. Direct parametric reconstruction as applied to in-vivo dynamic PET MPI data is therefore a promising method for producing MBF maps with lower variance. PMID:28379843

Direct parametric reconstruction in dynamic PET myocardial perfusion imaging: in vivo studies

NASA Astrophysics Data System (ADS)

Petibon, Yoann; Rakvongthai, Yothin; El Fakhri, Georges; Ouyang, Jinsong

2017-05-01

Dynamic PET myocardial perfusion imaging (MPI) used in conjunction with tracer kinetic modeling enables the quantification of absolute myocardial blood flow (MBF). However, MBF maps computed using the traditional indirect method (i.e. post-reconstruction voxel-wise fitting of kinetic model to PET time-activity-curves-TACs) suffer from poor signal-to-noise ratio (SNR). Direct reconstruction of kinetic parameters from raw PET projection data has been shown to offer parametric images with higher SNR compared to the indirect method. The aim of this study was to extend and evaluate the performance of a direct parametric reconstruction method using in vivo dynamic PET MPI data for the purpose of quantifying MBF. Dynamic PET MPI studies were performed on two healthy pigs using a Siemens Biograph mMR scanner. List-mode PET data for each animal were acquired following a bolus injection of ~7-8 mCi of 18F-flurpiridaz, a myocardial perfusion agent. Fully-3D dynamic PET sinograms were obtained by sorting the coincidence events into 16 temporal frames covering ~5 min after radiotracer administration. Additionally, eight independent noise realizations of both scans—each containing 1/8th of the total number of events—were generated from the original list-mode data. Dynamic sinograms were then used to compute parametric maps using the conventional indirect method and the proposed direct method. For both methods, a one-tissue compartment model accounting for spillover from the left and right ventricle blood-pools was used to describe the kinetics of 18F-flurpiridaz. An image-derived arterial input function obtained from a TAC taken in the left ventricle cavity was used for tracer kinetic analysis. For the indirect method, frame-by-frame images were estimated using two fully-3D reconstruction techniques: the standard ordered subset expectation maximization (OSEM) reconstruction algorithm on one side, and the one-step late maximum a posteriori (OSL-MAP) algorithm on the other side, which incorporates a quadratic penalty function. The parametric images were then calculated using voxel-wise weighted least-square fitting of the reconstructed myocardial PET TACs. For the direct method, parametric images were estimated directly from the dynamic PET sinograms using a maximum a posteriori (MAP) parametric reconstruction algorithm which optimizes an objective function comprised of the Poisson log-likelihood term, the kinetic model and a quadratic penalty function. Maximization of the objective function with respect to each set of parameters was achieved using a preconditioned conjugate gradient algorithm with a specifically developed pre-conditioner. The performance of the direct method was evaluated by comparing voxel- and segment-wise estimates of {{K}1} , the tracer transport rate (ml · min-1 · ml-1), to those obtained using the indirect method applied to both OSEM and OSL-MAP dynamic reconstructions. The proposed direct reconstruction method produced {{K}1} maps with visibly lower noise than the indirect method based on OSEM and OSL-MAP reconstructions. At normal count levels, the direct method was shown to outperform the indirect method based on OSL-MAP in the sense that at matched level of bias, reduced regional noise levels were obtained. At lower count levels, the direct method produced {{K}1} estimates with significantly lower standard deviation across noise realizations than the indirect method based on OSL-MAP at matched bias level. In all cases, the direct method yielded lower noise and standard deviation than the indirect method based on OSEM. Overall, the proposed direct reconstruction offered a better bias-variance tradeoff than the indirect method applied to either OSEM and OSL-MAP. Direct parametric reconstruction as applied to in vivo dynamic PET MPI data is therefore a promising method for producing MBF maps with lower variance.

Theoretical Analysis of Penalized Maximum-Likelihood Patlak Parametric Image Reconstruction in Dynamic PET for Lesion Detection.

PubMed

Yang, Li; Wang, Guobao; Qi, Jinyi

2016-04-01

Detecting cancerous lesions is a major clinical application of emission tomography. In a previous work, we studied penalized maximum-likelihood (PML) image reconstruction for lesion detection in static PET. Here we extend our theoretical analysis of static PET reconstruction to dynamic PET. We study both the conventional indirect reconstruction and direct reconstruction for Patlak parametric image estimation. In indirect reconstruction, Patlak parametric images are generated by first reconstructing a sequence of dynamic PET images, and then performing Patlak analysis on the time activity curves (TACs) pixel-by-pixel. In direct reconstruction, Patlak parametric images are estimated directly from raw sinogram data by incorporating the Patlak model into the image reconstruction procedure. PML reconstruction is used in both the indirect and direct reconstruction methods. We use a channelized Hotelling observer (CHO) to assess lesion detectability in Patlak parametric images. Simplified expressions for evaluating the lesion detectability have been derived and applied to the selection of the regularization parameter value to maximize detection performance. The proposed method is validated using computer-based Monte Carlo simulations. Good agreements between the theoretical predictions and the Monte Carlo results are observed. Both theoretical predictions and Monte Carlo simulation results show the benefit of the indirect and direct methods under optimized regularization parameters in dynamic PET reconstruction for lesion detection, when compared with the conventional static PET reconstruction.

Diagnosing and Mapping Pulmonary Emphysema on X-Ray Projection Images: Incremental Value of Grating-Based X-Ray Dark-Field Imaging

PubMed Central

Meinel, Felix G.; Schwab, Felix; Schleede, Simone; Bech, Martin; Herzen, Julia; Achterhold, Klaus; Auweter, Sigrid; Bamberg, Fabian; Yildirim, Ali Ö.; Bohla, Alexander; Eickelberg, Oliver; Loewen, Rod; Gifford, Martin; Ruth, Ronald; Reiser, Maximilian F.; Pfeiffer, Franz; Nikolaou, Konstantin

2013-01-01

Purpose To assess whether grating-based X-ray dark-field imaging can increase the sensitivity of X-ray projection images in the diagnosis of pulmonary emphysema and allow for a more accurate assessment of emphysema distribution. Materials and Methods Lungs from three mice with pulmonary emphysema and three healthy mice were imaged ex vivo using a laser-driven compact synchrotron X-ray source. Median signal intensities of transmission (T), dark-field (V) and a combined parameter (normalized scatter) were compared between emphysema and control group. To determine the diagnostic value of each parameter in differentiating between healthy and emphysematous lung tissue, a receiver-operating-characteristic (ROC) curve analysis was performed both on a per-pixel and a per-individual basis. Parametric maps of emphysema distribution were generated using transmission, dark-field and normalized scatter signal and correlated with histopathology. Results Transmission values relative to water were higher for emphysematous lungs than for control lungs (1.11 vs. 1.06, p<0.001). There was no difference in median dark-field signal intensities between both groups (0.66 vs. 0.66). Median normalized scatter was significantly lower in the emphysematous lungs compared to controls (4.9 vs. 10.8, p<0.001), and was the best parameter for differentiation of healthy vs. emphysematous lung tissue. In a per-pixel analysis, the area under the ROC curve (AUC) for the normalized scatter value was significantly higher than for transmission (0.86 vs. 0.78, p<0.001) and dark-field value (0.86 vs. 0.52, p<0.001) alone. Normalized scatter showed very high sensitivity for a wide range of specificity values (94% sensitivity at 75% specificity). Using the normalized scatter signal to display the regional distribution of emphysema provides color-coded parametric maps, which show the best correlation with histopathology. Conclusion In a murine model, the complementary information provided by X-ray transmission and dark-field images adds incremental diagnostic value in detecting pulmonary emphysema and visualizing its regional distribution as compared to conventional X-ray projections. PMID:23555692

Diagnosing and mapping pulmonary emphysema on X-ray projection images: incremental value of grating-based X-ray dark-field imaging.

PubMed

Meinel, Felix G; Schwab, Felix; Schleede, Simone; Bech, Martin; Herzen, Julia; Achterhold, Klaus; Auweter, Sigrid; Bamberg, Fabian; Yildirim, Ali Ö; Bohla, Alexander; Eickelberg, Oliver; Loewen, Rod; Gifford, Martin; Ruth, Ronald; Reiser, Maximilian F; Pfeiffer, Franz; Nikolaou, Konstantin

2013-01-01

To assess whether grating-based X-ray dark-field imaging can increase the sensitivity of X-ray projection images in the diagnosis of pulmonary emphysema and allow for a more accurate assessment of emphysema distribution. Lungs from three mice with pulmonary emphysema and three healthy mice were imaged ex vivo using a laser-driven compact synchrotron X-ray source. Median signal intensities of transmission (T), dark-field (V) and a combined parameter (normalized scatter) were compared between emphysema and control group. To determine the diagnostic value of each parameter in differentiating between healthy and emphysematous lung tissue, a receiver-operating-characteristic (ROC) curve analysis was performed both on a per-pixel and a per-individual basis. Parametric maps of emphysema distribution were generated using transmission, dark-field and normalized scatter signal and correlated with histopathology. Transmission values relative to water were higher for emphysematous lungs than for control lungs (1.11 vs. 1.06, p<0.001). There was no difference in median dark-field signal intensities between both groups (0.66 vs. 0.66). Median normalized scatter was significantly lower in the emphysematous lungs compared to controls (4.9 vs. 10.8, p<0.001), and was the best parameter for differentiation of healthy vs. emphysematous lung tissue. In a per-pixel analysis, the area under the ROC curve (AUC) for the normalized scatter value was significantly higher than for transmission (0.86 vs. 0.78, p<0.001) and dark-field value (0.86 vs. 0.52, p<0.001) alone. Normalized scatter showed very high sensitivity for a wide range of specificity values (94% sensitivity at 75% specificity). Using the normalized scatter signal to display the regional distribution of emphysema provides color-coded parametric maps, which show the best correlation with histopathology. In a murine model, the complementary information provided by X-ray transmission and dark-field images adds incremental diagnostic value in detecting pulmonary emphysema and visualizing its regional distribution as compared to conventional X-ray projections.

High-power picosecond fiber source for coherent Raman microscopy

PubMed Central

Kieu, Khanh; Saar, Brian G.; Holtom, Gary R.; Xie, X. Sunney; Wise, Frank W.

2011-01-01

We report a high-power picosecond fiber pump laser system for coherent Raman microscopy (CRM). The fiber laser system generates 3.5 ps pulses with 6 W average power at 1030 nm. Frequency doubling yields more than 2 W of green light, which can be used to pump an optical parametric oscillator to produce the pump and the Stokes beams for CRM. Detailed performance data on the laser and the various wavelength conversion steps are discussed, together with representative CRM images of fresh animal tissue obtained with the new source. PMID:19571996

Clinical feasibility study of combined opto-acoustic and ultrasonic imaging modality providing coregistered functional and anatomical maps of breast tumors

NASA Astrophysics Data System (ADS)

Zalev, Jason; Clingman, Bryan; Smith, Remie J.; Herzog, Don; Miller, Tom; Stavros, A. Thomas; Ermilov, Sergey; Conjusteau, André; Tsyboulski, Dmitri; Oraevsky, Alexander A.; Kist, Kenneth; Dornbluth, N. C.; Otto, Pamela

2013-03-01

We report on findings from the clinical feasibility study of the ImagioTM. Breast Imaging System, which acquires two-dimensional opto-acoustic (OA) images co-registered with conventional ultrasound using a specialized duplex hand-held probe. Dual-wavelength opto-acoustic technology is used to generate parametric maps based upon total hemoglobin and its oxygen saturation in breast tissues. This may provide functional diagnostic information pertaining to tumor metabolism and microvasculature, which is complementary to morphological information obtained with conventional gray-scale ultrasound. We present co-registered opto-acoustic and ultrasonic images of malignant and benign tumors from a recent clinical feasibility study. The clinical results illustrate that the technology may have the capability to improve the efficacy of breast tumor diagnosis. In doing so, it may have the potential to reduce biopsies and to characterize cancers that were not seen well with conventional gray-scale ultrasound alone.

TU-H-CAMPUS-IeP3-02: Neurovascular 4D Parametric Imaging Using Co-Registration of Biplane DSA Sequences with 3D Vascular Geometry Obtained From Cone Beam CT

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

Balasubramoniam, A; Bednarek, D; Rudin, S

Purpose: To create 4D parametric images using biplane Digital Subtraction Angiography (DSA) sequences co-registered with the 3D vascular geometry obtained from Cone Beam-CT (CBCT). Methods: We investigated a method to derive multiple 4D Parametric Imaging (PI) maps using only one CBCT acquisition. During this procedure a 3D-DSA geometry is stored and used subsequently for all 4D images. Each time a biplane DSA is acquired, we calculate 2D parametric maps of Bolus Arrival Time (BAT), Mean Transit Time (MTT) and Time to Peak (TTP). Arterial segments which are nearly parallel with one of the biplane imaging planes in the 2D parametricmore » maps are co-registered with the 3D geometry. The values in the remaining vascular network are found using spline interpolation since the points chosen for co-registration on the vasculature are discrete and remaining regions need to be interpolated. To evaluate the method we used a patient CT volume data set for 3D printing a neurovascular phantom containing a complete Circle of Willis. We connected the phantom to a flow loop with a peristaltic pump, simulating physiological flow conditions. Contrast media was injected with an automatic injector at 10 ml/sec. Images were acquired with a Toshiba Infinix C-arm and 4D parametric image maps of the vasculature were calculated. Results: 4D BAT, MTT, and TTP parametric image maps of the Circle of Willis were derived. We generated color-coded 3D geometries which avoided artifacts due to vessel overlap or foreshortening in the projection direction. Conclusion: The software was tested successfully and multiple 4D parametric images were obtained from biplane DSA sequences without the need to acquire additional 3D-DSA runs. This can benefit the patient by reducing the contrast media and the radiation dose normally associated with these procedures. Partial support from NIH Grant R01-EB002873 and Toshiba Medical Systems Corp.« less

Depth-resolved imaging of colon tumor using optical coherence tomography and fluorescence laminar optical tomography (Conference Presentation)

NASA Astrophysics Data System (ADS)

Tang, Qinggong; Frank, Aaron; Wang, Jianting; Chen, Chao-wei; Jin, Lily; Lin, Jon; Chan, Joanne M.; Chen, Yu

2016-03-01

Early detection of neoplastic changes remains a critical challenge in clinical cancer diagnosis and treatment. Many cancers arise from epithelial layers such as those of the gastrointestinal (GI) tract. Current standard endoscopic technology is unable to detect those subsurface lesions. Since cancer development is associated with both morphological and molecular alterations, imaging technologies that can quantitative image tissue's morphological and molecular biomarkers and assess the depth extent of a lesion in real time, without the need for tissue excision, would be a major advance in GI cancer diagnostics and therapy. In this research, we investigated the feasibility of multi-modal optical imaging including high-resolution optical coherence tomography (OCT) and depth-resolved high-sensitivity fluorescence laminar optical tomography (FLOT) for structural and molecular imaging. APC (adenomatous polyposis coli) mice model were imaged using OCT and FLOT and the correlated histopathological diagnosis was obtained. Quantitative structural (the scattering coefficient) and molecular imaging parameters (fluorescence intensity) from OCT and FLOT images were developed for multi-parametric analysis. This multi-modal imaging method has demonstrated the feasibility for more accurate diagnosis with 87.4% (87.3%) for sensitivity (specificity) which gives the most optimal diagnosis (the largest area under receiver operating characteristic (ROC) curve). This project results in a new non-invasive multi-modal imaging platform for improved GI cancer detection, which is expected to have a major impact on detection, diagnosis, and characterization of GI cancers, as well as a wide range of epithelial cancers.

Dynamic PET simulator via tomographic emission projection for kinetic modeling and parametric image studies.

PubMed

Häggström, Ida; Beattie, Bradley J; Schmidtlein, C Ross

2016-06-01

To develop and evaluate a fast and simple tool called dpetstep (Dynamic PET Simulator of Tracers via Emission Projection), for dynamic PET simulations as an alternative to Monte Carlo (MC), useful for educational purposes and evaluation of the effects of the clinical environment, postprocessing choices, etc., on dynamic and parametric images. The tool was developed in matlab using both new and previously reported modules of petstep (PET Simulator of Tracers via Emission Projection). Time activity curves are generated for each voxel of the input parametric image, whereby effects of imaging system blurring, counting noise, scatters, randoms, and attenuation are simulated for each frame. Each frame is then reconstructed into images according to the user specified method, settings, and corrections. Reconstructed images were compared to MC data, and simple Gaussian noised time activity curves (GAUSS). dpetstep was 8000 times faster than MC. Dynamic images from dpetstep had a root mean square error that was within 4% on average of that of MC images, whereas the GAUSS images were within 11%. The average bias in dpetstep and MC images was the same, while GAUSS differed by 3% points. Noise profiles in dpetstep images conformed well to MC images, confirmed visually by scatter plot histograms, and statistically by tumor region of interest histogram comparisons that showed no significant differences (p < 0.01). Compared to GAUSS, dpetstep images and noise properties agreed better with MC. The authors have developed a fast and easy one-stop solution for simulations of dynamic PET and parametric images, and demonstrated that it generates both images and subsequent parametric images with very similar noise properties to those of MC images, in a fraction of the time. They believe dpetstep to be very useful for generating fast, simple, and realistic results, however since it uses simple scatter and random models it may not be suitable for studies investigating these phenomena. dpetstep can be downloaded free of cost from https://github.com/CRossSchmidtlein/dPETSTEP.

Neuroscience imaging enabled by new highly tunable and high peak power femtosecond lasers

NASA Astrophysics Data System (ADS)

Hakulinen, T.; Klein, J.

2017-02-01

Neuroscience applications benefit from recent developments in industrial femtosecond laser technology. New laser sources provide several megawatts of peak power at wavelength of 1040 nm, which enables simultaneous optogenetics photoactivation of tens or even hundreds of neurons using red shifted opsins. Another recent imaging trend is to move towards longer wavelengths, which would enable access to deeper layers of tissue due to lower scattering and lower absorption in the tissue. Femtosecond lasers pumping a non-collinear optical parametric amplifier (NOPA) enable the access to longer wavelengths with high peak powers. High peak powers of >10 MW at 1300 nm and 1700 nm allow effective 3-photon excitation of green and red shifted calcium indicators respectively and access to deeper, sub-cortex layers of the brain. Early results include in vivo detection of spontaneous activity in hippocampus within an intact mouse brain, where neurons express GCaMP6 activated in a 3-photon process at 1320 nm.

In-Situ Characterization of Tissue Blood Flow, Blood Content, and Water State Using New Techniques in Magnetic Resonance Imaging.

NASA Astrophysics Data System (ADS)

Conturo, Thomas Edward

Tissue blood flow, blood content, and water state have been characterized in-situ with new nuclear magnetic resonance imaging techniques. The sensitivities of standard techniques to the physiologic tissue parameters spin density (N_{rm r}) and relaxation times (T_1 and T_2 ) are mathematically defined. A new driven inversion method is developed so that tissue T_1 and T_2 changes produce cooperative intensity changes, yielding high contrast, high signal to noise, and sensitivity to a wider range of tissue parameters. The actual tissue parameters were imaged by automated collection of multiple-echo data having multiple T _1 dependence. Data are simultaneously fit by three-parameters to a closed-form expression, producing lower inter-parameter correlation and parameter noise than in separate T_1 or T_2 methods or pre-averaged methods. Accurate parameters are obtained at different field strengths. Parametric images of pathology demonstrate high sensitivity to tissue heterogeneity, and water content is determined in many tissues. Erythrocytes were paramagnetically labeled to study blood content and relaxation mechanisms. Liver and spleen relaxation were enhanced following 10% exchange of animal blood volumes. Rapid water exchange between intracellular and extracellular compartments was validated. Erythrocytes occupied 12.5% of renal cortex volume, and blood content was uniform in the liver, spleen and kidney. The magnitude and direction of flow velocity was then imaged. To eliminate directional artifacts, a bipolar gradient technique sensitized to flow in different directions was developed. Phase angle was reconstructed instead of intensity since the former has a 2pi -fold higher dynamic range. Images of flow through curves demonstrated secondary flow with a centrifugally-biased laminar profile and stationary velocity peaks along the curvature. Portal vein flow velocities were diminished or reversed in cirrhosis. Image artifacts have been characterized and removed. The foldover in magnified images was eliminated by exciting limited regions with orthogonal pi/2 and pi pulses. Off-midline regions were imaged by tandemly offsetting the phase-encoding and excitation. Artifacts due to non-steady-state conditions were demonstrated. The approach to steady state was defined by operators and vectors, and any repeated series of RF pulses was proven to produce a steady-state. The vector difference between the magnetization and its steady state value is relatively constant during the approach. The repetition time relative to T_1 is the main determinant of approach rate, and off-resonant RF pulses incoherent with the magnetization produce a more rapid approach than on-resonant pulses.

Quantitative imaging of protein targets in the human brain with PET

NASA Astrophysics Data System (ADS)

Gunn, Roger N.; Slifstein, Mark; Searle, Graham E.; Price, Julie C.

2015-11-01

PET imaging of proteins in the human brain with high affinity radiolabelled molecules has a history stretching back over 30 years. During this period the portfolio of protein targets that can be imaged has increased significantly through successes in radioligand discovery and development. This portfolio now spans six major categories of proteins; G-protein coupled receptors, membrane transporters, ligand gated ion channels, enzymes, misfolded proteins and tryptophan-rich sensory proteins. In parallel to these achievements in radiochemical sciences there have also been significant advances in the quantitative analysis and interpretation of the imaging data including the development of methods for image registration, image segmentation, tracer compartmental modeling, reference tissue kinetic analysis and partial volume correction. In this review, we analyze the activity of the field around each of the protein targets in order to give a perspective on the historical focus and the possible future trajectory of the field. The important neurobiology and pharmacology is introduced for each of the six protein classes and we present established radioligands for each that have successfully transitioned to quantitative imaging in humans. We present a standard quantitative analysis workflow for these radioligands which takes the dynamic PET data, associated blood and anatomical MRI data as the inputs to a series of image processing and bio-mathematical modeling steps before outputting the outcome measure of interest on either a regional or parametric image basis. The quantitative outcome measures are then used in a range of different imaging studies including tracer discovery and development studies, cross sectional studies, classification studies, intervention studies and longitudinal studies. Finally we consider some of the confounds, challenges and subtleties that arise in practice when trying to quantify and interpret PET neuroimaging data including motion artifacts, partial volume effects, age effects, image registration and normalization, input functions and metabolites, parametric imaging, receptor internalization and genetic factors.

Parametric Methods for Dynamic 11C-Phenytoin PET Studies.

PubMed

Mansor, Syahir; Yaqub, Maqsood; Boellaard, Ronald; Froklage, Femke E; de Vries, Anke; Bakker, Esther D M; Voskuyl, Rob A; Eriksson, Jonas; Schwarte, Lothar A; Verbeek, Joost; Windhorst, Albert D; Lammertsma, Adriaan A

2017-03-01

In this study, the performance of various methods for generating quantitative parametric images of dynamic 11 C-phenytoin PET studies was evaluated. Methods: Double-baseline 60-min dynamic 11 C-phenytoin PET studies, including online arterial sampling, were acquired for 6 healthy subjects. Parametric images were generated using Logan plot analysis, a basis function method, and spectral analysis. Parametric distribution volume (V T ) and influx rate ( K 1 ) were compared with those obtained from nonlinear regression analysis of time-activity curves. In addition, global and regional test-retest (TRT) variability was determined for parametric K 1 and V T values. Results: Biases in V T observed with all parametric methods were less than 5%. For K 1 , spectral analysis showed a negative bias of 16%. The mean TRT variabilities of V T and K 1 were less than 10% for all methods. Shortening the scan duration to 45 min provided similar V T and K 1 with comparable TRT performance compared with 60-min data. Conclusion: Among the various parametric methods tested, the basis function method provided parametric V T and K 1 values with the least bias compared with nonlinear regression data and showed TRT variabilities lower than 5%, also for smaller volume-of-interest sizes (i.e., higher noise levels) and shorter scan duration. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Preclinical evaluation of parametric image reconstruction of [18F]FMISO PET: correlation with ex vivo immunohistochemistry

NASA Astrophysics Data System (ADS)

Cheng, Xiaoyin; Bayer, Christine; Maftei, Constantin-Alin; Astner, Sabrina T.; Vaupel, Peter; Ziegler, Sibylle I.; Shi, Kuangyu

2014-01-01

Compared to indirect methods, direct parametric image reconstruction (PIR) has the advantage of high quality and low statistical errors. However, it is not yet clear if this improvement in quality is beneficial for physiological quantification. This study aimed to evaluate direct PIR for the quantification of tumor hypoxia using the hypoxic fraction (HF) assessed from immunohistological data as a physiological reference. Sixteen mice with xenografted human squamous cell carcinomas were scanned with dynamic [18F]FMISO PET. Afterward, tumors were sliced and stained with H&E and the hypoxia marker pimonidazole. The hypoxic signal was segmented using k-means clustering and HF was specified as the ratio of the hypoxic area over the viable tumor area. The parametric Patlak slope images were obtained by indirect voxel-wise modeling on reconstructed images using filtered back projection and ordered-subset expectation maximization (OSEM) and by direct PIR (e.g., parametric-OSEM, POSEM). The mean and maximum Patlak slopes of the tumor area were investigated and compared with HF. POSEM resulted in generally higher correlations between slope and HF among the investigated methods. A strategy for the delineation of the hypoxic tumor volume based on thresholding parametric images at half maximum of the slope is recommended based on the results of this study.

Pre- and post-synaptic dopamine imaging and its relation with frontostriatal cognitive function in Parkinson disease: PET studies with [11C]NNC 112 and [18F]FDOPA.

PubMed

Cropley, Vanessa L; Fujita, Masahiro; Bara-Jimenez, William; Brown, Amira K; Zhang, Xiang-Yang; Sangare, Janet; Herscovitch, Peter; Pike, Victor W; Hallett, Mark; Nathan, Pradeep J; Innis, Robert B

2008-07-15

Frontostriatal cognitive dysfunction is common in Parkinson disease (PD), but the explanation for its heterogeneous expressions remains unclear. This study examined the dopamine system within the frontostriatal circuitry with positron emission tomography (PET) to investigate pre- and post-synaptic dopamine function in relation to the executive processes in PD. Fifteen non-demented PD patients and 14 healthy controls underwent [(18)F]FDOPA (for dopamine synthesis) and [(11)C]NNC 112 (for D(1) receptors) PET scans and cognitive testing. Parametric images of [(18)F]FDOPA uptake (K(i)) and [(11)C]NNC 112 binding potential (BP(ND)) were calculated using reference tissue models. Group differences in K(i) and BP(ND) were assessed with both volume of interest and statistical parametric mapping, and were correlated with cognitive tests. Measurement of [(18)F]FDOPA uptake in cerebral cortex was questionable because of higher K(i) values in white than adjacent gray matter. These paradoxical results were likely to be caused by violations of the reference tissue model assumption rendering interpretation of cortical [(18)F]FDOPA uptake in PD difficult. We found no regional differences in D(1) receptor density between controls and PD, and no overall differences in frontostriatal performance. Although D(1) receptor density did not relate to frontostriatal cognition, K(i) decreases in the putamen predicted performance on the Wisconsin Card Sorting Test in PD only. These results suggest that striatal dopamine denervation may contribute to some frontostriatal cognitive impairment in moderate stage PD.

ATMAD: robust image analysis for Automatic Tissue MicroArray De-arraying.

PubMed

Nguyen, Hoai Nam; Paveau, Vincent; Cauchois, Cyril; Kervrann, Charles

2018-04-19

Over the last two decades, an innovative technology called Tissue Microarray (TMA), which combines multi-tissue and DNA microarray concepts, has been widely used in the field of histology. It consists of a collection of several (up to 1000 or more) tissue samples that are assembled onto a single support - typically a glass slide - according to a design grid (array) layout, in order to allow multiplex analysis by treating numerous samples under identical and standardized conditions. However, during the TMA manufacturing process, the sample positions can be highly distorted from the design grid due to the imprecision when assembling tissue samples and the deformation of the embedding waxes. Consequently, these distortions may lead to severe errors of (histological) assay results when the sample identities are mismatched between the design and its manufactured output. The development of a robust method for de-arraying TMA, which localizes and matches TMA samples with their design grid, is therefore crucial to overcome the bottleneck of this prominent technology. In this paper, we propose an Automatic, fast and robust TMA De-arraying (ATMAD) approach dedicated to images acquired with brightfield and fluorescence microscopes (or scanners). First, tissue samples are localized in the large image by applying a locally adaptive thresholding on the isotropic wavelet transform of the input TMA image. To reduce false detections, a parametric shape model is considered for segmenting ellipse-shaped objects at each detected position. Segmented objects that do not meet the size and the roundness criteria are discarded from the list of tissue samples before being matched with the design grid. Sample matching is performed by estimating the TMA grid deformation under the thin-plate model. Finally, thanks to the estimated deformation, the true tissue samples that were preliminary rejected in the early image processing step are recognized by running a second segmentation step. We developed a novel de-arraying approach for TMA analysis. By combining wavelet-based detection, active contour segmentation, and thin-plate spline interpolation, our approach is able to handle TMA images with high dynamic, poor signal-to-noise ratio, complex background and non-linear deformation of TMA grid. In addition, the deformation estimation produces quantitative information to asset the manufacturing quality of TMAs.

Automatic correction of intensity nonuniformity from sparseness of gradient distribution in medical images.

PubMed

Zheng, Yuanjie; Grossman, Murray; Awate, Suyash P; Gee, James C

2009-01-01

We propose to use the sparseness property of the gradient probability distribution to estimate the intensity nonuniformity in medical images, resulting in two novel automatic methods: a non-parametric method and a parametric method. Our methods are easy to implement because they both solve an iteratively re-weighted least squares problem. They are remarkably accurate as shown by our experiments on images of different imaged objects and from different imaging modalities.

Automatic Correction of Intensity Nonuniformity from Sparseness of Gradient Distribution in Medical Images

PubMed Central

Zheng, Yuanjie; Grossman, Murray; Awate, Suyash P.; Gee, James C.

2013-01-01

We propose to use the sparseness property of the gradient probability distribution to estimate the intensity nonuniformity in medical images, resulting in two novel automatic methods: a non-parametric method and a parametric method. Our methods are easy to implement because they both solve an iteratively re-weighted least squares problem. They are remarkably accurate as shown by our experiments on images of different imaged objects and from different imaging modalities. PMID:20426191

Statistical parametric mapping of the regional distribution and ontogenetic scaling of foot pressures during walking in Asian elephants (Elephas maximus).

PubMed

Panagiotopoulou, Olga; Pataky, Todd C; Hill, Zoe; Hutchinson, John R

2012-05-01

Foot pressure distributions during locomotion have causal links with the anatomical and structural configurations of the foot tissues and the mechanics of locomotion. Elephant feet have five toes bound in a flexible pad of fibrous tissue (digital cushion). Does this specialized foot design control peak foot pressures in such giant animals? And how does body size, such as during ontogenetic growth, influence foot pressures? We addressed these questions by studying foot pressure distributions in elephant feet and their correlation with body mass and centre of pressure trajectories, using statistical parametric mapping (SPM), a neuro-imaging technology. Our results show a positive correlation between body mass and peak pressures, with the highest pressures dominated by the distal ends of the lateral toes (digits 3, 4 and 5). We also demonstrate that pressure reduction in the elephant digital cushion is a complex interaction of its viscoelastic tissue structure and its centre of pressure trajectories, because there is a tendency to avoid rear 'heel' contact as an elephant grows. Using SPM, we present a complete map of pressure distributions in elephant feet during ontogeny by performing statistical analysis at the pixel level across the entire plantar/palmar surface. We hope that our study will build confidence in the potential clinical and scaling applications of mammalian foot pressures, given our findings in support of a link between regional peak pressures and pathogenesis in elephant feet.

MRI-guided attenuation correction in whole-body PET/MR: assessment of the effect of bone attenuation.

PubMed

Akbarzadeh, A; Ay, M R; Ahmadian, A; Alam, N Riahi; Zaidi, H

2013-02-01

Hybrid PET/MRI presents many advantages in comparison with its counterpart PET/CT in terms of improved soft-tissue contrast, decrease in radiation exposure, and truly simultaneous and multi-parametric imaging capabilities. However, the lack of well-established methodology for MR-based attenuation correction is hampering further development and wider acceptance of this technology. We assess the impact of ignoring bone attenuation and using different tissue classes for generation of the attenuation map on the accuracy of attenuation correction of PET data. This work was performed using simulation studies based on the XCAT phantom and clinical input data. For the latter, PET and CT images of patients were used as input for the analytic simulation model using realistic activity distributions where CT-based attenuation correction was utilized as reference for comparison. For both phantom and clinical studies, the reference attenuation map was classified into various numbers of tissue classes to produce three (air, soft tissue and lung), four (air, lungs, soft tissue and cortical bones) and five (air, lungs, soft tissue, cortical bones and spongeous bones) class attenuation maps. The phantom studies demonstrated that ignoring bone increases the relative error by up to 6.8% in the body and up to 31.0% for bony regions. Likewise, the simulated clinical studies showed that the mean relative error reached 15% for lesions located in the body and 30.7% for lesions located in bones, when neglecting bones. These results demonstrate an underestimation of about 30% of tracer uptake when neglecting bone, which in turn imposes substantial loss of quantitative accuracy for PET images produced by hybrid PET/MRI systems. Considering bones in the attenuation map will considerably improve the accuracy of MR-guided attenuation correction in hybrid PET/MR to enable quantitative PET imaging on hybrid PET/MR technologies.

Robust biological parametric mapping: an improved technique for multimodal brain image analysis

NASA Astrophysics Data System (ADS)

Yang, Xue; Beason-Held, Lori; Resnick, Susan M.; Landman, Bennett A.

2011-03-01

Mapping the quantitative relationship between structure and function in the human brain is an important and challenging problem. Numerous volumetric, surface, region of interest and voxelwise image processing techniques have been developed to statistically assess potential correlations between imaging and non-imaging metrics. Recently, biological parametric mapping has extended the widely popular statistical parametric approach to enable application of the general linear model to multiple image modalities (both for regressors and regressands) along with scalar valued observations. This approach offers great promise for direct, voxelwise assessment of structural and functional relationships with multiple imaging modalities. However, as presented, the biological parametric mapping approach is not robust to outliers and may lead to invalid inferences (e.g., artifactual low p-values) due to slight mis-registration or variation in anatomy between subjects. To enable widespread application of this approach, we introduce robust regression and robust inference in the neuroimaging context of application of the general linear model. Through simulation and empirical studies, we demonstrate that our robust approach reduces sensitivity to outliers without substantial degradation in power. The robust approach and associated software package provides a reliable way to quantitatively assess voxelwise correlations between structural and functional neuroimaging modalities.

Dynamic whole body PET parametric imaging: II. Task-oriented statistical estimation

PubMed Central

Karakatsanis, Nicolas A.; Lodge, Martin A.; Zhou, Y.; Wahl, Richard L.; Rahmim, Arman

2013-01-01

In the context of oncology, dynamic PET imaging coupled with standard graphical linear analysis has been previously employed to enable quantitative estimation of tracer kinetic parameters of physiological interest at the voxel level, thus, enabling quantitative PET parametric imaging. However, dynamic PET acquisition protocols have been confined to the limited axial field-of-view (~15–20cm) of a single bed position and have not been translated to the whole-body clinical imaging domain. On the contrary, standardized uptake value (SUV) PET imaging, considered as the routine approach in clinical oncology, commonly involves multi-bed acquisitions, but is performed statically, thus not allowing for dynamic tracking of the tracer distribution. Here, we pursue a transition to dynamic whole body PET parametric imaging, by presenting, within a unified framework, clinically feasible multi-bed dynamic PET acquisition protocols and parametric imaging methods. In a companion study, we presented a novel clinically feasible dynamic (4D) multi-bed PET acquisition protocol as well as the concept of whole body PET parametric imaging employing Patlak ordinary least squares (OLS) regression to estimate the quantitative parameters of tracer uptake rate Ki and total blood distribution volume V. In the present study, we propose an advanced hybrid linear regression framework, driven by Patlak kinetic voxel correlations, to achieve superior trade-off between contrast-to-noise ratio (CNR) and mean squared error (MSE) than provided by OLS for the final Ki parametric images, enabling task-based performance optimization. Overall, whether the observer's task is to detect a tumor or quantitatively assess treatment response, the proposed statistical estimation framework can be adapted to satisfy the specific task performance criteria, by adjusting the Patlak correlation-coefficient (WR) reference value. The multi-bed dynamic acquisition protocol, as optimized in the preceding companion study, was employed along with extensive Monte Carlo simulations and an initial clinical FDG patient dataset to validate and demonstrate the potential of the proposed statistical estimation methods. Both simulated and clinical results suggest that hybrid regression in the context of whole-body Patlak Ki imaging considerably reduces MSE without compromising high CNR. Alternatively, for a given CNR, hybrid regression enables larger reductions than OLS in the number of dynamic frames per bed, allowing for even shorter acquisitions of ~30min, thus further contributing to the clinical adoption of the proposed framework. Compared to the SUV approach, whole body parametric imaging can provide better tumor quantification, and can act as a complement to SUV, for the task of tumor detection. PMID:24080994

Dynamic whole-body PET parametric imaging: II. Task-oriented statistical estimation.

PubMed

Karakatsanis, Nicolas A; Lodge, Martin A; Zhou, Y; Wahl, Richard L; Rahmim, Arman

2013-10-21

In the context of oncology, dynamic PET imaging coupled with standard graphical linear analysis has been previously employed to enable quantitative estimation of tracer kinetic parameters of physiological interest at the voxel level, thus, enabling quantitative PET parametric imaging. However, dynamic PET acquisition protocols have been confined to the limited axial field-of-view (~15-20 cm) of a single-bed position and have not been translated to the whole-body clinical imaging domain. On the contrary, standardized uptake value (SUV) PET imaging, considered as the routine approach in clinical oncology, commonly involves multi-bed acquisitions, but is performed statically, thus not allowing for dynamic tracking of the tracer distribution. Here, we pursue a transition to dynamic whole-body PET parametric imaging, by presenting, within a unified framework, clinically feasible multi-bed dynamic PET acquisition protocols and parametric imaging methods. In a companion study, we presented a novel clinically feasible dynamic (4D) multi-bed PET acquisition protocol as well as the concept of whole-body PET parametric imaging employing Patlak ordinary least squares (OLS) regression to estimate the quantitative parameters of tracer uptake rate Ki and total blood distribution volume V. In the present study, we propose an advanced hybrid linear regression framework, driven by Patlak kinetic voxel correlations, to achieve superior trade-off between contrast-to-noise ratio (CNR) and mean squared error (MSE) than provided by OLS for the final Ki parametric images, enabling task-based performance optimization. Overall, whether the observer's task is to detect a tumor or quantitatively assess treatment response, the proposed statistical estimation framework can be adapted to satisfy the specific task performance criteria, by adjusting the Patlak correlation-coefficient (WR) reference value. The multi-bed dynamic acquisition protocol, as optimized in the preceding companion study, was employed along with extensive Monte Carlo simulations and an initial clinical (18)F-deoxyglucose patient dataset to validate and demonstrate the potential of the proposed statistical estimation methods. Both simulated and clinical results suggest that hybrid regression in the context of whole-body Patlak Ki imaging considerably reduces MSE without compromising high CNR. Alternatively, for a given CNR, hybrid regression enables larger reductions than OLS in the number of dynamic frames per bed, allowing for even shorter acquisitions of ~30 min, thus further contributing to the clinical adoption of the proposed framework. Compared to the SUV approach, whole-body parametric imaging can provide better tumor quantification, and can act as a complement to SUV, for the task of tumor detection.

Dynamic PET simulator via tomographic emission projection for kinetic modeling and parametric image studies

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

Häggström, Ida, E-mail: haeggsti@mskcc.org; Beattie, Bradley J.; Schmidtlein, C. Ross

2016-06-15

Purpose: To develop and evaluate a fast and simple tool called dPETSTEP (Dynamic PET Simulator of Tracers via Emission Projection), for dynamic PET simulations as an alternative to Monte Carlo (MC), useful for educational purposes and evaluation of the effects of the clinical environment, postprocessing choices, etc., on dynamic and parametric images. Methods: The tool was developed in MATLAB using both new and previously reported modules of PETSTEP (PET Simulator of Tracers via Emission Projection). Time activity curves are generated for each voxel of the input parametric image, whereby effects of imaging system blurring, counting noise, scatters, randoms, and attenuationmore » are simulated for each frame. Each frame is then reconstructed into images according to the user specified method, settings, and corrections. Reconstructed images were compared to MC data, and simple Gaussian noised time activity curves (GAUSS). Results: dPETSTEP was 8000 times faster than MC. Dynamic images from dPETSTEP had a root mean square error that was within 4% on average of that of MC images, whereas the GAUSS images were within 11%. The average bias in dPETSTEP and MC images was the same, while GAUSS differed by 3% points. Noise profiles in dPETSTEP images conformed well to MC images, confirmed visually by scatter plot histograms, and statistically by tumor region of interest histogram comparisons that showed no significant differences (p < 0.01). Compared to GAUSS, dPETSTEP images and noise properties agreed better with MC. Conclusions: The authors have developed a fast and easy one-stop solution for simulations of dynamic PET and parametric images, and demonstrated that it generates both images and subsequent parametric images with very similar noise properties to those of MC images, in a fraction of the time. They believe dPETSTEP to be very useful for generating fast, simple, and realistic results, however since it uses simple scatter and random models it may not be suitable for studies investigating these phenomena. dPETSTEP can be downloaded free of cost from https://github.com/CRossSchmidtlein/dPETSTEP.« less

Is there more valuable information in PWI datasets for a voxel-wise acute ischemic stroke tissue outcome prediction than what is represented by typical perfusion maps?

NASA Astrophysics Data System (ADS)

Forkert, Nils Daniel; Siemonsen, Susanne; Dalski, Michael; Verleger, Tobias; Kemmling, Andre; Fiehler, Jens

2014-03-01

The acute ischemic stroke is a leading cause for death and disability in the industry nations. In case of a present acute ischemic stroke, the prediction of the future tissue outcome is of high interest for the clinicians as it can be used to support therapy decision making. Within this context, it has already been shown that the voxel-wise multi-parametric tissue outcome prediction leads to more promising results compared to single channel perfusion map thresholding. Most previously published multi-parametric predictions employ information from perfusion maps derived from perfusion-weighted MRI together with other image sequences such as diffusion-weighted MRI. However, it remains unclear if the typically calculated perfusion maps used for this purpose really include all valuable information from the PWI dataset for an optimal tissue outcome prediction. To investigate this problem in more detail, two different methods to predict tissue outcome using a k-nearest-neighbor approach were developed in this work and evaluated based on 18 datasets of acute stroke patients with known tissue outcome. The first method integrates apparent diffusion coefficient and perfusion parameter (Tmax, MTT, CBV, CBF) information for the voxel-wise prediction, while the second method employs also apparent diffusion coefficient information but the complete perfusion information in terms of the voxel-wise residue functions instead of the perfusion parameter maps for the voxel-wise prediction. Overall, the comparison of the results of the two prediction methods for the 18 patients using a leave-one-out cross validation revealed no considerable differences. Quantitatively, the parameter-based prediction of tissue outcome led to a mean Dice coefficient of 0.474, while the prediction using the residue functions led to a mean Dice coefficient of 0.461. Thus, it may be concluded from the results of this study that the perfusion parameter maps typically derived from PWI datasets include all valuable perfusion information required for a voxel-based tissue outcome prediction, while the complete analysis of the residue functions does not add further benefits for the voxel-wise tissue outcome prediction and is also computationally more expensive.

Selecting a Separable Parametric Spatiotemporal Covariance Structure for Longitudinal Imaging Data

PubMed Central

George, Brandon; Aban, Inmaculada

2014-01-01

Longitudinal imaging studies allow great insight into how the structure and function of a subject’s internal anatomy changes over time. Unfortunately, the analysis of longitudinal imaging data is complicated by inherent spatial and temporal correlation: the temporal from the repeated measures, and the spatial from the outcomes of interest being observed at multiple points in a patients body. We propose the use of a linear model with a separable parametric spatiotemporal error structure for the analysis of repeated imaging data. The model makes use of spatial (exponential, spherical, and Matérn) and temporal (compound symmetric, autoregressive-1, Toeplitz, and unstructured) parametric correlation functions. A simulation study, inspired by a longitudinal cardiac imaging study on mitral regurgitation patients, compared different information criteria for selecting a particular separable parametric spatiotemporal correlation structure as well as the effects on Type I and II error rates for inference on fixed effects when the specified model is incorrect. Information criteria were found to be highly accurate at choosing between separable parametric spatiotemporal correlation structures. Misspecification of the covariance structure was found to have the ability to inflate the Type I error or have an overly conservative test size, which corresponded to decreased power. An example with clinical data is given illustrating how the covariance structure procedure can be done in practice, as well as how covariance structure choice can change inferences about fixed effects. PMID:25293361

Stimulated echo diffusion tensor imaging (STEAM-DTI) with varying diffusion times as a probe of breast tissue.

PubMed

Teruel, Jose R; Cho, Gene Y; Moccaldi Rt, Melanie; Goa, Pål E; Bathen, Tone F; Feiweier, Thorsten; Kim, Sungheon G; Moy, Linda; Sigmund, Eric E

2017-01-01

To explore the application of diffusion tensor imaging (DTI) for breast tissue and breast pathologies using a stimulated-echo acquisition mode (STEAM) with variable diffusion times. In this Health Insurance Portability and Accountability Act-compliant study, approved by the local institutional review board, eight patients and six healthy volunteers underwent an MRI examination at 3 Tesla including STEAM-DTI with several diffusion times ranging from 68.5 to 902.5 ms. A DTI model was fitted to the data for each diffusion time, and parametric maps of mean diffusivity, fractional anisotropy, axial diffusivity, and radial diffusivity were computed for healthy fibroglandular tissue (FGT) and lesions. The median value of radial diffusivity for FGT was fitted to a linear decay to obtain an estimation of the surface-to-volume ratio, from which the radial diameter was calculated. For healthy FGT, radial diffusivity presented a linear decay with the square root of the diffusion time resulting in a range of estimated radial diameters from 202 to 496 µm, while axial diffusivity presented a nearly time-independent diffusion. Residual fat signal was reduced at longer diffusion times due to the shorter T1 of fat. Residual fat signal to the overall signal in the healthy volunteers' FGT was found to range from 2.39% to 2.55% (shortest mixing time), and from 0.40% to 0.51% (longest mixing time) for the b500 images. The use of variable diffusion times may provide an in vivo noninvasive tool to probe diffusion lengths in breast tissue and breast pathology, and might aid by improving fat suppression at longer diffusion times. 2 J. Magn. Reson. Imaging 2017;45:84-93. © 2016 International Society for Magnetic Resonance in Medicine.

Parametric mapping of [18F]fluoromisonidazole positron emission tomography using basis functions.

PubMed

Hong, Young T; Beech, John S; Smith, Rob; Baron, Jean-Claude; Fryer, Tim D

2011-02-01

In this study, we show a basis function method (BAFPIC) for voxelwise calculation of kinetic parameters (K(1), k(2), k(3), K(i)) and blood volume using an irreversible two-tissue compartment model. BAFPIC was applied to rat ischaemic stroke micro-positron emission tomography data acquired with the hypoxia tracer [(18)F]fluoromisonidazole because irreversible two-tissue compartmental modelling provided good fits to data from both hypoxic and normoxic tissues. Simulated data show that BAFPIC produces kinetic parameters with significantly lower variability and bias than nonlinear least squares (NLLS) modelling in hypoxic tissue. The advantage of BAFPIC over NLLS is less pronounced in normoxic tissue. K(i) determined from BAFPIC has lower variability than that from the Patlak-Gjedde graphical analysis (PGA) by up to 40% and lower bias, except for normoxic tissue at mid-high noise levels. Consistent with the simulation results, BAFPIC parametric maps of real data suffer less noise-induced variability than do NLLS and PGA. Delineation of hypoxia on BAFPIC k(3) maps is aided by low variability in normoxic tissue, which matches that in K(i) maps. BAFPIC produces K(i) values that correlate well with those from PGA (r(2)=0.93 to 0.97; slope 0.99 to 1.05, absolute intercept <0.00002 mL/g per min). BAFPIC is a computationally efficient method of determining parametric maps with low bias and variance.

THz-wave parametric sources and imaging applications

NASA Astrophysics Data System (ADS)

Kawase, Kodo

2004-12-01

We have studied the generation of terahertz (THz) waves by optical parametric processes based on laser light scattering from the polariton mode of nonlinear crystals. Using parametric oscillation of MgO-doped LiNbO3 crystal pumped by a nano-second Q-switched Nd:YAG laser, we have realized a widely tunable coherent THz-wave sources with a simple configuration. We have also developed a novel basic technology for THz imaging, which allows detection and identification of chemicals by introducing the component spatial pattern analysis. The spatial distributions of the chemicals were obtained from terahertz multispectral trasillumination images, using absorption spectra previously measured with a widely tunable THz-wave parametric oscillator. Further we have applied this technique to the detection and identification of illicit drugs concealed in envelopes. The samples we used were methamphetamine and MDMA, two of the most widely consumed illegal drugs in Japan, and aspirin as a reference.

Effects of Regularisation Priors and Anatomical Partial Volume Correction on Dynamic PET Data

NASA Astrophysics Data System (ADS)

Caldeira, Liliana L.; Silva, Nuno da; Scheins, Jürgen J.; Gaens, Michaela E.; Shah, N. Jon

2015-08-01

Dynamic PET provides temporal information about the tracer uptake. However, each PET frame has usually low statistics, resulting in noisy images. Furthermore, PET images suffer from partial volume effects. The goal of this study is to understand the effects of prior regularisation on dynamic PET data and subsequent anatomical partial volume correction. The Median Root Prior (MRP) regularisation method was used in this work during reconstruction. The quantification and noise in image-domain and time-domain (time-activity curves) as well as the impact on parametric images is assessed and compared with Ordinary Poisson Ordered Subset Expectation Maximisation (OP-OSEM) reconstruction with and without Gaussian filter. This study shows the improvement in PET images and time-activity curves (TAC) in terms of noise as well as in the parametric images when using prior regularisation in dynamic PET data. Anatomical partial volume correction improves the TAC and consequently, parametric images. Therefore, the use of MRP with anatomical partial volume correction is of interest for dynamic PET studies.

Comparison of scalar measures used in magnetic resonance diffusion tensor imaging.

PubMed

Bahn, M M

1999-07-01

The tensors derived from diffusion tensor imaging describe complex diffusion in tissues. However, it is difficult to compare tensors directly or to produce images that contain all of the information of the tensor. Therefore, it is convenient to produce scalar measures that extract desired aspects of the tensor. These measures map the three-dimensional eigenvalues of the diffusion tensor into scalar values. The measures impose an order on eigenvalue space. Many invariant scalar measures have been introduced in the literature. In the present manuscript, a general approach for producing invariant scalar measures is introduced. Because it is often difficult to determine in clinical practice which of the many measures is best to apply to a given situation, two formalisms are introduced for the presentation, definition, and comparison of measures applied to eigenvalues: (1) normalized eigenvalue space, and (2) parametric eigenvalue transformation plots. All of the anisotropy information contained in the three eigenvalues can be retained and displayed in a two-dimensional plot, the normalized eigenvalue plot. An example is given of how to determine the best measure to use for a given situation by superimposing isometric contour lines from various anisotropy measures on plots of actual measured eigenvalue data points. Parametric eigenvalue transformation plots allow comparison of how different measures impose order on normalized eigenvalue space to determine whether the measures are equivalent and how the measures differ. These formalisms facilitate the comparison of scalar invariant measures for diffusion tensor imaging. Normalized eigenvalue space allows presentation of eigenvalue anisotropy information. Copyright 1999 Academic Press.

Enhanced multi-protocol analysis via intelligent supervised embedding (EMPrAvISE): detecting prostate cancer on multi-parametric MRI

NASA Astrophysics Data System (ADS)

Viswanath, Satish; Bloch, B. Nicholas; Chappelow, Jonathan; Patel, Pratik; Rofsky, Neil; Lenkinski, Robert; Genega, Elizabeth; Madabhushi, Anant

2011-03-01

Currently, there is significant interest in developing methods for quantitative integration of multi-parametric (structural, functional) imaging data with the objective of building automated meta-classifiers to improve disease detection, diagnosis, and prognosis. Such techniques are required to address the differences in dimensionalities and scales of individual protocols, while deriving an integrated multi-parametric data representation which best captures all disease-pertinent information available. In this paper, we present a scheme called Enhanced Multi-Protocol Analysis via Intelligent Supervised Embedding (EMPrAvISE); a powerful, generalizable framework applicable to a variety of domains for multi-parametric data representation and fusion. Our scheme utilizes an ensemble of embeddings (via dimensionality reduction, DR); thereby exploiting the variance amongst multiple uncorrelated embeddings in a manner similar to ensemble classifier schemes (e.g. Bagging, Boosting). We apply this framework to the problem of prostate cancer (CaP) detection on 12 3 Tesla pre-operative in vivo multi-parametric (T2-weighted, Dynamic Contrast Enhanced, and Diffusion-weighted) magnetic resonance imaging (MRI) studies, in turn comprising a total of 39 2D planar MR images. We first align the different imaging protocols via automated image registration, followed by quantification of image attributes from individual protocols. Multiple embeddings are generated from the resultant high-dimensional feature space which are then combined intelligently to yield a single stable solution. Our scheme is employed in conjunction with graph embedding (for DR) and probabilistic boosting trees (PBTs) to detect CaP on multi-parametric MRI. Finally, a probabilistic pairwise Markov Random Field algorithm is used to apply spatial constraints to the result of the PBT classifier, yielding a per-voxel classification of CaP presence. Per-voxel evaluation of detection results against ground truth for CaP extent on MRI (obtained by spatially registering pre-operative MRI with available whole-mount histological specimens) reveals that EMPrAvISE yields a statistically significant improvement (AUC=0.77) over classifiers constructed from individual protocols (AUC=0.62, 0.62, 0.65, for T2w, DCE, DWI respectively) as well as one trained using multi-parametric feature concatenation (AUC=0.67).

Lung Motion Model Validation Experiments, Free-Breathing Tissue Densitometry, and Ventilation Mapping using Fast Helical CT Imaging

NASA Astrophysics Data System (ADS)

Dou, Hsiang-Tai

The uncertainties due to respiratory motion present significant challenges to accurate characterization of cancerous tissues both in terms of imaging and treatment. Currently available clinical lung imaging techniques are subject to inferior image quality and incorrect motion estimation, with consequences that can systematically impact the downstream treatment delivery and outcome. The main objective of this thesis is the development of the techniques of fast helical computed tomography (CT) imaging and deformable image registration for the radiotherapy applications in accurate breathing motion modeling, lung tissue density modeling and ventilation imaging. Fast helical CT scanning was performed on 64-slice CT scanner using the shortest available gantry rotation time and largest pitch value such that scanning of the thorax region amounts to just two seconds, which is less than typical breathing cycle in humans. The scanning was conducted under free breathing condition. Any portion of the lung anatomy undergoing such scanning protocol would be irradiated for only a quarter second, effectively removing any motion induced image artifacts. The resulting CT data were pristine volumetric images that record the lung tissue position and density in a fraction of the breathing cycle. Following our developed protocol, multiple fast helical CT scans were acquired to sample the tissue positions in different breathing states. To measure the tissue displacement, deformable image registration was performed that registers the non-reference images to the reference one. In modeling breathing motion, external breathing surrogate signal was recorded synchronously with the CT image slices. This allowed for the tissue-specific displacement to be modeled as parametrization of the recorded breathing signal using the 5D lung motion model. To assess the accuracy of the motion model in describing tissue position change, the model was used to simulate the original high-pitch helical CT scan geometries, employed as ground truth data. Image similarity between the simulated and ground truth scans was evaluated. The model validation experiments were conducted in a patient cohort of seventeen patients to assess the model robustness and inter-patient variation. The model error averaged over multiple tracked positions from several breathing cycles was found to be on the order of one millimeter. In modeling the density change under free breathing condition, the determinant of Jacobian matrix from the registration-derived deformation vector field yielded volume change information of the lung tissues. Correlation of the Jacobian values to the corresponding voxel Housfield units (HU) reveals that the density variation for the majority of lung tissues can be very well described by mass conservation relationship. Different tissue types were identified and separately modeled. Large trials of validation experiments were performed. The averaged deviation between the modeled and the reference lung density was 30 HU, which was estimated to be the background CT noise level. In characterizing the lung ventilation function, a novel method was developed to determine the extent of lung tissue volume change. Information on volume change was derived from the deformable image registration of the fast helical CT images in terms of Jacobian values with respect to a reference image. Assuming the multiple volume change measurements are independently and identically distributed, statistical formulation was derived to model ventilation distribution of each lung voxels and empirical minimum and maximum probability distribution of the Jacobian values was computed. Ventilation characteristic was evaluated as the difference of the expectation value from these extremal distributions. The resulting ventilation map was compared with an independently obtained ventilation image derived directly from the lung intensities and good correlation was found using statistical test. In addition, dynamic ventilation characterization was investigated by estimating the voxel-specific ventilation distribution. Ventilation maps were generated at different percentile levels using the tissue volume expansion metrics.

Selecting a separable parametric spatiotemporal covariance structure for longitudinal imaging data.

PubMed

George, Brandon; Aban, Inmaculada

2015-01-15

Longitudinal imaging studies allow great insight into how the structure and function of a subject's internal anatomy changes over time. Unfortunately, the analysis of longitudinal imaging data is complicated by inherent spatial and temporal correlation: the temporal from the repeated measures and the spatial from the outcomes of interest being observed at multiple points in a patient's body. We propose the use of a linear model with a separable parametric spatiotemporal error structure for the analysis of repeated imaging data. The model makes use of spatial (exponential, spherical, and Matérn) and temporal (compound symmetric, autoregressive-1, Toeplitz, and unstructured) parametric correlation functions. A simulation study, inspired by a longitudinal cardiac imaging study on mitral regurgitation patients, compared different information criteria for selecting a particular separable parametric spatiotemporal correlation structure as well as the effects on types I and II error rates for inference on fixed effects when the specified model is incorrect. Information criteria were found to be highly accurate at choosing between separable parametric spatiotemporal correlation structures. Misspecification of the covariance structure was found to have the ability to inflate the type I error or have an overly conservative test size, which corresponded to decreased power. An example with clinical data is given illustrating how the covariance structure procedure can be performed in practice, as well as how covariance structure choice can change inferences about fixed effects. Copyright © 2014 John Wiley & Sons, Ltd.

Advanced Imaging Methods for Long-Baseline Optical Interferometry

NASA Astrophysics Data System (ADS)

Le Besnerais, G.; Lacour, S.; Mugnier, L. M.; Thiebaut, E.; Perrin, G.; Meimon, S.

2008-11-01

We address the data processing methods needed for imaging with a long baseline optical interferometer. We first describe parametric reconstruction approaches and adopt a general formulation of nonparametric image reconstruction as the solution of a constrained optimization problem. Within this framework, we present two recent reconstruction methods, Mira and Wisard, representative of the two generic approaches for dealing with the missing phase information. Mira is based on an implicit approach and a direct optimization of a Bayesian criterion while Wisard adopts a self-calibration approach and an alternate minimization scheme inspired from radio-astronomy. Both methods can handle various regularization criteria. We review commonly used regularization terms and introduce an original quadratic regularization called ldquosoft support constraintrdquo that favors the object compactness. It yields images of quality comparable to nonquadratic regularizations on the synthetic data we have processed. We then perform image reconstructions, both parametric and nonparametric, on astronomical data from the IOTA interferometer, and discuss the respective roles of parametric and nonparametric approaches for optical interferometric imaging.

Towards an acoustic model-based poroelastic imaging method: I. Theoretical foundation.

PubMed

Berry, Gearóid P; Bamber, Jeffrey C; Armstrong, Cecil G; Miller, Naomi R; Barbone, Paul E

2006-04-01

The ultrasonic measurement and imaging of tissue elasticity is currently under wide investigation and development as a clinical tool for the assessment of a broad range of diseases, but little account in this field has yet been taken of the fact that soft tissue is porous and contains mobile fluid. The ability to squeeze fluid out of tissue may have implications for conventional elasticity imaging, and may present opportunities for new investigative tools. When a homogeneous, isotropic, fluid-saturated poroelastic material with a linearly elastic solid phase and incompressible solid and fluid constituents is subjected to stress, the behaviour of the induced internal strain field is influenced by three material constants: the Young's modulus (E(s)) and Poisson's ratio (nu(s)) of the solid matrix and the permeability (k) of the solid matrix to the pore fluid. New analytical expressions were derived and used to model the time-dependent behaviour of the strain field inside simulated homogeneous cylindrical samples of such a poroelastic material undergoing sustained unconfined compression. A model-based reconstruction technique was developed to produce images of parameters related to the poroelastic material constants (E(s), nu(s), k) from a comparison of the measured and predicted time-dependent spatially varying radial strain. Tests of the method using simulated noisy strain data showed that it is capable of producing three unique parametric images: an image of the Poisson's ratio of the solid matrix, an image of the axial strain (which was not time-dependent subsequent to the application of the compression) and an image representing the product of the aggregate modulus E(s)(1-nu(s))/(1+nu(s))(1-2nu(s)) of the solid matrix and the permeability of the solid matrix to the pore fluid. The analytical expressions were further used to numerically validate a finite element model and to clarify previous work on poroelastography.

Magnetic Resonance Imaging Allows the Evaluation of Tissue Damage and Regeneration in a Mouse Model of Critical Limb Ischemia.

PubMed

Zaccagnini, Germana; Palmisano, Anna; Canu, Tamara; Maimone, Biagina; Lo Russo, Francesco M; Ambrogi, Federico; Gaetano, Carlo; De Cobelli, Francesco; Del Maschio, Alessandro; Esposito, Antonio; Martelli, Fabio

2015-01-01

Magnetic resonance imaging (MRI) provides non-invasive, repetitive measures in the same individual, allowing the study of a physio-pathological event over time. In this study, we tested the performance of 7 Tesla multi-parametric MRI to monitor the dynamic changes of mouse skeletal muscle injury and regeneration upon acute ischemia induced by femoral artery dissection. T2-mapping (T2 relaxation time), diffusion-tensor imaging (Fractional Anisotropy) and perfusion by Dynamic Contrast-Enhanced MRI (K-trans) were measured and imaging results were correlated with histological morphometric analysis in both Gastrocnemius and Tibialis anterior muscles. We found that tissue damage positively correlated with T2-relaxation time, while myofiber regeneration and capillary density positively correlated with Fractional Anisotropy. Interestingly, K-trans positively correlated with capillary density. Accordingly, repeated MRI measurements between day 1 and day 28 after surgery in ischemic muscles showed that: 1) T2-relaxation time rapidly increased upon ischemia and then gradually declined, returning almost to basal level in the last phases of the regeneration process; 2) Fractional Anisotropy dropped upon ischemic damage induction and then recovered along with muscle regeneration and neoangiogenesis; 3) K-trans reached a minimum upon ischemia, then progressively recovered. Overall, Gastrocnemius and Tibialis anterior muscles displayed similar patterns of MRI parameters dynamic, with more marked responses and less variability in Tibialis anterior. We conclude that MRI provides quantitative information about both tissue damage after ischemia and the subsequent vascular and muscle regeneration, accounting for the differences between subjects and, within the same individual, between different muscles.

Development of a computer aided diagnosis model for prostate cancer classification on multi-parametric MRI

NASA Astrophysics Data System (ADS)

Alfano, R.; Soetemans, D.; Bauman, G. S.; Gibson, E.; Gaed, M.; Moussa, M.; Gomez, J. A.; Chin, J. L.; Pautler, S.; Ward, A. D.

2018-02-01

Multi-parametric MRI (mp-MRI) is becoming a standard in contemporary prostate cancer screening and diagnosis, and has shown to aid physicians in cancer detection. It offers many advantages over traditional systematic biopsy, which has shown to have very high clinical false-negative rates of up to 23% at all stages of the disease. However beneficial, mp-MRI is relatively complex to interpret and suffers from inter-observer variability in lesion localization and grading. Computer-aided diagnosis (CAD) systems have been developed as a solution as they have the power to perform deterministic quantitative image analysis. We measured the accuracy of such a system validated using accurately co-registered whole-mount digitized histology. We trained a logistic linear classifier (LOGLC), support vector machine (SVC), k-nearest neighbour (KNN) and random forest classifier (RFC) in a four part ROI based experiment against: 1) cancer vs. non-cancer, 2) high-grade (Gleason score ≥4+3) vs. low-grade cancer (Gleason score <4+3), 3) high-grade vs. other tissue components and 4) high-grade vs. benign tissue by selecting the classifier with the highest AUC using 1-10 features from forward feature selection. The CAD model was able to classify malignant vs. benign tissue and detect high-grade cancer with high accuracy. Once fully validated, this work will form the basis for a tool that enhances the radiologist's ability to detect malignancies, potentially improving biopsy guidance, treatment selection, and focal therapy for prostate cancer patients, maximizing the potential for cure and increasing quality of life.

Taking advantage of acoustic inhomogeneities in photoacoustic measurements

NASA Astrophysics Data System (ADS)

Da Silva, Anabela; Handschin, Charles; Riedinger, Christophe; Piasecki, Julien; Mensah, Serge; Litman, Amélie; Akhouayri, Hassan

2016-03-01

Photoacoustic offers promising perspectives in probing and imaging subsurface optically absorbing structures in biological tissues. The optical uence absorbed is partly dissipated into heat accompanied with microdilatations that generate acoustic pressure waves, the intensity which is related to the amount of fluuence absorbed. Hence the photoacoustic signal measured offers access, at least potentially, to a local monitoring of the absorption coefficient, in 3D if tomographic measurements are considered. However, due to both the diffusing and absorbing nature of the surrounding tissues, the major part of the uence is deposited locally at the periphery of the tissue, generating an intense acoustic pressure wave that may hide relevant photoacoustic signals. Experimental strategies have been developed in order to measure exclusively the photoacoustic waves generated by the structure of interest (orthogonal illumination and detection). Temporal or more sophisticated filters (wavelets) can also be applied. However, the measurement of this primary acoustic wave carries a lot of information about the acoustically inhomogeneous nature of the medium. We propose a protocol that includes the processing of this primary intense acoustic wave, leading to the quantification of the surrounding medium sound speed, and, if appropriate to an acoustical parametric image of the heterogeneities. This information is then included as prior knowledge in the photoacoustic reconstruction scheme to improve the localization and quantification.

Strategies for the generation of parametric images of [11C]PIB with plasma input functions considering discriminations and reproducibility.

PubMed

Edison, Paul; Brooks, David J; Turkheimer, Federico E; Archer, Hilary A; Hinz, Rainer

2009-11-01

Pittsburgh compound B or [11C]PIB is an amyloid imaging agent which shows a clear differentiation between subjects with Alzheimer's disease (AD) and controls. However the observed signal difference in other forms of dementia such as dementia with Lewy bodies (DLB) is smaller, and mild cognitively impaired (MCI) subjects and some healthy elderly normals may show intermediate levels of [11C]PIB binding. The cerebellum, a commonly used reference region for non-specific tracer uptake in [11C]PIB studies in AD may not be valid in Prion disorders or monogenic forms of AD. The aim of this work was to: 1-compare methods for generating parametric maps of [11C]PIB retention in tissue using a plasma input function in respect of their ability to discriminate between AD subjects and controls and 2-estimate the test-retest reproducibility in AD subjects. 12 AD subjects (5 of which underwent a repeat scan within 6 weeks) and 10 control subjects had 90 minute [11C]PIB dynamic PET scans, and arterial plasma input functions were measured. Parametric maps were generated with graphical analysis of reversible binding (Logan plot), irreversible binding (Patlak plot), and spectral analysis. Between group differentiation was calculated using Student's t-test and comparisons between different methods were made using p values. Reproducibility was assessed by intraclass correlation coefficients (ICC). We found that the 75 min value of the impulse response function showed the best group differentiation and had a higher ICC than volume of distribution maps generated from Logan and spectral analysis. Patlak analysis of [11C]PIB binding was the least reproducible.

Optimal Parameters to Determine the Apparent Diffusion Coefficient in Diffusion Weighted Imaging via Simulation

NASA Astrophysics Data System (ADS)

Perera, Dimuthu

Diffusion weighted (DW) Imaging is a non-invasive MR technique that provides information about the tissue microstructure using the diffusion of water molecules. The diffusion is generally characterized by the apparent diffusion coefficient (ADC) parametric map. The purpose of this study is to investigate in silico how the calculation of ADC is affected by image SNR, b-values, and the true tissue ADC. Also, to provide optimal parameter combination depending on the percentage accuracy and precision for prostate peripheral region cancer application. Moreover, to suggest parameter choices for any type of tissue, while providing the expected accuracy and precision. In this research DW images were generated assuming a mono-exponential signal model at two different b-values and for known true ADC values. Rician noise of different levels was added to the DWI images to adjust the image SNR. Using the two DWI images, ADC was calculated using a mono-exponential model for each set of b-values, SNR, and true ADC. 40,000 ADC data were collected for each parameter setting to determine the mean and the standard-deviation of the calculated ADC, as well as the percentage accuracy and precision with respect to the true ADC. The accuracy was calculated using the difference between known and calculated ADC. The precision was calculated using the standard-deviation of calculated ADC. The optimal parameters for a specific study was determined when both the percentage accuracy and precision were minimized. In our study, we simulated two true ADCs (ADC 0.00102 for tumor and 0.00180 mm2/s for normal prostate peripheral region tissue). Image SNR was varied from 2 to 100 and b-values were varied from 0 to 2000s/mm2. The results show that the percentage accuracy and percentage precision were minimized with image SNR. To increase SNR, 10 signal-averagings (NEX) were used considering the limitation in total scan time. The optimal NEX combination for tumor and normal tissue for prostate peripheral region was 1: 9. Also, the minimum percentage accuracy and percentage precision were obtained when low b-value is 0 and high b-value is 800 mm2/s for normal tissue and 1400 mm2/s for tumor tissue. Results also showed that for tissues with 1 x 10-3 < ADC < 2.1 x 10-3 mm 2/s the parameter combination at SNR = 20, b-value pair 0, 800 mm 2/s with NEX = 1:9 can calculate ADC with a percentage accuracy of less than 2% and percentage precision of 6-8%. Also, for tissues with 0.6 x 10-3 < ADC < 1.25 x 10-3 mm2 /s the parameter combination at SNR = 20, b-value pair 0, 1400 mm 2/s with NEX =1:9 can calculate ADC with a percentage accuracy of less than 2% and percentage precision of 6-8%.

Prospective multi-centre Voxel Based Morphometry study employing scanner specific segmentations: Procedure development using CaliBrain structural MRI data

PubMed Central

2009-01-01

Background Structural Magnetic Resonance Imaging (sMRI) of the brain is employed in the assessment of a wide range of neuropsychiatric disorders. In order to improve statistical power in such studies it is desirable to pool scanning resources from multiple centres. The CaliBrain project was designed to provide for an assessment of scanner differences at three centres in Scotland, and to assess the practicality of pooling scans from multiple-centres. Methods We scanned healthy subjects twice on each of the 3 scanners in the CaliBrain project with T1-weighted sequences. The tissue classifier supplied within the Statistical Parametric Mapping (SPM5) application was used to map the grey and white tissue for each scan. We were thus able to assess within scanner variability and between scanner differences. We have sought to correct for between scanner differences by adjusting the probability mappings of tissue occupancy (tissue priors) used in SPM5 for tissue classification. The adjustment procedure resulted in separate sets of tissue priors being developed for each scanner and we refer to these as scanner specific priors. Results Voxel Based Morphometry (VBM) analyses and metric tests indicated that the use of scanner specific priors reduced tissue classification differences between scanners. However, the metric results also demonstrated that the between scanner differences were not reduced to the level of within scanner variability, the ideal for scanner harmonisation. Conclusion Our results indicate the development of scanner specific priors for SPM can assist in pooling of scan resources from different research centres. This can facilitate improvements in the statistical power of quantitative brain imaging studies. PMID:19445668

Three-Dimensional Unstained Live-Cell Imaging Using Stimulated Parametric Emission Microscopy

NASA Astrophysics Data System (ADS)

Dang, Hieu M.; Kawasumi, Takehito; Omura, Gen; Umano, Toshiyuki; Kajiyama, Shin'ichiro; Ozeki, Yasuyuki; Itoh, Kazuyoshi; Fukui, Kiichi

2009-09-01

The ability to perform high-resolution unstained live imaging is very important to in vivo study of cell structures and functions. Stimulated parametric emission (SPE) microscopy is a nonlinear-optical microscopy based on ultra-fast electronic nonlinear-optical responses. For the first time, we have successfully applied this technique to archive three-dimensional (3D) images of unstained sub-cellular structures, such as, microtubules, nuclei, nucleoli, etc. in live cells. Observation of a complete cell division confirms the ability of SPE microscopy for long time-scale imaging.

Gated frequency-resolved optical imaging with an optical parametric amplifier

DOEpatents

Cameron, S.M.; Bliss, D.E.; Kimmel, M.W.; Neal, D.R.

1999-08-10

A system for detecting objects in a turbid media utilizes an optical parametric amplifier as an amplifying gate for received light from the media. An optical gating pulse from a second parametric amplifier permits the system to respond to and amplify only ballistic photons from the object in the media. 13 figs.

Gated frequency-resolved optical imaging with an optical parametric amplifier

DOEpatents

Cameron, Stewart M.; Bliss, David E.; Kimmel, Mark W.; Neal, Daniel R.

1999-01-01

A system for detecting objects in a turbid media utilizes an optical parametric amplifier as an amplifying gate for received light from the media. An optical gating pulse from a second parametric amplifier permits the system to respond to and amplify only ballistic photons from the object in the media.

Efficient Parallel Levenberg-Marquardt Model Fitting towards Real-Time Automated Parametric Imaging Microscopy

PubMed Central

Zhu, Xiang; Zhang, Dianwen

2013-01-01

We present a fast, accurate and robust parallel Levenberg-Marquardt minimization optimizer, GPU-LMFit, which is implemented on graphics processing unit for high performance scalable parallel model fitting processing. GPU-LMFit can provide a dramatic speed-up in massive model fitting analyses to enable real-time automated pixel-wise parametric imaging microscopy. We demonstrate the performance of GPU-LMFit for the applications in superresolution localization microscopy and fluorescence lifetime imaging microscopy. PMID:24130785

A finite element model of remote palpation of breast lesions using radiation force: factors affecting tissue displacement.

PubMed

Nightingale, K R; Nightingale, R W; Palmeri, M L; Trahey, G E

2000-01-01

The early detection of breast cancer reduces patient mortality. The most common method of breast cancer detection is palpation. However, lesions that lie deep within the breast are difficult to palpate when they are small. Thus, a method of remote palpation, which may allow the detection of small lesions lying deep within the breast, is currently under investigation. In this method, acoustic radiation force is used to apply localized forces within tissue (to tissue volumes on the order of 2 mm3) and the resulting tissue displacements are mapped using ultrasonic correlation based methods. A volume of tissue that is stiffer than the surrounding medium (i.e., a lesion) distributes the force throughout the tissue beneath it, resulting in larger regions of displacement, and smaller maximum displacements. The resulting displacement maps may be used to image tissue stiffness. A finite-element-model (FEM) of acoustic remote palpation is presented in this paper. Using this model, a parametric analysis of the affect of varying tissue and acoustic beam characteristics on radiation force induced tissue displacements is performed. The results are used to evaluate the potential of acoustic remote palpation to provide useful diagnostic information in a clinical setting. The potential for using a single diagnostic transducer to both generate radiation force and track the resulting displacements is investigated.

DCE-MRI, DW-MRI, and MRS in Cancer: Challenges and Advantages of Implementing Qualitative and Quantitative Multi-parametric Imaging in the Clinic

PubMed Central

Winfield, Jessica M.; Payne, Geoffrey S.; Weller, Alex; deSouza, Nandita M.

2016-01-01

Abstract Multi-parametric magnetic resonance imaging (mpMRI) offers a unique insight into tumor biology by combining functional MRI techniques that inform on cellularity (diffusion-weighted MRI), vascular properties (dynamic contrast-enhanced MRI), and metabolites (magnetic resonance spectroscopy) and has scope to provide valuable information for prognostication and response assessment. Challenges in the application of mpMRI in the clinic include the technical considerations in acquiring good quality functional MRI data, development of robust techniques for analysis, and clinical interpretation of the results. This article summarizes the technical challenges in acquisition and analysis of multi-parametric MRI data before reviewing the key applications of multi-parametric MRI in clinical research and practice. PMID:27748710

Non-invasive breast biopsy method using GD-DTPA contrast enhanced MRI series and F-18-FDG PET/CT dynamic image series

NASA Astrophysics Data System (ADS)

Magri, Alphonso William

This study was undertaken to develop a nonsurgical breast biopsy from Gd-DTPA Contrast Enhanced Magnetic Resonance (CE-MR) images and F-18-FDG PET/CT dynamic image series. A five-step process was developed to accomplish this. (1) Dynamic PET series were nonrigidly registered to the initial frame using a finite element method (FEM) based registration that requires fiducial skin markers to sample the displacement field between image frames. A commercial FEM package (ANSYS) was used for meshing and FEM calculations. Dynamic PET image series registrations were evaluated using similarity measurements SAVD and NCC. (2) Dynamic CE-MR series were nonrigidly registered to the initial frame using two registration methods: a multi-resolution free-form deformation (FFD) registration driven by normalized mutual information, and a FEM-based registration method. Dynamic CE-MR image series registrations were evaluated using similarity measurements, localization measurements, and qualitative comparison of motion artifacts. FFD registration was found to be superior to FEM-based registration. (3) Nonlinear curve fitting was performed for each voxel of the PET/CT volume of activity versus time, based on a realistic two-compartmental Patlak model. Three parameters for this model were fitted; two of them describe the activity levels in the blood and in the cellular compartment, while the third characterizes the washout rate of F-18-FDG from the cellular compartment. (4) Nonlinear curve fitting was performed for each voxel of the MR volume of signal intensity versus time, based on a realistic two-compartment Brix model. Three parameters for this model were fitted: rate of Gd exiting the compartment, representing the extracellular space of a lesion; rate of Gd exiting a blood compartment; and a parameter that characterizes the strength of signal intensities. Curve fitting used for PET/CT and MR series was accomplished by application of the Levenburg-Marquardt nonlinear regression algorithm. The best-fit parameters were used to create 3D parametric images. Compartmental modeling evaluation was based on the ability of parameter values to differentiate between tissue types. This evaluation was used on registered and unregistered image series and found that registration improved results. (5) PET and MR parametric images were registered through FEM- and FFD-based registration. Parametric image registration was evaluated using similarity measurements, target registration error, and qualitative comparison. Comparing FFD and FEM-based registration results showed that the FEM method is superior. This five-step process constitutes a novel multifaceted approach to a nonsurgical breast biopsy that successfully executes each step. Comparison of this method to biopsy still needs to be done with a larger set of subject data.

Terahertz parametric sources and imaging applications

NASA Astrophysics Data System (ADS)

Kawase, Kodo; Ogawa, Yuichi; Minamide, Hiroaki; Ito, Hiromasa

2005-07-01

We have studied the generation of terahertz (THz) waves by optical parametric processes based on laser light scattering from the polariton mode of nonlinear crystals. Using parametric oscillation of LiNbO3 or MgO-doped LiNbO3 crystal pumped by a nano-second Q-switched Nd:YAG laser, we have realized a widely tunable coherent THz-wave source with a simple configuration. We report the detailed characteristics of the oscillation and the radiation including tunability, spatial and temporal coherency, uni-directivity, and efficiency. A Fourier transform limited THz-wave spectrum narrowing was achieved by introducing the injection seeding method. Further, we have developed a spectroscopic THz imaging system using a THz-wave parametric oscillator, which allows detection and identification of drugs concealed in envelopes, by introducing the component spatial pattern analysis. Several images of the envelope are recorded at different THz frequencies and then processed. The final result is an image that reveals what substances are present in the envelope, in what quantity, and how they are distributed across the envelope area. The example presented here shows the identification of three drugs, two of which are illegal, while one is an over-the-counter drug.

K-edge ratio method for identification of multiple nanoparticulate contrast agents by spectral CT imaging

PubMed Central

Ghadiri, H; Ay, M R; Shiran, M B; Soltanian-Zadeh, H

2013-01-01

Objective: Recently introduced energy-sensitive X-ray CT makes it feasible to discriminate different nanoparticulate contrast materials. The purpose of this work is to present a K-edge ratio method for differentiating multiple simultaneous contrast agents using spectral CT. Methods: The ratio of two images relevant to energy bins straddling the K-edge of the materials is calculated using an analytic CT simulator. In the resulting parametric map, the selected contrast agent regions can be identified using a thresholding algorithm. The K-edge ratio algorithm is applied to spectral images of simulated phantoms to identify and differentiate up to four simultaneous and targeted CT contrast agents. Results: We show that different combinations of simultaneous CT contrast agents can be identified by the proposed K-edge ratio method when energy-sensitive CT is used. In the K-edge parametric maps, the pixel values for biological tissues and contrast agents reach a maximum of 0.95, whereas for the selected contrast agents, the pixel values are larger than 1.10. The number of contrast agents that can be discriminated is limited owing to photon starvation. For reliable material discrimination, minimum photon counts corresponding to 140 kVp, 100 mAs and 5-mm slice thickness must be used. Conclusion: The proposed K-edge ratio method is a straightforward and fast method for identification and discrimination of multiple simultaneous CT contrast agents. Advances in knowledge: A new spectral CT-based algorithm is proposed which provides a new concept of molecular CT imaging by non-iteratively identifying multiple contrast agents when they are simultaneously targeting different organs. PMID:23934964

Method of the active contour for segmentation of bone systems on bitmap images

NASA Astrophysics Data System (ADS)

Vu, Hai Anh; Safonov, Roman A.; Kolesnikova, Anna S.; Kirillova, Irina V.; Kossovich, Leonid U.

2018-02-01

It is developed within a method of the active contours the approach, which is allowing to realize separation of a contour of a object of the image in case of its segmentation. This approach exceeds a parametric method on speed, but also does not concede to it on decision accuracy. The approach is offered within this operation will allow to realize allotment of a contour with high accuracy of the image and quicker than a parametric method of the active contours.

WE-D-BRF-05: Quantitative Dual-Energy CT Imaging for Proton Stopping Power Computation

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

Han, D; Williamson, J; Siebers, J

2014-06-15

Purpose: To extend the two-parameter separable basis-vector model (BVM) to estimation of proton stopping power from dual-energy CT (DECT) imaging. Methods: BVM assumes that the photon cross sections of any unknown material can be represented as a linear combination of the corresponding quantities for two bracketing basis materials. We show that both the electron density (ρe) and mean excitation energy (Iex) can be modeled by BVM, enabling stopping power to be estimated from the Bethe-Bloch equation. We have implemented an idealized post-processing dual energy imaging (pDECT) simulation consisting of monogenetic 45 keV and 80 keV scanning beams with polystyrene-water andmore » water-CaCl2 solution basis pairs for soft tissues and bony tissues, respectively. The coefficients of 24 standard ICRU tissue compositions were estimated by pDECT. The corresponding ρe, Iex, and stopping power tables were evaluated via BVM and compared to tabulated ICRU 44 reference values. Results: BVM-based pDECT was found to estimate ρe and Iex with average and maximum errors of 0.5% and 2%, respectively, for the 24 tissues. Proton stopping power values at 175 MeV, show average/maximum errors of 0.8%/1.4%. For adipose, muscle and bone, these errors result range prediction accuracies less than 1%. Conclusion: A new two-parameter separable DECT model (BVM) for estimating proton stopping power was developed. Compared to competing parametric fit DECT models, BVM has the comparable prediction accuracy without necessitating iterative solution of nonlinear equations or a sample-dependent empirical relationship between effective atomic number and Iex. Based on the proton BVM, an efficient iterative statistical DECT reconstruction model is under development.« less

δ-aminolevulinic acid–induced protoporphyrin IX concentration correlates with histopathologic markers of malignancy in human gliomas: the need for quantitative fluorescence-guided resection to identify regions of increasing malignancy

PubMed Central

Valdés, Pablo A.; Kim, Anthony; Brantsch, Marco; Niu, Carolyn; Moses, Ziev B.; Tosteson, Tor D.; Wilson, Brian C.; Paulsen, Keith D.; Roberts, David W.; Harris, Brent T.

2011-01-01

Extent of resection is a major goal and prognostic factor in the treatment of gliomas. In this study we evaluate whether quantitative ex vivo tissue measurements of δ-aminolevulinic acid–induced protoporphyrin IX (PpIX) identify regions of increasing malignancy in low- and high-grade gliomas beyond the capabilities of current fluorescence imaging in patients undergoing fluorescence-guided resection (FGR). Surgical specimens were collected from 133 biopsies in 23 patients and processed for ex vivo neuropathological analysis: PpIX fluorimetry to measure PpIX concentrations (CPpIX) and Ki-67 immunohistochemistry to assess tissue proliferation. Samples displaying visible levels of fluorescence showed significantly higher levels of CPpIX and tissue proliferation. CPpIX was strongly correlated with histopathological score (nonparametric) and tissue proliferation (parametric), such that increasing levels of CPpIX were identified with regions of increasing malignancy. Furthermore, a large percentage of tumor-positive biopsy sites (∼40%) that were not visibly fluorescent under the operating microscope had levels of CPpIX greater than 0.1 µg/mL, which indicates that significant PpIX accumulation exists below the detection threshold of current fluorescence imaging. Although PpIX fluorescence is recognized as a visual biomarker for neurosurgical resection guidance, these data show that it is quantitatively related at the microscopic level to increasing malignancy in both low- and high-grade gliomas. This work suggests a need for improved PpIX fluorescence detection technologies to achieve better sensitivity and quantification of PpIX in tissue during surgery. PMID:21798847

Fitting C 2 Continuous Parametric Surfaces to Frontiers Delimiting Physiologic Structures

PubMed Central

Bayer, Jason D.

2014-01-01

We present a technique to fit C 2 continuous parametric surfaces to scattered geometric data points forming frontiers delimiting physiologic structures in segmented images. Such mathematical representation is interesting because it facilitates a large number of operations in modeling. While the fitting of C 2 continuous parametric curves to scattered geometric data points is quite trivial, the fitting of C 2 continuous parametric surfaces is not. The difficulty comes from the fact that each scattered data point should be assigned a unique parametric coordinate, and the fit is quite sensitive to their distribution on the parametric plane. We present a new approach where a polygonal (quadrilateral or triangular) surface is extracted from the segmented image. This surface is subsequently projected onto a parametric plane in a manner to ensure a one-to-one mapping. The resulting polygonal mesh is then regularized for area and edge length. Finally, from this point, surface fitting is relatively trivial. The novelty of our approach lies in the regularization of the polygonal mesh. Process performance is assessed with the reconstruction of a geometric model of mouse heart ventricles from a computerized tomography scan. Our results show an excellent reproduction of the geometric data with surfaces that are C 2 continuous. PMID:24782911

Pharmacokinetics Application in Biophysics Experiments

NASA Astrophysics Data System (ADS)

Millet, Philippe; Lemoigne, Yves

Among the available computerised tomography devices, the Positron Emission Tomography (PET) has the advantage to be sensitive to pico-molar concentrations of radiotracers inside living matter. Devices adapted to small animal imaging are now commercially available and allow us to study the function rather than the structure of living tissues by in vivo analysis. PET methodology, from the physics of electron-positron annihilation to the biophysics involved in tracers, is treated by other authors in this book. The basics of coincidence detection, image reconstruction, spatial resolution and sensitivity are discussed in the paper by R. Ott. The use of compartment analysis combined with pharmacokinetics is described here to illustrate an application to neuroimaging and to show how parametric imaging can bring insight on the in vivo bio-distribution of a radioactive tracer with small animal PET scanners. After reporting on the use of an intracerebral β+ radiosensitive probe (βP), we describe a small animal PET experiment used to measure the density of 5HT 1 a receptors in rat brain.

Physics, Techniques and Review of Neuroradiological Applications of Diffusion Kurtosis Imaging (DKI).

PubMed

Marrale, M; Collura, G; Brai, M; Toschi, N; Midiri, F; La Tona, G; Lo Casto, A; Gagliardo, C

2016-12-01

In recent years many papers about diagnostic applications of diffusion tensor imaging (DTI) have been published. This is because DTI allows to evaluate in vivo and in a non-invasive way the process of diffusion of water molecules in biological tissues. However, the simplified description of the diffusion process assumed in DTI does not permit to completely map the complex underlying cellular components and structures, which hinder and restrict the diffusion of water molecules. These limitations can be partially overcome by means of diffusion kurtosis imaging (DKI). The aim of this paper is the description of the theory of DKI, a new topic of growing interest in radiology. DKI is a higher order diffusion model that is a straightforward extension of the DTI model. Here, we analyze the physics underlying this method, we report our MRI acquisition protocol with the preprocessing pipeline used and the DKI parametric maps obtained on a 1.5 T scanner, and we review the most relevant clinical applications of this technique in various neurological diseases.

[Microcytomorphometric video-image detection of nuclear chromatin in ovarian cancer].

PubMed

Grzonka, Dariusz; Kamiński, Kazimierz; Kaźmierczak, Wojciech

2003-09-01

Technology of detection of tissue preparates precisious evaluates contents of nuclear chromatine, largeness and shape of cellular nucleus, indicators of mitosis, DNA index, ploidy, phase-S fraction and other parameters. Methods of detection of picture are: microcytomorphometry video-image (MCMM-VI), flow, double flow and activated by fluorescence. Diagnostic methods of malignant neoplasm of ovary are still nonspecific and not precise, that is a reason of unsatisfied results of treatment. Evaluation of microcytomorphometric measurements of nuclear chromatine histopathologic tissue preparates (HP) of ovarian cancer and comparison to normal ovarian tissue. Estimated 10 paraffin embedded tissue preparates of serous ovarian cancer, 4 preparates mucinous cancer and 2 cases of tumor Kruckenberg patients operated in Clinic of Perinatology and Gynaecology Silesian Medical Academy in Zabrze in period 2001-2002, MCMM-VI estimation based on computer aided analysis system: microscope Axioscop 20, camera tv JVCTK-C 1380, CarlZeiss KS Vision 400 rel.3.0 software. Following MCMM-VI parameters assessed: count of pathologic nucleus, diameter of nucleus, area, min/max diameter ratio, equivalent circle diameter (Dcircle), mean of brightness (mean D), integrated optical density (IOD = area x mean D), DNA index and 2.5 c exceeding rate percentage (2.5 c ER%). MCMM-VI performed on the 160 areas of 16 preparates of cancer and 100 areas of normal ovarian tissue. Statistical analysis was performed by used t-Student test. We obtained stastistically significant higher values parameters of nuclear chromatine, DI, 2.5 c ER of mucinous cancer and tumor Kruckenberg comparison to serous cancer. MCMM-VI parameters of chromatine malignant ovarian neoplasm were statistically significantly higher than normal ovarian tissue. Cytometric and karyometric parametres of nuclear chromatine estimated MCMM-VI are useful in the diagnostics and prognosis of ovarian cancer.

Visualizing Epithelial Expression in Vertical and Horizontal Planes With Dual Axes Confocal Endomicroscope Using Compact Distal Scanner.

PubMed

Li, Gaoming; Li, Haijun; Duan, Xiyu; Zhou, Quan; Zhou, Juan; Oldham, Kenn R; Wang, Thomas D

2017-07-01

The epithelium is a thin layer of tissue that lines hollow organs, such as colon. Visualizing in vertical cross sections with sub-cellular resolution is essential to understanding early disease mechanisms that progress naturally in the plane perpendicular to the tissue surface. The dual axes confocal architecture collects optical sections in tissue by directing light at an angle incident to the surface using separate illumination and collection beams to reduce effects of scattering, enhance dynamic range, and increase imaging depth. This configuration allows for images to be collected in the vertical as well as horizontal planes. We designed a fast, compact monolithic scanner based on the principle of parametric resonance. The mirrors were fabricated using microelectromechanical systems (MEMS) technology and were coated with aluminum to maximize near-infrared reflectivity. We achieved large axial displacements [Formula: see text] and wide lateral deflections >20°. The MEMS chip has a 3.2×2.9 mm 2 form factor that allows for efficient packaging in the distal end of an endomicroscope. Imaging can be performed in either the vertical or horizontal planes with [Formula: see text] depth or 1 ×1 mm 2 area, respectively, at 5 frames/s. We systemically administered a Cy5.5-labeled peptide that is specific for EGFR, and collected near-infrared fluorescence images ex vivo from pre-malignant mouse colonic epithelium to reveal the spatial distribution of this molecular target. Here, we demonstrate a novel scanning mechanism in a dual axes confocal endomicroscope that collects optical sections of near-infrared fluorescence in either vertical or horizontal planes to visualize molecular expression in the epithelium.

Automated diagnosis of prostate cancer in multi-parametric MRI based on multimodal convolutional neural networks

NASA Astrophysics Data System (ADS)

Le, Minh Hung; Chen, Jingyu; Wang, Liang; Wang, Zhiwei; Liu, Wenyu; (Tim Cheng, Kwang-Ting; Yang, Xin

2017-08-01

Automated methods for prostate cancer (PCa) diagnosis in multi-parametric magnetic resonance imaging (MP-MRIs) are critical for alleviating requirements for interpretation of radiographs while helping to improve diagnostic accuracy (Artan et al 2010 IEEE Trans. Image Process. 19 2444-55, Litjens et al 2014 IEEE Trans. Med. Imaging 33 1083-92, Liu et al 2013 SPIE Medical Imaging (International Society for Optics and Photonics) p 86701G, Moradi et al 2012 J. Magn. Reson. Imaging 35 1403-13, Niaf et al 2014 IEEE Trans. Image Process. 23 979-91, Niaf et al 2012 Phys. Med. Biol. 57 3833, Peng et al 2013a SPIE Medical Imaging (International Society for Optics and Photonics) p 86701H, Peng et al 2013b Radiology 267 787-96, Wang et al 2014 BioMed. Res. Int. 2014). This paper presents an automated method based on multimodal convolutional neural networks (CNNs) for two PCa diagnostic tasks: (1) distinguishing between cancerous and noncancerous tissues and (2) distinguishing between clinically significant (CS) and indolent PCa. Specifically, our multimodal CNNs effectively fuse apparent diffusion coefficients (ADCs) and T2-weighted MP-MRI images (T2WIs). To effectively fuse ADCs and T2WIs we design a new similarity loss function to enforce consistent features being extracted from both ADCs and T2WIs. The similarity loss is combined with the conventional classification loss functions and integrated into the back-propagation procedure of CNN training. The similarity loss enables better fusion results than existing methods as the feature learning processes of both modalities are mutually guided, jointly facilitating CNN to ‘see’ the true visual patterns of PCa. The classification results of multimodal CNNs are further combined with the results based on handcrafted features using a support vector machine classifier. To achieve a satisfactory accuracy for clinical use, we comprehensively investigate three critical factors which could greatly affect the performance of our multimodal CNNs but have not been carefully studied previously. (1) Given limited training data, how can these be augmented in sufficient numbers and variety for fine-tuning deep CNN networks for PCa diagnosis? (2) How can multimodal MP-MRI information be effectively combined in CNNs? (3) What is the impact of different CNN architectures on the accuracy of PCa diagnosis? Experimental results on extensive clinical data from 364 patients with a total of 463 PCa lesions and 450 identified noncancerous image patches demonstrate that our system can achieve a sensitivity of 89.85% and a specificity of 95.83% for distinguishing cancer from noncancerous tissues and a sensitivity of 100% and a specificity of 76.92% for distinguishing indolent PCa from CS PCa. This result is significantly superior to the state-of-the-art method relying on handcrafted features.

Simultaneous parametric generation and up-conversion of entangled optical images

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

Saygin, M. Yu., E-mail: mihasyu@gmail.com; Chirkin, A. S., E-mail: aschirkin@rambler.r

A quantum theory of parametric amplification and frequency conversion of an optical image in coupled nonlinear optical processes that include one parametric amplification process at high-frequency pumping and two up-conversion processes in the same pump field is developed. The field momentum operator that takes into account the diffraction and group velocities of the waves is used to derive the quantum equations related to the spatial dynamics of the images during the interaction. An optical scheme for the amplification and conversion of a close image is considered. The mean photon number density and signal-to-noise ratio are calculated in the fixed-pump-field approximationmore » for images at various frequencies. It has been established that the signal-to-noise ratio decreases with increasing interaction length in the amplified image and increases in the images at the generated frequencies, tending to asymptotic values for all interacting waves. The variance of the difference of the numbers of photons is calculated for various pairs of frequencies. The quantum entanglement of the optical images formed in a high-frequency pump field is shown to be converted to higher frequencies during the generation of sum frequencies. Thus, two pairs of entangled optical images are produced in the process considered.« less

THz-wave parametric source and its imaging applications

NASA Astrophysics Data System (ADS)

Kawase, Kodo

2004-08-01

Widely tunable coherent terahertz (THz) wave generation has been demonstrated based on the parametric oscillation using MgO doped LiNbO3 crystal pumped by a Q-switched Nd:YAG laser. This method exhibits multiple advantages like wide tunability, coherency and compactness of its system. We have developed a novel basic technology for terahertz (THz) imaging, which allows detection and identification of chemicals by introducing the component spatial pattern analysis. The spatial distributions of the chemicals were obtained from terahertz multispectral transillumination images, using absorption spectra previously measured with a widely tunable THz-wave parametric oscillator. Further we have applied this technique to the detection and identification of illicit drugs concealed in envelopes. The samples we used were methamphetamine and MDMA, two of the most widely consumed illegal drugs in Japan, and aspirin as a reference.

Complex Geometric Models of Diffusion and Relaxation in Healthy and Damaged White Matter

PubMed Central

Farrell, Jonathan A.D.; Smith, Seth A.; Reich, Daniel S.; Calabresi, Peter A.; van Zijl, Peter C.M.

2010-01-01

Which aspects of tissue microstructure affect diffusion weighted MRI signals? Prior models, many of which use Monte-Carlo simulations, have focused on relatively simple models of the cellular microenvironment and have not considered important anatomic details. With the advent of higher-order analysis models for diffusion imaging, such as high-angular-resolution diffusion imaging (HARDI), more realistic models are necessary. This paper presents and evaluates the reproducibility of simulations of diffusion in complex geometries. Our framework is quantitative, does not require specialized hardware, is easily implemented with little programming experience, and is freely available as open-source software. Models may include compartments with different diffusivities, permeabilities, and T2 time constants using both parametric (e.g., spheres and cylinders) and arbitrary (e.g., mesh-based) geometries. Three-dimensional diffusion displacement-probability functions are mapped with high reproducibility, and thus can be readily used to assess reproducibility of diffusion-derived contrasts. PMID:19739233

GRAPE: a graphical pipeline environment for image analysis in adaptive magnetic resonance imaging.

PubMed

Gabr, Refaat E; Tefera, Getaneh B; Allen, William J; Pednekar, Amol S; Narayana, Ponnada A

2017-03-01

We present a platform, GRAphical Pipeline Environment (GRAPE), to facilitate the development of patient-adaptive magnetic resonance imaging (MRI) protocols. GRAPE is an open-source project implemented in the Qt C++ framework to enable graphical creation, execution, and debugging of real-time image analysis algorithms integrated with the MRI scanner. The platform provides the tools and infrastructure to design new algorithms, and build and execute an array of image analysis routines, and provides a mechanism to include existing analysis libraries, all within a graphical environment. The application of GRAPE is demonstrated in multiple MRI applications, and the software is described in detail for both the user and the developer. GRAPE was successfully used to implement and execute three applications in MRI of the brain, performed on a 3.0-T MRI scanner: (i) a multi-parametric pipeline for segmenting the brain tissue and detecting lesions in multiple sclerosis (MS), (ii) patient-specific optimization of the 3D fluid-attenuated inversion recovery MRI scan parameters to enhance the contrast of brain lesions in MS, and (iii) an algebraic image method for combining two MR images for improved lesion contrast. GRAPE allows graphical development and execution of image analysis algorithms for inline, real-time, and adaptive MRI applications.

Standards of ultrasound imaging of the adrenal glands

PubMed Central

Jakubowski, Wiesław S.; Dobruch-Sobczak, Katarzyna; Kasperlik-Załuska, Anna A.

2015-01-01

Adrenal glands are paired endocrine glands located over the upper renal poles. Adrenal pathologies have various clinical presentations. They can coexist with the hyperfunction of individual cortical zones or the medulla, insufficiency of the adrenal cortex or retained normal hormonal function. The most common adrenal masses are tumors incidentally detected in imaging examinations (ultrasound, tomography, magnetic resonance imaging), referred to as incidentalomas. They include a range of histopathological entities but cortical adenomas without hormonal hyperfunction are the most common. Each abdominal ultrasound scan of a child or adult should include the assessment of the suprarenal areas. If a previously non-reported, incidental solid focal lesion exceeding 1 cm (incidentaloma) is detected in the suprarenal area, computed tomography or magnetic resonance imaging should be conducted to confirm its presence and for differentiation and the tumor functional status should be determined. Ultrasound imaging is also used to monitor adrenal incidentaloma that is not eligible for a surgery. The paper presents recommendations concerning the performance and assessment of ultrasound examinations of the adrenal glands and their pathological lesions. The article includes new ultrasound techniques, such as tissue harmonic imaging, spatial compound imaging, three-dimensional ultrasound, elastography, contrast-enhanced ultrasound and parametric imaging. The guidelines presented above are consistent with the recommendations of the Polish Ultrasound Society. PMID:26807295

Parametric imaging of collagen structural changes in human osteoarthritic cartilage using optical polarization tractography

NASA Astrophysics Data System (ADS)

Ravanfar, Mohammadreza; Pfeiffer, Ferris M.; Bozynski, Chantelle C.; Wang, Yuanbo; Yao, Gang

2017-12-01

Collagen degeneration is an important pathological feature of osteoarthritis. The purpose of this study is to investigate whether the polarization-sensitive optical coherence tomography (PSOCT)-based optical polarization tractography (OPT) can be useful in imaging collagen structural changes in human osteoarthritic cartilage samples. OPT eliminated the banding artifacts in conventional PSOCT by calculating the depth-resolved local birefringence and fiber orientation. A close comparison between OPT and PSOCT showed that OPT provided improved visualization and characterization of the zonal structure in human cartilage. Experimental results obtained in this study also underlined the importance of knowing the collagen fiber orientation in conventional polarized light microscopy assessment. In addition, parametric OPT imaging was achieved by quantifying the surface roughness, birefringence, and fiber dispersion in the superficial zone of the cartilage. These quantitative parametric images provided complementary information on the structural changes in cartilage, which can be useful for a comprehensive evaluation of collagen damage in osteoarthritic cartilage.

Sci—Fri PM: Topics — 06: The influence of regional dose sensitivity on salivary loss and recovery in the parotid gland

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

Clark, H; BC Cancer Agency, Surrey, B.C.; BC Cancer Agency, Vancouver, B.C.

Purpose: The Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC 2010) survey of radiation dose-volume effects on salivary gland function has called for improved understanding of intragland dose sensitivity and the effectiveness of partial sparing in salivary glands. Regional dose susceptibility of sagittally- and coronally-sub-segmented parotid gland has been studied. Specifically, we examine whether individual consideration of sub-segments leads to improved prediction of xerostomia compared with whole parotid mean dose. Methods: Data from 102 patients treated for head-and-neck cancers at the BC Cancer Agency were used in this study. Whole mouth stimulated saliva was collected before (baseline), threemore » months, and one year after cessation of radiotherapy. Organ volumes were contoured using treatment planning CT images and sub-segmented into regional portions. Both non-parametric (local regression) and parametric (mean dose exponential fitting) methods were employed. A bootstrap technique was used for reliability estimation and cross-comparison. Results: Salivary loss is described well using non-parametric and mean dose models. Parametric fits suggest a significant distinction in dose response between medial-lateral and anterior-posterior aspects of the parotid (p<0.01). Least-squares and least-median squares estimates differ significantly (p<0.00001), indicating fits may be skewed by noise or outliers. Salivary recovery exhibits a weakly arched dose response: the highest recovery is seen at intermediate doses. Conclusions: Salivary function loss is strongly dose dependent. In contrast no useful dose dependence was observed for function recovery. Regional dose dependence was observed, but may have resulted from a bias in dose distributions.« less

Nonlinear PET parametric image reconstruction with MRI information using kernel method

NASA Astrophysics Data System (ADS)

Gong, Kuang; Wang, Guobao; Chen, Kevin T.; Catana, Ciprian; Qi, Jinyi

2017-03-01

Positron Emission Tomography (PET) is a functional imaging modality widely used in oncology, cardiology, and neurology. It is highly sensitive, but suffers from relatively poor spatial resolution, as compared with anatomical imaging modalities, such as magnetic resonance imaging (MRI). With the recent development of combined PET/MR systems, we can improve the PET image quality by incorporating MR information. Previously we have used kernel learning to embed MR information in static PET reconstruction and direct Patlak reconstruction. Here we extend this method to direct reconstruction of nonlinear parameters in a compartment model by using the alternating direction of multiplier method (ADMM) algorithm. Simulation studies show that the proposed method can produce superior parametric images compared with existing methods.

Combining Segmented Grey and White Matter Images Improves Voxel-based Morphometry for the Case of Dilated Lateral Ventricles.

PubMed

Goto, Masami; Abe, Osamu; Aoki, Shigeki; Kamagata, Koji; Hori, Masaaki; Miyati, Tosiaki; Gomi, Tsutomu; Takeda, Tohoru

2018-01-18

To evaluate the error in segmented tissue images and to show the usefulness of the brain image in voxel-based morphometry (VBM) using Statistical Parametric Mapping (SPM) 12 software and 3D T 1 -weighted magnetic resonance images (3D-T 1 WIs) processed to simulate idiopathic normal pressure hydrocephalus (iNPH). VBM analysis was performed on sagittal 3D-T 1 WIs obtained in 22 healthy volunteers using a 1.5T MR scanner. Regions of interest for the lateral ventricles of all subjects were carefully outlined on the original 3D-T 1 WIs, and two types of simulated 3D-T 1 WI were also prepared (non-dilated 3D-T 1 WI as normal control and dilated 3D-T 1 WI to simulate iNPH). All simulated 3D-T 1 WIs were segmented into gray matter, white matter, and cerebrospinal fluid images, and normalized to standard space. A brain image was made by adding the gray and white matter images. After smoothing with a 6-mm isotropic Gaussian kernel, group comparisons (dilated vs non-dilated) were made for gray and white matter, cerebrospinal fluid, and brain images using a paired t-test. In evaluation of tissue volume, estimation error was larger using gray or white matter images than using the brain image, and estimation errors in gray and white matter volume change were found for the brain surface. To our knowledge, this is the first VBM study to show the possibility that VBM of gray and white matter volume on the brain surface may be more affected by individual differences in the level of dilation of the lateral ventricles than by individual differences in gray and white matter volumes. We recommend that VBM evaluation in patients with iNPH should be performed using the brain image rather than the gray and white matter images.

Comparison of Absolute Apparent Diffusion Coefficient (ADC) Values in ADC Maps Generated Across Different Postprocessing Software: Reproducibility in Endometrial Carcinoma.

PubMed

Ghosh, Adarsh; Singh, Tulika; Singla, Veenu; Bagga, Rashmi; Khandelwal, Niranjan

2017-12-01

Apparent diffusion coefficient (ADC) maps are usually generated by builtin software provided by the MRI scanner vendors; however, various open-source postprocessing software packages are available for image manipulation and parametric map generation. The purpose of this study is to establish the reproducibility of absolute ADC values obtained using different postprocessing software programs. DW images with three b values were obtained with a 1.5-T MRI scanner, and the trace images were obtained. ADC maps were automatically generated by the in-line software provided by the vendor during image generation and were also separately generated on postprocessing software. These ADC maps were compared on the basis of ROIs using paired t test, Bland-Altman plot, mountain plot, and Passing-Bablok regression plot. There was a statistically significant difference in the mean ADC values obtained from the different postprocessing software programs when the same baseline trace DW images were used for the ADC map generation. For using ADC values as a quantitative cutoff for histologic characterization of tissues, standardization of the postprocessing algorithm is essential across processing software packages, especially in view of the implementation of vendor-neutral archiving.

Aging in deep gray matter and white matter revealed by diffusional kurtosis imaging.

PubMed

Gong, Nan-Jie; Wong, Chun-Sing; Chan, Chun-Chung; Leung, Lam-Ming; Chu, Yiu-Ching

2014-10-01

Diffusion tensor imaging has already been extensively used to probe microstructural alterations in white matter tracts, and scarcely, in deep gray matter. However, results in literature regarding age-related degenerative mechanisms in white matter tracts and parametric changes in the putamen are inconsistent. Diffusional kurtosis imaging is a mathematical extension of diffusion tensor imaging, which could more comprehensively mirror microstructure, particularly in isotropic tissues such as gray matter. In this study, we used the diffusional kurtosis imaging method and a white-matter model that provided metrics of explicit neurobiological interpretations in healthy participants (58 in total, aged from 25 to 84 years). Tract-based whole-brain analyses and regions-of-interest (anterior and posterior limbs of the internal capsule, cerebral peduncle, fornix, genu and splenium of corpus callosum, globus pallidus, substantia nigra, red nucleus, putamen, caudate nucleus, and thalamus) analyses were performed to examine parametric differences across regions and correlations with age. In white matter tracts, evidence was found supportive for anterior-posterior gradient and not completely supportive for retrogenesis theory. Age-related degenerations appeared to be broadly driven by axonal loss. Demyelination may also be a major driving mechanism, although confined to the anterior brain. In terms of deep gray matter, higher mean kurtosis and fractional anisotropy in the globus pallidus, substantia nigra, and red nucleus reflected higher microstructural complexity and directionality compared with the putamen, caudate nucleus, and thalamus. In particular, the unique age-related positive correlations for fractional anisotropy, mean kurtosis, and radial kurtosis in the putamen opposite to those in other regions call for further investigation of exact underlying mechanisms. In summary, the results suggested that diffusional kurtosis can provide measurements in a new dimension that were complementary to diffusivity metrics. Kurtosis together with diffusivity can more comprehensively characterize microstructural compositions and age-related changes than diffusivity alone. Combined with proper model, it may also assist in providing neurobiological interpretations of the identified alterations. Copyright © 2014 Elsevier Inc. All rights reserved.

4D-PET reconstruction using a spline-residue model with spatial and temporal roughness penalties

NASA Astrophysics Data System (ADS)

Ralli, George P.; Chappell, Michael A.; McGowan, Daniel R.; Sharma, Ricky A.; Higgins, Geoff S.; Fenwick, John D.

2018-05-01

4D reconstruction of dynamic positron emission tomography (dPET) data can improve the signal-to-noise ratio in reconstructed image sequences by fitting smooth temporal functions to the voxel time-activity-curves (TACs) during the reconstruction, though the optimal choice of function remains an open question. We propose a spline-residue model, which describes TACs as weighted sums of convolutions of the arterial input function with cubic B-spline basis functions. Convolution with the input function constrains the spline-residue model at early time-points, potentially enhancing noise suppression in early time-frames, while still allowing a wide range of TAC descriptions over the entire imaged time-course, thus limiting bias. Spline-residue based 4D-reconstruction is compared to that of a conventional (non-4D) maximum a posteriori (MAP) algorithm, and to 4D-reconstructions based on adaptive-knot cubic B-splines, the spectral model and an irreversible two-tissue compartment (‘2C3K’) model. 4D reconstructions were carried out using a nested-MAP algorithm including spatial and temporal roughness penalties. The algorithms were tested using Monte-Carlo simulated scanner data, generated for a digital thoracic phantom with uptake kinetics based on a dynamic [18F]-Fluromisonidazole scan of a non-small cell lung cancer patient. For every algorithm, parametric maps were calculated by fitting each voxel TAC within a sub-region of the reconstructed images with the 2C3K model. Compared to conventional MAP reconstruction, spline-residue-based 4D reconstruction achieved  >50% improvements for five of the eight combinations of the four kinetics parameters for which parametric maps were created with the bias and noise measures used to analyse them, and produced better results for 5/8 combinations than any of the other reconstruction algorithms studied, while spectral model-based 4D reconstruction produced the best results for 2/8. 2C3K model-based 4D reconstruction generated the most biased parametric maps. Inclusion of a temporal roughness penalty function improved the performance of 4D reconstruction based on the cubic B-spline, spectral and spline-residue models.

Scene-based nonuniformity correction and enhancement: pixel statistics and subpixel motion.

PubMed

Zhao, Wenyi; Zhang, Chao

2008-07-01

We propose a framework for scene-based nonuniformity correction (NUC) and nonuniformity correction and enhancement (NUCE) that is required for focal-plane array-like sensors to obtain clean and enhanced-quality images. The core of the proposed framework is a novel registration-based nonuniformity correction super-resolution (NUCSR) method that is bootstrapped by statistical scene-based NUC methods. Based on a comprehensive imaging model and an accurate parametric motion estimation, we are able to remove severe/structured nonuniformity and in the presence of subpixel motion to simultaneously improve image resolution. One important feature of our NUCSR method is the adoption of a parametric motion model that allows us to (1) handle many practical scenarios where parametric motions are present and (2) carry out perfect super-resolution in principle by exploring available subpixel motions. Experiments with real data demonstrate the efficiency of the proposed NUCE framework and the effectiveness of the NUCSR method.

Investigating a continuous shear strain function for depth-dependent properties of native and tissue engineering cartilage using pixel-size data.

PubMed

Motavalli, Mostafa; Whitney, G Adam; Dennis, James E; Mansour, Joseph M

2013-12-01

A previously developed novel imaging technique for determining the depth dependent properties of cartilage in simple shear is implemented. Shear displacement is determined from images of deformed lines photobleached on a sample, and shear strain is obtained from the derivative of the displacement. We investigated the feasibility of an alternative systematic approach to numerical differentiation for computing the shear strain that is based on fitting a continuous function to the shear displacement. Three models for a continuous shear displacement function are evaluated: polynomials, cubic splines, and non-parametric locally weighted scatter plot curves. Four independent approaches are then applied to identify the best-fit model and the accuracy of the first derivative. One approach is based on the Akaiki Information Criteria, and the Bayesian Information Criteria. The second is based on a method developed to smooth and differentiate digitized data from human motion. The third method is based on photobleaching a predefined circular area with a specific radius. Finally, we integrate the shear strain and compare it with the total shear deflection of the sample measured experimentally. Results show that 6th and 7th order polynomials are the best models for the shear displacement and its first derivative. In addition, failure of tissue-engineered cartilage, consistent with previous results, demonstrates the qualitative value of this imaging approach. © 2013 Elsevier Ltd. All rights reserved.

Monitoring tumor response of prostate cancer to radiation therapy by multi-parametric 1H and hyperpolarized 13C magnetic resonance imaging

NASA Astrophysics Data System (ADS)

Zhang, Vickie Yi

Radiation therapy is one of the most common curative therapies for patients with localized prostate cancer, but despite excellent success rates, a significant number of patients suffer post- treatment cancer recurrence. The accurate characterization of early tumor response remains a major challenge for the clinical management of these patients. Multi-parametric MRI/1H MR spectroscopy imaging (MRSI) has been shown to increase the diagnostic performance in evaluating the effectiveness of radiation therapy. 1H MRSI can detect altered metabolic profiles in cancerous tissue. In this project, the concentrations of prostate metabolites from snap-frozen biopsies of recurrent cancer after failed radiation therapy were correlated with histopathological findings to identify quantitative biomarkers that predict for residual aggressive versus indolent cancer. The total choline to creatine ratio was significantly higher in recurrent aggressive versus indolent cancer, suggesting that use of a higher threshold tCho/Cr ratio in future in vivo 1H MRSI studies could improve the selection and therapeutic planning for patients after failed radiation therapy. Varying radiation doses may cause a diverse effect on prostate cancer micro-environment and metabolism, which could hold the key to improving treatment protocols for individual patients. The recent development and clinical translation of hyperpolarized 13C MRI have provided the ability to monitor both changes in the tumor micro-environment and its metabolism using a multi-probe approach, [1-13C]pyruvate and 13C urea, combined with 1H Multi-parametric MRI. In this thesis, hyperpolarized 13C MRI, 1H dynamic contrast enhancement, and diffusion weighted imaging were used to identify early radiation dose response in a transgenic prostate cancer model. Hyperpolarized pyruvate to lactate metabolism significantly decreased in a dose dependent fashion by 1 day after radiation therapy, prior to any changes observed using 1H DCE and diffusion weighted imaging. Hyperpolarized 13C urea and 1H DCE both show increase in perfusion/permeability by 4 days post-radiation. In tumor region treated with high dose radiation, ADC values significantly increased post-radiation, suggesting a decrease in cellular density. These dose dependent changes can be used as markers of early tumor response to the impact of increasing doses of radiation therapy. In addition, a spectral-spatial pulse sequence was developed for the 14T to dynamically observe kinetic information in a transgenic prostate cancer model before and after radiation therapy. A novel modeling approach was proposed to parameterize perfusion in the kinetic modeling of pyruvate to lactate conversion for better characterization of pyruvate metabolism. Unlike single time point HP 13C urea imaging, quantitative pharmacokinetic parameters such as blood flow and extracellular extravascular volume fraction can be extracted from dynamic acquisitions. Blood flow measured by hyperpolarized 13C urea was highly correlated with Ktrans measured by 1H DCE, suggesting hyperpolarized urea might be able to provide similar information as 1H DCE. The results of this thesis show that Multi-parametric MRI, including functional MRI, 1H MRSI, and hyperpolarized 13C, holds great potential for evaluating early tumor response to radiation therapy of prostate cancer. The findings of this thesis will be useful in designing future studies for using combined Multi-parametric 1H and hyperpolarized 13C MRI to improve planning and assessing radiation therapy in individual prostate cancer patients.

Quantitative Imaging Biomarkers of NAFLD

PubMed Central

Kinner, Sonja; Reeder, Scott B.

2016-01-01

Conventional imaging modalities, including ultrasonography (US), computed tomography (CT), and magnetic resonance (MR), play an important role in the diagnosis and management of patients with nonalcoholic fatty liver disease (NAFLD) by allowing noninvasive diagnosis of hepatic steatosis. However, conventional imaging modalities are limited as biomarkers of NAFLD for various reasons. Multi-parametric quantitative MRI techniques overcome many of the shortcomings of conventional imaging and allow comprehensive and objective evaluation of NAFLD. MRI can provide unconfounded biomarkers of hepatic fat, iron, and fibrosis in a single examination—a virtual biopsy has become a clinical reality. In this article, we will review the utility and limitation of conventional US, CT, and MR imaging for the diagnosis NAFLD. Recent advances in imaging biomarkers of NAFLD are also discussed with an emphasis in multi-parametric quantitative MRI. PMID:26848588

ACCELERATING MR PARAMETER MAPPING USING SPARSITY-PROMOTING REGULARIZATION IN PARAMETRIC DIMENSION

PubMed Central

Velikina, Julia V.; Alexander, Andrew L.; Samsonov, Alexey

2013-01-01

MR parameter mapping requires sampling along additional (parametric) dimension, which often limits its clinical appeal due to a several-fold increase in scan times compared to conventional anatomic imaging. Data undersampling combined with parallel imaging is an attractive way to reduce scan time in such applications. However, inherent SNR penalties of parallel MRI due to noise amplification often limit its utility even at moderate acceleration factors, requiring regularization by prior knowledge. In this work, we propose a novel regularization strategy, which utilizes smoothness of signal evolution in the parametric dimension within compressed sensing framework (p-CS) to provide accurate and precise estimation of parametric maps from undersampled data. The performance of the method was demonstrated with variable flip angle T1 mapping and compared favorably to two representative reconstruction approaches, image space-based total variation regularization and an analytical model-based reconstruction. The proposed p-CS regularization was found to provide efficient suppression of noise amplification and preservation of parameter mapping accuracy without explicit utilization of analytical signal models. The developed method may facilitate acceleration of quantitative MRI techniques that are not suitable to model-based reconstruction because of complex signal models or when signal deviations from the expected analytical model exist. PMID:23213053

Joint reconstruction of dynamic PET activity and kinetic parametric images using total variation constrained dictionary sparse coding

NASA Astrophysics Data System (ADS)

Yu, Haiqing; Chen, Shuhang; Chen, Yunmei; Liu, Huafeng

2017-05-01

Dynamic positron emission tomography (PET) is capable of providing both spatial and temporal information of radio tracers in vivo. In this paper, we present a novel joint estimation framework to reconstruct temporal sequences of dynamic PET images and the coefficients characterizing the system impulse response function, from which the associated parametric images of the system macro parameters for tracer kinetics can be estimated. The proposed algorithm, which combines statistical data measurement and tracer kinetic models, integrates a dictionary sparse coding (DSC) into a total variational minimization based algorithm for simultaneous reconstruction of the activity distribution and parametric map from measured emission sinograms. DSC, based on the compartmental theory, provides biologically meaningful regularization, and total variation regularization is incorporated to provide edge-preserving guidance. We rely on techniques from minimization algorithms (the alternating direction method of multipliers) to first generate the estimated activity distributions with sub-optimal kinetic parameter estimates, and then recover the parametric maps given these activity estimates. These coupled iterative steps are repeated as necessary until convergence. Experiments with synthetic, Monte Carlo generated data, and real patient data have been conducted, and the results are very promising.

Infrared laser damage thresholds in corneal tissue phantoms using femtosecond laser pulses

NASA Astrophysics Data System (ADS)

Boretsky, Adam R.; Clary, Joseph E.; Noojin, Gary D.; Rockwell, Benjamin A.

2018-02-01

Ultrafast lasers have become a fixture in many biomedical, industrial, telecommunications, and defense applications in recent years. These sources are capable of generating extremely high peak power that can cause laser-induced tissue breakdown through the formation of a plasma upon exposure. Despite the increasing prevalence of such lasers, current safety standards (ANSI Z136.1-2014) do not include maximum permissible exposure (MPE) values for the cornea with pulse durations less than one nanosecond. This study was designed to measure damage thresholds in corneal tissue phantoms in the near-infrared and mid-infrared to identify the wavelength dependence of laser damage thresholds from 1200-2500 nm. A high-energy regenerative amplifier and optical parametric amplifier outputting 100 femtosecond pulses with pulse energies up to 2 mJ were used to perform exposures and determine damage thresholds in transparent collagen gel tissue phantoms. Three-dimensional imaging, primarily optical coherence tomography, was used to evaluate tissue phantoms following exposure to determine ablation characteristics at the surface and within the bulk material. The determination of laser damage thresholds in the near-IR and mid-IR for ultrafast lasers will help to guide safety standards and establish the appropriate MPE levels for exposure sensitive ocular tissue such as the cornea. These data will help promote the safe use of ultrafast lasers for a wide range of applications.

Documenting the location of systematic transrectal ultrasound-guided prostate biopsies: correlation with multi-parametric MRI.

PubMed

Turkbey, Baris; Xu, Sheng; Kruecker, Jochen; Locklin, Julia; Pang, Yuxi; Shah, Vijay; Bernardo, Marcelino; Baccala, Angelo; Rastinehad, Ardeshir; Benjamin, Compton; Merino, Maria J; Wood, Bradford J; Choyke, Peter L; Pinto, Peter A

2011-03-29

During transrectal ultrasound (TRUS)-guided prostate biopsies, the actual location of the biopsy site is rarely documented. Here, we demonstrate the capability of TRUS-magnetic resonance imaging (MRI) image fusion to document the biopsy site and correlate biopsy results with multi-parametric MRI findings. Fifty consecutive patients (median age 61 years) with a median prostate-specific antigen (PSA) level of 5.8 ng/ml underwent 12-core TRUS-guided biopsy of the prostate. Pre-procedural T2-weighted magnetic resonance images were fused to TRUS. A disposable needle guide with miniature tracking sensors was attached to the TRUS probe to enable fusion with MRI. Real-time TRUS images during biopsy and the corresponding tracking information were recorded. Each biopsy site was superimposed onto the MRI. Each biopsy site was classified as positive or negative for cancer based on the results of each MRI sequence. Sensitivity, specificity, and receiver operating curve (ROC) area under the curve (AUC) values were calculated for multi-parametric MRI. Gleason scores for each multi-parametric MRI pattern were also evaluated. Six hundred and 5 systemic biopsy cores were analyzed in 50 patients, of whom 20 patients had 56 positive cores. MRI identified 34 of 56 positive cores. Overall, sensitivity, specificity, and ROC area values for multi-parametric MRI were 0.607, 0.727, 0.667, respectively. TRUS-MRI fusion after biopsy can be used to document the location of each biopsy site, which can then be correlated with MRI findings. Based on correlation with tracked biopsies, T2-weighted MRI and apparent diffusion coefficient maps derived from diffusion-weighted MRI are the most sensitive sequences, whereas the addition of delayed contrast enhancement MRI and three-dimensional magnetic resonance spectroscopy demonstrated higher specificity consistent with results obtained using radical prostatectomy specimens.

Usefulness of myocardial parametric imaging to evaluate myocardial viability in experimental and in clinical studies.

PubMed

Korosoglou, G; Hansen, A; Bekeredjian, R; Filusch, A; Hardt, S; Wolf, D; Schellberg, D; Katus, H A; Kuecherer, H

2006-03-01

To evaluate whether myocardial parametric imaging (MPI) is superior to visual assessment for the evaluation of myocardial viability. Myocardial contrast echocardiography (MCE) was assessed in 11 pigs before, during, and after left anterior descending coronary artery occlusion and in 32 patients with ischaemic heart disease by using intravenous SonoVue administration. In experimental studies perfusion defect area assessment by MPI was compared with visually guided perfusion defect planimetry. Histological assessment of necrotic tissue was the standard reference. In clinical studies viability was assessed on a segmental level by (1) visual analysis of myocardial opacification; (2) quantitative estimation of myocardial blood flow in regions of interest; and (3) MPI. Functional recovery between three and six months after revascularisation was the standard reference. In experimental studies, compared with visually guided perfusion defect planimetry, planimetric assessment of infarct size by MPI correlated more significantly with histology (r2 = 0.92 versus r2 = 0.56) and had a lower intraobserver variability (4% v 15%, p < 0.05). In clinical studies, MPI had higher specificity (66% v 43%, p < 0.05) than visual MCE and good accuracy (81%) for viability detection. It was less time consuming (3.4 (1.6) v 9.2 (2.4) minutes per image, p < 0.05) than quantitative blood flow estimation by regions of interest and increased the agreement between observers interpreting myocardial perfusion (kappa = 0.87 v kappa = 0.75, p < 0.05). MPI is useful for the evaluation of myocardial viability both in animals and in patients. It is less time consuming than quantification analysis by regions of interest and less observer dependent than visual analysis. Thus, strategies incorporating this technique may be valuable for the evaluation of myocardial viability in clinical routine.

Deriving stable multi-parametric MRI radiomic signatures in the presence of inter-scanner variations: survival prediction of glioblastoma via imaging pattern analysis and machine learning techniques

NASA Astrophysics Data System (ADS)

Rathore, Saima; Bakas, Spyridon; Akbari, Hamed; Shukla, Gaurav; Rozycki, Martin; Davatzikos, Christos

2018-02-01

There is mounting evidence that assessment of multi-parametric magnetic resonance imaging (mpMRI) profiles can noninvasively predict survival in many cancers, including glioblastoma. The clinical adoption of mpMRI as a prognostic biomarker, however, depends on its applicability in a multicenter setting, which is hampered by inter-scanner variations. This concept has not been addressed in existing studies. We developed a comprehensive set of within-patient normalized tumor features such as intensity profile, shape, volume, and tumor location, extracted from multicenter mpMRI of two large (npatients=353) cohorts, comprising the Hospital of the University of Pennsylvania (HUP, npatients=252, nscanners=3) and The Cancer Imaging Archive (TCIA, npatients=101, nscanners=8). Inter-scanner harmonization was conducted by normalizing the tumor intensity profile, with that of the contralateral healthy tissue. The extracted features were integrated by support vector machines to derive survival predictors. The predictors' generalizability was evaluated within each cohort, by two cross-validation configurations: i) pooled/scanner-agnostic, and ii) across scanners (training in multiple scanners and testing in one). The median survival in each configuration was used as a cut-off to divide patients in long- and short-survivors. Accuracy (ACC) for predicting long- versus short-survivors, for these configurations was ACCpooled=79.06% and ACCpooled=84.7%, ACCacross=73.55% and ACCacross=74.76%, in HUP and TCIA datasets, respectively. The hazard ratio at 95% confidence interval was 3.87 (2.87-5.20, P<0.001) and 6.65 (3.57-12.36, P<0.001) for HUP and TCIA datasets, respectively. Our findings suggest that adequate data normalization coupled with machine learning classification allows robust prediction of survival estimates on mpMRI acquired by multiple scanners.

Biological Parametric Mapping: A Statistical Toolbox for Multi-Modality Brain Image Analysis

PubMed Central

Casanova, Ramon; Ryali, Srikanth; Baer, Aaron; Laurienti, Paul J.; Burdette, Jonathan H.; Hayasaka, Satoru; Flowers, Lynn; Wood, Frank; Maldjian, Joseph A.

2006-01-01

In recent years multiple brain MR imaging modalities have emerged; however, analysis methodologies have mainly remained modality specific. In addition, when comparing across imaging modalities, most researchers have been forced to rely on simple region-of-interest type analyses, which do not allow the voxel-by-voxel comparisons necessary to answer more sophisticated neuroscience questions. To overcome these limitations, we developed a toolbox for multimodal image analysis called biological parametric mapping (BPM), based on a voxel-wise use of the general linear model. The BPM toolbox incorporates information obtained from other modalities as regressors in a voxel-wise analysis, thereby permitting investigation of more sophisticated hypotheses. The BPM toolbox has been developed in MATLAB with a user friendly interface for performing analyses, including voxel-wise multimodal correlation, ANCOVA, and multiple regression. It has a high degree of integration with the SPM (statistical parametric mapping) software relying on it for visualization and statistical inference. Furthermore, statistical inference for a correlation field, rather than a widely-used T-field, has been implemented in the correlation analysis for more accurate results. An example with in-vivo data is presented demonstrating the potential of the BPM methodology as a tool for multimodal image analysis. PMID:17070709

Limited-angle tomography for analyzer-based phase-contrast X-ray imaging

PubMed Central

Majidi, Keivan; Wernick, Miles N; Li, Jun; Muehleman, Carol; Brankov, Jovan G

2014-01-01

Multiple-Image Radiography (MIR) is an analyzer-based phase-contrast X-ray imaging method (ABI), which is emerging as a potential alternative to conventional radiography. MIR simultaneously generates three planar parametric images containing information about scattering, refraction and attenuation properties of the object. The MIR planar images are linear tomographic projections of the corresponding object properties, which allows reconstruction of volumetric images using computed tomography (CT) methods. However, when acquiring a full range of linear projections around the tissue of interest is not feasible or the scanning time is limited, limited-angle tomography techniques can be used to reconstruct these volumetric images near the central plane, which is the plane that contains the pivot point of the tomographic movement. In this work, we use computer simulations to explore the applicability of limited-angle tomography to MIR. We also investigate the accuracy of reconstructions as a function of number of tomographic angles for a fixed total radiation exposure. We use this function to find an optimal range of angles over which data should be acquired for limited-angle tomography MIR (LAT-MIR). Next, we apply the LAT-MIR technique to experimentally acquired MIR projections obtained in a cadaveric human thumb study. We compare the reconstructed slices near the central plane to the same slices reconstructed by CT-MIR using the full angular view around the object. Finally, we perform a task-based evaluation of LAT-MIR performance for different numbers of angular views, and use template matching to detect cartilage in the refraction image near the central plane. We use the signal-to-noise ratio of this test as the detectability metric to investigate an optimum range of tomographic angles for detecting soft tissues in LAT-MIR. Both results show that there is an optimum range of angular view for data acquisition where LAT-MIR yields the best performance, comparable to CT-MIR only if one considers volumetric images near the central plane and not the whole volume. PMID:24898008

Limited-angle tomography for analyzer-based phase-contrast x-ray imaging

NASA Astrophysics Data System (ADS)

Majidi, Keivan; Wernick, Miles N.; Li, Jun; Muehleman, Carol; Brankov, Jovan G.

2014-07-01

Multiple-image radiography (MIR) is an analyzer-based phase-contrast x-ray imaging method, which is emerging as a potential alternative to conventional radiography. MIR simultaneously generates three planar parametric images containing information about scattering, refraction and attenuation properties of the object. The MIR planar images are linear tomographic projections of the corresponding object properties, which allows reconstruction of volumetric images using computed tomography (CT) methods. However, when acquiring a full range of linear projections around the tissue of interest is not feasible or the scanning time is limited, limited-angle tomography techniques can be used to reconstruct these volumetric images near the central plane, which is the plane that contains the pivot point of the tomographic movement. In this work, we use computer simulations to explore the applicability of limited-angle tomography to MIR. We also investigate the accuracy of reconstructions as a function of number of tomographic angles for a fixed total radiation exposure. We use this function to find an optimal range of angles over which data should be acquired for limited-angle tomography MIR (LAT-MIR). Next, we apply the LAT-MIR technique to experimentally acquired MIR projections obtained in a cadaveric human thumb study. We compare the reconstructed slices near the central plane to the same slices reconstructed by CT-MIR using the full angular view around the object. Finally, we perform a task-based evaluation of LAT-MIR performance for different numbers of angular views, and use template matching to detect cartilage in the refraction image near the central plane. We use the signal-to-noise ratio of this test as the detectability metric to investigate an optimum range of tomographic angles for detecting soft tissues in LAT-MIR. Both results show that there is an optimum range of angular view for data acquisition where LAT-MIR yields the best performance, comparable to CT-MIR only if one considers volumetric images near the central plane and not the whole volume.

Feature selection and classification of multiparametric medical images using bagging and SVM

NASA Astrophysics Data System (ADS)

Fan, Yong; Resnick, Susan M.; Davatzikos, Christos

2008-03-01

This paper presents a framework for brain classification based on multi-parametric medical images. This method takes advantage of multi-parametric imaging to provide a set of discriminative features for classifier construction by using a regional feature extraction method which takes into account joint correlations among different image parameters; in the experiments herein, MRI and PET images of the brain are used. Support vector machine classifiers are then trained based on the most discriminative features selected from the feature set. To facilitate robust classification and optimal selection of parameters involved in classification, in view of the well-known "curse of dimensionality", base classifiers are constructed in a bagging (bootstrap aggregating) framework for building an ensemble classifier and the classification parameters of these base classifiers are optimized by means of maximizing the area under the ROC (receiver operating characteristic) curve estimated from their prediction performance on left-out samples of bootstrap sampling. This classification system is tested on a sex classification problem, where it yields over 90% classification rates for unseen subjects. The proposed classification method is also compared with other commonly used classification algorithms, with favorable results. These results illustrate that the methods built upon information jointly extracted from multi-parametric images have the potential to perform individual classification with high sensitivity and specificity.

Toward uniform implementation of parametric map Digital Imaging and Communication in Medicine standard in multisite quantitative diffusion imaging studies.

PubMed

Malyarenko, Dariya; Fedorov, Andriy; Bell, Laura; Prah, Melissa; Hectors, Stefanie; Arlinghaus, Lori; Muzi, Mark; Solaiyappan, Meiyappan; Jacobs, Michael; Fung, Maggie; Shukla-Dave, Amita; McManus, Kevin; Boss, Michael; Taouli, Bachir; Yankeelov, Thomas E; Quarles, Christopher Chad; Schmainda, Kathleen; Chenevert, Thomas L; Newitt, David C

2018-01-01

This paper reports on results of a multisite collaborative project launched by the MRI subgroup of Quantitative Imaging Network to assess current capability and provide future guidelines for generating a standard parametric diffusion map Digital Imaging and Communication in Medicine (DICOM) in clinical trials that utilize quantitative diffusion-weighted imaging (DWI). Participating sites used a multivendor DWI DICOM dataset of a single phantom to generate parametric maps (PMs) of the apparent diffusion coefficient (ADC) based on two models. The results were evaluated for numerical consistency among models and true phantom ADC values, as well as for consistency of metadata with attributes required by the DICOM standards. This analysis identified missing metadata descriptive of the sources for detected numerical discrepancies among ADC models. Instead of the DICOM PM object, all sites stored ADC maps as DICOM MR objects, generally lacking designated attributes and coded terms for quantitative DWI modeling. Source-image reference, model parameters, ADC units and scale, deemed important for numerical consistency, were either missing or stored using nonstandard conventions. Guided by the identified limitations, the DICOM PM standard has been amended to include coded terms for the relevant diffusion models. Open-source software has been developed to support conversion of site-specific formats into the standard representation.

New regularization scheme for blind color image deconvolution

NASA Astrophysics Data System (ADS)

Chen, Li; He, Yu; Yap, Kim-Hui

2011-01-01

This paper proposes a new regularization scheme to address blind color image deconvolution. Color images generally have a significant correlation among the red, green, and blue channels. Conventional blind monochromatic deconvolution algorithms handle each color image channels independently, thereby ignoring the interchannel correlation present in the color images. In view of this, a unified regularization scheme for image is developed to recover edges of color images and reduce color artifacts. In addition, by using the color image properties, a spectral-based regularization operator is adopted to impose constraints on the blurs. Further, this paper proposes a reinforcement regularization framework that integrates a soft parametric learning term in addressing blind color image deconvolution. A blur modeling scheme is developed to evaluate the relevance of manifold parametric blur structures, and the information is integrated into the deconvolution scheme. An optimization procedure called alternating minimization is then employed to iteratively minimize the image- and blur-domain cost functions. Experimental results show that the method is able to achieve satisfactory restored color images under different blurring conditions.

Near-self-imaging cavity for three-mode optoacoustic parametric amplifiers using silicon microresonators.

PubMed

Liu, Jian; Torres, F A; Ma, Yubo; Zhao, C; Ju, L; Blair, D G; Chao, S; Roch-Jeune, I; Flaminio, R; Michel, C; Liu, K-Y

2014-02-10

Three-mode optoacoustic parametric amplifiers (OAPAs), in which a pair of photon modes are strongly coupled to an acoustic mode, provide a general platform for investigating self-cooling, parametric instability and very sensitive transducers. Their realization requires an optical cavity with tunable transverse modes and a high quality-factor mirror resonator. This paper presents the design of a table-top OAPA based on a near-self-imaging cavity design, using a silicon torsional microresonator. The design achieves a tuning coefficient for the optical mode spacing of 2.46  MHz/mm. This allows tuning of the mode spacing between amplification and self-cooling regimes of the OAPA device. Based on demonstrated resonator parameters (frequencies ∼400  kHz and quality-factors ∼7.5×10(5) we predict that the OAPA can achieve parametric instability with 1.6 μW of input power and mode cooling by a factor of 1.9×10(4) with 30 mW of input power.

Parametric boundary reconstruction algorithm for industrial CT metrology application.

PubMed

Yin, Zhye; Khare, Kedar; De Man, Bruno

2009-01-01

High-energy X-ray computed tomography (CT) systems have been recently used to produce high-resolution images in various nondestructive testing and evaluation (NDT/NDE) applications. The accuracy of the dimensional information extracted from CT images is rapidly approaching the accuracy achieved with a coordinate measuring machine (CMM), the conventional approach to acquire the metrology information directly. On the other hand, CT systems generate the sinogram which is transformed mathematically to the pixel-based images. The dimensional information of the scanned object is extracted later by performing edge detection on reconstructed CT images. The dimensional accuracy of this approach is limited by the grid size of the pixel-based representation of CT images since the edge detection is performed on the pixel grid. Moreover, reconstructed CT images usually display various artifacts due to the underlying physical process and resulting object boundaries from the edge detection fail to represent the true boundaries of the scanned object. In this paper, a novel algorithm to reconstruct the boundaries of an object with uniform material composition and uniform density is presented. There are three major benefits in the proposed approach. First, since the boundary parameters are reconstructed instead of image pixels, the complexity of the reconstruction algorithm is significantly reduced. The iterative approach, which can be computationally intensive, will be practical with the parametric boundary reconstruction. Second, the object of interest in metrology can be represented more directly and accurately by the boundary parameters instead of the image pixels. By eliminating the extra edge detection step, the overall dimensional accuracy and process time can be improved. Third, since the parametric reconstruction approach shares the boundary representation with other conventional metrology modalities such as CMM, boundary information from other modalities can be directly incorporated as prior knowledge to improve the convergence of an iterative approach. In this paper, the feasibility of parametric boundary reconstruction algorithm is demonstrated with both simple and complex simulated objects. Finally, the proposed algorithm is applied to the experimental industrial CT system data.

Geometric Continuity: A Parametrization Independent Measure of Continuity for Computer Aided Geometric Design

DTIC Science & Technology

1985-08-01

in a. typography system, the surface of a. ship hull, or the skin of a.n airplane. To define objects such as these, higher order curve a.nd surface...rate). Thus, a parametrization contains infor- mation about the geometry (the shape or image of the curve), the orientation, and the rate. Figure 2.3...2.3. Each of the curves above has the same image ; they only differ in orientation and rate. Orientation is indicated by arrowheads and rate is

MO-F-CAMPUS-J-05: Toward MRI-Only Radiotherapy: Novel Tissue Segmentation and Pseudo-CT Generation Techniques Based On T1 MRI Sequences

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

Aouadi, S; McGarry, M; Hammoud, R

Purpose: To develop and validate a 4 class tissue segmentation approach (air cavities, background, bone and soft-tissue) on T1 -weighted brain MRI and to create a pseudo-CT for MRI-only radiation therapy verification. Methods: Contrast-enhanced T1-weighted fast-spin-echo sequences (TR = 756ms, TE= 7.152ms), acquired on a 1.5T GE MRI-Simulator, are used.MRIs are firstly pre-processed to correct for non uniformity using the non parametric, non uniformity intensity normalization algorithm. Subsequently, a logarithmic inverse scaling log(1/image) is applied, prior to segmentation, to better differentiate bone and air from soft-tissues. Finally, the following method is enrolled to classify intensities into air cavities, background, bonemore » and soft-tissue:Thresholded region growing with seed points in image corners is applied to get a mask of Air+Bone+Background. The background is, afterward, separated by the scan-line filling algorithm. The air mask is extracted by morphological opening followed by a post-processing based on knowledge about air regions geometry. The remaining rough bone pre-segmentation is refined by applying 3D geodesic active contours; bone segmentation evolves by the sum of internal forces from contour geometry and external force derived from image gradient magnitude.Pseudo-CT is obtained by assigning −1000HU to air and background voxels, performing linear mapping of soft-tissue MR intensities in [-400HU, 200HU] and inverse linear mapping of bone MR intensities in [200HU, 1000HU]. Results: Three brain patients having registered MRI and CT are used for validation. CT intensities classification into 4 classes is performed by thresholding. Dice and misclassification errors are quantified. Correct classifications for soft-tissue, bone, and air are respectively 89.67%, 77.8%, and 64.5%. Dice indices are acceptable for bone (0.74) and soft-tissue (0.91) but low for air regions (0.48). Pseudo-CT produces DRRs with acceptable clinical visual agreement to CT-based DRR. Conclusion: The proposed approach makes it possible to use T1-weighted MRI to generate accurate pseudo-CT from 4-class segmentation.« less

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.

Classical imaging with undetected light

NASA Astrophysics Data System (ADS)

Cardoso, A. C.; Berruezo, L. P.; Ávila, D. F.; Lemos, G. B.; Pimenta, W. M.; Monken, C. H.; Saldanha, P. L.; Pádua, S.

2018-03-01

We obtained the phase and intensity images of an object by detecting classical light which never interacted with it. With a double passage of a pump and a signal laser beams through a nonlinear crystal, we observe interference between the two idler beams produced by stimulated parametric down conversion. The object is placed in the amplified signal beam after its first passage through the crystal and the image is observed in the interference of the generated idler beams. High contrast images can be obtained even for objects with small transmittance coefficient due to the geometry of the interferometer and to the stimulated parametric emission. Like its quantum counterpart, this three-color imaging concept can be useful when the object must be probed with light at a wavelength for which detectors are not available.

A simple parametric model observer for quality assurance in computer tomography

NASA Astrophysics Data System (ADS)

Anton, M.; Khanin, A.; Kretz, T.; Reginatto, M.; Elster, C.

2018-04-01

Model observers are mathematical classifiers that are used for the quality assessment of imaging systems such as computer tomography. The quality of the imaging system is quantified by means of the performance of a selected model observer. For binary classification tasks, the performance of the model observer is defined by the area under its ROC curve (AUC). Typically, the AUC is estimated by applying the model observer to a large set of training and test data. However, the recording of these large data sets is not always practical for routine quality assurance. In this paper we propose as an alternative a parametric model observer that is based on a simple phantom, and we provide a Bayesian estimation of its AUC. It is shown that a limited number of repeatedly recorded images (10–15) is already sufficient to obtain results suitable for the quality assessment of an imaging system. A MATLAB® function is provided for the calculation of the results. The performance of the proposed model observer is compared to that of the established channelized Hotelling observer and the nonprewhitening matched filter for simulated images as well as for images obtained from a low-contrast phantom on an x-ray tomography scanner. The results suggest that the proposed parametric model observer, along with its Bayesian treatment, can provide an efficient, practical alternative for the quality assessment of CT imaging systems.

Parametric Architecture in the Urban Space

NASA Astrophysics Data System (ADS)

Januszkiewicz, Krystyna; Kowalski, Karol G.

2017-10-01

The paper deals with the parametric architecture which is trying to introduce a new spatial language in the context for urban tissue that correspond to the artistic consciousness and the attitude of information and digital technologies era. The first part of the paper defines the main features of parametric architecture (such as: folding, continuity and curvilinearity) which are are characteristic of the new style of named the “parametricism”. This architecture is a strong emphasis on geometry, materiality, feasibility and sustainability, what emerges is an explicit agenda promoting material ornamentation, spatial spectacle and formal theatricality. The second part presents result of case study, especially parametric public use buildings, within the tissue of city. The analyzed objects are: The Sage Gateshead (1998-2004) in Gateshead, Kunsthaus in Graz (2000-2003), the Weltstadthaus (2003-2005) in Cologne, The Golden Terraces in Warsaw (2000-2007), the Metropol Parasol in Seville (2005-2011) the King Cross Station (2005-2012) in London, the headquarters of the Pathé Foundation (2006-2014) in Paris. Each of the enumerated examples shows a diverse approach to designing in the urban space, which reflect the age of digital technologies and the information society. In conclusion emphasizes, that new concept of the spatialization of architecture is the equivalent of the democratization of the political system, the liberalization of the economy, among other examples.

A linear, separable two-parameter model for dual energy CT imaging of proton stopping power computation

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

Han, Dong, E-mail: radon.han@gmail.com; Williamson, Jeffrey F.; Siebers, Jeffrey V.

2016-01-15

Purpose: To evaluate the accuracy and robustness of a simple, linear, separable, two-parameter model (basis vector model, BVM) in mapping proton stopping powers via dual energy computed tomography (DECT) imaging. Methods: The BVM assumes that photon cross sections (attenuation coefficients) of unknown materials are linear combinations of the corresponding radiological quantities of dissimilar basis substances (i.e., polystyrene, CaCl{sub 2} aqueous solution, and water). The authors have extended this approach to the estimation of electron density and mean excitation energy, which are required parameters for computing proton stopping powers via the Bethe–Bloch equation. The authors compared the stopping power estimation accuracymore » of the BVM with that of a nonlinear, nonseparable photon cross section Torikoshi parametric fit model (VCU tPFM) as implemented by the authors and by Yang et al. [“Theoretical variance analysis of single- and dual-energy computed tomography methods for calculating proton stopping power ratios of biological tissues,” Phys. Med. Biol. 55, 1343–1362 (2010)]. Using an idealized monoenergetic DECT imaging model, proton ranges estimated by the BVM, VCU tPFM, and Yang tPFM were compared to International Commission on Radiation Units and Measurements (ICRU) published reference values. The robustness of the stopping power prediction accuracy of tissue composition variations was assessed for both of the BVM and VCU tPFM. The sensitivity of accuracy to CT image uncertainty was also evaluated. Results: Based on the authors’ idealized, error-free DECT imaging model, the root-mean-square error of BVM proton stopping power estimation for 175 MeV protons relative to ICRU reference values for 34 ICRU standard tissues is 0.20%, compared to 0.23% and 0.68% for the Yang and VCU tPFM models, respectively. The range estimation errors were less than 1 mm for the BVM and Yang tPFM models, respectively. The BVM estimation accuracy is not dependent on tissue type and proton energy range. The BVM is slightly more vulnerable to CT image intensity uncertainties than the tPFM models. Both the BVM and tPFM prediction accuracies were robust to uncertainties of tissue composition and independent of the choice of reference values. This reported accuracy does not include the impacts of I-value uncertainties and imaging artifacts and may not be achievable on current clinical CT scanners. Conclusions: The proton stopping power estimation accuracy of the proposed linear, separable BVM model is comparable to or better than that of the nonseparable tPFM models proposed by other groups. In contrast to the tPFM, the BVM does not require an iterative solving for effective atomic number and electron density at every voxel; this improves the computational efficiency of DECT imaging when iterative, model-based image reconstruction algorithms are used to minimize noise and systematic imaging artifacts of CT images.« less

Using Spatial Correlations of SPDC Sources for Increasing the Signal to Noise Ratio in Images

NASA Astrophysics Data System (ADS)

Ruíz, A. I.; Caudillo, R.; Velázquez, V. M.; Barrios, E.

2017-05-01

We experimentally show that, by using spatial correlations of photon pairs produced by Spontaneous Parametric Down-Conversion, it is possible to increase the Signal to Noise Ratio in images of objects illuminated with those photons; in comparison, objects illuminated with light from a laser present a minor ratio. Our simple experimental set-up was capable to produce an average improvement in signal to noise ratio of 11dB of Parametric Down-Converted light over laser light. This simple method can be easily implemented for obtaining high contrast images of faint objects and for transmitting information with low noise.

Topics in the two-dimensional sampling and reconstruction of images. [in remote sensing

NASA Technical Reports Server (NTRS)

Schowengerdt, R.; Gray, S.; Park, S. K.

1984-01-01

Mathematical analysis of image sampling and interpolative reconstruction is summarized and extended to two dimensions for application to data acquired from satellite sensors such as the Thematic mapper and SPOT. It is shown that sample-scene phase influences the reconstruction of sampled images, adds a considerable blur to the average system point spread function, and decreases the average system modulation transfer function. It is also determined that the parametric bicubic interpolator with alpha = -0.5 is more radiometrically accurate than the conventional bicubic interpolator with alpha = -1, and this at no additional cost. Finally, the parametric bicubic interpolator is found to be suitable for adaptive implementation by relating the alpha parameter to the local frequency content of an image.

Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps.

PubMed

Sadeghi-Naini, Ali; Suraweera, Harini; Tran, William Tyler; Hadizad, Farnoosh; Bruni, Giancarlo; Rastegar, Rashin Fallah; Curpen, Belinda; Czarnota, Gregory J

2017-10-20

This study evaluated, for the first time, the efficacy of quantitative ultrasound (QUS) spectral parametric maps in conjunction with texture-analysis techniques to differentiate non-invasively benign versus malignant breast lesions. Ultrasound B-mode images and radiofrequency data were acquired from 78 patients with suspicious breast lesions. QUS spectral-analysis techniques were performed on radiofrequency data to generate parametric maps of mid-band fit, spectral slope, spectral intercept, spacing among scatterers, average scatterer diameter, and average acoustic concentration. Texture-analysis techniques were applied to determine imaging biomarkers consisting of mean, contrast, correlation, energy and homogeneity features of parametric maps. These biomarkers were utilized to classify benign versus malignant lesions with leave-one-patient-out cross-validation. Results were compared to histopathology findings from biopsy specimens and radiology reports on MR images to evaluate the accuracy of technique. Among the biomarkers investigated, one mean-value parameter and 14 textural features demonstrated statistically significant differences (p < 0.05) between the two lesion types. A hybrid biomarker developed using a stepwise feature selection method could classify the legions with a sensitivity of 96%, a specificity of 84%, and an AUC of 0.97. Findings from this study pave the way towards adapting novel QUS-based frameworks for breast cancer screening and rapid diagnosis in clinic.

Kinetic evaluation and test-retest reproducibility of [11C]UCB-J, a novel radioligand for positron emission tomography imaging of synaptic vesicle glycoprotein 2A in humans.

PubMed

Finnema, Sjoerd J; Nabulsi, Nabeel B; Mercier, Joël; Lin, Shu-Fei; Chen, Ming-Kai; Matuskey, David; Gallezot, Jean-Dominique; Henry, Shannan; Hannestad, Jonas; Huang, Yiyun; Carson, Richard E

2017-01-01

Synaptic vesicle glycoprotein 2A (SV2A) is ubiquitously present in presynaptic terminals. Here we report kinetic modeling and test-retest reproducibility assessment of the SV2A positron emission tomography (PET) radioligand [ 11 C]UCB-J in humans. Five volunteers were examined twice on the HRRT after bolus injection of [ 11 C]UCB-J. Arterial blood samples were collected for measurements of radiometabolites and free fraction. Regional time-activity curves were analyzed with 1-tissue (1T) and 2-tissue (2T) compartment models to estimate volumes of distribution ( V T ). Parametric maps were generated using the 1T model. [ 11 C]UCB-J metabolized fairly quickly, with parent fraction of 36 ± 13% at 15 min after injection. Plasma free fraction was 32 ± 1%. Regional time-activity curves displayed rapid kinetics and were well described by the 1T model, except for the cerebellum and hippocampus. V T values estimated with the 2T model were similar to 1T values. Parametric maps were of high quality and V T values correlated well with time activity curve (TAC)-based estimates. Shortening of acquisition time from 120 min to 60 min had a negligible effect on V T values. The mean absolute test-retest reproducibility for V T was 3-9% across regions. In conclusion, [ 11 C]UCB-J exhibited excellent PET tracer characteristics and has potential as a general purpose tool for measuring synaptic density in neurodegenerative disorders.

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.

Fuzzy C-mean clustering on kinetic parameter estimation with generalized linear least square algorithm in SPECT

NASA Astrophysics Data System (ADS)

Choi, Hon-Chit; Wen, Lingfeng; Eberl, Stefan; Feng, Dagan

2006-03-01

Dynamic Single Photon Emission Computed Tomography (SPECT) has the potential to quantitatively estimate physiological parameters by fitting compartment models to the tracer kinetics. The generalized linear least square method (GLLS) is an efficient method to estimate unbiased kinetic parameters and parametric images. However, due to the low sensitivity of SPECT, noisy data can cause voxel-wise parameter estimation by GLLS to fail. Fuzzy C-Mean (FCM) clustering and modified FCM, which also utilizes information from the immediate neighboring voxels, are proposed to improve the voxel-wise parameter estimation of GLLS. Monte Carlo simulations were performed to generate dynamic SPECT data with different noise levels and processed by general and modified FCM clustering. Parametric images were estimated by Logan and Yokoi graphical analysis and GLLS. The influx rate (K I), volume of distribution (V d) were estimated for the cerebellum, thalamus and frontal cortex. Our results show that (1) FCM reduces the bias and improves the reliability of parameter estimates for noisy data, (2) GLLS provides estimates of micro parameters (K I-k 4) as well as macro parameters, such as volume of distribution (Vd) and binding potential (BP I & BP II) and (3) FCM clustering incorporating neighboring voxel information does not improve the parameter estimates, but improves noise in the parametric images. These findings indicated that it is desirable for pre-segmentation with traditional FCM clustering to generate voxel-wise parametric images with GLLS from dynamic SPECT data.

Sensitivity enhancement in swept-source optical coherence tomography by parametric balanced detector and amplifier

PubMed Central

Kang, Jiqiang; Wei, Xiaoming; Li, Bowen; Wang, Xie; Yu, Luoqin; Tan, Sisi; Jinata, Chandra; Wong, Kenneth K. Y.

2016-01-01

We proposed a sensitivity enhancement method of the interference-based signal detection approach and applied it on a swept-source optical coherence tomography (SS-OCT) system through all-fiber optical parametric amplifier (FOPA) and parametric balanced detector (BD). The parametric BD was realized by combining the signal and phase conjugated idler band that was newly-generated through FOPA, and specifically by superimposing these two bands at a photodetector. The sensitivity enhancement by FOPA and parametric BD in SS-OCT were demonstrated experimentally. The results show that SS-OCT with FOPA and SS-OCT with parametric BD can provide more than 9 dB and 12 dB sensitivity improvement, respectively, when compared with the conventional SS-OCT in a spectral bandwidth spanning over 76 nm. To further verify and elaborate their sensitivity enhancement, a bio-sample imaging experiment was conducted on loach eyes by conventional SS-OCT setup, SS-OCT with FOPA and parametric BD at different illumination power levels. All these results proved that using FOPA and parametric BD could improve the sensitivity significantly in SS-OCT systems. PMID:27446655

Coupled B-snake grids and constrained thin-plate splines for analysis of 2-D tissue deformations from tagged MRI.

PubMed

Amini, A A; Chen, Y; Curwen, R W; Mani, V; Sun, J

1998-06-01

Magnetic resonance imaging (MRI) is unique in its ability to noninvasively and selectively alter tissue magnetization and create tagged patterns within a deforming body such as the heart muscle. The resulting patterns define a time-varying curvilinear coordinate system on the tissue, which we track with coupled B-snake grids. B-spline bases provide local control of shape, compact representation, and parametric continuity. Efficient spline warps are proposed which warp an area in the plane such that two embedded snake grids obtained from two tagged frames are brought into registration, interpolating a dense displacement vector field. The reconstructed vector field adheres to the known displacement information at the intersections, forces corresponding snakes to be warped into one another, and for all other points in the plane, where no information is available, a C1 continuous vector field is interpolated. The implementation proposed in this paper improves on our previous variational-based implementation and generalizes warp methods to include biologically relevant contiguous open curves, in addition to standard landmark points. The methods are validated with a cardiac motion simulator, in addition to in-vivo tagging data sets.

Computation of the intensities of parametric holographic scattering patterns in photorefractive crystals.

PubMed

Schwalenberg, Simon

2005-06-01

The present work represents a first attempt to perform computations of output intensity distributions for different parametric holographic scattering patterns. Based on the model for parametric four-wave mixing processes in photorefractive crystals and taking into account realistic material properties, we present computed images of selected scattering patterns. We compare these calculated light distributions to the corresponding experimental observations. Our analysis is especially devoted to dark scattering patterns as they make high demands on the underlying model.

Digital double random amplitude image encryption method based on the symmetry property of the parametric discrete Fourier transform

NASA Astrophysics Data System (ADS)

Bekkouche, Toufik; Bouguezel, Saad

2018-03-01

We propose a real-to-real image encryption method. It is a double random amplitude encryption method based on the parametric discrete Fourier transform coupled with chaotic maps to perform the scrambling. The main idea behind this method is the introduction of a complex-to-real conversion by exploiting the inherent symmetry property of the transform in the case of real-valued sequences. This conversion allows the encrypted image to be real-valued instead of being a complex-valued image as in all existing double random phase encryption methods. The advantage is to store or transmit only one image instead of two images (real and imaginary parts). Computer simulation results and comparisons with the existing double random amplitude encryption methods are provided for peak signal-to-noise ratio, correlation coefficient, histogram analysis, and key sensitivity.

Correlation of morphological and molecular parameters for colon cancer

NASA Astrophysics Data System (ADS)

Yuan, Shuai; Roney, Celeste A.; Li, Qian; Jiang, James; Cable, Alex; Summers, Ronald M.; Chen, Yu

2010-02-01

Colorectal cancer (CRC) is the second leading cause of cancer death in the United States. There is great interest in studying the relationship among microstructures and molecular processes of colorectal cancer during its progression at early stages. In this study, we use our multi-modality optical system that could obtain co-registered optical coherence tomography (OCT) and fluorescence molecular imaging (FMI) images simultaneously to study CRC. The overexpressed carbohydrate α-L-fucose on the surfaces of polyps facilitates the bond of adenomatous polyps with UEA-1 and is used as biomarker. Tissue scattering coefficient derived from OCT axial scan is used as quantitative value of structural information. Both structural images from OCT and molecular images show spatial heterogeneity of tumors. Correlations between those values are analyzed and demonstrate that scattering coefficients are positively correlated with FMI signals in conjugated. In UEA-1 conjugated samples (8 polyps and 8 control regions), the correlation coefficient is ranged from 0.45 to 0.99. These findings indicate that the microstructure of polyps is changed gradually during cancer progression and the change is well correlated with certain molecular process. Our study demonstrated that multi-parametric imaging is able to simultaneously detect morphology and molecular information and it can enable spatially and temporally correlated studies of structure-function relationships during tumor progression.

A discriminative model-constrained EM approach to 3D MRI brain tissue classification and intensity non-uniformity correction

NASA Astrophysics Data System (ADS)

Wels, Michael; Zheng, Yefeng; Huber, Martin; Hornegger, Joachim; Comaniciu, Dorin

2011-06-01

We describe a fully automated method for tissue classification, which is the segmentation into cerebral gray matter (GM), cerebral white matter (WM), and cerebral spinal fluid (CSF), and intensity non-uniformity (INU) correction in brain magnetic resonance imaging (MRI) volumes. It combines supervised MRI modality-specific discriminative modeling and unsupervised statistical expectation maximization (EM) segmentation into an integrated Bayesian framework. While both the parametric observation models and the non-parametrically modeled INUs are estimated via EM during segmentation itself, a Markov random field (MRF) prior model regularizes segmentation and parameter estimation. Firstly, the regularization takes into account knowledge about spatial and appearance-related homogeneity of segments in terms of pairwise clique potentials of adjacent voxels. Secondly and more importantly, patient-specific knowledge about the global spatial distribution of brain tissue is incorporated into the segmentation process via unary clique potentials. They are based on a strong discriminative model provided by a probabilistic boosting tree (PBT) for classifying image voxels. It relies on the surrounding context and alignment-based features derived from a probabilistic anatomical atlas. The context considered is encoded by 3D Haar-like features of reduced INU sensitivity. Alignment is carried out fully automatically by means of an affine registration algorithm minimizing cross-correlation. Both types of features do not immediately use the observed intensities provided by the MRI modality but instead rely on specifically transformed features, which are less sensitive to MRI artifacts. Detailed quantitative evaluations on standard phantom scans and standard real-world data show the accuracy and robustness of the proposed method. They also demonstrate relative superiority in comparison to other state-of-the-art approaches to this kind of computational task: our method achieves average Dice coefficients of 0.93 ± 0.03 (WM) and 0.90 ± 0.05 (GM) on simulated mono-spectral and 0.94 ± 0.02 (WM) and 0.92 ± 0.04 (GM) on simulated multi-spectral data from the BrainWeb repository. The scores are 0.81 ± 0.09 (WM) and 0.82 ± 0.06 (GM) and 0.87 ± 0.05 (WM) and 0.83 ± 0.12 (GM) for the two collections of real-world data sets—consisting of 20 and 18 volumes, respectively—provided by the Internet Brain Segmentation Repository.

A discriminative model-constrained EM approach to 3D MRI brain tissue classification and intensity non-uniformity correction.

PubMed

Wels, Michael; Zheng, Yefeng; Huber, Martin; Hornegger, Joachim; Comaniciu, Dorin

2011-06-07

We describe a fully automated method for tissue classification, which is the segmentation into cerebral gray matter (GM), cerebral white matter (WM), and cerebral spinal fluid (CSF), and intensity non-uniformity (INU) correction in brain magnetic resonance imaging (MRI) volumes. It combines supervised MRI modality-specific discriminative modeling and unsupervised statistical expectation maximization (EM) segmentation into an integrated Bayesian framework. While both the parametric observation models and the non-parametrically modeled INUs are estimated via EM during segmentation itself, a Markov random field (MRF) prior model regularizes segmentation and parameter estimation. Firstly, the regularization takes into account knowledge about spatial and appearance-related homogeneity of segments in terms of pairwise clique potentials of adjacent voxels. Secondly and more importantly, patient-specific knowledge about the global spatial distribution of brain tissue is incorporated into the segmentation process via unary clique potentials. They are based on a strong discriminative model provided by a probabilistic boosting tree (PBT) for classifying image voxels. It relies on the surrounding context and alignment-based features derived from a probabilistic anatomical atlas. The context considered is encoded by 3D Haar-like features of reduced INU sensitivity. Alignment is carried out fully automatically by means of an affine registration algorithm minimizing cross-correlation. Both types of features do not immediately use the observed intensities provided by the MRI modality but instead rely on specifically transformed features, which are less sensitive to MRI artifacts. Detailed quantitative evaluations on standard phantom scans and standard real-world data show the accuracy and robustness of the proposed method. They also demonstrate relative superiority in comparison to other state-of-the-art approaches to this kind of computational task: our method achieves average Dice coefficients of 0.93 ± 0.03 (WM) and 0.90 ± 0.05 (GM) on simulated mono-spectral and 0.94 ± 0.02 (WM) and 0.92 ± 0.04 (GM) on simulated multi-spectral data from the BrainWeb repository. The scores are 0.81 ± 0.09 (WM) and 0.82 ± 0.06 (GM) and 0.87 ± 0.05 (WM) and 0.83 ± 0.12 (GM) for the two collections of real-world data sets-consisting of 20 and 18 volumes, respectively-provided by the Internet Brain Segmentation Repository.

Tracking the mammary architectural features and detecting breast cancer with magnetic resonance diffusion tensor imaging.

PubMed

Nissan, Noam; Furman-Haran, Edna; Feinberg-Shapiro, Myra; Grobgeld, Dov; Eyal, Erez; Zehavi, Tania; Degani, Hadassa

2014-12-15

Breast cancer is the most common cause of cancer among women worldwide. Early detection of breast cancer has a critical role in improving the quality of life and survival of breast cancer patients. In this paper a new approach for the detection of breast cancer is described, based on tracking the mammary architectural elements using diffusion tensor imaging (DTI). The paper focuses on the scanning protocols and image processing algorithms and software that were designed to fit the diffusion properties of the mammary fibroglandular tissue and its changes during malignant transformation. The final output yields pixel by pixel vector maps that track the architecture of the entire mammary ductal glandular trees and parametric maps of the diffusion tensor coefficients and anisotropy indices. The efficiency of the method to detect breast cancer was tested by scanning women volunteers including 68 patients with breast cancer confirmed by histopathology findings. Regions with cancer cells exhibited a marked reduction in the diffusion coefficients and in the maximal anisotropy index as compared to the normal breast tissue, providing an intrinsic contrast for delineating the boundaries of malignant growth. Overall, the sensitivity of the DTI parameters to detect breast cancer was found to be high, particularly in dense breasts, and comparable to the current standard breast MRI method that requires injection of a contrast agent. Thus, this method offers a completely non-invasive, safe and sensitive tool for breast cancer detection.

Anatomical parameterization for volumetric meshing of the liver

NASA Astrophysics Data System (ADS)

Vera, Sergio; González Ballester, Miguel A.; Gil, Debora

2014-03-01

A coordinate system describing the interior of organs is a powerful tool for a systematic localization of injured tissue. If the same coordinate values are assigned to specific anatomical landmarks, the coordinate system allows integration of data across different medical image modalities. Harmonic mappings have been used to produce parametric coordinate systems over the surface of anatomical shapes, given their flexibility to set values at specific locations through boundary conditions. However, most of the existing implementations in medical imaging restrict to either anatomical surfaces, or the depth coordinate with boundary conditions is given at sites of limited geometric diversity. In this paper we present a method for anatomical volumetric parameterization that extends current harmonic parameterizations to the interior anatomy using information provided by the volume medial surface. We have applied the methodology to define a common reference system for the liver shape and functional anatomy. This reference system sets a solid base for creating anatomical models of the patient's liver, and allows comparing livers from several patients in a common framework of reference.

In Situ Optical Mapping of Voltage and Calcium in the Heart

PubMed Central

Ewart, Paul; Ashley, Euan A.; Loew, Leslie M.; Kohl, Peter; Bollensdorff, Christian; Woods, Christopher E.

2012-01-01

Electroanatomic mapping the interrelation of intracardiac electrical activation with anatomic locations has become an important tool for clinical assessment of complex arrhythmias. Optical mapping of cardiac electrophysiology combines high spatiotemporal resolution of anatomy and physiological function with fast and simultaneous data acquisition. If applied to the clinical setting, this could improve both diagnostic potential and therapeutic efficacy of clinical arrhythmia interventions. The aim of this study was to explore this utility in vivo using a rat model. To this aim, we present a single-camera imaging and multiple light-emitting-diode illumination system that reduces economic and technical implementation hurdles to cardiac optical mapping. Combined with a red-shifted calcium dye and a new near-infrared voltage-sensitive dye, both suitable for use in blood-perfused tissue, we demonstrate the feasibility of in vivo multi-parametric imaging of the mammalian heart. Our approach combines recording of electrophysiologically-relevant parameters with observation of structural substrates and is adaptable, in principle, to trans-catheter percutaneous approaches. PMID:22876327

Determination of in vivo mechanical properties of long bones from their impedance response curves

NASA Technical Reports Server (NTRS)

Borders, S. G.

1981-01-01

A mathematical model consisting of a uniform, linear, visco-elastic, Euler-Bernoulli beam to represent the ulna or tibia of the vibrating forearm or leg system is developed. The skin and tissue compressed between the probe and bone is represented by a spring in series with the beam. The remaining skin and tissue surrounding the bone is represented by a visco-elastic foundation with mass. An extensive parametric study is carried out to determine the effect of each parameter of the mathematical model on its impedance response. A system identification algorithm is developed and programmed on a digital computer to determine the parametric values of the model which best simulate the data obtained from an impedance test.

Dissecting hemisphere-specific contributions to visual spatial imagery using parametric brain mapping.

PubMed

Bien, Nina; Sack, Alexander T

2014-07-01

In the current study we aimed to empirically test previously proposed accounts of a division of labour between the left and right posterior parietal cortices during visuospatial mental imagery. The representation of mental images in the brain has been a topic of debate for several decades. Although the posterior parietal cortex is involved bilaterally, previous studies have postulated that hemispheric specialisation might result in a division of labour between the left and right parietal cortices. In the current fMRI study, we used an elaborated version of a behaviourally-controlled spatial imagery paradigm, the mental clock task, which involves mental image generation and a subsequent spatial comparison between two angles. By systematically varying the difference between the two angles that are mentally compared, we induced a symbolic distance effect: smaller differences between the two angles result in higher task difficulty. We employed parametrically weighed brain imaging to reveal brain areas showing a graded activation pattern in accordance with the induced distance effect. The parametric difficulty manipulation influenced behavioural data and brain activation patterns in a similar matter. Moreover, since this difficulty manipulation only starts to play a role from the angle comparison phase onwards, it allows for a top-down dissociation between the initial mental image formation, and the subsequent angle comparison phase of the spatial imagery task. Employing parametrically weighed fMRI analysis enabled us to top-down disentangle brain activation related to mental image formation, and activation reflecting spatial angle comparison. The results provide first empirical evidence for the repeatedly proposed division of labour between the left and right posterior parietal cortices during spatial imagery. Copyright © 2014 Elsevier Inc. All rights reserved.

Least-squares reverse time migration in elastic media

NASA Astrophysics Data System (ADS)

Ren, Zhiming; Liu, Yang; Sen, Mrinal K.

2017-02-01

Elastic reverse time migration (RTM) can yield accurate subsurface information (e.g. PP and PS reflectivity) by imaging the multicomponent seismic data. However, the existing RTM methods are still insufficient to provide satisfactory results because of the finite recording aperture, limited bandwidth and imperfect illumination. Besides, the P- and S-wave separation and the polarity reversal correction are indispensable in conventional elastic RTM. Here, we propose an iterative elastic least-squares RTM (LSRTM) method, in which the imaging accuracy is improved gradually with iteration. We first use the Born approximation to formulate the elastic de-migration operator, and employ the Lagrange multiplier method to derive the adjoint equations and gradients with respect to reflectivity. Then, an efficient inversion workflow (only four forward computations needed in each iteration) is introduced to update the reflectivity. Synthetic and field data examples reveal that the proposed LSRTM method can obtain higher-quality images than the conventional elastic RTM. We also analyse the influence of model parametrizations and misfit functions in elastic LSRTM. We observe that Lamé parameters, velocity and impedance parametrizations have similar and plausible migration results when the structures of different models are correlated. For an uncorrelated subsurface model, velocity and impedance parametrizations produce fewer artefacts caused by parameter crosstalk than the Lamé coefficient parametrization. Correlation- and convolution-type misfit functions are effective when amplitude errors are involved and the source wavelet is unknown, respectively. Finally, we discuss the dependence of elastic LSRTM on migration velocities and its antinoise ability. Imaging results determine that the new elastic LSRTM method performs well as long as the low-frequency components of migration velocities are correct. The quality of images of elastic LSRTM degrades with increasing noise.

Terahertz parametric sources and imaging applications

NASA Astrophysics Data System (ADS)

Yamashita, M.; Ogawa, Y.; Otani, C.; Kawase, K.

2005-12-01

We have studied the generation of terahertz (THz) waves by optical parametric processes based on laser light scattering from the polariton mode of nonlinear crystals. Using parametric oscillation of LiNbO 3 or MgO-doped LiNbO 3 crystal pumped by a nano-second Q-switched Nd:YAG laser, we have realized a widely tunable coherent THz-wave sources with a simple configuration. We report the detailed characteristics of the oscillation and the radiation including tunability, spatial and temporal coherency, uni directivity, and efficiency. A Fourier transform limited THz-wave spectrum narrowing was achieved by introducing the injection seeding method. Further, we have developed a spectroscopic THz imaging system using a TPO, which allows detection and identification of drugs concealed in envelopes, by introducing the component spatial pattern analysis. Several images of the envelope are recorded at different THz frequencies and then processed. The final result is an image that reveals what substances are present in the envelope, in what quantity, and how they are distributed across the envelope area. The example presented here shows the identification of three drugs, two of which illegal, while one is an over-the-counter drug.

Brain segmentation and the generation of cortical surfaces

NASA Technical Reports Server (NTRS)

Joshi, M.; Cui, J.; Doolittle, K.; Joshi, S.; Van Essen, D.; Wang, L.; Miller, M. I.

1999-01-01

This paper describes methods for white matter segmentation in brain images and the generation of cortical surfaces from the segmentations. We have developed a system that allows a user to start with a brain volume, obtained by modalities such as MRI or cryosection, and constructs a complete digital representation of the cortical surface. The methodology consists of three basic components: local parametric modeling and Bayesian segmentation; surface generation and local quadratic coordinate fitting; and surface editing. Segmentations are computed by parametrically fitting known density functions to the histogram of the image using the expectation maximization algorithm [DLR77]. The parametric fits are obtained locally rather than globally over the whole volume to overcome local variations in gray levels. To represent the boundary of the gray and white matter we use triangulated meshes generated using isosurface generation algorithms [GH95]. A complete system of local parametric quadratic charts [JWM+95] is superimposed on the triangulated graph to facilitate smoothing and geodesic curve tracking. Algorithms for surface editing include extraction of the largest closed surface. Results for several macaque brains are presented comparing automated and hand surface generation. Copyright 1999 Academic Press.

Diffusion tensor magnetic resonance imaging of the pancreas.

PubMed

Nissan, Noam; Golan, Talia; Furman-Haran, Edna; Apter, Sara; Inbar, Yael; Ariche, Arie; Bar-Zakay, Barak; Goldes, Yuri; Schvimer, Michael; Grobgeld, Dov; Degani, Hadassa

2014-01-01

To develop a diffusion-tensor-imaging (DTI) protocol that is sensitive to the complex diffusion and perfusion properties of the healthy and malignant pancreas tissues. Twenty-eight healthy volunteers and nine patients with pancreatic-ductal-adenocacinoma (PDAC), were scanned at 3T with T2-weighted and DTI sequences. Healthy volunteers were also scanned with multi-b diffusion-weighted-imaging (DWI), whereas a standard clinical protocol complemented the PDAC patients' scans. Image processing at pixel resolution yielded parametric maps of three directional diffusion coefficients λ1, λ2, λ3, apparent diffusion coefficient (ADC), and fractional anisotropy (FA), as well as a λ1-vector map, and a main diffusion-direction map. DTI measurements of healthy pancreatic tissue at b-values 0,500 s/mm² yielded: λ1 = (2.65±0.35)×10⁻³, λ2 = (1.87±0.22)×10⁻³, λ3 = (1.20±0.18)×10⁻³, ADC = (1.91±0.22)×10⁻³ (all in mm²/s units) and FA = 0.38±0.06. Using b-values of 100,500 s/mm² led to a significant reduction in λ1, λ2, λ3 and ADC (p<.0001) and a significant increase (p<0.0001) in FA. The reduction in the diffusion coefficients suggested a contribution of a fast intra-voxel-incoherent-motion (IVIM) component at b≤100 s/mm², which was confirmed by the multi-b DWI results. In PDACs, λ1, λ2, λ3 and ADC in both 0,500 s/mm² and 100,500 s/mm² b-values sets, as well as the reduction in these diffusion coefficients between the two sets, were significantly lower in comparison to the distal normal pancreatic tissue, suggesting higher cellularity and diminution of the fast-IVIM component in the cancer tissue. DTI using two reference b-values 0 and 100 s/mm² enabled characterization of the water diffusion and anisotropy of the healthy pancreas, taking into account a contribution of IVIM. The reduction in the diffusion coefficients of PDAC, as compared to normal pancreatic tissue, and the smaller change in these coefficients in PDAC when the reference b-value was modified from 0 to 100 s/mm², helped identifying the presence of malignancy.

TU-EF-204-02: Hiigh Quality and Sub-MSv Cerebral CT Perfusion Imaging

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

Li, Ke; Niu, Kai; Wu, Yijing

2015-06-15

Purpose: CT Perfusion (CTP) imaging is of great importance in acute ischemic stroke management due to its potential to detect hypoperfused yet salvageable tissue and distinguish it from definitely unsalvageable tissue. However, current CTP imaging suffers from poor image quality and high radiation dose (up to 5 mSv). The purpose of this work was to demonstrate that technical innovations such as Prior Image Constrained Compressed Sensing (PICCS) have the potential to address these challenges and achieve high quality and sub-mSv CTP imaging. Methods: (1) A spatial-temporal 4D cascaded system model was developed to indentify the bottlenecks in the current CTPmore » technology; (2) A task-based framework was developed to optimize the CTP system parameters; (3) Guided by (1) and (2), PICCS was customized for the reconstruction of CTP source images. Digital anthropomorphic perfusion phantoms, animal studies, and preliminary human subject studies were used to validate and evaluate the potentials of using these innovations to advance the CTP technology. Results: The 4D cascaded model was validated in both phantom and canine stroke models. Based upon this cascaded model, it has been discovered that, as long as the spatial resolution and noise properties of the 4D source CT images are given, the 3D MTF and NPS of the final CTP maps can be analytically derived for a given set of processing methods and parameters. The cascaded model analysis also identified that the most critical technical factor in CTP is how to acquire and reconstruct high quality source images; it has very little to do with the denoising techniques often used after parametric perfusion calculations. This explained why PICCS resulted in a five-fold dose reduction or substantial improvement in image quality. Conclusion: Technical innovations generated promising results towards achieving high quality and sub-mSv CTP imaging for reliable and safe assessment of acute ischemic strokes. K. Li, K. Niu, Y. Wu: Nothing to disclose. G.-H. Chen: Research funded, GE Healthcare; Research funded, Siemens AX.« less

Dynamic PET Image reconstruction for parametric imaging using the HYPR kernel method

NASA Astrophysics Data System (ADS)

Spencer, Benjamin; Qi, Jinyi; Badawi, Ramsey D.; Wang, Guobao

2017-03-01

Dynamic PET image reconstruction is a challenging problem because of the ill-conditioned nature of PET and the lowcounting statistics resulted from short time-frames in dynamic imaging. The kernel method for image reconstruction has been developed to improve image reconstruction of low-count PET data by incorporating prior information derived from high-count composite data. In contrast to most of the existing regularization-based methods, the kernel method embeds image prior information in the forward projection model and does not require an explicit regularization term in the reconstruction formula. Inspired by the existing highly constrained back-projection (HYPR) algorithm for dynamic PET image denoising, we propose in this work a new type of kernel that is simpler to implement and further improves the kernel-based dynamic PET image reconstruction. Our evaluation study using a physical phantom scan with synthetic FDG tracer kinetics has demonstrated that the new HYPR kernel-based reconstruction can achieve a better region-of-interest (ROI) bias versus standard deviation trade-off for dynamic PET parametric imaging than the post-reconstruction HYPR denoising method and the previously used nonlocal-means kernel.

Accuracy and variability of tumor burden measurement on multi-parametric MRI

NASA Astrophysics Data System (ADS)

Salarian, Mehrnoush; Gibson, Eli; Shahedi, Maysam; Gaed, Mena; Gómez, José A.; Moussa, Madeleine; Romagnoli, Cesare; Cool, Derek W.; Bastian-Jordan, Matthew; Chin, Joseph L.; Pautler, Stephen; Bauman, Glenn S.; Ward, Aaron D.

2014-03-01

Measurement of prostate tumour volume can inform prognosis and treatment selection, including an assessment of the suitability and feasibility of focal therapy, which can potentially spare patients the deleterious side effects of radical treatment. Prostate biopsy is the clinical standard for diagnosis but provides limited information regarding tumour volume due to sparse tissue sampling. A non-invasive means for accurate determination of tumour burden could be of clinical value and an important step toward reduction of overtreatment. Multi-parametric magnetic resonance imaging (MPMRI) is showing promise for prostate cancer diagnosis. However, the accuracy and inter-observer variability of prostate tumour volume estimation based on separate expert contouring of T2-weighted (T2W), dynamic contrastenhanced (DCE), and diffusion-weighted (DW) MRI sequences acquired using an endorectal coil at 3T is currently unknown. We investigated this question using a histologic reference standard based on a highly accurate MPMRIhistology image registration and a smooth interpolation of planimetric tumour measurements on histology. Our results showed that prostate tumour volumes estimated based on MPMRI consistently overestimated histological reference tumour volumes. The variability of tumour volume estimates across the different pulse sequences exceeded interobserver variability within any sequence. Tumour volume estimates on DCE MRI provided the lowest inter-observer variability and the highest correlation with histology tumour volumes, whereas the apparent diffusion coefficient (ADC) maps provided the lowest volume estimation error. If validated on a larger data set, the observed correlations could support the development of automated prostate tumour volume segmentation algorithms as well as correction schemes for tumour burden estimation on MPMRI.

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

Ray, Jaideep; Lee, Jina; Lefantzi, Sophia

The estimation of fossil-fuel CO2 emissions (ffCO2) from limited ground-based and satellite measurements of CO2 concentrations will form a key component of the monitoring of treaties aimed at the abatement of greenhouse gas emissions. To that end, we construct a multiresolution spatial parametrization for fossil-fuel CO2 emissions (ffCO2), to be used in atmospheric inversions. Such a parametrization does not currently exist. The parametrization uses wavelets to accurately capture the multiscale, nonstationary nature of ffCO2 emissions and employs proxies of human habitation, e.g., images of lights at night and maps of built-up areas to reduce the dimensionality of the multiresolution parametrization.more » The parametrization is used in a synthetic data inversion to test its suitability for use in atmospheric inverse problem. This linear inverse problem is predicated on observations of ffCO2 concentrations collected at measurement towers. We adapt a convex optimization technique, commonly used in the reconstruction of compressively sensed images, to perform sparse reconstruction of the time-variant ffCO2 emission field. We also borrow concepts from compressive sensing to impose boundary conditions i.e., to limit ffCO2 emissions within an irregularly shaped region (the United States, in our case). We find that the optimization algorithm performs a data-driven sparsification of the spatial parametrization and retains only of those wavelets whose weights could be estimated from the observations. Further, our method for the imposition of boundary conditions leads to a 10computational saving over conventional means of doing so. We conclude with a discussion of the accuracy of the estimated emissions and the suitability of the spatial parametrization for use in inverse problems with a significant degree of regularization.« less

Peri-implant soft tissue colour around titanium and zirconia abutments: a prospective randomized controlled clinical study.

PubMed

Cosgarea, Raluca; Gasparik, Cristina; Dudea, Diana; Culic, Bogdan; Dannewitz, Bettina; Sculean, Anton

2015-05-01

To objectively determine the difference in colour between the peri-implant soft tissue at titanium and zirconia abutments. Eleven patients, each with two contralaterally inserted osteointegrated dental implants, were included in this study. The implants were restored either with titanium abutments and porcelain-fused-to-metal crowns, or with zirconia abutments and ceramic crowns. Prior and after crown cementation, multi-spectral images of the peri-implant soft tissues and the gingiva of the neighbouring teeth were taken with a colorimeter. The colour parameters L*, a*, b*, c* and the colour differences ΔE were calculated. Descriptive statistics, including non-parametric tests and correlation coefficients, were used for statistical analyses of the data. Compared to the gingiva of the neighbouring teeth, the peri-implant soft tissue around titanium and zirconia (test group), showed distinguishable ΔE both before and after crown cementation. Colour differences around titanium were statistically significant different (P = 0.01) only at 1 mm prior to crown cementation compared to zirconia. Compared to the gingiva of the neighbouring teeth, statistically significant (P < 0.01) differences were found for all colour parameter, either before or after crown cementation for both abutments; more significant differences were registered for titanium abutments. Tissue thickness correlated positively with c*-values for titanium at 1 mm and 2 mm from the gingival margin. Within their limits, the present data indicate that: (i) The peri-implant soft tissue around titanium and zirconia showed colour differences when compared to the soft tissue around natural teeth, and (ii) the peri-implant soft tissue around zirconia demonstrated a better colour match to the soft tissue at natural teeth than titanium. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Lesion registration for longitudinal disease tracking in an imaging informatics-based multiple sclerosis eFolder

NASA Astrophysics Data System (ADS)

Ma, Kevin; Liu, Joseph; Zhang, Xuejun; Lerner, Alex; Shiroishi, Mark; Amezcua, Lilyana; Liu, Brent

2016-03-01

We have designed and developed a multiple sclerosis eFolder system for patient data storage, image viewing, and automatic lesion quantification results stored in DICOM-SR format. The web-based system aims to be integrated in DICOM-compliant clinical and research environments to aid clinicians in patient treatments and data analysis. The system needs to quantify lesion volumes, identify and register lesion locations to track shifts in volume and quantity of lesions in a longitudinal study. In order to perform lesion registration, we have developed a brain warping and normalizing methodology using Statistical Parametric Mapping (SPM) MATLAB toolkit for brain MRI. Patients' brain MR images are processed via SPM's normalization processes, and the brain images are analyzed and warped according to the tissue probability map. Lesion identification and contouring are completed by neuroradiologists, and lesion volume quantification is completed by the eFolder's CAD program. Lesion comparison results in longitudinal studies show key growth and active regions. The results display successful lesion registration and tracking over a longitudinal study. Lesion change results are graphically represented in the web-based user interface, and users are able to correlate patient progress and changes in the MRI images. The completed lesion and disease tracking tool would enable the eFolder to provide complete patient profiles, improve the efficiency of patient care, and perform comprehensive data analysis through an integrated imaging informatics system.

Analysis of Parametric Adaptive Signal Detection with Applications to Radars and Hyperspectral Imaging

DTIC Science & Technology

2010-02-01

98 8.4.5 Training Screening ............................. .................................................................99 8.5 Experimental...associated with the proposed parametric model. Several im- portant issues are discussed, including model order selection, training screening , and time...parameters associated with the NS-AR model. In addition, we develop model order selection, training screening , and time-series based whitening and

Dynamic Contrast-Enhanced Magnetic Resonance Imaging of the Metastatic Potential of Melanoma Xenografts

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

Ovrebo, Kirsti Marie; Ellingsen, Christine; Galappathi, Kanthi

2012-05-01

Purpose: Gadolinium diethylene-triamine penta-acetic acid (Gd-DTPA)-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been suggested as a useful noninvasive method for characterizing the physiologic microenvironment of tumors. In the present study, we investigated whether Gd-DTPA-based DCE-MRI has the potential to provide biomarkers for hypoxia-associated metastatic dissemination. Methods and Materials: C-10 and D-12 melanoma xenografts were used as experimental tumor models. Pimonidazole was used as a hypoxia marker. A total of 60 tumors were imaged, and parametric images of K{sup trans} (volume transfer constant of Gd-DTPA) and v{sub e} (fractional distribution volume of Gd-DTPA) were produced by pharmacokinetic analysis of themore » DCE-MRI series. The host mice were killed immediately after DCE-MRI, and the primary tumor and the lungs were resected and prepared for histologic assessment of the fraction of pimonidazole-positive hypoxic tissue and the presence of lung metastases, respectively. Results: Metastases were found in 11 of 26 mice with C-10 tumors and 14 of 34 mice with D-12 tumors. The primary tumors of the metastatic-positive mice had a greater fraction of hypoxic tissue (p = 0.00031, C-10; p < 0.00001, D-12), a lower median K{sup trans} (p = 0.0011, C-10; p < 0.00001, D-12), and a lower median v{sub e} (p = 0.014, C-10; p = 0.016, D-12) than the primary tumors of the metastatic-negative mice. Conclusions: These findings support the clinical attempts to establish DCE-MRI as a method for providing biomarkers for tumor aggressiveness and suggests that primary tumors characterized by low K{sup trans} and low v{sub e} values could have a high probability of hypoxia-associated metastatic spread.« less

Extracting 3D Parametric Curves from 2D Images of Helical Objects.

PubMed

Willcocks, Chris G; Jackson, Philip T G; Nelson, Carl J; Obara, Boguslaw

2017-09-01

Helical objects occur in medicine, biology, cosmetics, nanotechnology, and engineering. Extracting a 3D parametric curve from a 2D image of a helical object has many practical applications, in particular being able to extract metrics such as tortuosity, frequency, and pitch. We present a method that is able to straighten the image object and derive a robust 3D helical curve from peaks in the object boundary. The algorithm has a small number of stable parameters that require little tuning, and the curve is validated against both synthetic and real-world data. The results show that the extracted 3D curve comes within close Hausdorff distance to the ground truth, and has near identical tortuosity for helical objects with a circular profile. Parameter insensitivity and robustness against high levels of image noise are demonstrated thoroughly and quantitatively.

Stimulated parametric emission microscopy.

PubMed

Isobe, Keisuke; Kataoka, Shogo; Murase, Rena; Watanabe, Wataru; Higashi, Tsunehito; Kawakami, Shigeki; Matsunaga, Sachihiro; Fukui, Kiichi; Itoh, Kazuyoshi

2006-01-23

We propose a novel microscopy technique based on the four-wave mixing (FWM) process that is enhanced by two-photon electronic resonance induced by a pump pulse along with stimulated emission induced by a dump pulse. A Ti:sapphire laser and an optical parametric oscillator are used as light sources for the pump and dump pulses, respectively. We demonstrate that our proposed FWM technique can be used to obtain a one-dimensional image of ethanol-thinned Coumarin 120 solution sandwiched between a hole-slide glass and a cover slip, and a two-dimensional image of a leaf of Camellia sinensis.

Simultaneous single-shot readout of multi-qubit circuits using a traveling-wave parametric amplifier

NASA Astrophysics Data System (ADS)

O'Brien, Kevin

Observing and controlling the state of ever larger quantum systems is critical for advancing quantum computation. Utilizing a Josephson traveling wave parametric amplifier (JTWPA), we demonstrate simultaneous multiplexed single shot readout of 10 transmon qubits in a planar architecture. We employ digital image sideband rejection to eliminate noise at the image frequencies. We quantify crosstalk and infidelity due to simultaneous readout and control of multiple qubits. Based on current amplifier technology, this approach can scale to simultaneous readout of at least 20 qubits. This work was supported by the Army Research Office.

Scanning ion conductance microscopy: a convergent high-resolution technology for multi-parametric analysis of living cardiovascular cells

PubMed Central

Miragoli, Michele; Moshkov, Alexey; Novak, Pavel; Shevchuk, Andrew; Nikolaev, Viacheslav O.; El-Hamamsy, Ismail; Potter, Claire M. F.; Wright, Peter; Kadir, S.H. Sheikh Abdul; Lyon, Alexander R.; Mitchell, Jane A.; Chester, Adrian H.; Klenerman, David; Lab, Max J.; Korchev, Yuri E.; Harding, Sian E.; Gorelik, Julia

2011-01-01

Cardiovascular diseases are complex pathologies that include alterations of various cell functions at the levels of intact tissue, single cells and subcellular signalling compartments. Conventional techniques to study these processes are extremely divergent and rely on a combination of individual methods, which usually provide spatially and temporally limited information on single parameters of interest. This review describes scanning ion conductance microscopy (SICM) as a novel versatile technique capable of simultaneously reporting various structural and functional parameters at nanometre resolution in living cardiovascular cells at the level of the whole tissue, single cells and at the subcellular level, to investigate the mechanisms of cardiovascular disease. SICM is a multimodal imaging technology that allows concurrent and dynamic analysis of membrane morphology and various functional parameters (cell volume, membrane potentials, cellular contraction, single ion-channel currents and some parameters of intracellular signalling) in intact living cardiovascular cells and tissues with nanometre resolution at different levels of organization (tissue, cellular and subcellular levels). Using this technique, we showed that at the tissue level, cell orientation in the inner and outer aortic arch distinguishes atheroprone and atheroprotected regions. At the cellular level, heart failure leads to a pronounced loss of T-tubules in cardiac myocytes accompanied by a reduction in Z-groove ratio. We also demonstrated the capability of SICM to measure the entire cell volume as an index of cellular hypertrophy. This method can be further combined with fluorescence to simultaneously measure cardiomyocyte contraction and intracellular calcium transients or to map subcellular localization of membrane receptors coupled to cyclic adenosine monophosphate production. The SICM pipette can be used for patch-clamp recordings of membrane potential and single channel currents. In conclusion, SICM provides a highly informative multimodal imaging platform for functional analysis of the mechanisms of cardiovascular diseases, which should facilitate identification of novel therapeutic strategies. PMID:21325316

Efficient use of mobile devices for quantification of pressure injury images.

PubMed

Garcia-Zapirain, Begonya; Sierra-Sosa, Daniel; Ortiz, David; Isaza-Monsalve, Mariano; Elmaghraby, Adel

2018-01-01

Pressure Injuries are chronic wounds that are formed due to the constriction of the soft tissues against bone prominences. In order to assess these injuries, the medical personnel carry out the evaluation and diagnosis using visual methods and manual measurements, which can be inaccurate and may generate discomfort in the patients. By using segmentation techniques, the Pressure Injuries can be extracted from an image and accurately parameterized, leading to a correct diagnosis. In general, these techniques are based on the solution of differential equations and the involved numerical methods are demanding in terms of computational resources. In previous work, we proposed a technique developed using toroidal parametric equations for image decomposition and segmentation without solving differential equations. In this paper, we present the development of a mobile application useful for the non-contact assessment of Pressure Injuries based on the toroidal decomposition from images. The usage of this technique allows us to achieve an accurate segmentation almost 8 times faster than Active Contours without Edges (ACWE) and Dynamic Contours methods. We describe the techniques and the implementation for Android devices using Python and Kivy. This application allows for the segmentation and parameterization of injuries, obtain relevant information for the diagnosis and tracking the evolution of patient's injuries.

Time-efficient high-resolution whole-brain three-dimensional macromolecular proton fraction mapping

PubMed Central

Yarnykh, Vasily L.

2015-01-01

Purpose Macromolecular proton fraction (MPF) mapping is a quantitative MRI method that reconstructs parametric maps of a relative amount of macromolecular protons causing the magnetization transfer (MT) effect and provides a biomarker of myelination in neural tissues. This study aimed to develop a high-resolution whole-brain MPF mapping technique utilizing a minimal possible number of source images for scan time reduction. Methods The described technique is based on replacement of an actually acquired reference image without MT saturation by a synthetic one reconstructed from R1 and proton density maps, thus requiring only three source images. This approach enabled whole-brain three-dimensional MPF mapping with isotropic 1.25×1.25×1.25 mm3 voxel size and scan time of 20 minutes. The synthetic reference method was validated against standard MPF mapping with acquired reference images based on data from 8 healthy subjects. Results Mean MPF values in segmented white and gray matter appeared in close agreement with no significant bias and small within-subject coefficients of variation (<2%). High-resolution MPF maps demonstrated sharp white-gray matter contrast and clear visualization of anatomical details including gray matter structures with high iron content. Conclusions Synthetic reference method improves resolution of MPF mapping and combines accurate MPF measurements with unique neuroanatomical contrast features. PMID:26102097

Modeling envelope statistics of blood and myocardium for segmentation of echocardiographic images.

PubMed

Nillesen, Maartje M; Lopata, Richard G P; Gerrits, Inge H; Kapusta, Livia; Thijssen, Johan M; de Korte, Chris L

2008-04-01

The objective of this study was to investigate the use of speckle statistics as a preprocessing step for segmentation of the myocardium in echocardiographic images. Three-dimensional (3D) and biplane image sequences of the left ventricle of two healthy children and one dog (beagle) were acquired. Pixel-based speckle statistics of manually segmented blood and myocardial regions were investigated by fitting various probability density functions (pdf). The statistics of heart muscle and blood could both be optimally modeled by a K-pdf or Gamma-pdf (Kolmogorov-Smirnov goodness-of-fit test). Scale and shape parameters of both distributions could differentiate between blood and myocardium. Local estimation of these parameters was used to obtain parametric images, where window size was related to speckle size (5 x 2 speckles). Moment-based and maximum-likelihood estimators were used. Scale parameters were still able to differentiate blood from myocardium; however, smoothing of edges of anatomical structures occurred. Estimation of the shape parameter required a larger window size, leading to unacceptable blurring. Using these parameters as an input for segmentation resulted in unreliable segmentation. Adaptive mean squares filtering was then introduced using the moment-based scale parameter (sigma(2)/mu) of the Gamma-pdf to automatically steer the two-dimensional (2D) local filtering process. This method adequately preserved sharpness of the edges. In conclusion, a trade-off between preservation of sharpness of edges and goodness-of-fit when estimating local shape and scale parameters is evident for parametric images. For this reason, adaptive filtering outperforms parametric imaging for the segmentation of echocardiographic images.

Quantification of Dynamic [18F]FDG Pet Studies in Acute Lung Injury.

PubMed

Grecchi, Elisabetta; Veronese, Mattia; Moresco, Rosa Maria; Bellani, Giacomo; Pesenti, Antonio; Messa, Cristina; Bertoldo, Alessandra

2016-02-01

This work aims to investigate lung glucose metabolism using 2-deoxy-2-[(18)F]fluoro-D-glucose ([(18)F]FDG) positron emission tomography (PET) imaging in acute lung injury (ALI) patients. Eleven ALI patients and five healthy controls underwent a dynamic [(18)F]FDG PET/X-ray computed tomography (CT) scan. The standardized uptake values (SUV) and three different methods for the quantification of glucose metabolism (i.e., ratio, Patlak, and spectral analysis iterative filter, SAIF) were applied both at the region and the voxel levels. SUV reported a lower correlation than the ratio with the net tracer uptake. Patlak and SAIF analyses did not show any significant spatial or quantitative (R(2) > 0.80) difference. The additional information provided by SAIF showed that in lung inflammation, elevated tracer uptake is coupled with abnormal tracer exchanges within and between lung tissue compartments. Full kinetic modeling provides a multi-parametric description of glucose metabolism in the lungs. This allows characterizing the spatial distribution of lung inflammation as well as returning the functional state of the tissues.

Multi-color autofluorescence and scattering spectroscopy provides rapid assessment of kidney function following ischemic injury

NASA Astrophysics Data System (ADS)

Raman, Rajesh N.; Pivetti, Chris D.; Ramsamooj, Rajendra; Troppmann, Christoph; Demos, Stavros G.

2018-02-01

A major source of kidneys for transplant comes from deceased donors whose tissues have suffered an unknown amount of warm ischemia prior to retrieval, with no quantitative means to assess function before transplant. Toward addressing this need, non-contact monitoring of optical signatures in rat kidneys was performed in vivo during ischemia and reperfusion. Kidney autofluorescence images were captured under ultraviolet illumination (355 nm, 325 nm, and 266 nm) in order to provide information on related metabolic and non-metabolic response. In addition, light scattering images under 355 nm, 325 nm, and 266 nm, 500 nm illumination were monitored to report on changes in kidney optical properties giving rise to the observed autofluorescence signals during these processes. During reperfusion, various signal ratios were generated from the recorded signals and then parametrized. Time-dependent parameters derived from the ratio of autofluorescence under 355 nm excitation to that under 266 nm excitation, as well as from 500 nm scattered signal, were found capable of discriminating dysfunctional kidneys from those that were functional (p < 0.01) within hours of reperfusion. Kidney dysfunction was confirmed by subsequent survival study and histology following autopsy up to a week later. Physiologic changes potentially giving rise to the observed signals, including those in cellular metabolism, vascular response, tissue microstructure, and microenvironment chemistry, are discussed.

Performance assessment of automated tissue characterization for prostate H and E stained histopathology

NASA Astrophysics Data System (ADS)

DiFranco, Matthew D.; Reynolds, Hayley M.; Mitchell, Catherine; Williams, Scott; Allan, Prue; Haworth, Annette

2015-03-01

Reliable automated prostate tumor detection and characterization in whole-mount histology images is sought in many applications, including post-resection tumor staging and as ground-truth data for multi-parametric MRI interpretation. In this study, an ensemble-based supervised classification algorithm for high-resolution histology images was trained on tile-based image features including histogram and gray-level co-occurrence statistics. The algorithm was assessed using different combinations of H and E prostate slides from two separate medical centers and at two different magnifications (400x and 200x), with the aim of applying tumor classification models to new data. Slides from both datasets were annotated by expert pathologists in order to identify homogeneous cancerous and non-cancerous tissue regions of interest, which were then categorized as (1) low-grade tumor (LG-PCa), including Gleason 3 and high-grade prostatic intraepithelial neoplasia (HG-PIN), (2) high-grade tumor (HG-PCa), including various Gleason 4 and 5 patterns, or (3) non-cancerous, including benign stroma and benign prostatic hyperplasia (BPH). Classification models for both LG-PCa and HG-PCa were separately trained using a support vector machine (SVM) approach, and per-tile tumor prediction maps were generated from the resulting ensembles. Results showed high sensitivity for predicting HG-PCa with an AUC up to 0.822 using training data from both medical centres, while LG-PCa showed a lower sensitivity of 0.763 with the same training data. Visual inspection of cancer probability heatmaps from 9 patients showed that 17/19 tumors were detected, and HG-PCa generally reported less false positives than LG-PCa.

Microvascular flow estimation by contrast-assisted ultrasound B-scan and statistical parametric images.

PubMed

Tsui, Po-Hsiang; Yeh, Chih-Kuang; Chang, Chien-Cheng

2009-05-01

The microbubbles destruction/replenishment technique has been previously applied to estimating blood flow in the microcirculation. The rate of increase of the time-intensity curve (TIC) due to microbubbles flowing into the region of interest (ROI), as measured from B-mode images, closely reflects the flow velocity. In previous studies, we proposed a new approach called the time-Nakagami-parameter curve (TNC) obtained from Nakagami images to monitor microbubble replenishment for quantifying the microvascular flow velocity. This study aimed to further explore some effects that may affect the TNC to estimate the microflow, including microbubble concentration, ultrasound transmitting energy, attenuation, intrinsic noise, and tissue clutter. In order to well control each effect production, we applied a typical simulation method to investigate the TIC and TNC. The rates of increase of the TIC and TNC were expressed by the rate constants beta(I) and beta(N), respectively, of a monoexponential model. The results show that beta(N) quantifies the microvascular flow velocity similarly to the conventional beta(I) . Moreover, the measures of beta(I) and beta(N) are not influenced by microbubble concentration, transducer excitation energy, and attenuation effect. Although the effect of intrinsic signals contributed by noise and blood would influence the TNC behavior, the TNC method has a better tolerance of tissue clutter than the TIC does, allowing the presence of some clutter components in the ROI. The results suggest that the TNC method can be used as a complementary tool for the conventional TIC to reduce the wall filter requirements for blood flow measurement in the microcirculation.

Quantitative evaluation of microvascular blood flow by contrast-enhanced ultrasound (CEUS).

PubMed

Greis, Christian

2011-01-01

Ultrasound contrast agents consist of tiny gas-filled microbubbles the size of red blood cells. Due to their size distribution, they are purely intravascular tracers which do not extravasate into the interstitial fluid, and thus they are perfect agents for imaging blood distribution and flow. Using ultrasound scanners with contrast-specific software, the specific microbubble-derived echo signals can be separated from tissue signals in realtime, allowing selective imaging of the contrast agent. The signal intensity obtained lies in a linear relationship to the amount of microbubbles in the target organ, which allows easy and reliable assessment of relative blood volume. Imaging of the contrast wash-in and wash-out after bolus injection, or more precisely using the flash-replenishment technique, allows assessment of regional blood flow velocity. Commercially available quantification software packages can calculate time-related intensity values from the contrast wash-in and wash-out phase for each image pixel from stored video clips. After fitting of a mathematical model curve according to the respective kinetic model (bolus or flash-replenishment kinetics), time/intensity curves (TIC) can be calculated from single pixels or user-defined regions of interest (ROI). Characteristic parameters of these TICs (e.g. peak intensity, area under the curve, wash-in rate, etc.) can be displayed as color-coded parametric maps on top of the anatomical image, to identify cold and hot spots with abnormal perfusion.

White matter changes after stroke in type 2 diabetic rats measured by diffusion magnetic resonance imaging.

PubMed

Ding, Guangliang; Chen, Jieli; Chopp, Michael; Li, Lian; Yan, Tao; Davoodi-Bojd, Esmaeil; Li, Qingjiang; Davarani, Siamak Pn; Jiang, Quan

2017-01-01

Diffusion-related magnetic resonance imaging parametric maps may be employed to characterize white matter of brain. We hypothesize that entropy of diffusion anisotropy may be most effective for detecting therapeutic effects of bone marrow stromal cell treatment of ischemia in type 2 diabetes mellitus rats. Type 2 diabetes mellitus was induced in adult male Wistar rats. These rats were then subjected to 2 h of middle cerebral artery occlusion, and received bone marrow stromal cell (5 × 10 6 , n = 8) or an equal volume of saline (n = 8) via tail vein injection at three days after middle cerebral artery occlusion. Magnetic resonance imaging was performed on day one and then weekly for five weeks post middle cerebral artery occlusion. The diffusion metrics complementarily permitted characterization of axons and axonal myelination. All six magnetic resonance imaging diffusion metrics, confirmed by histological measures, demonstrated that bone marrow stromal cell treatment significantly (p < 0.05) improved magnetic resonance imaging diffusion indices of white matter in type 2 diabetes mellitus rats after middle cerebral artery occlusion compared with the saline-treated rats. Superior to the fractional anisotropy metric that provided measures related to organization of neuronal fiber bundles, the entropy metric can also identify microstructures and low-density axonal fibers of cerebral tissue after stroke in type 2 diabetes mellitus rats. © The Author(s) 2015.

White matter changes after stroke in type 2 diabetic rats measured by diffusion magnetic resonance imaging

PubMed Central

Ding, Guangliang; Chen, Jieli; Chopp, Michael; Li, Lian; Yan, Tao; Davoodi-Bojd, Esmaeil; Li, Qingjiang; Davarani, Siamak PN

2015-01-01

Diffusion-related magnetic resonance imaging parametric maps may be employed to characterize white matter of brain. We hypothesize that entropy of diffusion anisotropy may be most effective for detecting therapeutic effects of bone marrow stromal cell treatment of ischemia in type 2 diabetes mellitus rats. Type 2 diabetes mellitus was induced in adult male Wistar rats. These rats were then subjected to 2 h of middle cerebral artery occlusion, and received bone marrow stromal cell (5 × 106, n = 8) or an equal volume of saline (n = 8) via tail vein injection at three days after middle cerebral artery occlusion. Magnetic resonance imaging was performed on day one and then weekly for five weeks post middle cerebral artery occlusion. The diffusion metrics complementarily permitted characterization of axons and axonal myelination. All six magnetic resonance imaging diffusion metrics, confirmed by histological measures, demonstrated that bone marrow stromal cell treatment significantly (p < 0.05) improved magnetic resonance imaging diffusion indices of white matter in type 2 diabetes mellitus rats after middle cerebral artery occlusion compared with the saline-treated rats. Superior to the fractional anisotropy metric that provided measures related to organization of neuronal fiber bundles, the entropy metric can also identify microstructures and low-density axonal fibers of cerebral tissue after stroke in type 2 diabetes mellitus rats. PMID:26685128

PSFGAN: a generative adversarial network system for separating quasar point sources and host galaxy light

NASA Astrophysics Data System (ADS)

Stark, Dominic; Launet, Barthelemy; Schawinski, Kevin; Zhang, Ce; Koss, Michael; Turp, M. Dennis; Sartori, Lia F.; Zhang, Hantian; Chen, Yiru; Weigel, Anna K.

2018-06-01

The study of unobscured active galactic nuclei (AGN) and quasars depends on the reliable decomposition of the light from the AGN point source and the extended host galaxy light. The problem is typically approached using parametric fitting routines using separate models for the host galaxy and the point spread function (PSF). We present a new approach using a Generative Adversarial Network (GAN) trained on galaxy images. We test the method using Sloan Digital Sky Survey r-band images with artificial AGN point sources added that are then removed using the GAN and with parametric methods using GALFIT. When the AGN point source is more than twice as bright as the host galaxy, we find that our method, PSFGAN, can recover point source and host galaxy magnitudes with smaller systematic error and a lower average scatter (49 per cent). PSFGAN is more tolerant to poor knowledge of the PSF than parametric methods. Our tests show that PSFGAN is robust against a broadening in the PSF width of ± 50 per cent if it is trained on multiple PSFs. We demonstrate that while a matched training set does improve performance, we can still subtract point sources using a PSFGAN trained on non-astronomical images. While initial training is computationally expensive, evaluating PSFGAN on data is more than 40 times faster than GALFIT fitting two components. Finally, PSFGAN is more robust and easy to use than parametric methods as it requires no input parameters.

Computational design and engineering of polymeric orthodontic aligners.

PubMed

Barone, S; Paoli, A; Razionale, A V; Savignano, R

2016-10-05

Transparent and removable aligners represent an effective solution to correct various orthodontic malocclusions through minimally invasive procedures. An aligner-based treatment requires patients to sequentially wear dentition-mating shells obtained by thermoforming polymeric disks on reference dental models. An aligner is shaped introducing a geometrical mismatch with respect to the actual tooth positions to induce a loading system, which moves the target teeth toward the correct positions. The common practice is based on selecting the aligner features (material, thickness, and auxiliary elements) by only considering clinician's subjective assessments. In this article, a computational design and engineering methodology has been developed to reconstruct anatomical tissues, to model parametric aligner shapes, to simulate orthodontic movements, and to enhance the aligner design. The proposed approach integrates computer-aided technologies, from tomographic imaging to optical scanning, from parametric modeling to finite element analyses, within a 3-dimensional digital framework. The anatomical modeling provides anatomies, including teeth (roots and crowns), jaw bones, and periodontal ligaments, which are the references for the down streaming parametric aligner shaping. The biomechanical interactions between anatomical models and aligner geometries are virtually reproduced using a finite element analysis software. The methodology allows numerical simulations of patient-specific conditions and the comparative analyses of different aligner configurations. In this article, the digital framework has been used to study the influence of various auxiliary elements on the loading system delivered to a maxillary and a mandibular central incisor during an orthodontic tipping movement. Numerical simulations have shown a high dependency of the orthodontic tooth movement on the auxiliary element configuration, which should then be accurately selected to maximize the aligner's effectiveness. Copyright © 2016 John Wiley & Sons, Ltd.

Extracting relevant information for cancer diagnosis from dynamic full field OCT through image processing and learning

NASA Astrophysics Data System (ADS)

Apelian, Clément; Gastaud, Clément; Boccara, A. Claude

2017-02-01

For a large number of cancer surgeries, the lack of reliable intraoperative diagnosis leads to reoperations or bad outcomes for the patients. To deliver better diagnosis, we developed Dynamic Full Field OCT (D-FFOCT) as a complement to FFOCT. FFOCT already presents interesting results for cancer diagnosis e.g. Mohs surgery and reaching 96% accuracy on prostate cancer. D-FFOCT accesses the dynamic processes of metabolism and gives new tools to diagnose the state of a tissue at the cellular level to complement FFOCT contrast. We developed a processing framework that intends to maximize the information provided by the FFOCT technology as well as D-FFOCT and synthetize this as a meaningful image. We use different time processing to generate metrics (standard deviation of time signals, decorrelation times and more) and spatial processing to sort out structures and the corresponding imaging modality, which is the most appropriate. Sorting was achieved through quadratic discriminant analysis in a N-dimension parametric space corresponding to our metrics. Combining the best imaging modalities for each structure leads to a rich morphology image. This image displaying the morphology is then colored to represent the dynamic behavior of these structures (slow or fast) and to be quickly analyzed by doctors. Therefore, we achieved a micron resolved image, rich of both FFOCT ability of imaging fixed and highly backscattering structures as well as D-FFOCT ability of imaging low level scattering cellular level details. We believe that this morphological contrast close to histology and the dynamic behavior contrast will push forward the limits of intraoperative diagnosis further on.

Usefulness of myocardial parametric imaging to evaluate myocardial viability in experimental and in clinical studies

PubMed Central

Korosoglou, G; Hansen, A; Bekeredjian, R; Filusch, A; Hardt, S; Wolf, D; Schellberg, D; Katus, H A; Kuecherer, H

2006-01-01

Objective To evaluate whether myocardial parametric imaging (MPI) is superior to visual assessment for the evaluation of myocardial viability. Methods and results Myocardial contrast echocardiography (MCE) was assessed in 11 pigs before, during, and after left anterior descending coronary artery occlusion and in 32 patients with ischaemic heart disease by using intravenous SonoVue administration. In experimental studies perfusion defect area assessment by MPI was compared with visually guided perfusion defect planimetry. Histological assessment of necrotic tissue was the standard reference. In clinical studies viability was assessed on a segmental level by (1) visual analysis of myocardial opacification; (2) quantitative estimation of myocardial blood flow in regions of interest; and (3) MPI. Functional recovery between three and six months after revascularisation was the standard reference. In experimental studies, compared with visually guided perfusion defect planimetry, planimetric assessment of infarct size by MPI correlated more significantly with histology (r2  =  0.92 versus r2  =  0.56) and had a lower intraobserver variability (4% v 15%, p < 0.05). In clinical studies, MPI had higher specificity (66% v 43%, p < 0.05) than visual MCE and good accuracy (81%) for viability detection. It was less time consuming (3.4 (1.6) v 9.2 (2.4) minutes per image, p < 0.05) than quantitative blood flow estimation by regions of interest and increased the agreement between observers interpreting myocardial perfusion (κ  =  0.87 v κ  =  0.75, p < 0.05). Conclusion MPI is useful for the evaluation of myocardial viability both in animals and in patients. It is less time consuming than quantification analysis by regions of interest and less observer dependent than visual analysis. Thus, strategies incorporating this technique may be valuable for the evaluation of myocardial viability in clinical routine. PMID:15939722

Use of Brain MRI Atlases to Determine Boundaries of Age-Related Pathology: The Importance of Statistical Method

PubMed Central

Dickie, David Alexander; Job, Dominic E.; Gonzalez, David Rodriguez; Shenkin, Susan D.; Wardlaw, Joanna M.

2015-01-01

Introduction Neurodegenerative disease diagnoses may be supported by the comparison of an individual patient’s brain magnetic resonance image (MRI) with a voxel-based atlas of normal brain MRI. Most current brain MRI atlases are of young to middle-aged adults and parametric, e.g., mean ±standard deviation (SD); these atlases require data to be Gaussian. Brain MRI data, e.g., grey matter (GM) proportion images, from normal older subjects are apparently not Gaussian. We created a nonparametric and a parametric atlas of the normal limits of GM proportions in older subjects and compared their classifications of GM proportions in Alzheimer’s disease (AD) patients. Methods Using publicly available brain MRI from 138 normal subjects and 138 subjects diagnosed with AD (all 55–90 years), we created: a mean ±SD atlas to estimate parametrically the percentile ranks and limits of normal ageing GM; and, separately, a nonparametric, rank order-based GM atlas from the same normal ageing subjects. GM images from AD patients were then classified with respect to each atlas to determine the effect statistical distributions had on classifications of proportions of GM in AD patients. Results The parametric atlas often defined the lower normal limit of the proportion of GM to be negative (which does not make sense physiologically as the lowest possible proportion is zero). Because of this, for approximately half of the AD subjects, 25–45% of voxels were classified as normal when compared to the parametric atlas; but were classified as abnormal when compared to the nonparametric atlas. These voxels were mainly concentrated in the frontal and occipital lobes. Discussion To our knowledge, we have presented the first nonparametric brain MRI atlas. In conditions where there is increasing variability in brain structure, such as in old age, nonparametric brain MRI atlases may represent the limits of normal brain structure more accurately than parametric approaches. Therefore, we conclude that the statistical method used for construction of brain MRI atlases should be selected taking into account the population and aim under study. Parametric methods are generally robust for defining central tendencies, e.g., means, of brain structure. Nonparametric methods are advisable when studying the limits of brain structure in ageing and neurodegenerative disease. PMID:26023913

Diffusion Weighted Magnetic Resonance Imaging Assessment of Blood Flow in the Microvasculature of Abdominal Organs

NASA Astrophysics Data System (ADS)

Truica, Loredana Sorina

In this thesis, water diffusion in human liver and placenta is studied using diffusion weighted magnetic resonance imaging. For short, randomly oriented vascular segments, intravascular water motion is diffusion-like. For tissues with large vascular compartments the diffusion decay is bi-exponential with one component corresponding to diffusing water and the other to water in the microvasculature. This model, known as the intravoxel incoherent motion (IVIM) model, is seldom used with abdominal organs because of motion artifacts. This limitation was overcome for the experiments reported here by introducing: 1) parallel imaging, 2) navigator echo respiratory triggering (NRT), 3) a double echo diffusion sequence that inherently compensates for eddy current effects, 4) SPAIR fat suppression and 5) a superior approach to image analysis. In particular, the use of NRT allowed us to use a free breathing protocol instead of the previously required breath hold protocol. The resulting DWI images were of high quality and motion artifact free. Diffusion decays were measured over a larger portion of the decay than had previously been reported and the results are considerably better than those previously reported. For both studies, reliable measurements of the diffusion coefficient (D), pseudo-diffusion coefficient (D) and perfusion fraction (f), were obtained using a region of interest analysis as well as a pixel-by-pixel approach. To within experimental error, all patients had the same values of D (1.10 mum 2/ms +/- 0.16 mum2/ms), D* (46 mum2/ms +/- 17 mum2/ms) and f (44.0% +/- 6.9%) in liver and D (1.8 mum 2/ms +/- 0.2 mum2/ms), D* (30 mum 2/ms +/- 12 mmu2/ms), and f (40% +/- 6%) in the placenta. No dependence on gestational age was found for the placental study. Parametric maps of f and D* were consistent with blood flow patterns in both systems. The model worked well for both investigated organs even though their anatomical structures are quite different. A method for removing rectified noise bias from low intensity magnitude MR images measured with phased array coils is also presented. This algorithm has significance for diffusion decay measurements since it permits the use of low intensity data points which could, for example, allow the acquisition of high resolution parametric maps.

Marginal Space Deep Learning: Efficient Architecture for Volumetric Image Parsing.

PubMed

Ghesu, Florin C; Krubasik, Edward; Georgescu, Bogdan; Singh, Vivek; Yefeng Zheng; Hornegger, Joachim; Comaniciu, Dorin

2016-05-01

Robust and fast solutions for anatomical object detection and segmentation support the entire clinical workflow from diagnosis, patient stratification, therapy planning, intervention and follow-up. Current state-of-the-art techniques for parsing volumetric medical image data are typically based on machine learning methods that exploit large annotated image databases. Two main challenges need to be addressed, these are the efficiency in scanning high-dimensional parametric spaces and the need for representative image features which require significant efforts of manual engineering. We propose a pipeline for object detection and segmentation in the context of volumetric image parsing, solving a two-step learning problem: anatomical pose estimation and boundary delineation. For this task we introduce Marginal Space Deep Learning (MSDL), a novel framework exploiting both the strengths of efficient object parametrization in hierarchical marginal spaces and the automated feature design of Deep Learning (DL) network architectures. In the 3D context, the application of deep learning systems is limited by the very high complexity of the parametrization. More specifically 9 parameters are necessary to describe a restricted affine transformation in 3D, resulting in a prohibitive amount of billions of scanning hypotheses. The mechanism of marginal space learning provides excellent run-time performance by learning classifiers in clustered, high-probability regions in spaces of gradually increasing dimensionality. To further increase computational efficiency and robustness, in our system we learn sparse adaptive data sampling patterns that automatically capture the structure of the input. Given the object localization, we propose a DL-based active shape model to estimate the non-rigid object boundary. Experimental results are presented on the aortic valve in ultrasound using an extensive dataset of 2891 volumes from 869 patients, showing significant improvements of up to 45.2% over the state-of-the-art. To our knowledge, this is the first successful demonstration of the DL potential to detection and segmentation in full 3D data with parametrized representations.

Kinetic modeling of PET-FDG in the brain without blood sampling.

PubMed

Bentourkia, M'hamed

2006-12-01

The aim in this work is to report a new method to calculate parametric images from a single scan acquisition with positron emission tomography (PET) and fluorodeoxyglucose (FDG) in the human brain without blood sampling. It is usually practical for research or clinical purposes to inject the patient in an isolated room and to start the PET acquisition only for some 10-20 min, about 30 min after FDG injection. In order to calculate the cerebral metabolic rates for glucose (CMRG), usually several blood samples are required. The proposed method considers the relation between the uptake of the tracer in the cerebellum as a reference tissue and the population based input curve. Similar results were obtained for CMRG values with the present method in comparison to the usual autoradiographic and the non-linear least squares fitting of regions of interest.

Fractional motion model for characterization of anomalous diffusion from NMR signals.

PubMed

Fan, Yang; Gao, Jia-Hong

2015-07-01

Measuring molecular diffusion has been used to characterize the properties of living organisms and porous materials. NMR is able to detect the diffusion process in vivo and noninvasively. The fractional motion (FM) model is appropriate to describe anomalous diffusion phenomenon in crowded environments, such as living cells. However, no FM-based NMR theory has yet been established. Here, we present a general formulation of the FM-based NMR signal under the influence of arbitrary magnetic field gradient waveforms. An explicit analytic solution of the stretched exponential decay format for NMR signals with finite-width Stejskal-Tanner bipolar pulse magnetic field gradients is presented. Signals from a numerical simulation matched well with the theoretical prediction. In vivo diffusion-weighted brain images were acquired and analyzed using the proposed theory, and the resulting parametric maps exhibit remarkable contrasts between different brain tissues.

Fractional motion model for characterization of anomalous diffusion from NMR signals

NASA Astrophysics Data System (ADS)

Fan, Yang; Gao, Jia-Hong

2015-07-01

Measuring molecular diffusion has been used to characterize the properties of living organisms and porous materials. NMR is able to detect the diffusion process in vivo and noninvasively. The fractional motion (FM) model is appropriate to describe anomalous diffusion phenomenon in crowded environments, such as living cells. However, no FM-based NMR theory has yet been established. Here, we present a general formulation of the FM-based NMR signal under the influence of arbitrary magnetic field gradient waveforms. An explicit analytic solution of the stretched exponential decay format for NMR signals with finite-width Stejskal-Tanner bipolar pulse magnetic field gradients is presented. Signals from a numerical simulation matched well with the theoretical prediction. In vivo diffusion-weighted brain images were acquired and analyzed using the proposed theory, and the resulting parametric maps exhibit remarkable contrasts between different brain tissues.

Comparison of classification methods for voxel-based prediction of acute ischemic stroke outcome following intra-arterial intervention

NASA Astrophysics Data System (ADS)

Winder, Anthony J.; Siemonsen, Susanne; Flottmann, Fabian; Fiehler, Jens; Forkert, Nils D.

2017-03-01

Voxel-based tissue outcome prediction in acute ischemic stroke patients is highly relevant for both clinical routine and research. Previous research has shown that features extracted from baseline multi-parametric MRI datasets have a high predictive value and can be used for the training of classifiers, which can generate tissue outcome predictions for both intravenous and conservative treatments. However, with the recent advent and popularization of intra-arterial thrombectomy treatment, novel research specifically addressing the utility of predictive classi- fiers for thrombectomy intervention is necessary for a holistic understanding of current stroke treatment options. The aim of this work was to develop three clinically viable tissue outcome prediction models using approximate nearest-neighbor, generalized linear model, and random decision forest approaches and to evaluate the accuracy of predicting tissue outcome after intra-arterial treatment. Therefore, the three machine learning models were trained, evaluated, and compared using datasets of 42 acute ischemic stroke patients treated with intra-arterial thrombectomy. Classifier training utilized eight voxel-based features extracted from baseline MRI datasets and five global features. Evaluation of classifier-based predictions was performed via comparison to the known tissue outcome, which was determined in follow-up imaging, using the Dice coefficient and leave-on-patient-out cross validation. The random decision forest prediction model led to the best tissue outcome predictions with a mean Dice coefficient of 0.37. The approximate nearest-neighbor and generalized linear model performed equally suboptimally with average Dice coefficients of 0.28 and 0.27 respectively, suggesting that both non-linearity and machine learning are desirable properties of a classifier well-suited to the intra-arterial tissue outcome prediction problem.

Multi-parametric MRI findings of granulomatous prostatitis developing after intravesical bacillus calmette-guérin therapy.

PubMed

Gottlieb, Josh; Princenthal, Robert; Cohen, Martin I

2017-07-01

To evaluate the multi-parametric MRI (mpMRI) findings in patients with biopsy-proven granulomatous prostatitis and prior Bacillus Calmette-Guérin (BCG) exposure. MRI was performed in six patients with pathologically proven granulomatous prostatitis and a prior history of bladder cancer treated with intravesical BCG therapy. Multi-parametric prostate MRI images were recorded on a GE 750W or Philips Achieva 3.0 Tesla MRI scanner with high-resolution, small-field-of-view imaging consisting of axial T2, axial T1, coronal T2, sagittal T2, axial multiple b-value diffusion (multiple values up to 1200 or 1400), and dynamic contrast-enhanced 3D axial T1 with fat suppression sequence. Two different patterns of MR findings were observed. Five of the six patients had a low mean ADC value <1000 (decreased signal on ADC map images) and isointense signal on high-b-value imaging (b = 1200 or 1400), consistent with nonspecific granulomatous prostatitis. The other pattern seen in one of the six patients was decreased signal on the ADC map images with increased signal on the high-b-value sequence, revealing true restricted diffusion indistinguishable from aggressive prostate cancer. This patient had biopsy-confirmed acute BCG prostatitis. Our study suggests that patients with known BCG exposure and PI-RADS v2 scores ≤3, showing similar mpMRI findings as demonstrated, may not require prostate biopsy.

Parametric fMRI of paced motor responses uncovers novel whole-brain imaging biomarkers in spinocerebellar ataxia type 3.

PubMed

Duarte, João Valente; Faustino, Ricardo; Lobo, Mercês; Cunha, Gil; Nunes, César; Ferreira, Carlos; Januário, Cristina; Castelo-Branco, Miguel

2016-10-01

Machado-Joseph Disease, inherited type 3 spinocerebellar ataxia (SCA3), is the most common form worldwide. Neuroimaging and neuropathology have consistently demonstrated cerebellar alterations. Here we aimed to discover whole-brain functional biomarkers, based on parametric performance-level-dependent signals. We assessed 13 patients with early SCA3 and 14 healthy participants. We used a combined parametric behavioral/functional neuroimaging design to investigate disease fingerprints, as a function of performance levels, coupled with structural MRI and voxel-based morphometry. Functional magnetic resonance imaging (fMRI) was designed to parametrically analyze behavior and neural responses to audio-paced bilateral thumb movements at temporal frequencies of 1, 3, and 5 Hz. Our performance-level-based design probing neuronal correlates of motor coordination enabled the discovery that neural activation and behavior show critical loss of parametric modulation specifically in SCA3, associated with frequency-dependent cortico/subcortical activation/deactivation patterns. Cerebellar/cortical rate-dependent dissociation patterns could clearly differentiate between groups irrespective of grey matter loss. Our findings suggest functional reorganization of the motor network and indicate a possible role of fMRI as a tool to monitor disease progression in SCA3. Accordingly, fMRI patterns proved to be potential biomarkers in early SCA3, as tested by receiver operating characteristic analysis of both behavior and neural activation at different frequencies. Discrimination analysis based on BOLD signal in response to the applied parametric finger-tapping task significantly often reached >80% sensitivity and specificity in single regions-of-interest.Functional fingerprints based on cerebellar and cortical BOLD performance dependent signal modulation can thus be combined as diagnostic and/or therapeutic targets in hereditary ataxia. Hum Brain Mapp 37:3656-3668, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Novel approaches to address spectral distortions in photon counting x-ray CT using artificial neural networks

NASA Astrophysics Data System (ADS)

Touch, M.; Clark, D. P.; Barber, W.; Badea, C. T.

2016-04-01

Spectral CT using a photon-counting x-ray detector (PCXD) can potentially increase accuracy of measuring tissue composition. However, PCXD spectral measurements suffer from distortion due to charge sharing, pulse pileup, and Kescape energy loss. This study proposes two novel artificial neural network (ANN)-based algorithms: one to model and compensate for the distortion, and another one to directly correct for the distortion. The ANN-based distortion model was obtained by training to learn the distortion from a set of projections with a calibration scan. The ANN distortion was then applied in the forward statistical model to compensate for distortion in the projection decomposition. ANN was also used to learn to correct distortions directly in projections. The resulting corrected projections were used for reconstructing the image, denoising via joint bilateral filtration, and decomposition into three-material basis functions: Compton scattering, the photoelectric effect, and iodine. The ANN-based distortion model proved to be more robust to noise and worked better compared to using an imperfect parametric distortion model. In the presence of noise, the mean relative errors in iodine concentration estimation were 11.82% (ANN distortion model) and 16.72% (parametric model). With distortion correction, the mean relative error in iodine concentration estimation was improved by 50% over direct decomposition from distorted data. With our joint bilateral filtration, the resulting material image quality and iodine detectability as defined by the contrast-to-noise ratio were greatly enhanced allowing iodine concentrations as low as 2 mg/ml to be detected. Future work will be dedicated to experimental evaluation of our ANN-based methods using 3D-printed phantoms.

Correcting power and p-value calculations for bias in diffusion tensor imaging.

PubMed

Lauzon, Carolyn B; Landman, Bennett A

2013-07-01

Diffusion tensor imaging (DTI) provides quantitative parametric maps sensitive to tissue microarchitecture (e.g., fractional anisotropy, FA). These maps are estimated through computational processes and subject to random distortions including variance and bias. Traditional statistical procedures commonly used for study planning (including power analyses and p-value/alpha-rate thresholds) specifically model variability, but neglect potential impacts of bias. Herein, we quantitatively investigate the impacts of bias in DTI on hypothesis test properties (power and alpha-rate) using a two-sided hypothesis testing framework. We present theoretical evaluation of bias on hypothesis test properties, evaluate the bias estimation technique SIMEX for DTI hypothesis testing using simulated data, and evaluate the impacts of bias on spatially varying power and alpha rates in an empirical study of 21 subjects. Bias is shown to inflame alpha rates, distort the power curve, and cause significant power loss even in empirical settings where the expected difference in bias between groups is zero. These adverse effects can be attenuated by properly accounting for bias in the calculation of power and p-values. Copyright © 2013 Elsevier Inc. All rights reserved.

A lymphocyte spatial distribution graph-based method for automated classification of recurrence risk on lung cancer images

NASA Astrophysics Data System (ADS)

Garciá-Arteaga, Juan D.; Corredor, Germán.; Wang, Xiangxue; Velcheti, Vamsidhar; Madabhushi, Anant; Romero, Eduardo

2017-11-01

Tumor-infiltrating lymphocytes occurs when various classes of white blood cells migrate from the blood stream towards the tumor, infiltrating it. The presence of TIL is predictive of the response of the patient to therapy. In this paper, we show how the automatic detection of lymphocytes in digital H and E histopathological images and the quantitative evaluation of the global lymphocyte configuration, evaluated through global features extracted from non-parametric graphs, constructed from the lymphocytes' detected positions, can be correlated to the patient's outcome in early-stage non-small cell lung cancer (NSCLC). The method was assessed on a tissue microarray cohort composed of 63 NSCLC cases. From the evaluated graphs, minimum spanning trees and K-nn showed the highest predictive ability, yielding F1 Scores of 0.75 and 0.72 and accuracies of 0.67 and 0.69, respectively. The predictive power of the proposed methodology indicates that graphs may be used to develop objective measures of the infiltration grade of tumors, which can, in turn, be used by pathologists to improve the decision making and treatment planning processes.

Restoration of MRI data for intensity non-uniformities using local high order intensity statistics

PubMed Central

Hadjidemetriou, Stathis; Studholme, Colin; Mueller, Susanne; Weiner, Michael; Schuff, Norbert

2008-01-01

MRI at high magnetic fields (>3.0 T) is complicated by strong inhomogeneous radio-frequency fields, sometimes termed the “bias field”. These lead to non-biological intensity non-uniformities across the image. They can complicate further image analysis such as registration and tissue segmentation. Existing methods for intensity uniformity restoration have been optimized for 1.5 T, but they are less effective for 3.0 T MRI, and not at all satisfactory for higher fields. Also, many of the existing restoration algorithms require a brain template or use a prior atlas, which can restrict their practicalities. In this study an effective intensity uniformity restoration algorithm has been developed based on non-parametric statistics of high order local intensity co-occurrences. These statistics are restored with a non-stationary Wiener filter. The algorithm also assumes a smooth non-uniformity and is stable. It does not require a prior atlas and is robust to variations in anatomy. In geriatric brain imaging it is robust to variations such as enlarged ventricles and low contrast to noise ratio. The co-occurrence statistics improve robustness to whole head images with pronounced non-uniformities present in high field acquisitions. Its significantly improved performance and lower time requirements have been demonstrated by comparing it to the very commonly used N3 algorithm on BrainWeb MR simulator images as well as on real 4 T human head images. PMID:18621568

Ultrasound parametric imaging of hepatic steatosis using the homodyned-K distribution: An animal study.

PubMed

Fang, Jui; Zhou, Zhuhuang; Chang, Ning-Fang; Wan, Yung-Liang; Tsui, Po-Hsiang

2018-07-01

Hepatic steatosis is an abnormal state where excess lipid mass is accumulated in hepatocyte vesicles. Backscattered ultrasound signals received from the liver contain useful information regarding the degree of steatosis in the liver. The homodyned-K (HK) distribution has been demonstrated as a general model for ultrasound backscattering. The estimator based on the first three integer moments (denoted as "FTM") of the intensity has potential for practical applications because of its simplicity and low computational complexity. This study explored the diagnostic performance of HK parametric imaging based on the FTM method in the assessment of hepatic steatosis. Phantom experiments were initially conducted using the sliding window technique to determine an appropriate window size length (WSL) for HK parametric imaging. Subsequently, hepatic steatosis was induced in male Wistar rats fed a methionine- and choline-deficient (MCD) diet for 0 (i.e., normal control), 1, 2, 4, 6, and 8 weeks (n = 36; six rats in each group). After completing the scheduled MCD diet, ultrasound B-mode and HK imaging of the rat livers were performed in vivo and histopathological examinations were conducted to score the degree of hepatic steatosis. HK parameters μ (related to scatterer number density) and k (related to scatterer periodicity) were expressed as functions of the steatosis stage in terms of the median and interquartile range (IQR). Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic performance levels of the μ and k parameters. The results showed that an appropriate WSL for HK parametric imaging is seven times the pulse length of the transducer. The median value of the μ parameter increased monotonically from 0.194 (IQR: 0.18-0.23) to 0.893 (IQR: 0.64-1.04) as the steatosis stage increased. Concurrently, the median value of the k parameter increased from 0.279 (IQR: 0.26-0.31) to 0.5 (IQR: 0.41-0.54) in the early stages (normal to mild) and decreased to 0.39 (IQR: 0.29-0.45) in the advanced stages (moderate to severe). The areas under the ROC curves obtained using (μ, k) were (0.947, 0.804), (0.914, 0.575), and (0.813, 0.604) for the steatosis stages of ≥mild, ≥moderate, and ≥severe, respectively. The current findings suggest that ultrasound HK parametric imaging based on FTM estimation has great potential for future clinical diagnoses of hepatic steatosis. Copyright © 2018 Elsevier B.V. All rights reserved.

Methodological accuracy of image-based electron density assessment using dual-energy computed tomography.

PubMed

Möhler, Christian; Wohlfahrt, Patrick; Richter, Christian; Greilich, Steffen

2017-06-01

Electron density is the most important tissue property influencing photon and ion dose distributions in radiotherapy patients. Dual-energy computed tomography (DECT) enables the determination of electron density by combining the information on photon attenuation obtained at two different effective x-ray energy spectra. Most algorithms suggested so far use the CT numbers provided after image reconstruction as input parameters, i.e., are imaged-based. To explore the accuracy that can be achieved with these approaches, we quantify the intrinsic methodological and calibration uncertainty of the seemingly simplest approach. In the studied approach, electron density is calculated with a one-parametric linear superposition ('alpha blending') of the two DECT images, which is shown to be equivalent to an affine relation between the photon attenuation cross sections of the two x-ray energy spectra. We propose to use the latter relation for empirical calibration of the spectrum-dependent blending parameter. For a conclusive assessment of the electron density uncertainty, we chose to isolate the purely methodological uncertainty component from CT-related effects such as noise and beam hardening. Analyzing calculated spectrally weighted attenuation coefficients, we find universal applicability of the investigated approach to arbitrary mixtures of human tissue with an upper limit of the methodological uncertainty component of 0.2%, excluding high-Z elements such as iodine. The proposed calibration procedure is bias-free and straightforward to perform using standard equipment. Testing the calibration on five published data sets, we obtain very small differences in the calibration result in spite of different experimental setups and CT protocols used. Employing a general calibration per scanner type and voltage combination is thus conceivable. Given the high suitability for clinical application of the alpha-blending approach in combination with a very small methodological uncertainty, we conclude that further refinement of image-based DECT-algorithms for electron density assessment is not advisable. © 2017 American Association of Physicists in Medicine.

Diffusion Tensor Magnetic Resonance Imaging of the Pancreas

PubMed Central

Nissan, Noam; Golan, Talia; Furman-Haran, Edna; Apter, Sara; Inbar, Yael; Ariche, Arie; Bar-Zakay, Barak; Goldes, Yuri; Schvimer, Michael; Grobgeld, Dov; Degani, Hadassa

2014-01-01

Purpose To develop a diffusion-tensor-imaging (DTI) protocol that is sensitive to the complex diffusion and perfusion properties of the healthy and malignant pancreas tissues. Materials and Methods Twenty-eight healthy volunteers and nine patients with pancreatic-ductal-adenocacinoma (PDAC), were scanned at 3T with T2-weighted and DTI sequences. Healthy volunteers were also scanned with multi-b diffusion-weighted-imaging (DWI), whereas a standard clinical protocol complemented the PDAC patients’ scans. Image processing at pixel resolution yielded parametric maps of three directional diffusion coefficients λ1, λ2, λ3, apparent diffusion coefficient (ADC), and fractional anisotropy (FA), as well as a λ1-vector map, and a main diffusion-direction map. Results DTI measurements of healthy pancreatic tissue at b-values 0,500 s/mm2yielded: λ1 = (2.65±0.35)×10−3, λ2 = (1.87±0.22)×10−3, λ3 = (1.20±0.18)×10−3, ADC = (1.91±0.22)×10−3 (all in mm2/s units) and FA = 0.38±0.06. Using b-values of 100,500 s/mm2 led to a significant reduction in λ1, λ2, λ3 and ADC (p<.0001) and a significant increase (p<0.0001) in FA. The reduction in the diffusion coefficients suggested a contribution of a fast intra-voxel-incoherent-motion (IVIM) component at b≤100 s/mm2, which was confirmed by the multi-b DWI results. In PDACs, λ1, λ2, λ3 and ADC in both 0,500 s/mm2 and 100,500 s/mm2 b-values sets, as well as the reduction in these diffusion coefficients between the two sets, were significantly lower in comparison to the distal normal pancreatic tissue, suggesting higher cellularity and diminution of the fast-IVIM component in the cancer tissue. Conclusion DTI using two reference b-values 0 and 100 s/mm2 enabled characterization of the water diffusion and anisotropy of the healthy pancreas, taking into account a contribution of IVIM. The reduction in the diffusion coefficients of PDAC, as compared to normal pancreatic tissue, and the smaller change in these coefficients in PDAC when the reference b-value was modified from 0 to 100 s/mm2, helped identifying the presence of malignancy. PMID:25549366

Polarization switch of four-wave mixing in a lawtunable fiber optical parametric oscillator.

PubMed

Yang, Kangwen; Ye, Pengbo; Zheng, Shikai; Jiang, Jieshi; Huang, Kun; Hao, Qiang; Zeng, Heping

2018-02-05

We reported the simultaneous generation and selective manipulation of scalar and cross-phase modulation instabilities in a fiber optical parametric oscillator. Numerical and experimental results show independent control of parametric gain by changing the input pump polarization state. The resonant cavity enables power enhancement of 45 dB for the spontaneous sidebands, generating laser pulses tunable from 783 to 791 nm and 896 to 1005 nm due to the combination of four-wave mixing, cascaded Raman scattering and other nonlinear effects. This gain controlled, wavelength tunable, fiber-based laser source may find applications in the fields of nonlinear biomedical imaging and stimulated Raman spectroscopy.

Corrections of arterial input function for dynamic H215O PET to assess perfusion of pelvic tumours: arterial blood sampling versus image extraction

NASA Astrophysics Data System (ADS)

Lüdemann, L.; Sreenivasa, G.; Michel, R.; Rosner, C.; Plotkin, M.; Felix, R.; Wust, P.; Amthauer, H.

2006-06-01

Assessment of perfusion with 15O-labelled water (H215O) requires measurement of the arterial input function (AIF). The arterial time activity curve (TAC) measured using the peripheral sampling scheme requires corrections for delay and dispersion. In this study, parametrizations with and without arterial spillover correction for fitting of the tissue curve are evaluated. Additionally, a completely noninvasive method for generation of the AIF from a dynamic positron emission tomography (PET) acquisition is applied to assess perfusion of pelvic tumours. This method uses a volume of interest (VOI) to extract the TAC from the femoral artery. The VOI TAC is corrected for spillover using a separate tissue TAC and for recovery by determining the recovery coefficient on a coregistered CT data set. The techniques were applied in five patients with pelvic tumours who underwent a total of 11 examinations. Delay and dispersion correction of the blood TAC without arterial spillover correction yielded in seven examinations solutions inconsistent with physiology. Correction of arterial spillover increased the fitting accuracy and yielded consistent results in all patients. Generation of an AIF from PET image data was investigated as an alternative to arterial blood sampling and was shown to have an intrinsic potential to determine the AIF noninvasively and reproducibly. The AIF extracted from a VOI in a dynamic PET scan was similar in shape to the blood AIF but yielded significantly higher tissue perfusion values (mean of 104.0 ± 52.0%) and lower partition coefficients (-31.6 ± 24.2%). The perfusion values and partition coefficients determined with the VOI technique have to be corrected in order to compare the results with those of studies using a blood AIF.

An in vivo MRI Template Set for Morphometry, Tissue Segmentation, and fMRI Localization in Rats

PubMed Central

Valdés-Hernández, Pedro Antonio; Sumiyoshi, Akira; Nonaka, Hiroi; Haga, Risa; Aubert-Vásquez, Eduardo; Ogawa, Takeshi; Iturria-Medina, Yasser; Riera, Jorge J.; Kawashima, Ryuta

2011-01-01

Over the last decade, several papers have focused on the construction of highly detailed mouse high field magnetic resonance image (MRI) templates via non-linear registration to unbiased reference spaces, allowing for a variety of neuroimaging applications such as robust morphometric analyses. However, work in rats has only provided medium field MRI averages based on linear registration to biased spaces with the sole purpose of approximate functional MRI (fMRI) localization. This precludes any morphometric analysis in spite of the need of exploring in detail the neuroanatomical substrates of diseases in a recent advent of rat models. In this paper we present a new in vivo rat T2 MRI template set, comprising average images of both intensity and shape, obtained via non-linear registration. Also, unlike previous rat template sets, we include white and gray matter probabilistic segmentations, expanding its use to those applications demanding prior-based tissue segmentation, e.g., statistical parametric mapping (SPM) voxel-based morphometry. We also provide a preliminary digitalization of latest Paxinos and Watson atlas for anatomical and functional interpretations within the cerebral cortex. We confirmed that, like with previous templates, forepaw and hindpaw fMRI activations can be correctly localized in the expected atlas structure. To exemplify the use of our new MRI template set, were reported the volumes of brain tissues and cortical structures and probed their relationships with ontogenetic development. Other in vivo applications in the near future can be tensor-, deformation-, or voxel-based morphometry, morphological connectivity, and diffusion tensor-based anatomical connectivity. Our template set, freely available through the SPM extension website, could be an important tool for future longitudinal and/or functional extensive preclinical studies. PMID:22275894

A robust multi-kernel change detection framework for detecting leaf beetle defoliation using Landsat 7 ETM+ data

NASA Astrophysics Data System (ADS)

Anees, Asim; Aryal, Jagannath; O'Reilly, Małgorzata M.; Gale, Timothy J.; Wardlaw, Tim

2016-12-01

A robust non-parametric framework, based on multiple Radial Basic Function (RBF) kernels, is proposed in this study, for detecting land/forest cover changes using Landsat 7 ETM+ images. One of the widely used frameworks is to find change vectors (difference image) and use a supervised classifier to differentiate between change and no-change. The Bayesian Classifiers e.g. Maximum Likelihood Classifier (MLC), Naive Bayes (NB), are widely used probabilistic classifiers which assume parametric models, e.g. Gaussian function, for the class conditional distributions. However, their performance can be limited if the data set deviates from the assumed model. The proposed framework exploits the useful properties of Least Squares Probabilistic Classifier (LSPC) formulation i.e. non-parametric and probabilistic nature, to model class posterior probabilities of the difference image using a linear combination of a large number of Gaussian kernels. To this end, a simple technique, based on 10-fold cross-validation is also proposed for tuning model parameters automatically instead of selecting a (possibly) suboptimal combination from pre-specified lists of values. The proposed framework has been tested and compared with Support Vector Machine (SVM) and NB for detection of defoliation, caused by leaf beetles (Paropsisterna spp.) in Eucalyptus nitens and Eucalyptus globulus plantations of two test areas, in Tasmania, Australia, using raw bands and band combination indices of Landsat 7 ETM+. It was observed that due to multi-kernel non-parametric formulation and probabilistic nature, the LSPC outperforms parametric NB with Gaussian assumption in change detection framework, with Overall Accuracy (OA) ranging from 93.6% (κ = 0.87) to 97.4% (κ = 0.94) against 85.3% (κ = 0.69) to 93.4% (κ = 0.85), and is more robust to changing data distributions. Its performance was comparable to SVM, with added advantages of being probabilistic and capable of handling multi-class problems naturally with its original formulation.

A Non-Parametric Approach for the Activation Detection of Block Design fMRI Simulated Data Using Self-Organizing Maps and Support Vector Machine.

PubMed

Bahrami, Sheyda; Shamsi, Mousa

2017-01-01

Functional magnetic resonance imaging (fMRI) is a popular method to probe the functional organization of the brain using hemodynamic responses. In this method, volume images of the entire brain are obtained with a very good spatial resolution and low temporal resolution. However, they always suffer from high dimensionality in the face of classification algorithms. In this work, we combine a support vector machine (SVM) with a self-organizing map (SOM) for having a feature-based classification by using SVM. Then, a linear kernel SVM is used for detecting the active areas. Here, we use SOM for feature extracting and labeling the datasets. SOM has two major advances: (i) it reduces dimension of data sets for having less computational complexity and (ii) it is useful for identifying brain regions with small onset differences in hemodynamic responses. Our non-parametric model is compared with parametric and non-parametric methods. We use simulated fMRI data sets and block design inputs in this paper and consider the contrast to noise ratio (CNR) value equal to 0.6 for simulated datasets. fMRI simulated dataset has contrast 1-4% in active areas. The accuracy of our proposed method is 93.63% and the error rate is 6.37%.

Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features

PubMed Central

Hu, Yu-Chuan; Li, Gang; Yang, Yang; Han, Yu; Sun, Ying-Zhi; Liu, Zhi-Cheng; Tian, Qiang; Han, Zi-Yang; Liu, Le-De; Hu, Bin-Quan; Qiu, Zi-Yu; Wang, Wen; Cui, Guang-Bin

2017-01-01

Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization. PMID:28599282

Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.

PubMed

Zhang, Xin; Yan, Lin-Feng; Hu, Yu-Chuan; Li, Gang; Yang, Yang; Han, Yu; Sun, Ying-Zhi; Liu, Zhi-Cheng; Tian, Qiang; Han, Zi-Yang; Liu, Le-De; Hu, Bin-Quan; Qiu, Zi-Yu; Wang, Wen; Cui, Guang-Bin

2017-07-18

Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization.

Wavelet Filter Banks for Super-Resolution SAR Imaging

NASA Technical Reports Server (NTRS)

Sheybani, Ehsan O.; Deshpande, Manohar; Memarsadeghi, Nargess

2011-01-01

This paper discusses Innovative wavelet-based filter banks designed to enhance the analysis of super resolution Synthetic Aperture Radar (SAR) images using parametric spectral methods and signal classification algorithms, SAR finds applications In many of NASA's earth science fields such as deformation, ecosystem structure, and dynamics of Ice, snow and cold land processes, and surface water and ocean topography. Traditionally, standard methods such as Fast-Fourier Transform (FFT) and Inverse Fast-Fourier Transform (IFFT) have been used to extract Images from SAR radar data, Due to non-parametric features of these methods and their resolution limitations and observation time dependence, use of spectral estimation and signal pre- and post-processing techniques based on wavelets to process SAR radar data has been proposed. Multi-resolution wavelet transforms and advanced spectral estimation techniques have proven to offer efficient solutions to this problem.

Edge Sharpness Assessment by Parametric Modeling: Application to Magnetic Resonance Imaging.

PubMed

Ahmad, R; Ding, Y; Simonetti, O P

2015-05-01

In biomedical imaging, edge sharpness is an important yet often overlooked image quality metric. In this work, a semi-automatic method to quantify edge sharpness in the presence of significant noise is presented with application to magnetic resonance imaging (MRI). The method is based on parametric modeling of image edges. First, an edge map is automatically generated and one or more edges-of-interest (EOI) are manually selected using graphical user interface. Multiple exclusion criteria are then enforced to eliminate edge pixels that are potentially not suitable for sharpness assessment. Second, at each pixel of the EOI, an image intensity profile is read along a small line segment that runs locally normal to the EOI. Third, the profiles corresponding to all EOI pixels are individually fitted with a sigmoid function characterized by four parameters, including one that represents edge sharpness. Last, the distribution of the sharpness parameter is used to quantify edge sharpness. For validation, the method is applied to simulated data as well as MRI data from both phantom imaging and cine imaging experiments. This method allows for fast, quantitative evaluation of edge sharpness even in images with poor signal-to-noise ratio. Although the utility of this method is demonstrated for MRI, it can be adapted for other medical imaging applications.

A Parametric Computational Analysis into Galvanic Coupling Intrabody Communication.

PubMed

Callejon, M Amparo; Del Campo, P; Reina-Tosina, Javier; Roa, Laura M

2017-08-02

Intrabody Communication (IBC) uses the human body tissues as transmission media for electrical signals to interconnect personal health devices in wireless body area networks. The main goal of this work is to conduct a computational analysis covering some bioelectric issues that still have not been fully explained, such as the modeling of the skin-electrode impedance, the differences associated to the use of constant voltage or current excitation modes, or the influence on attenuation of the subject's anthropometrical and bioelectric properties. With this aim, a computational finite element model has been developed, allowing the IBC channel attenuation as well as the electric field and current density through arm tissues to be computed as a function of these parameters. As a conclusion, this parametric analysis has in turn permitted us to disclose some knowledge about the causes and effects of the above-mentioned issues, thus explaining and complementing previous results reported in the literature.

PET image reconstruction using multi-parametric anato-functional priors

NASA Astrophysics Data System (ADS)

Mehranian, Abolfazl; Belzunce, Martin A.; Niccolini, Flavia; Politis, Marios; Prieto, Claudia; Turkheimer, Federico; Hammers, Alexander; Reader, Andrew J.

2017-08-01

In this study, we investigate the application of multi-parametric anato-functional (MR-PET) priors for the maximum a posteriori (MAP) reconstruction of brain PET data in order to address the limitations of the conventional anatomical priors in the presence of PET-MR mismatches. In addition to partial volume correction benefits, the suitability of these priors for reconstruction of low-count PET data is also introduced and demonstrated, comparing to standard maximum-likelihood (ML) reconstruction of high-count data. The conventional local Tikhonov and total variation (TV) priors and current state-of-the-art anatomical priors including the Kaipio, non-local Tikhonov prior with Bowsher and Gaussian similarity kernels are investigated and presented in a unified framework. The Gaussian kernels are calculated using both voxel- and patch-based feature vectors. To cope with PET and MR mismatches, the Bowsher and Gaussian priors are extended to multi-parametric priors. In addition, we propose a modified joint Burg entropy prior that by definition exploits all parametric information in the MAP reconstruction of PET data. The performance of the priors was extensively evaluated using 3D simulations and two clinical brain datasets of [18F]florbetaben and [18F]FDG radiotracers. For simulations, several anato-functional mismatches were intentionally introduced between the PET and MR images, and furthermore, for the FDG clinical dataset, two PET-unique active tumours were embedded in the PET data. Our simulation results showed that the joint Burg entropy prior far outperformed the conventional anatomical priors in terms of preserving PET unique lesions, while still reconstructing functional boundaries with corresponding MR boundaries. In addition, the multi-parametric extension of the Gaussian and Bowsher priors led to enhanced preservation of edge and PET unique features and also an improved bias-variance performance. In agreement with the simulation results, the clinical results also showed that the Gaussian prior with voxel-based feature vectors, the Bowsher and the joint Burg entropy priors were the best performing priors. However, for the FDG dataset with simulated tumours, the TV and proposed priors were capable of preserving the PET-unique tumours. Finally, an important outcome was the demonstration that the MAP reconstruction of a low-count FDG PET dataset using the proposed joint entropy prior can lead to comparable image quality to a conventional ML reconstruction with up to 5 times more counts. In conclusion, multi-parametric anato-functional priors provide a solution to address the pitfalls of the conventional priors and are therefore likely to increase the diagnostic confidence in MR-guided PET image reconstructions.

Dielectric properties measurements of brown and white adipose tissue in rats from 0.5 to 10 GHz.

PubMed

Rodrigues, D B; Stauffer, P R; Colebeck, E; Hood, A Z; Salahi, S; Maccarini, P F; Topsakal, E

2016-01-01

Brown adipose tissue (BAT) plays an important role in whole body metabolism and with appropriate stimulus could potentially mediate weight gain and insulin sensitivity. Although imaging techniques are available to detect subsurface BAT, there are currently no viable methods for continuous acquisition of BAT energy expenditure. Microwave (MW) radiometry is an emerging technology that allows the quantification of tissue temperature variations at depths of several centimeters. Such temperature differentials may be correlated with variations in metabolic rate, thus providing a quantitative approach to monitor BAT metabolism. In order to optimize MW radiometry, numerical and experimental phantoms with accurate dielectric properties are required to develop and calibrate radiometric sensors. Thus, we present for the first time, the characterization of relative permittivity and electrical conductivity of brown (BAT) and white (WAT) adipose tissues in rats across the MW range 0.5-10GHz. Measurements were carried out in situ and post mortem in six female rats of approximately 200g. A Cole-Cole model was used to fit the experimental data into a parametric model that describes the variation of dielectric properties as a function of frequency. Measurements confirm that the dielectric properties of BAT ( ε r = 14.0-19.4, σ = 0.3-3.3S/m) are significantly higher than those of WAT ( ε r = 9.1-11.9, σ = 0.1-1.9S/m), in accordance with the higher water content of BAT.

Dielectric properties measurements of brown and white adipose tissue in rats from 0.5 to 10 GHz

PubMed Central

Rodrigues, D B; Stauffer, P R; Colebeck, E; Hood, A Z; Salahi, S; Maccarini, P F; Topsakal, E

2017-01-01

Brown adipose tissue (BAT) plays an important role in whole body metabolism and with appropriate stimulus could potentially mediate weight gain and insulin sensitivity. Although imaging techniques are available to detect subsurface BAT, there are currently no viable methods for continuous acquisition of BAT energy expenditure. Microwave (MW) radiometry is an emerging technology that allows the quantification of tissue temperature variations at depths of several centimeters. Such temperature differentials may be correlated with variations in metabolic rate, thus providing a quantitative approach to monitor BAT metabolism. In order to optimize MW radiometry, numerical and experimental phantoms with accurate dielectric properties are required to develop and calibrate radiometric sensors. Thus, we present for the first time, the characterization of relative permittivity and electrical conductivity of brown (BAT) and white (WAT) adipose tissues in rats across the MW range 0.5–10GHz. Measurements were carried out in situ and post mortem in six female rats of approximately 200g. A Cole-Cole model was used to fit the experimental data into a parametric model that describes the variation of dielectric properties as a function of frequency. Measurements confirm that the dielectric properties of BAT (εr = 14.0–19.4, σ = 0.3–3.3S/m) are significantly higher than those of WAT (εr = 9.1–11.9, σ = 0.1–1.9S/m), in accordance with the higher water content of BAT. PMID:29354288

Neural networks improve brain cancer detection with Raman spectroscopy in the presence of light artifacts

NASA Astrophysics Data System (ADS)

Jermyn, Michael; Desroches, Joannie; Mercier, Jeanne; St-Arnaud, Karl; Guiot, Marie-Christine; Petrecca, Kevin; Leblond, Frederic

2016-03-01

It is often difficult to identify cancer tissue during brain cancer (glioma) surgery. Gliomas invade into areas of normal brain, and this cancer invasion is frequently not detected using standard preoperative magnetic resonance imaging (MRI). This results in enduring invasive cancer following surgery and leads to recurrence. A hand-held Raman spectroscopy is able to rapidly detect cancer invasion in patients with grade 2-4 gliomas. However, ambient light sources can produce spectral artifacts which inhibit the ability to distinguish between cancer and normal tissue using the spectral information available. To address this issue, we have demonstrated that artificial neural networks (ANN) can accurately classify invasive cancer versus normal brain tissue, even when including measurements with significant spectral artifacts from external light sources. The non-parametric and adaptive model used by ANN makes it suitable for detecting complex non-linear spectral characteristics associated with different tissues and the confounding presence of light artifacts. The use of ANN for brain cancer detection with Raman spectroscopy, in the presence of light artifacts, improves the robustness and clinical translation potential for intraoperative use. Integration with the neurosurgical workflow is facilitated by accounting for the effect of light artifacts which may occur, due to operating room lights, neuronavigation systems, windows, or other light sources. The ability to rapidly detect invasive brain cancer under these conditions may reduce residual cancer remaining after surgery, and thereby improve patient survival.

Monte Carlo-based parametrization of the lateral dose spread for clinical treatment planning of scanned proton and carbon ion beams.

PubMed

Parodi, Katia; Mairani, Andrea; Sommerer, Florian

2013-07-01

Ion beam therapy using state-of-the-art pencil-beam scanning offers unprecedented tumour-dose conformality with superior sparing of healthy tissue and critical organs compared to conventional radiation modalities for external treatment of deep-seated tumours. For inverse plan optimization, the commonly employed analytical treatment-planning systems (TPSs) have to meet reasonable compromises in the accuracy of the pencil-beam modelling to ensure good performances in clinically tolerable execution times. In particular, the complex lateral spreading of ion beams in air and in the traversed tissue is typically approximated with ideal Gaussian-shaped distributions, enabling straightforward superimposition of several scattering contributions. This work presents the double Gaussian parametrization of scanned proton and carbon ion beams in water that has been introduced in an upgraded version of the worldwide first commercial ion TPS for clinical use at the Heidelberg Ion Beam Therapy Center (HIT). First, the Monte Carlo results obtained from a detailed implementation of the HIT beamline have been validated against available experimental data. Then, for generating the TPS lateral parametrization, radial beam broadening has been calculated in a water target placed at a representative position after scattering in the beamline elements and air for 20 initial beam energies for each ion species. The simulated profiles were finally fitted with an idealized double Gaussian distribution that did not perfectly describe the nature of the data, thus requiring a careful choice of the fitting conditions. The obtained parametrization is in clinical use not only at the HIT center, but also at the Centro Nazionale di Adroterapia Oncologica.

Monte Carlo-based parametrization of the lateral dose spread for clinical treatment planning of scanned proton and carbon ion beams

PubMed Central

Parodi, Katia; Mairani, Andrea; Sommerer, Florian

2013-01-01

Ion beam therapy using state-of-the-art pencil-beam scanning offers unprecedented tumour-dose conformality with superior sparing of healthy tissue and critical organs compared to conventional radiation modalities for external treatment of deep-seated tumours. For inverse plan optimization, the commonly employed analytical treatment-planning systems (TPSs) have to meet reasonable compromises in the accuracy of the pencil-beam modelling to ensure good performances in clinically tolerable execution times. In particular, the complex lateral spreading of ion beams in air and in the traversed tissue is typically approximated with ideal Gaussian-shaped distributions, enabling straightforward superimposition of several scattering contributions. This work presents the double Gaussian parametrization of scanned proton and carbon ion beams in water that has been introduced in an upgraded version of the worldwide first commercial ion TPS for clinical use at the Heidelberg Ion Beam Therapy Center (HIT). First, the Monte Carlo results obtained from a detailed implementation of the HIT beamline have been validated against available experimental data. Then, for generating the TPS lateral parametrization, radial beam broadening has been calculated in a water target placed at a representative position after scattering in the beamline elements and air for 20 initial beam energies for each ion species. The simulated profiles were finally fitted with an idealized double Gaussian distribution that did not perfectly describe the nature of the data, thus requiring a careful choice of the fitting conditions. The obtained parametrization is in clinical use not only at the HIT center, but also at the Centro Nazionale di Adroterapia Oncologica. PMID:23824133

Realization of the purely spatial Einstein-Podolsky-Rosen paradox in full-field images of spontaneous parametric down-conversion

NASA Astrophysics Data System (ADS)

Moreau, Paul-Antoine; Mougin-Sisini, Joé; Devaux, Fabrice; Lantz, Eric

2012-07-01

We demonstrate Einstein-Podolsky-Rosen (EPR) entanglement by detecting purely spatial quantum correlations in the near and far fields of spontaneous parametric down-conversion generated in a type-2 beta barium borate crystal. Full-field imaging is performed in the photon-counting regime with an electron-multiplying CCD camera. The data are used without any postselection, and we obtain a violation of Heisenberg inequalities with inferred quantities taking into account all the biphoton pairs in both the near and far fields by integration on the entire two-dimensional transverse planes. This ensures a rigorous demonstration of the EPR paradox in its original position-momentum form.

Non-parametric diffeomorphic image registration with the demons algorithm.

PubMed

Vercauteren, Tom; Pennec, Xavier; Perchant, Aymeric; Ayache, Nicholas

2007-01-01

We propose a non-parametric diffeomorphic image registration algorithm based on Thirion's demons algorithm. The demons algorithm can be seen as an optimization procedure on the entire space of displacement fields. The main idea of our algorithm is to adapt this procedure to a space of diffeomorphic transformations. In contrast to many diffeomorphic registration algorithms, our solution is computationally efficient since in practice it only replaces an addition of free form deformations by a few compositions. Our experiments show that in addition to being diffeomorphic, our algorithm provides results that are similar to the ones from the demons algorithm but with transformations that are much smoother and closer to the true ones in terms of Jacobians.

Monitoring combat wound healing by IR hyperspectral imaging

NASA Astrophysics Data System (ADS)

Howle, Chris R.; Spear, Abigail M.; Gazi, Ehsan; Crane, Nicole J.

2016-03-01

In recent conflicts, battlefield injuries consist largely of extensive soft injuries from blasts and high energy projectiles, including gunshot wounds. Repair of these large, traumatic wounds requires aggressive surgical treatment, including multiple surgical debridements to remove devitalised tissue and to reduce bacterial load. Identifying those patients with wound complications, such as infection and impaired healing, could greatly assist health care teams in providing the most appropriate and personalised care for combat casualties. Candidate technologies to enable this benefit include the fusion of imaging and optical spectroscopy to enable rapid identification of key markers. Hence, a novel system based on IR negative contrast imaging (NCI) is presented that employs an optical parametric oscillator (OPO) source comprising a periodically-poled LiNbO3 (PPLN) crystal. The crystal operates in the shortwave and midwave IR spectral regions (ca. 1.5 - 1.9 μm and 2.4 - 3.8 μm, respectively). Wavelength tuning is achieved by translating the crystal within the pump beam. System size and complexity are minimised by the use of single element detectors and the intracavity OPO design. Images are composed by raster scanning the monochromatic beam over the scene of interest; the reflection and/or absorption of the incident radiation by target materials and their surrounding environment provide a method for spatial location. Initial results using the NCI system to characterise wound biopsies are presented here.

A networked modular hardware and software system for MRI-guided robotic prostate interventions

NASA Astrophysics Data System (ADS)

Su, Hao; Shang, Weijian; Harrington, Kevin; Camilo, Alex; Cole, Gregory; Tokuda, Junichi; Hata, Nobuhiko; Tempany, Clare; Fischer, Gregory S.

2012-02-01

Magnetic resonance imaging (MRI) provides high resolution multi-parametric imaging, large soft tissue contrast, and interactive image updates making it an ideal modality for diagnosing prostate cancer and guiding surgical tools. Despite a substantial armamentarium of apparatuses and systems has been developed to assist surgical diagnosis and therapy for MRI-guided procedures over last decade, the unified method to develop high fidelity robotic systems in terms of accuracy, dynamic performance, size, robustness and modularity, to work inside close-bore MRI scanner still remains a challenge. In this work, we develop and evaluate an integrated modular hardware and software system to support the surgical workflow of intra-operative MRI, with percutaneous prostate intervention as an illustrative case. Specifically, the distinct apparatuses and methods include: 1) a robot controller system for precision closed loop control of piezoelectric motors, 2) a robot control interface software that connects the 3D Slicer navigation software and the robot controller to exchange robot commands and coordinates using the OpenIGTLink open network communication protocol, and 3) MRI scan plane alignment to the planned path and imaging of the needle as it is inserted into the target location. A preliminary experiment with ex-vivo phantom validates the system workflow, MRI-compatibility and shows that the robotic system has a better than 0.01mm positioning accuracy.

Whole-body PET parametric imaging employing direct 4D nested reconstruction and a generalized non-linear Patlak model

NASA Astrophysics Data System (ADS)

Karakatsanis, Nicolas A.; Rahmim, Arman

2014-03-01

Graphical analysis is employed in the research setting to provide quantitative estimation of PET tracer kinetics from dynamic images at a single bed. Recently, we proposed a multi-bed dynamic acquisition framework enabling clinically feasible whole-body parametric PET imaging by employing post-reconstruction parameter estimation. In addition, by incorporating linear Patlak modeling within the system matrix, we enabled direct 4D reconstruction in order to effectively circumvent noise amplification in dynamic whole-body imaging. However, direct 4D Patlak reconstruction exhibits a relatively slow convergence due to the presence of non-sparse spatial correlations in temporal kinetic analysis. In addition, the standard Patlak model does not account for reversible uptake, thus underestimating the influx rate Ki. We have developed a novel whole-body PET parametric reconstruction framework in the STIR platform, a widely employed open-source reconstruction toolkit, a) enabling accelerated convergence of direct 4D multi-bed reconstruction, by employing a nested algorithm to decouple the temporal parameter estimation from the spatial image update process, and b) enhancing the quantitative performance particularly in regions with reversible uptake, by pursuing a non-linear generalized Patlak 4D nested reconstruction algorithm. A set of published kinetic parameters and the XCAT phantom were employed for the simulation of dynamic multi-bed acquisitions. Quantitative analysis on the Ki images demonstrated considerable acceleration in the convergence of the nested 4D whole-body Patlak algorithm. In addition, our simulated and patient whole-body data in the postreconstruction domain indicated the quantitative benefits of our extended generalized Patlak 4D nested reconstruction for tumor diagnosis and treatment response monitoring.

Task-based detectability comparison of exponential transformation of free-response operating characteristic (EFROC) curve and channelized Hotelling observer (CHO)

NASA Astrophysics Data System (ADS)

Khobragade, P.; Fan, Jiahua; Rupcich, Franco; Crotty, Dominic J.; Gilat Schmidt, Taly

2016-03-01

This study quantitatively evaluated the performance of the exponential transformation of the free-response operating characteristic curve (EFROC) metric, with the Channelized Hotelling Observer (CHO) as a reference. The CHO has been used for image quality assessment of reconstruction algorithms and imaging systems and often it is applied to study the signal-location-known cases. The CHO also requires a large set of images to estimate the covariance matrix. In terms of clinical applications, this assumption and requirement may be unrealistic. The newly developed location-unknown EFROC detectability metric is estimated from the confidence scores reported by a model observer. Unlike the CHO, EFROC does not require a channelization step and is a non-parametric detectability metric. There are few quantitative studies available on application of the EFROC metric, most of which are based on simulation data. This study investigated the EFROC metric using experimental CT data. A phantom with four low contrast objects: 3mm (14 HU), 5mm (7HU), 7mm (5 HU) and 10 mm (3 HU) was scanned at dose levels ranging from 25 mAs to 270 mAs and reconstructed using filtered backprojection. The area under the curve values for CHO (AUC) and EFROC (AFE) were plotted with respect to different dose levels. The number of images required to estimate the non-parametric AFE metric was calculated for varying tasks and found to be less than the number of images required for parametric CHO estimation. The AFE metric was found to be more sensitive to changes in dose than the CHO metric. This increased sensitivity and the assumption of unknown signal location may be useful for investigating and optimizing CT imaging methods. Future work is required to validate the AFE metric against human observers.

Development of an endoluminal high-intensity ultrasound applicator for image-guided thermal therapy of pancreatic tumors

NASA Astrophysics Data System (ADS)

Adams, Matthew S.; Scott, Serena J.; Salgaonkar, Vasant A.; Jones, Peter D.; Plata-Camargo, Juan C.; Sommer, Graham; Diederich, Chris J.

2015-03-01

An ultrasound applicator for endoluminal thermal therapy of pancreatic tumors has been introduced and evaluated through acoustic/biothermal simulations and ex vivo experimental investigations. Endoluminal therapeutic ultrasound constitutes a minimally invasive conformal therapy and is compatible with ultrasound or MR-based image guidance. The applicator would be placed in the stomach or duodenal lumen, and sonication would be performed through the luminal wall into the tumor, with concurrent water cooling of the wall tissue to prevent its thermal injury. A finite-element (FEM) 3D acoustic and biothermal model was implemented for theoretical analysis of the approach. Parametric studies over transducer geometries and frequencies revealed that operating frequencies within 1-3 MHz maximize penetration depth and lesion volume while sparing damage to the luminal wall. Patient-specific FEM models of pancreatic head tumors were generated and used to assess the feasibility of performing endoluminal ultrasound thermal ablation and hyperthermia of pancreatic tumors. Results indicated over 80% of the volume of small tumors (~2 cm diameter) within 35 mm of the duodenum could be safely ablated in under 30 minutes or elevated to hyperthermic temperatures at steady-state. Approximately 60% of a large tumor (~5 cm diameter) model could be safely ablated by considering multiple positions of the applicator along the length of the duodenum to increase coverage. Prototype applicators containing two 3.2 MHz planar transducers were fabricated and evaluated in ex vivo porcine carcass heating experiments under MR temperature imaging (MRTI) guidance. The applicator was positioned in the stomach adjacent to the pancreas, and sonications were performed for 10 min at 5 W/cm2 applied intensity. MRTI indicated over 400C temperature rise in pancreatic tissue with heating penetration extending 3 cm from the luminal wall.

Integrated 68Gallium Labelled Prostate-Specific Membrane Antigen-11 Positron Emission Tomography/Magnetic Resonance Imaging Enhances Discriminatory Power of Multi-Parametric Prostate Magnetic Resonance Imaging.

PubMed

Al-Bayati, Mohammad; Grueneisen, Johannes; Lütje, Susanne; Sawicki, Lino M; Suntharalingam, Saravanabavaan; Tschirdewahn, Stephan; Forsting, Michael; Rübben, Herbert; Herrmann, Ken; Umutlu, Lale; Wetter, Axel

2018-01-01

To evaluate diagnostic accuracy of integrated 68Gallium labelled prostate-specific membrane antigen (68Ga-PSMA)-11 positron emission tomography (PET)/MRI in patients with primary prostate cancer (PCa) as compared to multi-parametric MRI. A total of 22 patients with recently diagnosed primary PCa underwent clinically indicated 68Ga-PSMA-11 PET/CT for initial staging followed by integrated 68Ga-PSMA-11 PET/MRI. Images of multi-parametric magnetic resonance imaging (mpMRI), PET and PET/MRI were evaluated separately by applying Prostate Imaging Reporting and Data System (PIRADSv2) for mpMRI and a 5-point Likert scale for PET and PET/MRI. Results were compared with pathology reports of biopsy or resection. Statistical analyses including receiver operating characteristics analysis were performed to compare the diagnostic performance of mpMRI, PET and PET/MRI. PET and integrated PET/MRI demonstrated a higher diagnostic accuracy than mpMRI (area under the curve: mpMRI: 0.679, PET and PET/MRI: 0.951). The proportion of equivocal results (PIRADS 3 and Likert 3) was considerably higher in mpMRI than in PET and PET/MRI. In a notable proportion of equivocal PIRADS results, PET led to a correct shift towards higher suspicion of malignancy and enabled correct lesion classification. Integrated 68Ga-PSMA-11 PET/MRI demonstrates higher diagnostic accuracy than mpMRI and is particularly valuable in tumours with equivocal results from PIRADS classification. © 2018 S. Karger AG, Basel.

Statistical parametric mapping of LORETA using high density EEG and individual MRI: application to mismatch negativities in schizophrenia.

PubMed

Park, Hae-Jeong; Kwon, Jun Soo; Youn, Tak; Pae, Ji Soo; Kim, Jae-Jin; Kim, Myung-Sun; Ha, Kyoo-Seob

2002-11-01

We describe a method for the statistical parametric mapping of low resolution electromagnetic tomography (LORETA) using high-density electroencephalography (EEG) and individual magnetic resonance images (MRI) to investigate the characteristics of the mismatch negativity (MMN) generators in schizophrenia. LORETA, using a realistic head model of the boundary element method derived from the individual anatomy, estimated the current density maps from the scalp topography of the 128-channel EEG. From the current density maps that covered the whole cortical gray matter (up to 20,000 points), volumetric current density images were reconstructed. Intensity normalization of the smoothed current density images was used to reduce the confounding effect of subject specific global activity. After transforming each image into a standard stereotaxic space, we carried out statistical parametric mapping of the normalized current density images. We applied this method to the source localization of MMN in schizophrenia. The MMN generators, produced by a deviant tone of 1,200 Hz (5% of 1,600 trials) under the standard tone of 1,000 Hz, 80 dB binaural stimuli with 300 msec of inter-stimulus interval, were measured in 14 right-handed schizophrenic subjects and 14 age-, gender-, and handedness-matched controls. We found that the schizophrenic group exhibited significant current density reductions of MMN in the left superior temporal gyrus and the left inferior parietal gyrus (P < 0. 0005). This study is the first voxel-by-voxel statistical mapping of current density using individual MRI and high-density EEG. Copyright 2002 Wiley-Liss, Inc.

Airways, vasculature, and interstitial tissue: anatomically informed computational modeling of human lungs for virtual clinical trials

NASA Astrophysics Data System (ADS)

Abadi, Ehsan; Sturgeon, Gregory M.; Agasthya, Greeshma; Harrawood, Brian; Hoeschen, Christoph; Kapadia, Anuj; Segars, W. P.; Samei, Ehsan

2017-03-01

This study aimed to model virtual human lung phantoms including both non-parenchymal and parenchymal structures. Initial branches of the non-parenchymal structures (airways, arteries, and veins) were segmented from anatomical data in each lobe separately. A volume-filling branching algorithm was utilized to grow the higher generations of the airways and vessels to the level of terminal branches. The diameters of the airways and vessels were estimated using established relationships between flow rates and diameters. The parenchyma was modeled based on secondary pulmonary lobule units. Polyhedral shapes with variable sizes were modeled, and the borders were assigned to interlobular septa. A heterogeneous background was added inside these units using a non-parametric texture synthesis algorithm which was informed by a high-resolution CT lung specimen dataset. A voxelized based CT simulator was developed to create synthetic helical CT images of the phantom with different pitch values. Results showed the progressive degradation in depiction of lung details with increased pitch. Overall, the enhanced lung models combined with the XCAT phantoms prove to provide a powerful toolset to perform virtual clinical trials in the context of thoracic imaging. Such trials, not practical using clinical datasets or simplistic phantoms, can quantitatively evaluate and optimize advanced imaging techniques towards patient-based care.

Analysis and classification of optical tomographic images of rheumatoid fingers with ANOVA and discriminate analysis

NASA Astrophysics Data System (ADS)

Montejo, Ludguier D.; Kim, Hyun K.; Häme, Yrjö; Jia, Jingfei; Montejo, Julio D.; Netz, Uwe J.; Blaschke, Sabine; Zwaka, Paul; Müeller, Gerhard A.; Beuthan, Jürgen; Hielscher, Andreas H.

2011-03-01

We present a study on the effectiveness of computer-aided diagnosis (CAD) of rheumatoid arthritis (RA) from frequency-domain diffuse optical tomographic (FDOT) images. FDOT is used to obtain the distribution of tissue optical properties. Subsequently, the non-parametric Kruskal-Wallis ANOVA test is employed to verify statistically significant differences between the optical parameters of patients affected by RA and healthy volunteers. Furthermore, quadratic discriminate analysis (QDA) of the absorption (μa) and scattering (μa or μ's) distributions is used to classify subjects as affected or not affected by RA. We evaluate the classification efficiency by determining the sensitivity (Se), specificity (Sp), and the Youden index (Y). We find that combining features extracted from μa and μa or μ's images allows for more accurate classification than when μa or μa or μ's features are considered individually on their own. Combining μa and μa or μ's features yields values of up to Y = 0.75 (Se = 0.84 and Sp = 0.91). The best results when μa or μ's features are considered individually are Y = 0.65 (Se = 0.85 and Sp = 0.80) and Y = 0.70 (Se = 0.80 and Sp = 0.90), respectively.

Ultrasound window-modulated compounding Nakagami imaging: Resolution improvement and computational acceleration for liver characterization.

PubMed

Ma, Hsiang-Yang; Lin, Ying-Hsiu; Wang, Chiao-Yin; Chen, Chiung-Nien; Ho, Ming-Chih; Tsui, Po-Hsiang

2016-08-01

Ultrasound Nakagami imaging is an attractive method for visualizing changes in envelope statistics. Window-modulated compounding (WMC) Nakagami imaging was reported to improve image smoothness. The sliding window technique is typically used for constructing ultrasound parametric and Nakagami images. Using a large window overlap ratio may improve the WMC Nakagami image resolution but reduces computational efficiency. Therefore, the objectives of this study include: (i) exploring the effects of the window overlap ratio on the resolution and smoothness of WMC Nakagami images; (ii) proposing a fast algorithm that is based on the convolution operator (FACO) to accelerate WMC Nakagami imaging. Computer simulations and preliminary clinical tests on liver fibrosis samples (n=48) were performed to validate the FACO-based WMC Nakagami imaging. The results demonstrated that the width of the autocorrelation function and the parameter distribution of the WMC Nakagami image reduce with the increase in the window overlap ratio. One-pixel shifting (i.e., sliding the window on the image data in steps of one pixel for parametric imaging) as the maximum overlap ratio significantly improves the WMC Nakagami image quality. Concurrently, the proposed FACO method combined with a computational platform that optimizes the matrix computation can accelerate WMC Nakagami imaging, allowing the detection of liver fibrosis-induced changes in envelope statistics. FACO-accelerated WMC Nakagami imaging is a new-generation Nakagami imaging technique with an improved image quality and fast computation. Copyright © 2016 Elsevier B.V. All rights reserved.

Classification of clinical significance of MRI prostate findings using 3D convolutional neural networks

NASA Astrophysics Data System (ADS)

Mehrtash, Alireza; Sedghi, Alireza; Ghafoorian, Mohsen; Taghipour, Mehdi; Tempany, Clare M.; Wells, William M.; Kapur, Tina; Mousavi, Parvin; Abolmaesumi, Purang; Fedorov, Andriy

2017-03-01

Prostate cancer (PCa) remains a leading cause of cancer mortality among American men. Multi-parametric magnetic resonance imaging (mpMRI) is widely used to assist with detection of PCa and characterization of its aggressiveness. Computer-aided diagnosis (CADx) of PCa in MRI can be used as clinical decision support system to aid radiologists in interpretation and reporting of mpMRI. We report on the development of a convolution neural network (CNN) model to support CADx in PCa based on the appearance of prostate tissue in mpMRI, conducted as part of the SPIE-AAPM-NCI PROSTATEx challenge. The performance of different combinations of mpMRI inputs to CNN was assessed and the best result was achieved using DWI and DCE-MRI modalities together with the zonal information of the finding. On the test set, the model achieved an area under the receiver operating characteristic curve of 0.80.

Tumor response estimation in radar-based microwave breast cancer detection.

PubMed

Kurrant, Douglas J; Fear, Elise C; Westwick, David T

2008-12-01

Radar-based microwave imaging techniques have been proposed for early stage breast cancer detection. A considerable challenge for the successful implementation of these techniques is the reduction of clutter, or components of the signal originating from objects other than the tumor. In particular, the reduction of clutter from the late-time scattered fields is required in order to detect small (subcentimeter diameter) tumors. In this paper, a method to estimate the tumor response contained in the late-time scattered fields is presented. The method uses a parametric function to model the tumor response. A maximum a posteriori estimation approach is used to evaluate the optimal values for the estimates of the parameters. A pattern classification technique is then used to validate the estimation. The ability of the algorithm to estimate a tumor response is demonstrated by using both experimental and simulated data obtained with a tissue sensing adaptive radar system.

Sparsity-promoting and edge-preserving maximum a posteriori estimators in non-parametric Bayesian inverse problems

NASA Astrophysics Data System (ADS)

Agapiou, Sergios; Burger, Martin; Dashti, Masoumeh; Helin, Tapio

2018-04-01

We consider the inverse problem of recovering an unknown functional parameter u in a separable Banach space, from a noisy observation vector y of its image through a known possibly non-linear map {{\\mathcal G}} . We adopt a Bayesian approach to the problem and consider Besov space priors (see Lassas et al (2009 Inverse Problems Imaging 3 87-122)), which are well-known for their edge-preserving and sparsity-promoting properties and have recently attracted wide attention especially in the medical imaging community. Our key result is to show that in this non-parametric setup the maximum a posteriori (MAP) estimates are characterized by the minimizers of a generalized Onsager-Machlup functional of the posterior. This is done independently for the so-called weak and strong MAP estimates, which as we show coincide in our context. In addition, we prove a form of weak consistency for the MAP estimators in the infinitely informative data limit. Our results are remarkable for two reasons: first, the prior distribution is non-Gaussian and does not meet the smoothness conditions required in previous research on non-parametric MAP estimates. Second, the result analytically justifies existing uses of the MAP estimate in finite but high dimensional discretizations of Bayesian inverse problems with the considered Besov priors.

Multiscale Reconstruction for Magnetic Resonance Fingerprinting

PubMed Central

Pierre, Eric Y.; Ma, Dan; Chen, Yong; Badve, Chaitra; Griswold, Mark A.

2015-01-01

Purpose To reduce acquisition time needed to obtain reliable parametric maps with Magnetic Resonance Fingerprinting. Methods An iterative-denoising algorithm is initialized by reconstructing the MRF image series at low image resolution. For subsequent iterations, the method enforces pixel-wise fidelity to the best-matching dictionary template then enforces fidelity to the acquired data at slightly higher spatial resolution. After convergence, parametric maps with desirable spatial resolution are obtained through template matching of the final image series. The proposed method was evaluated on phantom and in-vivo data using the highly-undersampled, variable-density spiral trajectory and compared with the original MRF method. The benefits of additional sparsity constraints were also evaluated. When available, gold standard parameter maps were used to quantify the performance of each method. Results The proposed approach allowed convergence to accurate parametric maps with as few as 300 time points of acquisition, as compared to 1000 in the original MRF work. Simultaneous quantification of T1, T2, proton density (PD) and B0 field variations in the brain was achieved in vivo for a 256×256 matrix for a total acquisition time of 10.2s, representing a 3-fold reduction in acquisition time. Conclusions The proposed iterative multiscale reconstruction reliably increases MRF acquisition speed and accuracy. PMID:26132462

Modelling of cardiac-related changes in lung resistivity measured with EITS.

PubMed

Zhao, T X; Brown, B H; Nopp, P; Wang, W; Leathard, A D; Lu, L Q

1996-11-01

Resistivity data from 9.6 kHZ to 1.2 MHz were recorded from eight normal subjects using an electrical impedance tomographic spectroscopy (EITS) system and then averaged to a mean cardiac cycle using the ECG gating technique. The Cole-Cole model, that is, extracellular resistance R connected in parallel with intracellular resistance S and membrane capacitance C in series, with a distribution parameter a, was applied to model the frequency characteristics and to produce parametric images. During systole, SC and RC were found to decrease and FR increase. The changes in R/S were not consistent among the subjects. We estimated the peak changes in R, S and C to be -2.5%, -3.3% and -7.6% respectively. The results can be explained by considering the blood vessels as spheres of different sizes with blood inside them. The decrease in R during systole might be caused by the increased blood content in relatively large vessels, whereas that in S by the increased blood volume in relatively small vessels. The capacitance of blood is normally smaller than that of lung tissue, whereas FR blood is higher than that of lung tissue. Hence, as blood content increases, C should decrease and FR increase.

On the sensitivity of teleseismic full-waveform inversion to earth parametrization, initial model and acquisition design

NASA Astrophysics Data System (ADS)

Beller, S.; Monteiller, V.; Combe, L.; Operto, S.; Nolet, G.

2018-02-01

Full-waveform inversion (FWI) is not yet a mature imaging technology for lithospheric imaging from teleseismic data. Therefore, its promise and pitfalls need to be assessed more accurately according to the specifications of teleseismic experiments. Three important issues are related to (1) the choice of the lithospheric parametrization for optimization and visualization, (2) the initial model and (3) the acquisition design, in particular in terms of receiver spread and sampling. These three issues are investigated with a realistic synthetic example inspired by the CIFALPS experiment in the Western Alps. Isotropic elastic FWI is implemented with an adjoint-state formalism and aims to update three parameter classes by minimization of a classical least-squares difference-based misfit function. Three different subsurface parametrizations, combining density (ρ) with P and S wave speeds (Vp and Vs) , P and S impedances (Ip and Is), or elastic moduli (λ and μ) are first discussed based on their radiation patterns before their assessment by FWI. We conclude that the (ρ, λ, μ) parametrization provides the FWI models that best correlate with the true ones after recombining a posteriori the (ρ, λ, μ) optimization parameters into Ip and Is. Owing to the low frequency content of teleseismic data, 1-D reference global models as PREM provide sufficiently accurate initial models for FWI after smoothing that is necessary to remove the imprint of the layering. Two kinds of station deployments are assessed: coarse areal geometry versus dense linear one. We unambiguously conclude that a coarse areal geometry should be favoured as it dramatically increases the penetration in depth of the imaging as well as the horizontal resolution. This results because the areal geometry significantly increases local wavenumber coverage, through a broader sampling of the scattering and dip angles, compared to a linear deployment.

Synthesis and Analysis of Custom Bi-directional Reflectivity Distribution Functions in DIRSIG

NASA Astrophysics Data System (ADS)

Dank, J.; Allen, D.

2016-09-01

The bi-directional reflectivity distribution (BRDF) function is a fundamental optical property of materials, characterizing important properties of light scattered by a surface. For accurate radiance calculations using synthetic targets and numerical simulations such as the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model, fidelity of the target BRDFs is critical. While fits to measured BRDF data can be used in DIRSIG, obtaining high-quality data over a large spectral continuum can be time-consuming and expensive, requiring significant investment in illumination sources, sensors, and other specialized hardware. As a consequence, numerous parametric BRDF models are available to approximate actual behavior; but these all have shortcomings. Further, DIRSIG doesn't allow direct visualization of BRDFs, making it difficult for the user to understand the numerical impact of various models. Here, we discuss the innovative use of "mixture maps" to synthesize custom BRDFs as linear combinations of parametric models and measured data. We also show how DIRSIG's interactive mode can be used to visualize and analyze both available parametric models currently used in DIRSIG and custom BRDFs developed using our methods.

Image-rotating, 4-mirror, ring optical parametric oscillator

DOEpatents

Smith, Arlee V.; Armstrong, Darrell J.

2004-08-10

A device for optical parametric amplification utilizing four mirrors oriented in a nonplanar configuration where the optical plane formed by two of the mirrors is orthogonal to the optical plane formed by the other two mirrors and with the ratio of lengths of the laser beam paths approximately constant regardless of the scale of the device. With a cavity length of less than approximately 110 mm, a conversion efficiency of greater than 45% can be achieved.

Multi-mode of Four and Six Wave Parametric Amplified Process

NASA Astrophysics Data System (ADS)

Zhu, Dayu; Yang, Yiheng; Zhang, Da; Liu, Ruizhou; Ma, Danmeng; Li, Changbiao; Zhang, Yanpeng

2017-03-01

Multiple quantum modes in correlated fields are essential for future quantum information processing and quantum computing. Here we report the generation of multi-mode phenomenon through parametric amplified four- and six-wave mixing processes in a rubidium atomic ensemble. The multi-mode properties in both frequency and spatial domains are studied. On one hand, the multi-mode behavior is dominantly controlled by the intensity of external dressing effect, or nonlinear phase shift through internal dressing effect, in frequency domain; on the other hand, the multi-mode behavior is visually demonstrated from the images of the biphoton fields directly, in spatial domain. Besides, the correlation of the two output fields is also demonstrated in both domains. Our approach supports efficient applications for scalable quantum correlated imaging.

Multi-mode of Four and Six Wave Parametric Amplified Process.

PubMed

Zhu, Dayu; Yang, Yiheng; Zhang, Da; Liu, Ruizhou; Ma, Danmeng; Li, Changbiao; Zhang, Yanpeng

2017-03-03

Multiple quantum modes in correlated fields are essential for future quantum information processing and quantum computing. Here we report the generation of multi-mode phenomenon through parametric amplified four- and six-wave mixing processes in a rubidium atomic ensemble. The multi-mode properties in both frequency and spatial domains are studied. On one hand, the multi-mode behavior is dominantly controlled by the intensity of external dressing effect, or nonlinear phase shift through internal dressing effect, in frequency domain; on the other hand, the multi-mode behavior is visually demonstrated from the images of the biphoton fields directly, in spatial domain. Besides, the correlation of the two output fields is also demonstrated in both domains. Our approach supports efficient applications for scalable quantum correlated imaging.

A distribution-based parametrization for improved tomographic imaging of solute plumes

USGS Publications Warehouse

Pidlisecky, Adam; Singha, K.; Day-Lewis, F. D.

2011-01-01

Difference geophysical tomography (e.g. radar, resistivity and seismic) is used increasingly for imaging fluid flow and mass transport associated with natural and engineered hydrologic phenomena, including tracer experiments, in situ remediation and aquifer storage and recovery. Tomographic data are collected over time, inverted and differenced against a background image to produce 'snapshots' revealing changes to the system; these snapshots readily provide qualitative information on the location and morphology of plumes of injected tracer, remedial amendment or stored water. In principle, geometric moments (i.e. total mass, centres of mass, spread, etc.) calculated from difference tomograms can provide further quantitative insight into the rates of advection, dispersion and mass transfer; however, recent work has shown that moments calculated from tomograms are commonly biased, as they are strongly affected by the subjective choice of regularization criteria. Conventional approaches to regularization (Tikhonov) and parametrization (image pixels) result in tomograms which are subject to artefacts such as smearing or pixel estimates taking on the sign opposite to that expected for the plume under study. Here, we demonstrate a novel parametrization for imaging plumes associated with hydrologic phenomena. Capitalizing on the mathematical analogy between moment-based descriptors of plumes and the moment-based parameters of probability distributions, we design an inverse problem that (1) is overdetermined and computationally efficient because the image is described by only a few parameters, (2) produces tomograms consistent with expected plume behaviour (e.g. changes of one sign relative to the background image), (3) yields parameter estimates that are readily interpreted for plume morphology and offer direct insight into hydrologic processes and (4) requires comparatively few data to achieve reasonable model estimates. We demonstrate the approach in a series of numerical examples based on straight-ray difference-attenuation radar monitoring of the transport of an ionic tracer, and show that the methodology outlined here is particularly effective when limited data are available. ?? 2011 The Authors Geophysical Journal International ?? 2011 RAS.

Dependence of quantitative accuracy of CT perfusion imaging on system parameters

NASA Astrophysics Data System (ADS)

Li, Ke; Chen, Guang-Hong

2017-03-01

Deconvolution is a popular method to calculate parametric perfusion parameters from four dimensional CT perfusion (CTP) source images. During the deconvolution process, the four dimensional space is squeezed into three-dimensional space by removing the temporal dimension, and a prior knowledge is often used to suppress noise associated with the process. These additional complexities confound the understanding about deconvolution-based CTP imaging system and how its quantitative accuracy depends on parameters and sub-operations involved in the image formation process. Meanwhile, there has been a strong clinical need in answering this question, as physicians often rely heavily on the quantitative values of perfusion parameters to make diagnostic decisions, particularly during an emergent clinical situation (e.g. diagnosis of acute ischemic stroke). The purpose of this work was to develop a theoretical framework that quantitatively relates the quantification accuracy of parametric perfusion parameters with CTP acquisition and post-processing parameters. This goal was achieved with the help of a cascaded systems analysis for deconvolution-based CTP imaging systems. Based on the cascaded systems analysis, the quantitative relationship between regularization strength, source image noise, arterial input function, and the quantification accuracy of perfusion parameters was established. The theory could potentially be used to guide developments of CTP imaging technology for better quantification accuracy and lower radiation dose.

A global goodness-of-fit test for receiver operating characteristic curve analysis via the bootstrap method.

PubMed

Zou, Kelly H; Resnic, Frederic S; Talos, Ion-Florin; Goldberg-Zimring, Daniel; Bhagwat, Jui G; Haker, Steven J; Kikinis, Ron; Jolesz, Ferenc A; Ohno-Machado, Lucila

2005-10-01

Medical classification accuracy studies often yield continuous data based on predictive models for treatment outcomes. A popular method for evaluating the performance of diagnostic tests is the receiver operating characteristic (ROC) curve analysis. The main objective was to develop a global statistical hypothesis test for assessing the goodness-of-fit (GOF) for parametric ROC curves via the bootstrap. A simple log (or logit) and a more flexible Box-Cox normality transformations were applied to untransformed or transformed data from two clinical studies to predict complications following percutaneous coronary interventions (PCIs) and for image-guided neurosurgical resection results predicted by tumor volume, respectively. We compared a non-parametric with a parametric binormal estimate of the underlying ROC curve. To construct such a GOF test, we used the non-parametric and parametric areas under the curve (AUCs) as the metrics, with a resulting p value reported. In the interventional cardiology example, logit and Box-Cox transformations of the predictive probabilities led to satisfactory AUCs (AUC=0.888; p=0.78, and AUC=0.888; p=0.73, respectively), while in the brain tumor resection example, log and Box-Cox transformations of the tumor size also led to satisfactory AUCs (AUC=0.898; p=0.61, and AUC=0.899; p=0.42, respectively). In contrast, significant departures from GOF were observed without applying any transformation prior to assuming a binormal model (AUC=0.766; p=0.004, and AUC=0.831; p=0.03), respectively. In both studies the p values suggested that transformations were important to consider before applying any binormal model to estimate the AUC. Our analyses also demonstrated and confirmed the predictive values of different classifiers for determining the interventional complications following PCIs and resection outcomes in image-guided neurosurgery.

Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization.

PubMed

Sauwen, Nicolas; Acou, Marjan; Sima, Diana M; Veraart, Jelle; Maes, Frederik; Himmelreich, Uwe; Achten, Eric; Huffel, Sabine Van

2017-05-04

Segmentation of gliomas in multi-parametric (MP-)MR images is challenging due to their heterogeneous nature in terms of size, appearance and location. Manual tumor segmentation is a time-consuming task and clinical practice would benefit from (semi-) automated segmentation of the different tumor compartments. We present a semi-automated framework for brain tumor segmentation based on non-negative matrix factorization (NMF) that does not require prior training of the method. L1-regularization is incorporated into the NMF objective function to promote spatial consistency and sparseness of the tissue abundance maps. The pathological sources are initialized through user-defined voxel selection. Knowledge about the spatial location of the selected voxels is combined with tissue adjacency constraints in a post-processing step to enhance segmentation quality. The method is applied to an MP-MRI dataset of 21 high-grade glioma patients, including conventional, perfusion-weighted and diffusion-weighted MRI. To assess the effect of using MP-MRI data and the L1-regularization term, analyses are also run using only conventional MRI and without L1-regularization. Robustness against user input variability is verified by considering the statistical distribution of the segmentation results when repeatedly analyzing each patient's dataset with a different set of random seeding points. Using L1-regularized semi-automated NMF segmentation, mean Dice-scores of 65%, 74 and 80% are found for active tumor, the tumor core and the whole tumor region. Mean Hausdorff distances of 6.1 mm, 7.4 mm and 8.2 mm are found for active tumor, the tumor core and the whole tumor region. Lower Dice-scores and higher Hausdorff distances are found without L1-regularization and when only considering conventional MRI data. Based on the mean Dice-scores and Hausdorff distances, segmentation results are competitive with state-of-the-art in literature. Robust results were found for most patients, although careful voxel selection is mandatory to avoid sub-optimal segmentation.

Machine learning-based dual-energy CT parametric mapping

NASA Astrophysics Data System (ADS)

Su, Kuan-Hao; Kuo, Jung-Wen; Jordan, David W.; Van Hedent, Steven; Klahr, Paul; Wei, Zhouping; Helo, Rose Al; Liang, Fan; Qian, Pengjiang; Pereira, Gisele C.; Rassouli, Negin; Gilkeson, Robert C.; Traughber, Bryan J.; Cheng, Chee-Wai; Muzic, Raymond F., Jr.

2018-06-01

The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Zeff), relative electron density (ρ e), mean excitation energy (I x ), and relative stopping power (RSP) from clinical dual-energy CT data. The maps could be used for material identification and radiation dose calculation. Machine learning methods of historical centroid (HC), random forest (RF), and artificial neural networks (ANN) were used to learn the relationship between dual-energy CT input data and ideal output parametric maps calculated for phantoms from the known compositions of 13 tissue substitutes. After training and model selection steps, the machine learning predictors were used to generate parametric maps from independent phantom and patient input data. Precision and accuracy were evaluated using the ideal maps. This process was repeated for a range of exposure doses, and performance was compared to that of the clinically-used dual-energy, physics-based method which served as the reference. The machine learning methods generated more accurate and precise parametric maps than those obtained using the reference method. Their performance advantage was particularly evident when using data from the lowest exposure, one-fifth of a typical clinical abdomen CT acquisition. The RF method achieved the greatest accuracy. In comparison, the ANN method was only 1% less accurate but had much better computational efficiency than RF, being able to produce parametric maps in 15 s. Machine learning methods outperformed the reference method in terms of accuracy and noise tolerance when generating parametric maps, encouraging further exploration of the techniques. Among the methods we evaluated, ANN is the most suitable for clinical use due to its combination of accuracy, excellent low-noise performance, and computational efficiency.

Machine learning-based dual-energy CT parametric mapping.

PubMed

Su, Kuan-Hao; Kuo, Jung-Wen; Jordan, David W; Van Hedent, Steven; Klahr, Paul; Wei, Zhouping; Al Helo, Rose; Liang, Fan; Qian, Pengjiang; Pereira, Gisele C; Rassouli, Negin; Gilkeson, Robert C; Traughber, Bryan J; Cheng, Chee-Wai; Muzic, Raymond F

2018-06-08

The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Z eff ), relative electron density (ρ e ), mean excitation energy (I x ), and relative stopping power (RSP) from clinical dual-energy CT data. The maps could be used for material identification and radiation dose calculation. Machine learning methods of historical centroid (HC), random forest (RF), and artificial neural networks (ANN) were used to learn the relationship between dual-energy CT input data and ideal output parametric maps calculated for phantoms from the known compositions of 13 tissue substitutes. After training and model selection steps, the machine learning predictors were used to generate parametric maps from independent phantom and patient input data. Precision and accuracy were evaluated using the ideal maps. This process was repeated for a range of exposure doses, and performance was compared to that of the clinically-used dual-energy, physics-based method which served as the reference. The machine learning methods generated more accurate and precise parametric maps than those obtained using the reference method. Their performance advantage was particularly evident when using data from the lowest exposure, one-fifth of a typical clinical abdomen CT acquisition. The RF method achieved the greatest accuracy. In comparison, the ANN method was only 1% less accurate but had much better computational efficiency than RF, being able to produce parametric maps in 15 s. Machine learning methods outperformed the reference method in terms of accuracy and noise tolerance when generating parametric maps, encouraging further exploration of the techniques. Among the methods we evaluated, ANN is the most suitable for clinical use due to its combination of accuracy, excellent low-noise performance, and computational efficiency.

A novel medical image data-based multi-physics simulation platform for computational life sciences.

PubMed

Neufeld, Esra; Szczerba, Dominik; Chavannes, Nicolas; Kuster, Niels

2013-04-06

Simulating and modelling complex biological systems in computational life sciences requires specialized software tools that can perform medical image data-based modelling, jointly visualize the data and computational results, and handle large, complex, realistic and often noisy anatomical models. The required novel solvers must provide the power to model the physics, biology and physiology of living tissue within the full complexity of the human anatomy (e.g. neuronal activity, perfusion and ultrasound propagation). A multi-physics simulation platform satisfying these requirements has been developed for applications including device development and optimization, safety assessment, basic research, and treatment planning. This simulation platform consists of detailed, parametrized anatomical models, a segmentation and meshing tool, a wide range of solvers and optimizers, a framework for the rapid development of specialized and parallelized finite element method solvers, a visualization toolkit-based visualization engine, a Python scripting interface for customized applications, a coupling framework, and more. Core components are cross-platform compatible and use open formats. Several examples of applications are presented: hyperthermia cancer treatment planning, tumour growth modelling, evaluating the magneto-haemodynamic effect as a biomarker and physics-based morphing of anatomical models.

Parametric PET/MR Fusion Imaging to Differentiate Aggressive from Indolent Primary Prostate Cancer with Application for Image-Guided Prostate Cancer Biopsies

DTIC Science & Technology

2014-10-01

Unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT The study investigates whether fusion PET/MRI imaging with 18F- choline PET/CT and...imaging with 18F- choline PET/CT and diffusion-weighted MRI can be successfully applied to target prostate cancer using image-guided prostate...Completed task. The 18F- choline synthesis was implemented and optimized for routine radiotracer production. RDRC committee approval as part of the IRB

Entangled-photon compressive ghost imaging

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

Zerom, Petros; Chan, Kam Wai Clifford; Howell, John C.

2011-12-15

We have experimentally demonstrated high-resolution compressive ghost imaging at the single-photon level using entangled photons produced by a spontaneous parametric down-conversion source and using single-pixel detectors. For a given mean-squared error, the number of photons needed to reconstruct a two-dimensional image is found to be much smaller than that in quantum ghost imaging experiments employing a raster scan. This procedure not only shortens the data acquisition time, but also suggests a more economical use of photons for low-light-level and quantum image formation.

Automated detection of extended sources in radio maps: progress from the SCORPIO survey

NASA Astrophysics Data System (ADS)

Riggi, S.; Ingallinera, A.; Leto, P.; Cavallaro, F.; Bufano, F.; Schillirò, F.; Trigilio, C.; Umana, G.; Buemi, C. S.; Norris, R. P.

2016-08-01

Automated source extraction and parametrization represents a crucial challenge for the next-generation radio interferometer surveys, such as those performed with the Square Kilometre Array (SKA) and its precursors. In this paper, we present a new algorithm, called CAESAR (Compact And Extended Source Automated Recognition), to detect and parametrize extended sources in radio interferometric maps. It is based on a pre-filtering stage, allowing image denoising, compact source suppression and enhancement of diffuse emission, followed by an adaptive superpixel clustering stage for final source segmentation. A parametrization stage provides source flux information and a wide range of morphology estimators for post-processing analysis. We developed CAESAR in a modular software library, also including different methods for local background estimation and image filtering, along with alternative algorithms for both compact and diffuse source extraction. The method was applied to real radio continuum data collected at the Australian Telescope Compact Array (ATCA) within the SCORPIO project, a pathfinder of the Evolutionary Map of the Universe (EMU) survey at the Australian Square Kilometre Array Pathfinder (ASKAP). The source reconstruction capabilities were studied over different test fields in the presence of compact sources, imaging artefacts and diffuse emission from the Galactic plane and compared with existing algorithms. When compared to a human-driven analysis, the designed algorithm was found capable of detecting known target sources and regions of diffuse emission, outperforming alternative approaches over the considered fields.

Building and using a statistical 3D motion atlas for analyzing myocardial contraction in MRI

NASA Astrophysics Data System (ADS)

Rougon, Nicolas F.; Petitjean, Caroline; Preteux, Francoise J.

2004-05-01

We address the issue of modeling and quantifying myocardial contraction from 4D MR sequences, and present an unsupervised approach for building and using a statistical 3D motion atlas for the normal heart. This approach relies on a state-of-the-art variational non rigid registration (NRR) technique using generalized information measures, which allows for robust intra-subject motion estimation and inter-subject anatomical alignment. The atlas is built from a collection of jointly acquired tagged and cine MR exams in short- and long-axis views. Subject-specific non parametric motion estimates are first obtained by incremental NRR of tagged images onto the end-diastolic (ED) frame. Individual motion data are then transformed into the coordinate system of a reference subject using subject-to-reference mappings derived by NRR of cine ED images. Finally, principal component analysis of aligned motion data is performed for each cardiac phase, yielding a mean model and a set of eigenfields encoding kinematic ariability. The latter define an organ-dedicated hierarchical motion basis which enables parametric motion measurement from arbitrary tagged MR exams. To this end, the atlas is transformed into subject coordinates by reference-to-subject NRR of ED cine frames. Atlas-based motion estimation is then achieved by parametric NRR of tagged images onto the ED frame, yielding a compact description of myocardial contraction during diastole.

Multiscale reconstruction for MR fingerprinting.

PubMed

Pierre, Eric Y; Ma, Dan; Chen, Yong; Badve, Chaitra; Griswold, Mark A

2016-06-01

To reduce the acquisition time needed to obtain reliable parametric maps with Magnetic Resonance Fingerprinting. An iterative-denoising algorithm is initialized by reconstructing the MRF image series at low image resolution. For subsequent iterations, the method enforces pixel-wise fidelity to the best-matching dictionary template then enforces fidelity to the acquired data at slightly higher spatial resolution. After convergence, parametric maps with desirable spatial resolution are obtained through template matching of the final image series. The proposed method was evaluated on phantom and in vivo data using the highly undersampled, variable-density spiral trajectory and compared with the original MRF method. The benefits of additional sparsity constraints were also evaluated. When available, gold standard parameter maps were used to quantify the performance of each method. The proposed approach allowed convergence to accurate parametric maps with as few as 300 time points of acquisition, as compared to 1000 in the original MRF work. Simultaneous quantification of T1, T2, proton density (PD), and B0 field variations in the brain was achieved in vivo for a 256 × 256 matrix for a total acquisition time of 10.2 s, representing a three-fold reduction in acquisition time. The proposed iterative multiscale reconstruction reliably increases MRF acquisition speed and accuracy. Magn Reson Med 75:2481-2492, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Research on respiratory motion correction method based on liver contrast-enhanced ultrasound images of single mode

NASA Astrophysics Data System (ADS)

Zhang, Ji; Li, Tao; Zheng, Shiqiang; Li, Yiyong

2015-03-01

To reduce the effects of respiratory motion in the quantitative analysis based on liver contrast-enhanced ultrasound (CEUS) image sequencesof single mode. The image gating method and the iterative registration method using model image were adopted to register liver contrast-enhanced ultrasound image sequences of single mode. The feasibility of the proposed respiratory motion correction method was explored preliminarily using 10 hepatocellular carcinomas CEUS cases. The positions of the lesions in the time series of 2D ultrasound images after correction were visually evaluated. Before and after correction, the quality of the weighted sum of transit time (WSTT) parametric images were also compared, in terms of the accuracy and spatial resolution. For the corrected and uncorrected sequences, their mean deviation values (mDVs) of time-intensity curve (TIC) fitting derived from CEUS sequences were measured. After the correction, the positions of the lesions in the time series of 2D ultrasound images were almost invariant. In contrast, the lesions in the uncorrected images all shifted noticeably. The quality of the WSTT parametric maps derived from liver CEUS image sequences were improved more greatly. Moreover, the mDVs of TIC fitting derived from CEUS sequences after the correction decreased by an average of 48.48+/-42.15. The proposed correction method could improve the accuracy of quantitative analysis based on liver CEUS image sequences of single mode, which would help in enhancing the differential diagnosis efficiency of liver tumors.

Dynamic contrast-enhanced CT of head and neck tumors: perfusion measurements using a distributed-parameter tracer kinetic model. Initial results and comparison with deconvolution-based analysis

NASA Astrophysics Data System (ADS)

Bisdas, Sotirios; Konstantinou, George N.; Sherng Lee, Puor; Thng, Choon Hua; Wagenblast, Jens; Baghi, Mehran; San Koh, Tong

2007-10-01

The objective of this work was to evaluate the feasibility of a two-compartment distributed-parameter (DP) tracer kinetic model to generate functional images of several physiologic parameters from dynamic contrast-enhanced CT data obtained of patients with extracranial head and neck tumors and to compare the DP functional images to those obtained by deconvolution-based DCE-CT data analysis. We performed post-processing of DCE-CT studies, obtained from 15 patients with benign and malignant head and neck cancer. We introduced a DP model of the impulse residue function for a capillary-tissue exchange unit, which accounts for the processes of convective transport and capillary-tissue exchange. The calculated parametric maps represented blood flow (F), intravascular blood volume (v1), extravascular extracellular blood volume (v2), vascular transit time (t1), permeability-surface area product (PS), transfer ratios k12 and k21, and the fraction of extracted tracer (E). Based on the same regions of interest (ROI) analysis, we calculated the tumor blood flow (BF), blood volume (BV) and mean transit time (MTT) by using a modified deconvolution-based analysis taking into account the extravasation of the contrast agent for PS imaging. We compared the corresponding values by using Bland-Altman plot analysis. We outlined 73 ROIs including tumor sites, lymph nodes and normal tissue. The Bland-Altman plot analysis revealed that the two methods showed an accepted degree of agreement for blood flow, and, thus, can be used interchangeably for measuring this parameter. Slightly worse agreement was observed between v1 in the DP model and BV but even here the two tracer kinetic analyses can be used interchangeably. Under consideration of whether both techniques may be used interchangeably was the case of t1 and MTT, as well as for measurements of the PS values. The application of the proposed DP model is feasible in the clinical routine and it can be used interchangeably for measuring blood flow and vascular volume with the commercially available reference standard of the deconvolution-based approach. The lack of substantial agreement between the measurements of vascular transit time and permeability-surface area product may be attributed to the different tracer kinetic principles employed by both models and the detailed capillary tissue exchange physiological modeling of the DP technique.

Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma.

PubMed

Hu, Leland S; Ning, Shuluo; Eschbacher, Jennifer M; Gaw, Nathan; Dueck, Amylou C; Smith, Kris A; Nakaji, Peter; Plasencia, Jonathan; Ranjbar, Sara; Price, Stephen J; Tran, Nhan; Loftus, Joseph; Jenkins, Robert; O'Neill, Brian P; Elmquist, William; Baxter, Leslie C; Gao, Fei; Frakes, David; Karis, John P; Zwart, Christine; Swanson, Kristin R; Sarkaria, Jann; Wu, Teresa; Mitchell, J Ross; Li, Jing

2015-01-01

Genetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT), despite the importance of characterizing this tumor segment, which universally recurs. In this study, we use multiple texture analysis and machine learning (ML) algorithms to analyze multi-parametric MRI, and produce new images indicating tumor-rich targets in GBM. We recruited primary GBM patients undergoing image-guided biopsies and acquired pre-operative MRI: CE-MRI, Dynamic-Susceptibility-weighted-Contrast-enhanced-MRI, and Diffusion Tensor Imaging. Following image coregistration and region of interest placement at biopsy locations, we compared MRI metrics and regional texture with histologic diagnoses of high- vs low-tumor content (≥80% vs <80% tumor nuclei) for corresponding samples. In a training set, we used three texture analysis algorithms and three ML methods to identify MRI-texture features that optimized model accuracy to distinguish tumor content. We confirmed model accuracy in a separate validation set. We collected 82 biopsies from 18 GBMs throughout ENH and BAT. The MRI-based model achieved 85% cross-validated accuracy to diagnose high- vs low-tumor in the training set (60 biopsies, 11 patients). The model achieved 81.8% accuracy in the validation set (22 biopsies, 7 patients). Multi-parametric MRI and texture analysis can help characterize and visualize GBM's spatial histologic heterogeneity to identify regional tumor-rich biopsy targets.

Excitation-resolved multispectral method for imaging pharmacokinetic parameters in dynamic fluorescent molecular tomography

NASA Astrophysics Data System (ADS)

Chen, Maomao; Zhou, Yuan; Su, Han; Zhang, Dong; Luo, Jianwen

2017-04-01

Imaging of the pharmacokinetic parameters in dynamic fluorescence molecular tomography (DFMT) can provide three-dimensional metabolic information for biological studies and drug development. However, owing to the ill-posed nature of the FMT inverse problem, the relatively low quality of the parametric images makes it difficult to investigate the different metabolic processes of the fluorescent targets with small distances. An excitation-resolved multispectral DFMT method is proposed; it is based on the fact that the fluorescent targets with different concentrations show different variations in the excitation spectral domain and can be considered independent signal sources. With an independent component analysis method, the spatial locations of different fluorescent targets can be decomposed, and the fluorescent yields of the targets at different time points can be recovered. Therefore, the metabolic process of each component can be independently investigated. Simulations and phantom experiments are carried out to evaluate the performance of the proposed method. The results demonstrated that the proposed excitation-resolved multispectral method can effectively improve the reconstruction accuracy of the parametric images in DFMT.

Widespread abnormality of the γ-aminobutyric acid-ergic system in Tourette syndrome

PubMed Central

Bagic, Anto; Simmons, Janine M.; Mari, Zoltan; Bonne, Omer; Xu, Ben; Kazuba, Diane; Herscovitch, Peter; Carson, Richard E.; Murphy, Dennis L.; Drevets, Wayne C.; Hallett, Mark

2012-01-01

Dysfunction of the γ-aminobutyric acid-ergic system in Tourette syndrome may conceivably underlie the symptoms of motor disinhibition presenting as tics and psychiatric manifestations, such as attention deficit hyperactivity disorder and obsessive–compulsive disorder. The purpose of this study was to identify a possible dysfunction of the γ-aminobutyric acid-ergic system in Tourette patients, especially involving the basal ganglia-thalamo-cortical circuits and the cerebellum. We studied 11 patients with Tourette syndrome and 11 healthy controls. Positron emission tomography procedure: after injection of 20 mCi of [11C]flumazenil, dynamic emission images of the brain were acquired. Structural magnetic resonance imaging scans were obtained to provide an anatomical framework for the positron emission tomography data analysis. Images of binding potential were created using the two-step version of the simplified reference tissue model. The binding potential images then were spatially normalized, smoothed and compared between groups using statistical parametric mapping. We found decreased binding of GABAA receptors in Tourette patients bilaterally in the ventral striatum, globus pallidus, thalamus, amygdala and right insula. In addition, the GABAA receptor binding was increased in the bilateral substantia nigra, left periaqueductal grey, right posterior cingulate cortex and bilateral cerebellum. These results are consistent with the longstanding hypothesis that circuits involving the basal ganglia and thalamus are disinhibited in Tourette syndrome patients. In addition, the abnormalities in GABAA receptor binding in the insula and cerebellum appear particularly noteworthy based upon recent evidence implicating these structures in the generation of tics. PMID:22577221

A new texture descriptor based on local micro-pattern for detection of architectural distortion in mammographic images

NASA Astrophysics Data System (ADS)

de Oliveira, Helder C. R.; Moraes, Diego R.; Reche, Gustavo A.; Borges, Lucas R.; Catani, Juliana H.; de Barros, Nestor; Melo, Carlos F. E.; Gonzaga, Adilson; Vieira, Marcelo A. C.

2017-03-01

This paper presents a new local micro-pattern texture descriptor for the detection of Architectural Distortion (AD) in digital mammography images. AD is a subtle contraction of breast parenchyma that may represent an early sign of breast cancer. Due to its subtlety and variability, AD is more difficult to detect compared to microcalcifications and masses, and is commonly found in retrospective evaluations of false-negative mammograms. Several computer-based systems have been proposed for automatic detection of AD, but their performance are still unsatisfactory. The proposed descriptor, Local Mapped Pattern (LMP), is a generalization of the Local Binary Pattern (LBP), which is considered one of the most powerful feature descriptor for texture classification in digital images. Compared to LBP, the LMP descriptor captures more effectively the minor differences between the local image pixels. Moreover, LMP is a parametric model which can be optimized for the desired application. In our work, the LMP performance was compared to the LBP and four Haralick's texture descriptors for the classification of 400 regions of interest (ROIs) extracted from clinical mammograms. ROIs were selected and divided into four classes: AD, normal tissue, microcalcifications and masses. Feature vectors were used as input to a multilayer perceptron neural network, with a single hidden layer. Results showed that LMP is a good descriptor to distinguish AD from other anomalies in digital mammography. LMP performance was slightly better than the LBP and comparable to Haralick's descriptors (mean classification accuracy = 83%).

Variogram methods for texture classification of atherosclerotic plaque ultrasound images

NASA Astrophysics Data System (ADS)

Jeromin, Oliver M.; Pattichis, Marios S.; Pattichis, Constantinos; Kyriacou, Efthyvoulos; Nicolaides, Andrew

2006-03-01

Stroke is the third leading cause of death in the western world and the major cause of disability in adults. The type and stenosis of extracranial carotid artery disease is often responsible for ischemic strokes, transient ischemic attacks (TIAs) or amaurosis fugax (AF). The identification and grading of stenosis can be done using gray scale ultrasound scans. The appearance of B-scan pictures containing various granular structures makes the use of texture analysis techniques suitable for computer assisted tissue characterization purposes. The objective of this study is to investigate the usefulness of variogram analysis in the assessment of ultrasound plague morphology. The variogram estimates the variance of random fields, from arbitrary samples in space. We explore stationary random field models based on the variogram, which can be applied in ultrasound plaque imaging leading to a Computer Aided Diagnosis (CAD) system for the early detection of symptomatic atherosclerotic plaques. Non-parametric tests on the variogram coefficients show that the cofficients coming from symptomatic versus asymptomatic plaques come from distinct distributions. Furthermore, we show significant improvement in class separation, when a log point-transformation is applied to the images, prior to variogram estimation. Model fitting using least squares is explored for anisotropic variograms along specific directions. Comparative classification results, show that variogram coefficients can be used for the early detection of symptomatic cases, and also exhibit the largest class distances between symptomatic and asymptomatic plaque images, as compared to over 60 other texture features, used in the literature.

The usability of the optical parametric amplification of light for high-angular-resolution imaging and fast astrometry

NASA Astrophysics Data System (ADS)

Kurek, A. R.; Stachowski, A.; Banaszek, K.; Pollo, A.

2018-05-01

High-angular-resolution imaging is crucial for many applications in modern astronomy and astrophysics. The fundamental diffraction limit constrains the resolving power of both ground-based and spaceborne telescopes. The recent idea of a quantum telescope based on the optical parametric amplification (OPA) of light aims to bypass this limit for the imaging of extended sources by an order of magnitude or more. We present an updated scheme of an OPA-based device and a more accurate model of the signal amplification by such a device. The semiclassical model that we present predicts that the noise in such a system will form so-called light speckles as a result of light interference in the optical path. Based on this model, we analysed the efficiency of OPA in increasing the angular resolution of the imaging of extended targets and the precise localization of a distant point source. According to our new model, OPA offers a gain in resolved imaging in comparison to classical optics. For a given time-span, we found that OPA can be more efficient in localizing a single distant point source than classical telescopes.

Automated Voxel-Based Analysis of Volumetric Dynamic Contrast-Enhanced CT Data Improves Measurement of Serial Changes in Tumor Vascular Biomarkers

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

Coolens, Catherine, E-mail: catherine.coolens@rmp.uhn.on.ca; Department of Radiation Oncology, University of Toronto, Toronto, Ontario; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario

2015-01-01

Objectives: Development of perfusion imaging as a biomarker requires more robust methodologies for quantification of tumor physiology that allow assessment of volumetric tumor heterogeneity over time. This study proposes a parametric method for automatically analyzing perfused tissue from volumetric dynamic contrast-enhanced (DCE) computed tomography (CT) scans and assesses whether this 4-dimensional (4D) DCE approach is more robust and accurate than conventional, region-of-interest (ROI)-based CT methods in quantifying tumor perfusion with preliminary evaluation in metastatic brain cancer. Methods and Materials: Functional parameter reproducibility and analysis of sensitivity to imaging resolution and arterial input function were evaluated in image sets acquired from amore » 320-slice CT with a controlled flow phantom and patients with brain metastases, whose treatments were planned for stereotactic radiation surgery and who consented to a research ethics board-approved prospective imaging biomarker study. A voxel-based temporal dynamic analysis (TDA) methodology was used at baseline, at day 7, and at day 20 after treatment. The ability to detect changes in kinetic parameter maps in clinical data sets was investigated for both 4D TDA and conventional 2D ROI-based analysis methods. Results: A total of 7 brain metastases in 3 patients were evaluated over the 3 time points. The 4D TDA method showed improved spatial efficacy and accuracy of perfusion parameters compared to ROI-based DCE analysis (P<.005), with a reproducibility error of less than 2% when tested with DCE phantom data. Clinically, changes in transfer constant from the blood plasma into the extracellular extravascular space (K{sub trans}) were seen when using TDA, with substantially smaller errors than the 2D method on both day 7 post radiation surgery (±13%; P<.05) and by day 20 (±12%; P<.04). Standard methods showed a decrease in K{sub trans} but with large uncertainty (111.6 ± 150.5) %. Conclusions: Parametric voxel-based analysis of 4D DCE CT data resulted in greater accuracy and reliability in measuring changes in perfusion CT-based kinetic metrics, which have the potential to be used as biomarkers in patients with metastatic brain cancer.« less

Multi-parametric studies of electrically-driven flyer plates

NASA Astrophysics Data System (ADS)

Neal, William; Bowden, Michael; Explosive Trains; Devices Collaboration

2015-06-01

Exploding foil initiator (EFI) detonators function by the acceleration of a flyer plate, by the electrical explosion of a metallic bridge, into an explosive pellet. The length, and therefore time, scales of this shock initation process is dominated by the magnitude and duration of the imparted shock pulse. To predict the dynamics of this initiation, it is critical to further understand the velocity, shape and thickness of this flyer plate. This study uses multi-parametric diagnostics to investigate the geometry and velocity of the flyer plate upon impact including the imparted electrical energy: photon Doppler velocimetry (PDV), dual axis imaging, time-resolved impact imaging, voltage and current. The investigation challenges the validity of traditional assumptions about the state of the flyer plate at impact and discusses the improved understanding of the process.

Simultaneous K-edge subtraction tomography for tracing strontium using parametric X-ray radiation

NASA Astrophysics Data System (ADS)

Hayakawa, Y.; Hayakawa, K.; Kaneda, T.; Nogami, K.; Sakae, T.; Sakai, T.; Sato, I.; Takahashi, Y.; Tanaka, T.

2017-07-01

The X-ray source based on parametric X-ray radiation (PXR) has been regularly providing a coherent X-ray beam for application studies at Nihon University. Recently, three dimensional (3D) computed tomography (CT) has become one of the most important applications of the PXR source. The methodology referred to as K-edge subtraction (KES) imaging is a particularly successful application utilizing the energy selectivity of PXR. In order to demonstrate the applicability of PXR-KES, a simultaneous KES experiment for a specimen containing strontium was performed using a PXR beam having an energy near the Sr K-edge of 16.1 keV. As a result, the 3D distribution of Sr was obtained by subtraction between the two simultaneously acquired tomographic images.

Hybrid Photoacoustic/Ultrasound Tomograph for Real-Time Finger Imaging.

PubMed

Oeri, Milan; Bost, Wolfgang; Sénégond, Nicolas; Tretbar, Steffen; Fournelle, Marc

2017-10-01

We report a target-enclosing, hybrid tomograph with a total of 768 elements based on capacitive micromachined ultrasound transducer technology and providing fast, high-resolution 2-D/3-D photoacoustic and ultrasound tomography tailored to finger imaging. A freely programmable ultrasound beamforming platform sampling data at 80 MHz was developed to realize plane wave transmission under multiple angles. A multiplexing unit enables the connection and control of a large number of elements. Fast image reconstruction is provided by GPU processing. The tomograph is composed of four independent and fully automated movable arc-shaped transducers, allowing imaging of all three finger joints. The system benefits from photoacoustics, yielding high optical contrast and enabling visualization of finger vascularization, and ultrasound provides morphologic information on joints and surrounding tissue. A diode-pumped, Q-switched Nd:YAG laser and an optical parametric oscillator are used to broaden the spectrum of emitted wavelengths to provide multispectral imaging. Custom-made optical fiber bundles enable illumination of the region of interest in the plane of acoustic detection. Precision in positioning of the probe in motion is ensured by use of a motor-driven guide slide. The current position of the probe is encoded by the stage and used to relate ultrasound and photoacoustic signals to the corresponding region of interest of the suspicious finger joint. The system is characterized in phantoms and a healthy human finger in vivo. The results obtained promise to provide new opportunities in finger diagnostics and establish photoacoustic/ultrasound-tomography in medical routine. Copyright © 2017 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Incremental Learning With Selective Memory (ILSM): Towards Fast Prostate Localization for Image Guided Radiotherapy

PubMed Central

Gao, Yaozong; Zhan, Yiqiang

2015-01-01

Image-guided radiotherapy (IGRT) requires fast and accurate localization of the prostate in 3-D treatment-guided radiotherapy, which is challenging due to low tissue contrast and large anatomical variation across patients. On the other hand, the IGRT workflow involves collecting a series of computed tomography (CT) images from the same patient under treatment. These images contain valuable patient-specific information yet are often neglected by previous works. In this paper, we propose a novel learning framework, namely incremental learning with selective memory (ILSM), to effectively learn the patient-specific appearance characteristics from these patient-specific images. Specifically, starting with a population-based discriminative appearance model, ILSM aims to “personalize” the model to fit patient-specific appearance characteristics. The model is personalized with two steps: backward pruning that discards obsolete population-based knowledge and forward learning that incorporates patient-specific characteristics. By effectively combining the patient-specific characteristics with the general population statistics, the incrementally learned appearance model can localize the prostate of a specific patient much more accurately. This work has three contributions: 1) the proposed incremental learning framework can capture patient-specific characteristics more effectively, compared to traditional learning schemes, such as pure patient-specific learning, population-based learning, and mixture learning with patient-specific and population data; 2) this learning framework does not have any parametric model assumption, hence, allowing the adoption of any discriminative classifier; and 3) using ILSM, we can localize the prostate in treatment CTs accurately (DSC ∼0.89) and fast (∼4 s), which satisfies the real-world clinical requirements of IGRT. PMID:24495983

SQUID magnetometry from nanometer to centimeter length scales

NASA Astrophysics Data System (ADS)

Hatridge, Michael Jonathan

Information stored in magnetic fields plays an important role in everyday life. This information exists over a remarkably wide range of sizes, so that magnetometry at a variety of length scales can extract useful information. Examples at centimeter to millimeter length scales include measurement of spatial and temporal character of fields generated in the human brain and heart, and active manipulation of spins in the human body for non-invasive magnetic resonance imaging (MRI). At micron length scales, magnetometry can be used to measure magnetic objects such as flux qubits; at nanometer length scales it can be used to study individual magnetic domains, and even individual spins. The development of Superconducting QUantum Interference Device (SQUID) based magnetometer for two such applications, in vivo prepolarized, ultra-low field MRI of humans and dispersive readout of SQUIDs for micro- and nanoscale magnetometry, are the focus of this thesis. Conventional MRI has developed into a powerful clinical tool for imaging the human body. This technique is based on nuclear magnetic resonance of protons with the addition application of three-dimensional magnetic field gradients to encode spatial information. Most clinical MRI systems involve magnetic fields generated by superconducting magnets, and the current trend is to higher magnetic fields than the widely used 1.5-T systems. Nonetheless, there is ongoing interest in the development of less expensive imagers operating at lower fields. The prepolarized, SQUID detected ultra-low field MRI (ULF MRI) developed by the Clarke group allows imaging in very weak fields (typically 132 muT, corresponding to a resonant frequency of 5.6 kHz). At these low field strengths, there is enhanced contrast in the longitudinal relaxation time of various tissue types, enabling imaging of objects which are not visible to conventional MRI, for instance prostate cancer. We are currently investigating the contrast between normal and cancerous prostate tissue in ex vivo prostate specimens in collaboration with the UCSF Genitourinary Oncology/Prostate SPORE Tissue Core. In characterizing pairs of nominally normal and cancerous tissue, we measure a marked difference in the longitudinal relaxation times, with an average value of cancerous tissue 0.66 times shorter than normal prostate tissue. However, in vivo imaging is required to definitively demonstrate the feasibility of ULF MR imaging of prostate cancer. To that end, we have worked to improve the performance of the system to facilitate human imaging. This is accomplished by increasing the prepolarizing field amplitude, and minimizing magnetic noise in the SQUID detector. We have achieved polarizing fields as high as 150 mT and SQUID effective field noise below 1 fT Hz-1/2, enabling us to demonstrate proof-of-principle in vivo images of the human forearm with 2 x 2 x 10 mm3 resolution in 6 minutes. On a much smaller spatial scale, there is currently fundamental and technological interest in measuring and manipulating nanoscale magnets, particularly in the quantum coherent regime. The observation of the dynamics of such systems requires a magnetometer with not only exceptional sensitivity but also high gain, wide bandwidth and low backaction. We demonstrate a dispersive magnetometer consisting of a two-junction SQUID in parallel with an integrated, lumped-element capacitor. Input flux signals are encoded as a phase modulation of the microwave drive tone applied to the magnetometer, resulting in a single quadrature voltage signal. For strong drive power, the nonlinearity of the resonator results in quantum limited, phase sensitive parametric amplification of this signal. We have achieved a bandwidth of 20 MHz---approximately two orders of magnitude higher than dispersive devices of comparable sensitivity---with an effective flux noise of 0.29 muphi0 Hz-12 . This performance is in excellent agreement with our theoretical model.

Macrophages Under Low Oxygen Culture Conditions Respond to Ion Parametric Resonance Magnetic Fields

EPA Science Inventory

Macrophages, when entering inflamed tissue, encounter low oxygen tension due to the impairment of blood supply and/or the massive infiltration of cells that consume oxygen. Previously, we showed that such macrophages release more bacteriotoxic hydrogen peroxide (H202) when expose...

Macrophages Under Low Oxygen Culture ConditionsRespond to Ion Parametric Resonance Magnetic Fields

EPA Science Inventory

Macrophages, when entering inflamed tissue, encounter low oxygen tension due to the impairment of blood supply and/or the massive infiltration of cells that consume oxygen. Previously, we showed that such macrophages release more bacteriotoxic hydrogen peroxide (H202) when expose...

Analyzing and Improving Image Quality in Reflective Ghost Imaging

DTIC Science & Technology

2011-02-01

photon quantum entanglement ," Phys. Rev. A 52, 3429 (1995). [2] A. Valencia, G. Scarcelli. M. D. Angelo, and Y. Shih. "Two- photon imaging with thermal...and reference fields, which were generated by spontaneous parametric downconversion (SPDC) and post- selection [1]. Because biphotons are entangled ...envelopes about center frequency we of linearly-polarized light fields normalized to have V/ photons /m 2s units as functions of their transverse

Semiautomated Workflow for Clinically Streamlined Glioma Parametric Response Mapping

PubMed Central

Keith, Lauren; Ross, Brian D.; Galbán, Craig J.; Luker, Gary D.; Galbán, Stefanie; Zhao, Binsheng; Guo, Xiaotao; Chenevert, Thomas L.; Hoff, Benjamin A.

2017-01-01

Management of glioblastoma multiforme remains a challenging problem despite recent advances in targeted therapies. Timely assessment of therapeutic agents is hindered by the lack of standard quantitative imaging protocols for determining targeted response. Clinical response assessment for brain tumors is determined by volumetric changes assessed at 10 weeks post-treatment initiation. Further, current clinical criteria fail to use advanced quantitative imaging approaches, such as diffusion and perfusion magnetic resonance imaging. Development of the parametric response mapping (PRM) applied to diffusion-weighted magnetic resonance imaging has provided a sensitive and early biomarker of successful cytotoxic therapy in brain tumors while maintaining a spatial context within the tumor. Although PRM provides an earlier readout than volumetry and sometimes greater sensitivity compared with traditional whole-tumor diffusion statistics, it is not routinely used for patient management; an automated and standardized software for performing the analysis and for the generation of a clinical report document is required for this. We present a semiautomated and seamless workflow for image coregistration, segmentation, and PRM classification of glioblastoma multiforme diffusion-weighted magnetic resonance imaging scans. The software solution can be integrated using local hardware or performed remotely in the cloud while providing connectivity to existing picture archive and communication systems. This is an important step toward implementing PRM analysis of solid tumors in routine clinical practice. PMID:28286871

A Computer-Aided Analysis Method of SPECT Brain Images for Quantitative Treatment Monitoring: Performance Evaluations and Clinical Applications.

PubMed

Zheng, Xiujuan; Wei, Wentao; Huang, Qiu; Song, Shaoli; Wan, Jieqing; Huang, Gang

2017-01-01

The objective and quantitative analysis of longitudinal single photon emission computed tomography (SPECT) images are significant for the treatment monitoring of brain disorders. Therefore, a computer aided analysis (CAA) method is introduced to extract a change-rate map (CRM) as a parametric image for quantifying the changes of regional cerebral blood flow (rCBF) in longitudinal SPECT brain images. The performances of the CAA-CRM approach in treatment monitoring are evaluated by the computer simulations and clinical applications. The results of computer simulations show that the derived CRMs have high similarities with their ground truths when the lesion size is larger than system spatial resolution and the change rate is higher than 20%. In clinical applications, the CAA-CRM approach is used to assess the treatment of 50 patients with brain ischemia. The results demonstrate that CAA-CRM approach has a 93.4% accuracy of recovered region's localization. Moreover, the quantitative indexes of recovered regions derived from CRM are all significantly different among the groups and highly correlated with the experienced clinical diagnosis. In conclusion, the proposed CAA-CRM approach provides a convenient solution to generate a parametric image and derive the quantitative indexes from the longitudinal SPECT brain images for treatment monitoring.

Effect of Logarithmic and Linear Frequency Scales on Parametric Modelling of Tissue Dielectric Data.

PubMed

Salahuddin, Saqib; Porter, Emily; Meaney, Paul M; O'Halloran, Martin

2017-02-01

The dielectric properties of biological tissues have been studied widely over the past half-century. These properties are used in a vast array of applications, from determining the safety of wireless telecommunication devices to the design and optimisation of medical devices. The frequency-dependent dielectric properties are represented in closed-form parametric models, such as the Cole-Cole model, for use in numerical simulations which examine the interaction of electromagnetic (EM) fields with the human body. In general, the accuracy of EM simulations depends upon the accuracy of the tissue dielectric models. Typically, dielectric properties are measured using a linear frequency scale; however, use of the logarithmic scale has been suggested historically to be more biologically descriptive. Thus, the aim of this paper is to quantitatively compare the Cole-Cole fitting of broadband tissue dielectric measurements collected with both linear and logarithmic frequency scales. In this way, we can determine if appropriate choice of scale can minimise the fit error and thus reduce the overall error in simulations. Using a well-established fundamental statistical framework, the results of the fitting for both scales are quantified. It is found that commonly used performance metrics, such as the average fractional error, are unable to examine the effect of frequency scale on the fitting results due to the averaging effect that obscures large localised errors. This work demonstrates that the broadband fit for these tissues is quantitatively improved when the given data is measured with a logarithmic frequency scale rather than a linear scale, underscoring the importance of frequency scale selection in accurate wideband dielectric modelling of human tissues.

Effect of Logarithmic and Linear Frequency Scales on Parametric Modelling of Tissue Dielectric Data

PubMed Central

Salahuddin, Saqib; Porter, Emily; Meaney, Paul M.; O’Halloran, Martin

2016-01-01

The dielectric properties of biological tissues have been studied widely over the past half-century. These properties are used in a vast array of applications, from determining the safety of wireless telecommunication devices to the design and optimisation of medical devices. The frequency-dependent dielectric properties are represented in closed-form parametric models, such as the Cole-Cole model, for use in numerical simulations which examine the interaction of electromagnetic (EM) fields with the human body. In general, the accuracy of EM simulations depends upon the accuracy of the tissue dielectric models. Typically, dielectric properties are measured using a linear frequency scale; however, use of the logarithmic scale has been suggested historically to be more biologically descriptive. Thus, the aim of this paper is to quantitatively compare the Cole-Cole fitting of broadband tissue dielectric measurements collected with both linear and logarithmic frequency scales. In this way, we can determine if appropriate choice of scale can minimise the fit error and thus reduce the overall error in simulations. Using a well-established fundamental statistical framework, the results of the fitting for both scales are quantified. It is found that commonly used performance metrics, such as the average fractional error, are unable to examine the effect of frequency scale on the fitting results due to the averaging effect that obscures large localised errors. This work demonstrates that the broadband fit for these tissues is quantitatively improved when the given data is measured with a logarithmic frequency scale rather than a linear scale, underscoring the importance of frequency scale selection in accurate wideband dielectric modelling of human tissues. PMID:28191324

Application of a Simplified Method for Estimating Perfusion Derived from Diffusion-Weighted MR Imaging in Glioma Grading.

PubMed

Cao, Mengqiu; Suo, Shiteng; Han, Xu; Jin, Ke; Sun, Yawen; Wang, Yao; Ding, Weina; Qu, Jianxun; Zhang, Xiaohua; Zhou, Yan

2017-01-01

Purpose : To evaluate the feasibility of a simplified method based on diffusion-weighted imaging (DWI) acquired with three b -values to measure tissue perfusion linked to microcirculation, to validate it against from perfusion-related parameters derived from intravoxel incoherent motion (IVIM) and dynamic contrast-enhanced (DCE) magnetic resonance (MR) imaging, and to investigate its utility to differentiate low- from high-grade gliomas. Materials and Methods : The prospective study was approved by the local institutional review board and written informed consent was obtained from all patients. From May 2016 and May 2017, 50 patients confirmed with glioma were assessed with multi- b -value DWI and DCE MR imaging at 3.0 T. Besides conventional apparent diffusion coefficient (ADC 0,1000 ) map, perfusion-related parametric maps for IVIM-derived perfusion fraction ( f ) and pseudodiffusion coefficient (D*), DCE MR imaging-derived pharmacokinetic metrics, including K trans , v e and v p , as well as a metric named simplified perfusion fraction (SPF), were generated. Correlation between perfusion-related parameters was analyzed by using the Spearman rank correlation. All imaging parameters were compared between the low-grade ( n = 19) and high-grade ( n = 31) groups by using the Mann-Whitney U test. The diagnostic performance for tumor grading was evaluated with receiver operating characteristic (ROC) analysis. Results : SPF showed strong correlation with IVIM-derived f and D* ( ρ = 0.732 and 0.716, respectively; both P < 0.001). Compared with f , SPF was more correlated with DCE MR imaging-derived K trans ( ρ = 0.607; P < 0.001) and v p ( ρ = 0.397; P = 0.004). Among all parameters, SPF achieved the highest accuracy for differentiating low- from high-grade gliomas, with an area under the ROC curve value of 0.942, which was significantly higher than that of ADC 0,1000 ( P = 0.004). By using SPF as a discriminative index, the diagnostic sensitivity and specificity were 87.1% and 94.7%, respectively, at the optimal cut-off value of 19.26%. Conclusion : The simplified method to measure tissue perfusion based on DWI by using three b -values may be helpful to differentiate low- from high-grade gliomas. SPF may serve as a valuable alternative to measure tumor perfusion in gliomas in a noninvasive, convenient and efficient way.

Brain tissues volume measurements from 2D MRI using parametric approach

NASA Astrophysics Data System (ADS)

L'vov, A. A.; Toropova, O. A.; Litovka, Yu. V.

2018-04-01

The purpose of the paper is to propose a fully automated method of volume assessment of structures within human brain. Our statistical approach uses maximum interdependency principle for decision making process of measurements consistency and unequal observations. Detecting outliers performed using maximum normalized residual test. We propose a statistical model which utilizes knowledge of tissues distribution in human brain and applies partial data restoration for precision improvement. The approach proposes completed computationally efficient and independent from segmentation algorithm used in the application.

[Detection of cerebral hypoperfusion using single photon emission computed tomography image analysis and statistical parametric mapping in patients with Parkinson's disease or progressive supranuclear palsy].

PubMed

Harada, Kengo; Saeki, Hiroshi; Matsuya, Eiji; Okita, Izumi

2013-11-01

We carried out differential diagnosis of brain blood flow images using single-photon emission computed tomography (SPECT) for patients with Parkinson's disease (PD) or progressive supranuclear paralysis (PSP) using statistical parametric mapping (SPM) and to whom we had applied anatomical standardization. We studied two groups and compared brain blood flow images using SPECT (N-isopropyl-4-iodoamphetamine [(123)I] hydrochloride injection, 222 MGq dosage i.v.). A total of 27 patients were studied using SPM: 18 with PD and 9 with PSP; humming bird sign on MRI was from moderate to medium. The decline of brain bloodstream in the PSP group was more notable in the midbrain, near the domain where the humming bird sign was observable, than in the PD group. The observable differences in brain bloodstream decline in the midbrain of PSP and PD patients suggest the potential usefulness of this technique's clinical application to distinction diagnosis.

New cancer-treatment model of photodynamic therapy combined with a type I topoisomerase inhibitor, CPT-11, against HeLa cell tumors in nude mice used by OPO parametric tunable laser

NASA Astrophysics Data System (ADS)

Yoshida, Takato O.; Matsuzawa, Eiji; Matsuo, Tetsumichi; Koide, Yukio; Terakawa, Susumu; Yokokura, Teruo; Hirano, Toru

1995-03-01

A new cancer-treatment model, photodynamic therapy (PDT) combined with a type I topoisomerase inhibitor, camptothecin derivative (CPT-11), against HeLa cell tumors in BALB/c nude mice has been developed using a wide-band tunable coherent light source operated on optical parametric oscillation (OPO parametric tunable laser). The Photosan-3 PDT and CPT-11 combined therapy was remarkably effective, that is the inhibition rate (I.R.) 40 - 80%, as compared to PDT only in vivo. The analysis of HpD (Photosan-3) and CPT-11 effects on cultured HeLa cells in vitro has been studied by a video-enhanced contrast differential interference contrast microscope (VEC-DIC). Photosan-3 with 600 nm light killed cells by mitochondrial damage within 50 min, but not with 700 nm light. CPT-11 with 700 - 400 nm light killed cells within 50 min after nucleolus damage appeared after around 30 min. The localization of CPT-11 in cells was observed as fluorescence images in the nucleus, particularly the nucleoral area produced clear images using an Argus 100.

Observation of Geometric Parametric Instability Induced by the Periodic Spatial Self-Imaging of Multimode Waves

NASA Astrophysics Data System (ADS)

Krupa, Katarzyna; Tonello, Alessandro; Barthélémy, Alain; Couderc, Vincent; Shalaby, Badr Mohamed; Bendahmane, Abdelkrim; Millot, Guy; Wabnitz, Stefan

2016-05-01

Spatiotemporal mode coupling in highly multimode physical systems permits new routes for exploring complex instabilities and forming coherent wave structures. We present here the first experimental demonstration of multiple geometric parametric instability sidebands, generated in the frequency domain through resonant space-time coupling, owing to the natural periodic spatial self-imaging of a multimode quasi-continuous-wave beam in a standard graded-index multimode fiber. The input beam was launched in the fiber by means of an amplified microchip laser emitting sub-ns pulses at 1064 nm. The experimentally observed frequency spacing among sidebands agrees well with analytical predictions and numerical simulations. The first-order peaks are located at the considerably large detuning of 123.5 THz from the pump. These results open the remarkable possibility to convert a near-infrared laser directly into a broad spectral range spanning visible and infrared wavelengths, by means of a single resonant parametric nonlinear effect occurring in the normal dispersion regime. As further evidence of our strong space-time coupling regime, we observed the striking effect that all of the different sideband peaks were carried by a well-defined and stable bell-shaped spatial profile.

Parametric modeling of the intervertebral disc space in 3D: application to CT images of the lumbar spine.

PubMed

Korez, Robert; Likar, Boštjan; Pernuš, Franjo; Vrtovec, Tomaž

2014-10-01

Gradual degeneration of intervertebral discs of the lumbar spine is one of the most common causes of low back pain. Although conservative treatment for low back pain may provide relief to most individuals, surgical intervention may be required for individuals with significant continuing symptoms, which is usually performed by replacing the degenerated intervertebral disc with an artificial implant. For designing implants with good bone contact and continuous force distribution, the morphology of the intervertebral disc space and vertebral body endplates is of considerable importance. In this study, we propose a method for parametric modeling of the intervertebral disc space in three dimensions (3D) and show its application to computed tomography (CT) images of the lumbar spine. The initial 3D model of the intervertebral disc space is generated according to the superquadric approach and therefore represented by a truncated elliptical cone, which is initialized by parameters obtained from 3D models of adjacent vertebral bodies. In an optimization procedure, the 3D model of the intervertebral disc space is incrementally deformed by adding parameters that provide a more detailed morphometric description of the observed shape, and aligned to the observed intervertebral disc space in the 3D image. By applying the proposed method to CT images of 20 lumbar spines, the shape and pose of each of the 100 intervertebral disc spaces were represented by a 3D parametric model. The resulting mean (±standard deviation) accuracy of modeling was 1.06±0.98mm in terms of radial Euclidean distance against manually defined ground truth points, with the corresponding success rate of 93% (i.e. 93 out of 100 intervertebral disc spaces were modeled successfully). As the resulting 3D models provide a description of the shape of intervertebral disc spaces in a complete parametric form, morphometric analysis was straightforwardly enabled and allowed the computation of the corresponding heights, widths and volumes, as well as of other geometric features that in detail describe the shape of intervertebral disc spaces. Copyright © 2014 Elsevier Ltd. All rights reserved.

Reliable clarity automatic-evaluation method for optical remote sensing images

NASA Astrophysics Data System (ADS)

Qin, Bangyong; Shang, Ren; Li, Shengyang; Hei, Baoqin; Liu, Zhiwen

2015-10-01

Image clarity, which reflects the sharpness degree at the edge of objects in images, is an important quality evaluate index for optical remote sensing images. Scholars at home and abroad have done a lot of work on estimation of image clarity. At present, common clarity-estimation methods for digital images mainly include frequency-domain function methods, statistical parametric methods, gradient function methods and edge acutance methods. Frequency-domain function method is an accurate clarity-measure approach. However, its calculation process is complicate and cannot be carried out automatically. Statistical parametric methods and gradient function methods are both sensitive to clarity of images, while their results are easy to be affected by the complex degree of images. Edge acutance method is an effective approach for clarity estimate, while it needs picking out the edges manually. Due to the limits in accuracy, consistent or automation, these existing methods are not applicable to quality evaluation of optical remote sensing images. In this article, a new clarity-evaluation method, which is based on the principle of edge acutance algorithm, is proposed. In the new method, edge detection algorithm and gradient search algorithm are adopted to automatically search the object edges in images. Moreover, The calculation algorithm for edge sharpness has been improved. The new method has been tested with several groups of optical remote sensing images. Compared with the existing automatic evaluation methods, the new method perform better both in accuracy and consistency. Thus, the new method is an effective clarity evaluation method for optical remote sensing images.

Histological correlation of 7 T multi-parametric MRI performed in ex-vivo Achilles tendon.

PubMed

Juras, Vladimir; Apprich, Sebastian; Pressl, Christina; Zbyn, Stefan; Szomolanyi, Pavol; Domayer, Stephan; Hofstaetter, Jochen G; Trattnig, Siegfried

2013-05-01

The goal of this in vitro validation study was to investigate the feasibility of biochemical MRI techniques, such as sodium imaging, T₂ mapping, fast imaging with steady state precession (FISP), and reversed FISP (PSIF), as potential markers for collagen, glycosaminoglycan and water content in the Achilles tendon. Five fresh cadaver ankles acquired from a local anatomy department were used in the study. To acquire a sodium signal from the Achilles tendon, a 3D-gradient-echo sequence, optimized for sodium imaging, was used with TE=7.71 ms and TR=17 ms. The T₂ relaxation times were obtained using a multi-echo, spin-echo technique with a repetition time (TR) of 1200 ms and six echo times. A 3D, partially balanced, steady-state gradient echo pulse sequence was used to acquire FISP and PSIF images, with TR/TE=6.96/2.46 ms. MRI parameters were correlated with each other, as well as with histologically assessed glycosaminoglycan and water content in cadaver Achilles tendons. The highest relevant Pearson correlation coefficient was found between sodium SNR and glycosaminoglycan content (r=0.71, p=0.007). Relatively high correlation was found between the PSIF signal and T2 values (r=0.51, p=0.036), and between the FISP signal and T₂ values (r=0.56, p=0.047). Other correlations were found to be below the moderate level. This study demonstrated the feasibility of progressive biochemical MRI methods for the imaging of the AT. A GAG-specific, contrast-free method (sodium imaging), as well as collagen- and water-sensitive methods (T₂ mapping, FISP, PSIF), may be used in fast-relaxing tissues, such as tendons, in reasonable scan times. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Impact of region contouring variability on image-based focal therapy evaluation

NASA Astrophysics Data System (ADS)

Gibson, Eli; Donaldson, Ian A.; Shah, Taimur T.; Hu, Yipeng; Ahmed, Hashim U.; Barratt, Dean C.

2016-03-01

Motivation: Focal therapy is an emerging low-morbidity treatment option for low-intermediate risk prostate cancer; however, challenges remain in accurately delivering treatment to specified targets and determining treatment success. Registered multi-parametric magnetic resonance imaging (MPMRI) acquired before and after treatment can support focal therapy evaluation and optimization; however, contouring variability, when defining the prostate, the clinical target volume (CTV) and the ablation region in images, reduces the precision of quantitative image-based focal therapy evaluation metrics. To inform the interpretation and clarify the limitations of such metrics, we investigated inter-observer contouring variability and its impact on four metrics. Methods: Pre-therapy and 2-week-post-therapy standard-of-care MPMRI were acquired from 5 focal cryotherapy patients. Two clinicians independently contoured, on each slice, the prostate (pre- and post-treatment) and the dominant index lesion CTV (pre-treatment) in the T2-weighted MRI, and the ablated region (post-treatment) in the dynamic-contrast- enhanced MRI. For each combination of clinician contours, post-treatment images were registered to pre-treatment images using a 3D biomechanical-model-based registration of prostate surfaces, and four metrics were computed: the proportion of the target tissue region that was ablated and the target:ablated region volume ratio for each of two targets (the CTV and an expanded planning target volume). Variance components analysis was used to measure the contribution of each type of contour to the variance in the therapy evaluation metrics. Conclusions: 14-23% of evaluation metric variance was attributable to contouring variability (including 6-12% from ablation region contouring); reducing this variability could improve the precision of focal therapy evaluation metrics.

Quantum optical measurement with tripartite entangled photons generated by triple parametric down-conversion

NASA Astrophysics Data System (ADS)

Cho, Minhaeng

2018-05-01

Parametric down-conversion is a second-order nonlinear optical process annihilating a pump photon and creating a pair of photons in the signal and idler modes. Then, by using two parametric down-converters and introducing a path indistinguishability for the two generated idler modes, a quantum coherence between two conjugate signal beams can be induced. Such a double spontaneous or stimulated parametric down-conversion scheme has been used to demonstrate quantum spectroscopy and imaging with undetected idler photons via measuring one-photon interference between their correlated signal beams. Recently, we considered another quantum optical measurement scheme utilizing W-type tripartite entangled signal photons that can be generated by employing three spontaneous parametric down-conversion crystals and by inducing coherences or path-indistinguishabilities between their correlated idler beams and between quantum vacuum fields. Here, we consider an extended triple stimulated parametric down-conversion scheme for quantum optical measurement of sample properties with undetected idler and photons. Noting the real effect of vacuum field indistinguishability on the fringe visibility as well as the role of zero-point field energy in the interferometry, we show that this scheme is an ideal and efficient way to create a coherent state of W-type entangled signal photons. We anticipate that this scheme would be of critical use in further developing quantum optical measurements in spectroscopy and microscopy with undetected photons.

Quantum optical measurement with tripartite entangled photons generated by triple parametric down-conversion.

PubMed

Cho, Minhaeng

2018-05-14

Parametric down-conversion is a second-order nonlinear optical process annihilating a pump photon and creating a pair of photons in the signal and idler modes. Then, by using two parametric down-converters and introducing a path indistinguishability for the two generated idler modes, a quantum coherence between two conjugate signal beams can be induced. Such a double spontaneous or stimulated parametric down-conversion scheme has been used to demonstrate quantum spectroscopy and imaging with undetected idler photons via measuring one-photon interference between their correlated signal beams. Recently, we considered another quantum optical measurement scheme utilizing W-type tripartite entangled signal photons that can be generated by employing three spontaneous parametric down-conversion crystals and by inducing coherences or path-indistinguishabilities between their correlated idler beams and between quantum vacuum fields. Here, we consider an extended triple stimulated parametric down-conversion scheme for quantum optical measurement of sample properties with undetected idler and photons. Noting the real effect of vacuum field indistinguishability on the fringe visibility as well as the role of zero-point field energy in the interferometry, we show that this scheme is an ideal and efficient way to create a coherent state of W-type entangled signal photons. We anticipate that this scheme would be of critical use in further developing quantum optical measurements in spectroscopy and microscopy with undetected photons.

Large-scale subject-specific cerebral arterial tree modeling using automated parametric mesh generation for blood flow simulation.

PubMed

Ghaffari, Mahsa; Tangen, Kevin; Alaraj, Ali; Du, Xinjian; Charbel, Fady T; Linninger, Andreas A

2017-12-01

In this paper, we present a novel technique for automatic parametric mesh generation of subject-specific cerebral arterial trees. This technique generates high-quality and anatomically accurate computational meshes for fast blood flow simulations extending the scope of 3D vascular modeling to a large portion of cerebral arterial trees. For this purpose, a parametric meshing procedure was developed to automatically decompose the vascular skeleton, extract geometric features and generate hexahedral meshes using a body-fitted coordinate system that optimally follows the vascular network topology. To validate the anatomical accuracy of the reconstructed vasculature, we performed statistical analysis to quantify the alignment between parametric meshes and raw vascular images using receiver operating characteristic curve. Geometric accuracy evaluation showed an agreement with area under the curves value of 0.87 between the constructed mesh and raw MRA data sets. Parametric meshing yielded on-average, 36.6% and 21.7% orthogonal and equiangular skew quality improvement over the unstructured tetrahedral meshes. The parametric meshing and processing pipeline constitutes an automated technique to reconstruct and simulate blood flow throughout a large portion of the cerebral arterial tree down to the level of pial vessels. This study is the first step towards fast large-scale subject-specific hemodynamic analysis for clinical applications. Copyright © 2017 Elsevier Ltd. All rights reserved.

Role of Nuclear Medicine in the Diagnosis of Benign Thyroid Diseases.

PubMed

Garberoglio, Sara; Testori, Ornella

2016-01-01

A deep understanding of thyroid pathophysiology is the basis for diagnosing and treating benign thyroid diseases with radioactive materials, known as radiopharmaceuticals, which are introduced into the body by injection or orally. After the radiotracer administration, the patient becomes the emitting source, and several devices have been studied to detect and capture these emissions (gamma or beta-negative) and transform them into photons, parametric images, numbers and molecular information. Thyroid scintigraphy is the only technique that allows the assessment of thyroid regional function and, therefore, the detection of areas of autonomously functioning thyroid nodules. Scintigraphy visualizes the distribution of active thyroid tissue and displays the differential accumulation of radionuclides in the investigated cells, thus providing a functional map. Moreover, this technique is a fundamental tool in the clinical and surgical management of thyroid diseases, including: single thyroid nodules with a suppressed thyroid-stimulating hormone level, for which fine-needle aspiration biopsy (FNAB) is used to identify hot nodules; multinodular goiters, especially larger ones, to identify cold or indeterminate areas requiring FNAB and hot areas that do not need cytologic evaluation, and to evaluate mediastinal extension; the diagnosis of ectopic thyroid tissue; subclinical hyperthyroidism to identify occult hyperfunctioning tissue; follicular lesions to identify a functioning cellular adenoma that could be benign, although such nodules are mostly cold on scintigraphy; to distinguish low-uptake from high-uptake thyrotoxicosis, and to determine eligibility for radioiodine therapy. © 2016 S. Karger AG, Basel.

Characterization and modelling of the spatially- and spectrally-varying point-spread function in hyperspectral imaging systems for computational correction of axial optical aberrations

NASA Astrophysics Data System (ADS)

Špiclin, Žiga; Bürmen, Miran; Pernuš, Franjo; Likar, Boštjan

2012-03-01

Spatial resolution of hyperspectral imaging systems can vary significantly due to axial optical aberrations that originate from wavelength-induced index-of-refraction variations of the imaging optics. For systems that have a broad spectral range, the spatial resolution will vary significantly both with respect to the acquisition wavelength and with respect to the spatial position within each spectral image. Variations of the spatial resolution can be effectively characterized as part of the calibration procedure by a local image-based estimation of the pointspread function (PSF) of the hyperspectral imaging system. The estimated PSF can then be used in the image deconvolution methods to improve the spatial resolution of the spectral images. We estimated the PSFs from the spectral images of a line grid geometric caliber. From individual line segments of the line grid, the PSF was obtained by a non-parametric estimation procedure that used an orthogonal series representation of the PSF. By using the non-parametric estimation procedure, the PSFs were estimated at different spatial positions and at different wavelengths. The variations of the spatial resolution were characterized by the radius and the fullwidth half-maximum of each PSF and by the modulation transfer function, computed from images of USAF1951 resolution target. The estimation and characterization of the PSFs and the image deconvolution based spatial resolution enhancement were tested on images obtained by a hyperspectral imaging system with an acousto-optic tunable filter in the visible spectral range. The results demonstrate that the spatial resolution of the acquired spectral images can be significantly improved using the estimated PSFs and image deconvolution methods.

Filtering high resolution hyperspectral imagery and analyzing it for quantification of water quality parameters and aquatic vegetation

NASA Astrophysics Data System (ADS)

Pande-Chhetri, Roshan

High resolution hyperspectral imagery (airborne or ground-based) is gaining momentum as a useful analytical tool in various fields including agriculture and aquatic systems. These images are often contaminated with stripes and noise resulting in lower signal-to-noise ratio, especially in aquatic regions where signal is naturally low. This research investigates effective methods for filtering high spatial resolution hyperspectral imagery and use of the imagery in water quality parameter estimation and aquatic vegetation classification. The striping pattern of the hyperspectral imagery is non-parametric and difficult to filter. In this research, a de-striping algorithm based on wavelet analysis and adaptive Fourier domain normalization was examined. The result of this algorithm was found superior to other available algorithms and yielded highest Peak Signal to Noise Ratio improvement. The algorithm was implemented on individual image bands and on selected bands of the Maximum Noise Fraction (MNF) transformed images. The results showed that image filtering in the MNF domain was efficient and produced best results. The study investigated methods of analyzing hyperspectral imagery to estimate water quality parameters and to map aquatic vegetation in case-2 waters. Ground-based hyperspectral imagery was analyzed to determine chlorophyll-a (Chl-a) concentrations in aquaculture ponds. Two-band and three-band indices were implemented and the effect of using submerged reflectance targets was evaluated. Laboratory measured values were found to be in strong correlation with two-band and three-band spectral indices computed from the hyperspectral image. Coefficients of determination (R2) values were found to be 0.833 and 0.862 without submerged targets and stronger values of 0.975 and 0.982 were obtained using submerged targets. Airborne hyperspectral images were used to detect and classify aquatic vegetation in a black river estuarine system. Image normalization for water surface reflectance and water depths was conducted and non-parametric classifiers such as ANN, SVM and SAM were tested and compared. Quality assessment indicated better classification and detection when non-parametric classifiers were applied to normalized or depth invariant transform images. Best classification accuracy of 73% was achieved when ANN is applied on normalized image and best detection accuracy of around 92% was obtained when SVM or SAM was applied on depth invariant images.

Flutriciclamide (18F-GE180) PET: First-in-Human PET Study of Novel Third-Generation In Vivo Marker of Human Translocator Protein.

PubMed

Fan, Zhen; Calsolaro, Valeria; Atkinson, Rebecca A; Femminella, Grazia D; Waldman, Adam; Buckley, Christopher; Trigg, William; Brooks, David J; Hinz, Rainer; Edison, Paul

2016-11-01

Neuroinflammation is associated with neurodegenerative disease. PET radioligands targeting the 18-kDa translocator protein (TSPO) have been used as in vivo markers of neuroinflammation, but there is an urgent need for novel probes with improved signal-to-noise ratio. Flutriciclamide ( 18 F-GE180) is a recently developed third-generation TSPO ligand. In this first study, we evaluated the optimum scan duration and kinetic modeling strategies for 18 F-GE180 PET in (older) healthy controls. Ten healthy controls, 6 TSPO high-affinity binders, and 4 mixed-affinity binders were recruited. All subjects underwent detailed neuropsychologic tests, MRI, and a 210-min 18 F-GE180 dynamic PET/CT scan using metabolite-corrected arterial plasma input function. We evaluated 5 different kinetic models: irreversible and reversible 2-tissue-compartment models, a reversible 1-tissue model, and 2 models with an extra irreversible vascular compartment. The minimal scan duration was established using 210-min scan data. The feasibility of generating parametric maps was also investigated using graphical analysis. 18 F-GE180 concentration was higher in plasma than in whole blood during the entire scan duration. The volume of distribution (V T ) was 0.17 in high-affinity binders and 0.12 in mixed-affinity binders using the kinetic model. The model that best represented brain 18 F-GE180 kinetics across regions was the reversible 2-tissue-compartment model (2TCM4k), and 90 min resulted as the optimum scan length required to obtain stable estimates. Logan graphical analysis with arterial input function gave a V T highly consistent with V T in the kinetic model, which could be used for voxelwise analysis. We report for the first time, to our knowledge, the kinetic properties of the novel third-generation TSPO PET ligand 18 F-GE180 in humans: 2TCM4k is the optimal method to quantify the brain uptake, 90 min is the optimal scan length, and the Logan approach could be used to generate parametric maps. Although these control subjects have shown relatively low V T , the methodology presented here forms the basis for quantification for future PET studies using 18 F-GE180 in different pathologies. © 2016 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Multi-parametric analysis of phagocyte antimicrobial responses using imaging flow cytometry.

PubMed

Havixbeck, Jeffrey J; Wong, Michael E; More Bayona, Juan A; Barreda, Daniel R

2015-08-01

We feature a multi-parametric approach based on an imaging flow cytometry platform for examining phagocyte antimicrobial responses against the gram-negative bacterium Aeromonas veronii. This pathogen is known to induce strong inflammatory responses across a broad range of animal species, including humans. We examined the contribution of A. veronii to the induction of early phagocyte inflammatory processes in RAW 264.7 murine macrophages in vitro. We found that A. veronii, both in live or heat-killed forms, induced similar levels of macrophage activation based on NF-κB translocation. Although these macrophages maintained high levels of viability following heat-killed or live challenges with A. veronii, we identified inhibition of macrophage proliferation as early as 1h post in vitro challenge. The characterization of phagocytic responses showed a time-dependent increase in phagocytosis upon A. veronii challenge, which was paired with a robust induction of intracellular respiratory burst responses. Interestingly, despite the overall increase in the production of reactive oxygen species (ROS) among RAW 264.7 macrophages, we found a significant reduction in the production of ROS among the macrophage subset that had bound A. veronii. Phagocytic uptake of the pathogen further decreased ROS production levels, even beyond those of unstimulated controls. Overall, this multi-parametric imaging flow cytometry-based approach allowed for segregation of unique phagocyte sub-populations and examination of their downstream antimicrobial responses, and should contribute to improved understanding of phagocyte responses against Aeromonas and other pathogens. Copyright © 2015 Elsevier B.V. All rights reserved.

Machine-learning in grading of gliomas based on multi-parametric magnetic resonance imaging at 3T.

PubMed

Citak-Er, Fusun; Firat, Zeynep; Kovanlikaya, Ilhami; Ture, Ugur; Ozturk-Isik, Esin

2018-06-15

The objective of this study was to assess the contribution of multi-parametric (mp) magnetic resonance imaging (MRI) quantitative features in the machine learning-based grading of gliomas with a multi-region-of-interests approach. Forty-three patients who were newly diagnosed as having a glioma were included in this study. The patients were scanned prior to any therapy using a standard brain tumor magnetic resonance (MR) imaging protocol that included T1 and T2-weighted, diffusion-weighted, diffusion tensor, MR perfusion and MR spectroscopic imaging. Three different regions-of-interest were drawn for each subject to encompass tumor, immediate tumor periphery, and distant peritumoral edema/normal. The normalized mp-MRI features were used to build machine-learning models for differentiating low-grade gliomas (WHO grades I and II) from high grades (WHO grades III and IV). In order to assess the contribution of regional mp-MRI quantitative features to the classification models, a support vector machine-based recursive feature elimination method was applied prior to classification. A machine-learning model based on support vector machine algorithm with linear kernel achieved an accuracy of 93.0%, a specificity of 86.7%, and a sensitivity of 96.4% for the grading of gliomas using ten-fold cross validation based on the proposed subset of the mp-MRI features. In this study, machine-learning based on multiregional and multi-parametric MRI data has proven to be an important tool in grading glial tumors accurately even in this limited patient population. Future studies are needed to investigate the use of machine learning algorithms for brain tumor classification in a larger patient cohort. Copyright © 2018. Published by Elsevier Ltd.

Simultaneous measurement and modulation of multiple physiological parameters in the isolated heart using optical techniques

PubMed Central

Lee, Peter; Yan, Ping; Ewart, Paul; Kohl, Peter

2012-01-01

Whole-heart multi-parametric optical mapping has provided valuable insight into the interplay of electro-physiological parameters, and this technology will continue to thrive as dyes are improved and technical solutions for imaging become simpler and cheaper. Here, we show the advantage of using improved 2nd-generation voltage dyes, provide a simple solution to panoramic multi-parametric mapping, and illustrate the application of flash photolysis of caged compounds for studies in the whole heart. For proof of principle, we used the isolated rat whole-heart model. After characterising the blue and green isosbestic points of di-4-ANBDQBS and di-4-ANBDQPQ, respectively, two voltage and calcium mapping systems are described. With two newly custom-made multi-band optical filters, (1) di-4-ANBDQBS and fluo-4 and (2) di-4-ANBDQPQ and rhod-2 mapping are demonstrated. Furthermore, we demonstrate three-parameter mapping using di-4-ANBDQPQ, rhod-2 and NADH. Using off-the-shelf optics and the di-4-ANBDQPQ and rhod-2 combination, we demonstrate panoramic multi-parametric mapping, affording a 360° spatiotemporal record of activity. Finally, local optical perturbation of calcium dynamics in the whole heart is demonstrated using the caged compound, o-nitrophenyl ethylene glycol tetraacetic acid (NP-EGTA), with an ultraviolet light-emitting diode (LED). Calcium maps (heart loaded with di-4-ANBDQPQ and rhod-2) demonstrate successful NP-EGTA loading and local flash photolysis. All imaging systems were built using only a single camera. In conclusion, using novel 2nd-generation voltage dyes, we developed scalable techniques for multi-parametric optical mapping of the whole heart from one point of view and panoramically. In addition to these parameter imaging approaches, we show that it is possible to use caged compounds and ultraviolet LEDs to locally perturb electrophysiological parameters in the whole heart. PMID:22886365

Dorsomedial SCN neuronal subpopulations subserve different functions in human dementia.

PubMed

Harper, David G; Stopa, Edward G; Kuo-Leblanc, Victoria; McKee, Ann C; Asayama, Kentaro; Volicer, Ladislav; Kowall, Neil; Satlin, Andrew

2008-06-01

The suprachiasmatic nuclei (SCN) are necessary and sufficient for the maintenance of circadian rhythms in primate and other mammalian species. The human dorsomedial SCN contains populations of non-species-specific vasopressin and species-specific neurotensin neurons. We made time-series recordings of core body temperature and locomotor activity in 19 elderly, male, end-stage dementia patients and 8 normal elderly controls. Following the death of the dementia patients, neuropathological diagnostic information and tissue samples from the hypothalamus were obtained. Hypothalamic tissue was also obtained from eight normal control cases that had not had activity or core temperature recordings previously. Core temperature was analysed for parametric, circadian features, and activity was analysed for non-parametric and parametric circadian features. These indices were then correlated with the degree of degeneration seen in the SCN (glia/neuron ratio) and neuronal counts from the dorsomedial SCN (vasopressin, neurotensin). Specific loss of SCN neurotensin neurons was associated with loss of activity and temperature amplitude without increase in activity fragmentation. Loss of SCN vasopressin neurons was associated with increased activity fragmentation but not loss of amplitude. Evidence for a circadian rhythm of vasopressinergic activity was seen in the dementia cases but no evidence was seen for a circadian rhythm in neurotensinergic activity. These results provide evidence that the SCN is necessary for the maintenance of the circadian rhythm in humans, information on the role of neuronal subpopulations in subserving this function and the utility of dementia in elaborating brain-behaviour relationships in the human.

Towards robust deconvolution of low-dose perfusion CT: sparse perfusion deconvolution using online dictionary learning.

PubMed

Fang, Ruogu; Chen, Tsuhan; Sanelli, Pina C

2013-05-01

Computed tomography perfusion (CTP) is an important functional imaging modality in the evaluation of cerebrovascular diseases, particularly in acute stroke and vasospasm. However, the post-processed parametric maps of blood flow tend to be noisy, especially in low-dose CTP, due to the noisy contrast enhancement profile and the oscillatory nature of the results generated by the current computational methods. In this paper, we propose a robust sparse perfusion deconvolution method (SPD) to estimate cerebral blood flow in CTP performed at low radiation dose. We first build a dictionary from high-dose perfusion maps using online dictionary learning and then perform deconvolution-based hemodynamic parameters estimation on the low-dose CTP data. Our method is validated on clinical data of patients with normal and pathological CBF maps. The results show that we achieve superior performance than existing methods, and potentially improve the differentiation between normal and ischemic tissue in the brain. Copyright © 2013 Elsevier B.V. All rights reserved.

Towards robust deconvolution of low-dose perfusion CT: Sparse perfusion deconvolution using online dictionary learning

PubMed Central

Fang, Ruogu; Chen, Tsuhan; Sanelli, Pina C.

2014-01-01

Computed tomography perfusion (CTP) is an important functional imaging modality in the evaluation of cerebrovascular diseases, particularly in acute stroke and vasospasm. However, the post-processed parametric maps of blood flow tend to be noisy, especially in low-dose CTP, due to the noisy contrast enhancement profile and the oscillatory nature of the results generated by the current computational methods. In this paper, we propose a robust sparse perfusion deconvolution method (SPD) to estimate cerebral blood flow in CTP performed at low radiation dose. We first build a dictionary from high-dose perfusion maps using online dictionary learning and then perform deconvolution-based hemodynamic parameters estimation on the low-dose CTP data. Our method is validated on clinical data of patients with normal and pathological CBF maps. The results show that we achieve superior performance than existing methods, and potentially improve the differentiation between normal and ischemic tissue in the brain. PMID:23542422

Strong-lensing analysis of A2744 with MUSE and Hubble Frontier Fields images

NASA Astrophysics Data System (ADS)

Mahler, G.; Richard, J.; Clément, B.; Lagattuta, D.; Schmidt, K.; Patrício, V.; Soucail, G.; Bacon, R.; Pello, R.; Bouwens, R.; Maseda, M.; Martinez, J.; Carollo, M.; Inami, H.; Leclercq, F.; Wisotzki, L.

2018-01-01

We present an analysis of Multi Unit Spectroscopic Explorer (MUSE) observations obtained on the massive Frontier Fields (FFs) cluster A2744. This new data set covers the entire multiply imaged region around the cluster core. The combined catalogue consists of 514 spectroscopic redshifts (with 414 new identifications). We use this redshift information to perform a strong-lensing analysis revising multiple images previously found in the deep FF images, and add three new MUSE-detected multiply imaged systems with no obvious Hubble Space Telescope counterpart. The combined strong-lensing constraints include a total of 60 systems producing 188 images altogether, out of which 29 systems and 83 images are spectroscopically confirmed, making A2744 one of the most well-constrained clusters to date. Thanks to the large amount of spectroscopic redshifts, we model the influence of substructures at larger radii, using a parametrization including two cluster-scale components in the cluster core and several group scale in the outskirts. The resulting model accurately reproduces all the spectroscopic multiple systems, reaching an rms of 0.67 arcsec in the image plane. The large number of MUSE spectroscopic redshifts gives us a robust model, which we estimate reduces the systematic uncertainty on the 2D mass distribution by up to ∼2.5 times the statistical uncertainty in the cluster core. In addition, from a combination of the parametrization and the set of constraints, we estimate the relative systematic uncertainty to be up to 9 per cent at 200 kpc.

[Technical background of data collection for parametric observation of total mesorectal excision (TME) in rectal cancer].

PubMed

Bláha, M; Hoch, J; Ferko, A; Ryška, A; Hovorková, E

Improvement in any human activity is preconditioned by inspection of results and providing feedback used for modification of the processes applied. Comparison of experts experience in the given field is another indispensable part leading to optimisation and improvement of processes, and optimally to implementation of standards. For the purpose of objective comparison and assessment of the processes, it is always necessary to describe the processes in a parametric way, to obtain representative data, to assess the achieved results, and to provide unquestionable and data-driven feedback based on such analysis. This may lead to a consensus on the definition of standards in the given area of health care. Total mesorectal excision (TME) is a standard procedure of rectal cancer (C20) surgical treatment. However, the quality of performed procedures varies in different health care facilities, which is given, among others, by internal processes and surgeons experience. Assessment of surgical treatment results is therefore of key importance. A pathologist who assesses the resected tissue can provide valuable feedback in this respect. An information system for the parametric assessment of TME performance is described in our article, including technical background in the form of a multicentre clinical registry and the structure of observed parameters. We consider the proposed system of TME parametric assessment as significant for improvement of TME performance, aimed at reducing local recurrences and at improving the overall prognosis of patients. rectal cancer total mesorectal excision parametric data clinical registries TME registry.

Imaging Strategies for Tissue Engineering Applications

PubMed Central

Nam, Seung Yun; Ricles, Laura M.; Suggs, Laura J.

2015-01-01

Tissue engineering has evolved with multifaceted research being conducted using advanced technologies, and it is progressing toward clinical applications. As tissue engineering technology significantly advances, it proceeds toward increasing sophistication, including nanoscale strategies for material construction and synergetic methods for combining with cells, growth factors, or other macromolecules. Therefore, to assess advanced tissue-engineered constructs, tissue engineers need versatile imaging methods capable of monitoring not only morphological but also functional and molecular information. However, there is no single imaging modality that is suitable for all tissue-engineered constructs. Each imaging method has its own range of applications and provides information based on the specific properties of the imaging technique. Therefore, according to the requirements of the tissue engineering studies, the most appropriate tool should be selected among a variety of imaging modalities. The goal of this review article is to describe available biomedical imaging methods to assess tissue engineering applications and to provide tissue engineers with criteria and insights for determining the best imaging strategies. Commonly used biomedical imaging modalities, including X-ray and computed tomography, positron emission tomography and single photon emission computed tomography, magnetic resonance imaging, ultrasound imaging, optical imaging, and emerging techniques and multimodal imaging, will be discussed, focusing on the latest trends of their applications in recent tissue engineering studies. PMID:25012069

A stereotaxic, population-averaged T1w ovine brain atlas including cerebral morphology and tissue volumes

PubMed Central

Nitzsche, Björn; Frey, Stephen; Collins, Louis D.; Seeger, Johannes; Lobsien, Donald; Dreyer, Antje; Kirsten, Holger; Stoffel, Michael H.; Fonov, Vladimir S.; Boltze, Johannes

2015-01-01

Standard stereotaxic reference systems play a key role in human brain studies. Stereotaxic coordinate systems have also been developed for experimental animals including non-human primates, dogs, and rodents. However, they are lacking for other species being relevant in experimental neuroscience including sheep. Here, we present a spatial, unbiased ovine brain template with tissue probability maps (TPM) that offer a detailed stereotaxic reference frame for anatomical features and localization of brain areas, thereby enabling inter-individual and cross-study comparability. Three-dimensional data sets from healthy adult Merino sheep (Ovis orientalis aries, 12 ewes and 26 neutered rams) were acquired on a 1.5 T Philips MRI using a T1w sequence. Data were averaged by linear and non-linear registration algorithms. Moreover, animals were subjected to detailed brain volume analysis including examinations with respect to body weight (BW), age, and sex. The created T1w brain template provides an appropriate population-averaged ovine brain anatomy in a spatial standard coordinate system. Additionally, TPM for gray (GM) and white (WM) matter as well as cerebrospinal fluid (CSF) classification enabled automatic prior-based tissue segmentation using statistical parametric mapping (SPM). Overall, a positive correlation of GM volume and BW explained about 15% of the variance of GM while a positive correlation between WM and age was found. Absolute tissue volume differences were not detected, indeed ewes showed significantly more GM per bodyweight as compared to neutered rams. The created framework including spatial brain template and TPM represent a useful tool for unbiased automatic image preprocessing and morphological characterization in sheep. Therefore, the reported results may serve as a starting point for further experimental and/or translational research aiming at in vivo analysis in this species. PMID:26089780

Comparison of interpolation functions to improve a rebinning-free CT-reconstruction algorithm.

PubMed

de las Heras, Hugo; Tischenko, Oleg; Xu, Yuan; Hoeschen, Christoph

2008-01-01

The robust algorithm OPED for the reconstruction of images from Radon data has been recently developed. This reconstructs an image from parallel data within a special scanning geometry that does not need rebinning but only a simple re-ordering, so that the acquired fan data can be used directly for the reconstruction. However, if the number of rays per fan view is increased, there appear empty cells in the sinogram. These cells need to be filled by interpolation before the reconstruction can be carried out. The present paper analyzes linear interpolation, cubic splines and parametric (or "damped") splines for the interpolation task. The reconstruction accuracy in the resulting images was measured by the Normalized Mean Square Error (NMSE), the Hilbert Angle, and the Mean Relative Error. The spatial resolution was measured by the Modulation Transfer Function (MTF). Cubic splines were confirmed to be the most recommendable method. The reconstructed images resulting from cubic spline interpolation show a significantly lower NMSE than the ones from linear interpolation and have the largest MTF for all frequencies. Parametric splines proved to be advantageous only for small sinograms (below 50 fan views).

TU-AB-BRC-03: Accurate Tissue Characterization for Monte Carlo Dose Calculation Using Dual-and Multi-Energy CT Data

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

Lalonde, A; Bouchard, H

Purpose: To develop a general method for human tissue characterization with dual-and multi-energy CT and evaluate its performance in determining elemental compositions and the associated proton stopping power relative to water (SPR) and photon mass absorption coefficients (EAC). Methods: Principal component analysis is used to extract an optimal basis of virtual materials from a reference dataset of tissues. These principal components (PC) are used to perform two-material decomposition using simulated DECT data. The elemental mass fraction and the electron density in each tissue is retrieved by measuring the fraction of each PC. A stoichiometric calibration method is adapted to themore » technique to make it suitable for clinical use. The present approach is compared with two others: parametrization and three-material decomposition using the water-lipid-protein (WLP) triplet. Results: Monte Carlo simulations using TOPAS for four reference tissues shows that characterizing them with only two PC is enough to get a submillimetric precision on proton range prediction. Based on the simulated DECT data of 43 references tissues, the proposed method is in agreement with theoretical values of protons SPR and low-kV EAC with a RMS error of 0.11% and 0.35%, respectively. In comparison, parametrization and WLP respectively yield RMS errors of 0.13% and 0.29% on SPR, and 2.72% and 2.19% on EAC. Furthermore, the proposed approach shows potential applications for spectral CT. Using five PC and five energy bins reduces the SPR RMS error to 0.03%. Conclusion: The proposed method shows good performance in determining elemental compositions from DECT data and physical quantities relevant to radiotherapy dose calculation and generally shows better accuracy and unbiased results compared to reference methods. The proposed method is particularly suitable for Monte Carlo calculations and shows promise in using more than two energies to characterize human tissue with CT.« less

Parametric mapping using spectral analysis for 11C-PBR28 PET reveals neuroinflammation in mild cognitive impairment subjects.

PubMed

Fan, Zhen; Dani, Melanie; Femminella, Grazia D; Wood, Melanie; Calsolaro, Valeria; Veronese, Mattia; Turkheimer, Federico; Gentleman, Steve; Brooks, David J; Hinz, Rainer; Edison, Paul

2018-07-01

Neuroinflammation and microglial activation play an important role in amnestic mild cognitive impairment (MCI) and Alzheimer's disease. In this study, we investigated the spatial distribution of neuroinflammation in MCI subjects, using spectral analysis (SA) to generate parametric maps and quantify 11 C-PBR28 PET, and compared these with compartmental and other kinetic models of quantification. Thirteen MCI and nine healthy controls were enrolled in this study. Subjects underwent 11 C-PBR28 PET scans with arterial cannulation. Spectral analysis with an arterial plasma input function was used to generate 11 C-PBR28 parametric maps. These maps were then compared with regional 11 C-PBR28 V T (volume of distribution) using a two-tissue compartment model and Logan graphic analysis. Amyloid load was also assessed with 18 F-Flutemetamol PET. With SA, three component peaks were identified in addition to blood volume. The 11 C-PBR28 impulse response function (IRF) at 90 min produced the lowest coefficient of variation. Single-subject analysis using this IRF demonstrated microglial activation in five out of seven amyloid-positive MCI subjects. IRF parametric maps of 11 C-PBR28 uptake revealed a group-wise significant increase in neuroinflammation in amyloid-positive MCI subjects versus HC in multiple cortical association areas, and particularly in the temporal lobe. Interestingly, compartmental analysis detected group-wise increase in 11 C-PBR28 binding in the thalamus of amyloid-positive MCI subjects, while Logan parametric maps did not perform well. This study demonstrates for the first time that spectral analysis can be used to generate parametric maps of 11 C-PBR28 uptake, and is able to detect microglial activation in amyloid-positive MCI subjects. IRF parametric maps of 11 C-PBR28 uptake allow voxel-wise single-subject analysis and could be used to evaluate microglial activation in individual subjects.

A first-in-man PET study of [18F]PSS232, a fluorinated ABP688 derivative for imaging metabotropic glutamate receptor subtype 5.

PubMed

Warnock, Geoffrey; Sommerauer, Michael; Mu, Linjing; Pla Gonzalez, Gloria; Geistlich, Susanne; Treyer, Valerie; Schibli, Roger; Buck, Alfred; Krämer, Stefanie D; Ametamey, Simon M

2018-06-01

Non-invasive imaging of metabotropic glutamate receptor 5 (mGlu 5 ) in the brain using PET is of interest in e.g., anxiety, depression, and Parkinson's disease. Widespread application of the most widely used mGlu 5 tracer, [ 11 C]ABP688, is limited by the short physical half-life of carbon-11. [ 18 F]PSS232 is a fluorinated analog with promising preclinical properties and high selectivity and specificity for mGlu 5 . In this first-in-man study, we evaluated the brain uptake pattern and kinetics of [ 18 F]PSS232 in healthy volunteers. [ 18 F]PSS232 PET was performed with ten healthy male volunteers aged 20-40 years. Seven of the subjects received a bolus injection and the remainder a bolus/infusion protocol. Cerebral blood flow was determined in seven subjects using [ 15 O]water PET. Arterial blood activity was measured using an online blood counter. Tracer kinetics were evaluated by compartment modeling and parametric maps were generated for both tracers. At 90 min post-injection, 59.2 ± 11.1% of total radioactivity in plasma corresponded to intact tracer. The regional first pass extraction fraction of [ 18 F]PSS232 ranged from 0.41 ± 0.06 to 0.55 ± 0.03 and brain distribution pattern matched that of [ 11 C]ABP688. Uptake kinetics followed a simple two-tissue compartment model. The volume of distribution of total tracer (V T , ml/cm 3 ) ranged from 1.18 ± 0.20 for white matter to 2.91 ± 0.51 for putamen. The respective mean distribution volume ratios (DVR) with cerebellum as the reference tissue were 0.88 ± 0.06 and 2.12 ± 0.10, respectively. The tissue/cerebellum ratios of a bolus/infusion protocol (30/70 dose ratio) were close to the DVR values. Brain uptake of [ 18 F]PSS232 matched the distribution of mGlu 5 and followed a two-tissue compartment model. The well-defined kinetics and the possibility to use reference tissue models, obviating the need for arterial blood sampling, make [ 18 F]PSS232 a promising fluorine-18 labeled radioligand for measuring mGlu 5 density in humans.

Ghost imaging with paired x-ray photons

NASA Astrophysics Data System (ADS)

Schori, A.; Borodin, D.; Tamasaku, K.; Shwartz, S.

2018-06-01

We report the experimental observation of ghost imaging with paired x-ray photons, which are generated by parametric downconversion. We use the one-to-one relation between the photon energies and the emission angles and the anticorrelation between the k -vectors of the signal and the idler photons to reconstruct the images of slits with nominally zero background levels. Further extension of our procedure can be used for the observation of various quantum phenomena at x-ray wavelengths.

A fuzzy feature fusion method for auto-segmentation of gliomas with multi-modality diffusion and perfusion magnetic resonance images in radiotherapy.

PubMed

Guo, Lu; Wang, Ping; Sun, Ranran; Yang, Chengwen; Zhang, Ning; Guo, Yu; Feng, Yuanming

2018-02-19

The diffusion and perfusion magnetic resonance (MR) images can provide functional information about tumour and enable more sensitive detection of the tumour extent. We aimed to develop a fuzzy feature fusion method for auto-segmentation of gliomas in radiotherapy planning using multi-parametric functional MR images including apparent diffusion coefficient (ADC), fractional anisotropy (FA) and relative cerebral blood volume (rCBV). For each functional modality, one histogram-based fuzzy model was created to transform image volume into a fuzzy feature space. Based on the fuzzy fusion result of the three fuzzy feature spaces, regions with high possibility belonging to tumour were generated automatically. The auto-segmentations of tumour in structural MR images were added in final auto-segmented gross tumour volume (GTV). For evaluation, one radiation oncologist delineated GTVs for nine patients with all modalities. Comparisons between manually delineated and auto-segmented GTVs showed that, the mean volume difference was 8.69% (±5.62%); the mean Dice's similarity coefficient (DSC) was 0.88 (±0.02); the mean sensitivity and specificity of auto-segmentation was 0.87 (±0.04) and 0.98 (±0.01) respectively. High accuracy and efficiency can be achieved with the new method, which shows potential of utilizing functional multi-parametric MR images for target definition in precision radiation treatment planning for patients with gliomas.

CuBe: parametric modeling of 3D foveal shape using cubic Bézier

PubMed Central

Yadav, Sunil Kumar; Motamedi, Seyedamirhosein; Oberwahrenbrock, Timm; Oertel, Frederike Cosima; Polthier, Konrad; Paul, Friedemann; Kadas, Ella Maria; Brandt, Alexander U.

2017-01-01

Optical coherence tomography (OCT) allows three-dimensional (3D) imaging of the retina, and is commonly used for assessing pathological changes of fovea and macula in many diseases. Many neuroinflammatory conditions are known to cause modifications to the fovea shape. In this paper, we propose a method for parametric modeling of the foveal shape. Our method exploits invariant features of the macula from OCT data and applies a cubic Bézier polynomial along with a least square optimization to produce a best fit parametric model of the fovea. Additionally, we provide several parameters of the foveal shape based on the proposed 3D parametric modeling. Our quantitative and visual results show that the proposed model is not only able to reconstruct important features from the foveal shape, but also produces less error compared to the state-of-the-art methods. Finally, we apply the model in a comparison of healthy control eyes and eyes from patients with neuroinflammatory central nervous system disorders and optic neuritis, and show that several derived model parameters show significant differences between the two groups. PMID:28966857

Fully automated prostate segmentation in 3D MR based on normalized gradient fields cross-correlation initialization and LOGISMOS refinement

NASA Astrophysics Data System (ADS)

Yin, Yin; Fotin, Sergei V.; Periaswamy, Senthil; Kunz, Justin; Haldankar, Hrishikesh; Muradyan, Naira; Cornud, François; Turkbey, Baris; Choyke, Peter

2012-02-01

Manual delineation of the prostate is a challenging task for a clinician due to its complex and irregular shape. Furthermore, the need for precisely targeting the prostate boundary continues to grow. Planning for radiation therapy, MR-ultrasound fusion for image-guided biopsy, multi-parametric MRI tissue characterization, and context-based organ retrieval are examples where accurate prostate delineation can play a critical role in a successful patient outcome. Therefore, a robust automated full prostate segmentation system is desired. In this paper, we present an automated prostate segmentation system for 3D MR images. In this system, the prostate is segmented in two steps: the prostate displacement and size are first detected, and then the boundary is refined by a shape model. The detection approach is based on normalized gradient fields cross-correlation. This approach is fast, robust to intensity variation and provides good accuracy to initialize a prostate mean shape model. The refinement model is based on a graph-search based framework, which contains both shape and topology information during deformation. We generated the graph cost using trained classifiers and used coarse-to-fine search and region-specific classifier training. The proposed algorithm was developed using 261 training images and tested on another 290 cases. The segmentation performance using mean DSC ranging from 0.89 to 0.91 depending on the evaluation subset demonstrates state of the art performance. Running time for the system is about 20 to 40 seconds depending on image size and resolution.

An SPM8-based approach for attenuation correction combining segmentation and nonrigid template formation: application to simultaneous PET/MR brain imaging.

PubMed

Izquierdo-Garcia, David; Hansen, Adam E; Förster, Stefan; Benoit, Didier; Schachoff, Sylvia; Fürst, Sebastian; Chen, Kevin T; Chonde, Daniel B; Catana, Ciprian

2014-11-01

We present an approach for head MR-based attenuation correction (AC) based on the Statistical Parametric Mapping 8 (SPM8) software, which combines segmentation- and atlas-based features to provide a robust technique to generate attenuation maps (μ maps) from MR data in integrated PET/MR scanners. Coregistered anatomic MR and CT images of 15 glioblastoma subjects were used to generate the templates. The MR images from these subjects were first segmented into 6 tissue classes (gray matter, white matter, cerebrospinal fluid, bone, soft tissue, and air), which were then nonrigidly coregistered using a diffeomorphic approach. A similar procedure was used to coregister the anatomic MR data for a new subject to the template. Finally, the CT-like images obtained by applying the inverse transformations were converted to linear attenuation coefficients to be used for AC of PET data. The method was validated on 16 new subjects with brain tumors (n = 12) or mild cognitive impairment (n = 4) who underwent CT and PET/MR scans. The μ maps and corresponding reconstructed PET images were compared with those obtained using the gold standard CT-based approach and the Dixon-based method available on the Biograph mMR scanner. Relative change (RC) images were generated in each case, and voxel- and region-of-interest-based analyses were performed. The leave-one-out cross-validation analysis of the data from the 15 atlas-generation subjects showed small errors in brain linear attenuation coefficients (RC, 1.38% ± 4.52%) compared with the gold standard. Similar results (RC, 1.86% ± 4.06%) were obtained from the analysis of the atlas-validation datasets. The voxel- and region-of-interest-based analysis of the corresponding reconstructed PET images revealed quantification errors of 3.87% ± 5.0% and 2.74% ± 2.28%, respectively. The Dixon-based method performed substantially worse (the mean RC values were 13.0% ± 10.25% and 9.38% ± 4.97%, respectively). Areas closer to the skull showed the largest improvement. We have presented an SPM8-based approach for deriving the head μ map from MR data to be used for PET AC in integrated PET/MR scanners. Its implementation is straightforward and requires only the morphologic data acquired with a single MR sequence. The method is accurate and robust, combining the strengths of both segmentation- and atlas-based approaches while minimizing their drawbacks. © 2014 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Thermal therapy of pancreatic tumors using endoluminal ultrasound: parametric and patient-specific modeling

PubMed Central

Adams, Matthew S.; Scott, Serena J.; Salgaonkar, Vasant A.; Sommer, Graham; Diederich, Chris J.

2016-01-01

Purpose To investigate endoluminal ultrasound applicator configurations for volumetric thermal ablation and hyperthermia of pancreatic tumors using 3D acoustic and biothermal finite element models. Materials and Methods Parametric studies compared endoluminal heating performance for varying applicator transducer configurations (planar, curvilinear-focused, or radial-diverging), frequencies (1–5 MHz), and anatomical conditions. Patient-specific pancreatic head and body tumor models were used to evaluate feasibility of generating hyperthermia and thermal ablation using an applicator positioned in the duodenal or stomach lumen. Temperature and thermal dose were calculated to define ablation (>240 EM43°C) and moderate hyperthermia (40–45 °C) boundaries, and to assess sparing of sensitive tissues. Proportional-integral control was incorporated to regulate maximum temperature to 70–80 °C for ablation and 45 °C for hyperthermia in target regions. Results Parametric studies indicated that 1–3 MHz planar transducers are most suitable for volumetric ablation, producing 5–8 cm3 lesion volumes for a stationary 5 minute sonication. Curvilinear-focused geometries produce more localized ablation to 20–45 mm depth from the GI tract and enhance thermal sparing (Tmax<42 °C) of the luminal wall. Patient anatomy simulations show feasibility in ablating 60.1–92.9% of head/body tumor volumes (4.3–37.2 cm3) with dose <15 EM43°C in the luminal wall for 18–48 min treatment durations, using 1–3 applicator placements in GI lumen. For hyperthermia, planar and radial-diverging transducers could maintain up to 8 cm3 and 15 cm3 of tissue, respectively, between 40–45 °C for a single applicator placement. Conclusions Modeling studies indicate the feasibility of endoluminal ultrasound for volumetric thermal ablation or hyperthermia treatment of pancreatic tumor tissue. PMID:27097663

Graded-threshold parametric response maps: towards a strategy for adaptive dose painting

NASA Astrophysics Data System (ADS)

Lausch, A.; Jensen, N.; Chen, J.; Lee, T. Y.; Lock, M.; Wong, E.

2014-03-01

Purpose: To modify the single-threshold parametric response map (ST-PRM) method for predicting treatment outcomes in order to facilitate its use for guidance of adaptive dose painting in intensity-modulated radiotherapy. Methods: Multiple graded thresholds were used to extend the ST-PRM method (Nat. Med. 2009;15(5):572-576) such that the full functional change distribution within tumours could be represented with respect to multiple confidence interval estimates for functional changes in similar healthy tissue. The ST-PRM and graded-threshold PRM (GT-PRM) methods were applied to functional imaging scans of 5 patients treated for hepatocellular carcinoma. Pre and post-radiotherapy arterial blood flow maps (ABF) were generated from CT-perfusion scans of each patient. ABF maps were rigidly registered based on aligning tumour centres of mass. ST-PRM and GT-PRM analyses were then performed on overlapping tumour regions within the registered ABF maps. Main findings: The ST-PRMs contained many disconnected clusters of voxels classified as having a significant change in function. While this may be useful to predict treatment response, it may pose challenges for identifying boost volumes or for informing dose-painting by numbers strategies. The GT-PRMs included all of the same information as ST-PRMs but also visualized the full tumour functional change distribution. Heterogeneous clusters in the ST-PRMs often became more connected in the GT-PRMs by voxels with similar functional changes. Conclusions: GT-PRMs provided additional information which helped to visualize relationships between significant functional changes identified by ST-PRMs. This may enhance ST-PRM utility for guiding adaptive dose painting.

A parametric study of hard tissue injury prediction using finite elements: consideration of geometric complexity, subfailure material properties, CT-thresholding, and element characteristics.

PubMed

Arregui-Dalmases, Carlos; Del Pozo, Eduardo; Duprey, Sonia; Lopez-Valdes, Francisco J; Lau, Anthony; Subit, Damien; Kent, Richard

2010-06-01

The objectives of this study were to examine the axial response of the clavicle under quasistatic compressions replicating the body boundary conditions and to quantify the sensitivity of finite element-predicted fracture in the clavicle to several parameters. Clavicles were harvested from 14 donors (age range 14-56 years). Quasistatic axial compression tests were performed using a custom rig designed to replicate in situ boundary conditions. Prior to testing, high-resolution computed tomography (CT) scans were taken of each clavicle. From those images, finite element models were constructed. Factors varied parametrically included the density used to threshold cortical bone in the CT scans, the presence of trabecular bone, the mesh density, Young's modulus, the maximum stress, and the element type (shell vs. solid, triangular vs. quadrilateral surface elements). The experiments revealed significant variability in the peak force (2.41 +/- 0.72 kN) and displacement to peak force (4.9 +/- 1.1 mm), with age (p < .05) and with some geometrical traits of the specimens. In the finite element models, the failure force and location were moderately dependent upon the Young's modulus. The fracture force was highly sensitive to the yield stress (80-110 MPa). Neither fracture location nor force was strongly dependent on mesh density as long as the element size was less than 5 x 5 mm(2). Both the fracture location and force were strongly dependent upon the threshold density used to define the thickness of the cortical shell.

Near-infrared spectroscopic tissue imaging for medical applications

DOEpatents

Demos,; Stavros, Staggs [Livermore, CA; Michael, C [Tracy, CA

2006-03-21

Near infrared imaging using elastic light scattering and tissue autofluorescence are explored for medical applications. The approach involves imaging using cross-polarized elastic light scattering and tissue autofluorescence in the Near Infra-Red (NIR) coupled with image processing and inter-image operations to differentiate human tissue components.

Near-infrared spectroscopic tissue imaging for medical applications

DOEpatents

Demos, Stavros [Livermore, CA; Staggs, Michael C [Tracy, CA

2006-12-12

Near infrared imaging using elastic light scattering and tissue autofluorescence are explored for medical applications. The approach involves imaging using cross-polarized elastic light scattering and tissue autofluorescence in the Near Infra-Red (NIR) coupled with image processing and inter-image operations to differentiate human tissue components.

Sex differences in amphetamine-induced displacement of [(18)F]fallypride in striatal and extrastriatal regions: a PET study.

PubMed

Riccardi, Patrizia; Zald, David; Li, Rui; Park, Sohee; Ansari, M Sib; Dawant, Benoit; Anderson, Sharlet; Woodward, Neil; Schmidt, Dennis; Baldwin, Ronald; Kessler, Robert

2006-09-01

The authors examined gender differences in d-amphetamine-induced displacements of [(18)F]fallypride in the striatal and extrastriatal brain regions and the correlations of these displacements with cognition and sensation seeking. Six women and seven men underwent positron emission tomography (PET) with [(18)F]fallypride before and after an oral dose of d-amphetamine. Percent displacements were calculated using regions of interest and parametric images of dopamine 2 (D(2)) receptor binding potential. Parametric images of dopamine release suggest that the female subjects had greater dopamine release than the male subjects in the right globus pallidus and right inferior frontal gyrus. Gender differences were observed in correlations of changes in cognition and sensation seeking with regional dopamine release. Findings revealed a greater dopamine release in women as well as gender differences in the relationship between regional dopamine release and sensation seeking and cognition.

Dynamic Human Body Modeling Using a Single RGB Camera.

PubMed

Zhu, Haiyu; Yu, Yao; Zhou, Yu; Du, Sidan

2016-03-18

In this paper, we present a novel automatic pipeline to build personalized parametric models of dynamic people using a single RGB camera. Compared to previous approaches that use monocular RGB images, our system can model a 3D human body automatically and incrementally, taking advantage of human motion. Based on coarse 2D and 3D poses estimated from image sequences, we first perform a kinematic classification of human body parts to refine the poses and obtain reconstructed body parts. Next, a personalized parametric human model is generated by driving a general template to fit the body parts and calculating the non-rigid deformation. Experimental results show that our shape estimation method achieves comparable accuracy with reconstructed models using depth cameras, yet requires neither user interaction nor any dedicated devices, leading to the feasibility of using this method on widely available smart phones.

Dynamic Human Body Modeling Using a Single RGB Camera

PubMed Central

Zhu, Haiyu; Yu, Yao; Zhou, Yu; Du, Sidan

2016-01-01

In this paper, we present a novel automatic pipeline to build personalized parametric models of dynamic people using a single RGB camera. Compared to previous approaches that use monocular RGB images, our system can model a 3D human body automatically and incrementally, taking advantage of human motion. Based on coarse 2D and 3D poses estimated from image sequences, we first perform a kinematic classification of human body parts to refine the poses and obtain reconstructed body parts. Next, a personalized parametric human model is generated by driving a general template to fit the body parts and calculating the non-rigid deformation. Experimental results show that our shape estimation method achieves comparable accuracy with reconstructed models using depth cameras, yet requires neither user interaction nor any dedicated devices, leading to the feasibility of using this method on widely available smart phones. PMID:26999159

Perceptual reversals during binocular rivalry: ERP components and their concomitant source differences.

PubMed

Britz, Juliane; Pitts, Michael A

2011-11-01

We used an intermittent stimulus presentation to investigate event-related potential (ERP) components associated with perceptual reversals during binocular rivalry. The combination of spatiotemporal ERP analysis with source imaging and statistical parametric mapping of the concomitant source differences yielded differences in three time windows: reversals showed increased activity in early visual (∼120 ms) and in inferior frontal and anterior temporal areas (∼400-600 ms) and decreased activity in the ventral stream (∼250-350 ms). The combination of source imaging and statistical parametric mapping suggests that these differences were due to differences in generator strength and not generator configuration, unlike the initiation of reversals in right inferior parietal areas. These results are discussed within the context of the extensive network of brain areas that has been implicated in the initiation, implementation, and appraisal of bistable perceptual reversals. Copyright © 2011 Society for Psychophysiological Research.

Fusion of multi-parametric MRI and temporal ultrasound for characterization of prostate cancer: in vivo feasibility study

NASA Astrophysics Data System (ADS)

Imani, Farhad; Ghavidel, Sahar; Abolmaesumi, Purang; Khallaghi, Siavash; Gibson, Eli; Khojaste, Amir; Gaed, Mena; Moussa, Madeleine; Gomez, Jose A.; Romagnoli, Cesare; Cool, Derek W.; Bastian-Jordan, Matthew; Kassam, Zahra; Siemens, D. Robert; Leveridge, Michael; Chang, Silvia; Fenster, Aaron; Ward, Aaron D.; Mousavi, Parvin

2016-03-01

Recently, multi-parametric Magnetic Resonance Imaging (mp-MRI) has been used to improve the sensitivity of detecting high-risk prostate cancer (PCa). Prior to biopsy, primary and secondary cancer lesions are identified on mp-MRI. The lesions are then targeted using TRUS guidance. In this paper, for the first time, we present a fused mp-MRI-temporal-ultrasound framework for characterization of PCa, in vivo. Cancer classification results obtained using temporal ultrasound are fused with those achieved using consolidated mp-MRI maps determined by multiple observers. We verify the outcome of our study using histopathology following deformable registration of ultrasound and histology images. Fusion of temporal ultrasound and mp-MRI for characterization of the PCa results in an area under the receiver operating characteristic curve (AUC) of 0.86 for cancerous regions with Gleason scores (GSs)>=3+3, and AUC of 0.89 for those with GSs>=3+4.

Generic distortion model for metrology under optical microscopes

NASA Astrophysics Data System (ADS)

Liu, Xingjian; Li, Zhongwei; Zhong, Kai; Chao, YuhJin; Miraldo, Pedro; Shi, Yusheng

2018-04-01

For metrology under optical microscopes, lens distortion is the dominant source of error. Previous distortion models and correction methods mostly rely on the assumption that parametric distortion models require a priori knowledge of the microscopes' lens systems. However, because of the numerous optical elements in a microscope, distortions can be hardly represented by a simple parametric model. In this paper, a generic distortion model considering both symmetric and asymmetric distortions is developed. Such a model is obtained by using radial basis functions (RBFs) to interpolate the radius and distortion values of symmetric distortions (image coordinates and distortion rays for asymmetric distortions). An accurate and easy to implement distortion correction method is presented. With the proposed approach, quantitative measurement with better accuracy can be achieved, such as in Digital Image Correlation for deformation measurement when used with an optical microscope. The proposed technique is verified by both synthetic and real data experiments.

Evaluation of Second-Level Inference in fMRI Analysis

PubMed Central

Roels, Sanne P.; Loeys, Tom; Moerkerke, Beatrijs

2016-01-01

We investigate the impact of decisions in the second-level (i.e., over subjects) inferential process in functional magnetic resonance imaging on (1) the balance between false positives and false negatives and on (2) the data-analytical stability, both proxies for the reproducibility of results. Second-level analysis based on a mass univariate approach typically consists of 3 phases. First, one proceeds via a general linear model for a test image that consists of pooled information from different subjects. We evaluate models that take into account first-level (within-subjects) variability and models that do not take into account this variability. Second, one proceeds via inference based on parametrical assumptions or via permutation-based inference. Third, we evaluate 3 commonly used procedures to address the multiple testing problem: familywise error rate correction, False Discovery Rate (FDR) correction, and a two-step procedure with minimal cluster size. Based on a simulation study and real data we find that the two-step procedure with minimal cluster size results in most stable results, followed by the familywise error rate correction. The FDR results in most variable results, for both permutation-based inference and parametrical inference. Modeling the subject-specific variability yields a better balance between false positives and false negatives when using parametric inference. PMID:26819578

Parametric fMRI analysis of visual encoding in the human medial temporal lobe.

PubMed

Rombouts, S A; Scheltens, P; Machielson, W C; Barkhof, F; Hoogenraad, F G; Veltman, D J; Valk, J; Witter, M P

1999-01-01

A number of functional brain imaging studies indicate that the medial temporal lobe system is crucially involved in encoding new information into memory. However, most studies were based on differences in brain activity between encoding of familiar vs. novel stimuli. To further study the underlying cognitive processes, we applied a parametric design of encoding. Seven healthy subjects were instructed to encode complex color pictures into memory. Stimuli were presented in a parametric fashion at different rates, thus representing different loads of encoding. Functional magnetic resonance imaging (fMRI) was used to assess changes in brain activation. To determine the number of pictures successfully stored into memory, recognition scores were determined afterwards. During encoding, brain activation occurred in the medial temporal lobe, comparable to the results obtained by others. Increasing the encoding load resulted in an increase in the number of successfully stored items. This was reflected in a significant increase in brain activation in the left lingual gyrus, in the left and right parahippocampal gyrus, and in the right inferior frontal gyrus. This study shows that fMRI can detect changes in brain activation during variation of one aspect of higher cognitive tasks. Further, it strongly supports the notion that the human medial temporal lobe is involved in encoding novel visual information into memory.

Clinical application of 3D arterial spin-labeled brain perfusion imaging for Alzheimer disease: comparison with brain perfusion SPECT.

PubMed

Takahashi, H; Ishii, K; Hosokawa, C; Hyodo, T; Kashiwagi, N; Matsuki, M; Ashikaga, R; Murakami, T

2014-05-01

Alzheimer disease is the most common neurodegenerative disorder with dementia, and a practical and economic biomarker for diagnosis of Alzheimer disease is needed. Three-dimensional arterial spin-labeling, with its high signal-to-noise ratio, enables measurement of cerebral blood flow precisely without any extrinsic tracers. We evaluated the performance of 3D arterial spin-labeling compared with SPECT, and demonstrated the 3D arterial spin-labeled imaging characteristics in the diagnosis of Alzheimer disease. This study included 68 patients with clinically suspected Alzheimer disease who underwent both 3D arterial spin-labeling and SPECT imaging. Two readers independently assessed both images. Kendall W coefficients of concordance (K) were computed, and receiver operating characteristic analyses were performed for each reader. The differences between the images in regional perfusion distribution were evaluated by means of statistical parametric mapping, and the incidence of hypoperfusion of the cerebral watershed area, referred to as "borderzone sign" in the 3D arterial spin-labeled images, was determined. Readers showed K = 0.82/0.73 for SPECT/3D arterial spin-labeled imaging, and the respective areas under the receiver operating characteristic curve were 0.82/0.69 for reader 1 and 0.80/0.69 for reader 2. Statistical parametric mapping showed that the perisylvian and medial parieto-occipital perfusion in the arterial spin-labeled images was significantly higher than that in the SPECT images. Borderzone sign was observed on 3D arterial spin-labeling in 70% of patients misdiagnosed with Alzheimer disease. The diagnostic performance of 3D arterial spin-labeling and SPECT for Alzheimer disease was almost equivalent. Three-dimensional arterial spin-labeled imaging was more influenced by hemodynamic factors than was SPECT imaging. © 2014 by American Journal of Neuroradiology.

Characterization of Metabolic, Diffusion, and Perfusion Properties in GBM: Contrast-Enhancing versus Non-Enhancing Tumor.

PubMed

Autry, Adam; Phillips, Joanna J; Maleschlijski, Stojan; Roy, Ritu; Molinaro, Annette M; Chang, Susan M; Cha, Soonmee; Lupo, Janine M; Nelson, Sarah J

2017-12-01

Although the contrast-enhancing (CE) lesion on T 1 -weighted MR images is widely used as a surrogate for glioblastoma (GBM), there are also non-enhancing regions of infiltrative tumor within the T 2 -weighted lesion, which elude radiologic detection. Because non-enhancing GBM (Enh-) challenges clinical patient management as latent disease, this study sought to characterize ex vivo metabolic profiles from Enh- and CE GBM (Enh+) samples, alongside histological and in vivo MR parameters, to assist in defining criteria for estimating total tumor burden. Fifty-six patients with newly diagnosed GBM received a multi-parametric pre-surgical MR examination. Targets for obtaining image-guided tissue samples were defined based on in vivo parameters that were suspicious for tumor. The actual location from where tissue samples were obtained was recorded, and half of each sample was analyzed for histopathology while the other half was scanned using HR-MAS spectroscopy. The Enh+ and Enh- tumor samples demonstrated comparable mitotic activity, but also significant heterogeneity in microvascular morphology. Ex vivo spectroscopic parameters indicated similar levels of total choline and N-acetylaspartate between these contrast-based radiographic subtypes of GBM, and characteristic differences in the levels of myo-inositol, creatine/phosphocreatine, and phosphoethanolamine. Analysis of in vivo parameters at the sample locations were consistent with histological and ex vivo metabolic data. The similarity between ex vivo levels of choline and NAA, and between in vivo levels of choline, NAA and nADC in Enh+ and Enh- tumor, indicate that these parameters can be used in defining non-invasive metrics of total tumor burden for patients with GBM. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Accounting for Non-Gaussian Sources of Spatial Correlation in Parametric Functional Magnetic Resonance Imaging Paradigms I: Revisiting Cluster-Based Inferences.

PubMed

Gopinath, Kaundinya; Krishnamurthy, Venkatagiri; Sathian, K

2018-02-01

In a recent study, Eklund et al. employed resting-state functional magnetic resonance imaging data as a surrogate for null functional magnetic resonance imaging (fMRI) datasets and posited that cluster-wise family-wise error (FWE) rate-corrected inferences made by using parametric statistical methods in fMRI studies over the past two decades may have been invalid, particularly for cluster defining thresholds less stringent than p < 0.001; this was principally because the spatial autocorrelation functions (sACF) of fMRI data had been modeled incorrectly to follow a Gaussian form, whereas empirical data suggested otherwise. Here, we show that accounting for non-Gaussian signal components such as those arising from resting-state neural activity as well as physiological responses and motion artifacts in the null fMRI datasets yields first- and second-level general linear model analysis residuals with nearly uniform and Gaussian sACF. Further comparison with nonparametric permutation tests indicates that cluster-based FWE corrected inferences made with Gaussian spatial noise approximations are valid.

Impact of time-of-flight on indirect 3D and direct 4D parametric image reconstruction in the presence of inconsistent dynamic PET data.

PubMed

Kotasidis, F A; Mehranian, A; Zaidi, H

2016-05-07

Kinetic parameter estimation in dynamic PET suffers from reduced accuracy and precision when parametric maps are estimated using kinetic modelling following image reconstruction of the dynamic data. Direct approaches to parameter estimation attempt to directly estimate the kinetic parameters from the measured dynamic data within a unified framework. Such image reconstruction methods have been shown to generate parametric maps of improved precision and accuracy in dynamic PET. However, due to the interleaving between the tomographic and kinetic modelling steps, any tomographic or kinetic modelling errors in certain regions or frames, tend to spatially or temporally propagate. This results in biased kinetic parameters and thus limits the benefits of such direct methods. Kinetic modelling errors originate from the inability to construct a common single kinetic model for the entire field-of-view, and such errors in erroneously modelled regions could spatially propagate. Adaptive models have been used within 4D image reconstruction to mitigate the problem, though they are complex and difficult to optimize. Tomographic errors in dynamic imaging on the other hand, can originate from involuntary patient motion between dynamic frames, as well as from emission/transmission mismatch. Motion correction schemes can be used, however, if residual errors exist or motion correction is not included in the study protocol, errors in the affected dynamic frames could potentially propagate either temporally, to other frames during the kinetic modelling step or spatially, during the tomographic step. In this work, we demonstrate a new strategy to minimize such error propagation in direct 4D image reconstruction, focusing on the tomographic step rather than the kinetic modelling step, by incorporating time-of-flight (TOF) within a direct 4D reconstruction framework. Using ever improving TOF resolutions (580 ps, 440 ps, 300 ps and 160 ps), we demonstrate that direct 4D TOF image reconstruction can substantially prevent kinetic parameter error propagation either from erroneous kinetic modelling, inter-frame motion or emission/transmission mismatch. Furthermore, we demonstrate the benefits of TOF in parameter estimation when conventional post-reconstruction (3D) methods are used and compare the potential improvements to direct 4D methods. Further improvements could possibly be achieved in the future by combining TOF direct 4D image reconstruction with adaptive kinetic models and inter-frame motion correction schemes.

Impact of time-of-flight on indirect 3D and direct 4D parametric image reconstruction in the presence of inconsistent dynamic PET data

NASA Astrophysics Data System (ADS)

Kotasidis, F. A.; Mehranian, A.; Zaidi, H.

2016-05-01

Kinetic parameter estimation in dynamic PET suffers from reduced accuracy and precision when parametric maps are estimated using kinetic modelling following image reconstruction of the dynamic data. Direct approaches to parameter estimation attempt to directly estimate the kinetic parameters from the measured dynamic data within a unified framework. Such image reconstruction methods have been shown to generate parametric maps of improved precision and accuracy in dynamic PET. However, due to the interleaving between the tomographic and kinetic modelling steps, any tomographic or kinetic modelling errors in certain regions or frames, tend to spatially or temporally propagate. This results in biased kinetic parameters and thus limits the benefits of such direct methods. Kinetic modelling errors originate from the inability to construct a common single kinetic model for the entire field-of-view, and such errors in erroneously modelled regions could spatially propagate. Adaptive models have been used within 4D image reconstruction to mitigate the problem, though they are complex and difficult to optimize. Tomographic errors in dynamic imaging on the other hand, can originate from involuntary patient motion between dynamic frames, as well as from emission/transmission mismatch. Motion correction schemes can be used, however, if residual errors exist or motion correction is not included in the study protocol, errors in the affected dynamic frames could potentially propagate either temporally, to other frames during the kinetic modelling step or spatially, during the tomographic step. In this work, we demonstrate a new strategy to minimize such error propagation in direct 4D image reconstruction, focusing on the tomographic step rather than the kinetic modelling step, by incorporating time-of-flight (TOF) within a direct 4D reconstruction framework. Using ever improving TOF resolutions (580 ps, 440 ps, 300 ps and 160 ps), we demonstrate that direct 4D TOF image reconstruction can substantially prevent kinetic parameter error propagation either from erroneous kinetic modelling, inter-frame motion or emission/transmission mismatch. Furthermore, we demonstrate the benefits of TOF in parameter estimation when conventional post-reconstruction (3D) methods are used and compare the potential improvements to direct 4D methods. Further improvements could possibly be achieved in the future by combining TOF direct 4D image reconstruction with adaptive kinetic models and inter-frame motion correction schemes.

Synthetic aperture radar target detection, feature extraction, and image formation techniques

NASA Technical Reports Server (NTRS)

Li, Jian

1994-01-01

This report presents new algorithms for target detection, feature extraction, and image formation with the synthetic aperture radar (SAR) technology. For target detection, we consider target detection with SAR and coherent subtraction. We also study how the image false alarm rates are related to the target template false alarm rates when target templates are used for target detection. For feature extraction from SAR images, we present a computationally efficient eigenstructure-based 2D-MODE algorithm for two-dimensional frequency estimation. For SAR image formation, we present a robust parametric data model for estimating high resolution range signatures of radar targets and for forming high resolution SAR images.

Tissues segmentation based on multi spectral medical images

NASA Astrophysics Data System (ADS)

Li, Ya; Wang, Ying

2017-11-01

Each band image contains the most obvious tissue feature according to the optical characteristics of different tissues in different specific bands for multispectral medical images. In this paper, the tissues were segmented by their spectral information at each multispectral medical images. Four Local Binary Patter descriptors were constructed to extract blood vessels based on the gray difference between the blood vessels and their neighbors. The segmented tissue in each band image was merged to a clear image.

Dielectric properties of dog brain tissue measured in vitro across the 0.3-3 GHz band.

PubMed

Mohammed, Beadaa; Bialkowski, Konstanty; Abbosh, Amin; Mills, Paul C; Bradley, Andrew P

2016-09-22

Dielectric properties of dead Greyhound female dogs' brain tissues at different ages were measured at room temperature across the frequency range of 0.3-3 GHz. Measurements were made on excised tissues, in vitro in the laboratory, to carry out dielectric tests on sample tissues. Each dataset for a brain tissue was parametrized using the Cole-Cole expression, and the relevant Cole-Cole parameters for four tissue types are provided. A comparison was made with the database available in literature for other animals and human brain tissue. Results of two types of tissues (white matter and skull) showed systematic variation in dielectric properties as a function of animal age, whereas no significant change related to age was noticed for other tissues. Results provide critical information regarding dielectric properties of animal tissues for a realistic animal head model that can be used to verify the validity and reliability of a microwave head scanner for animals prior to testing on live animals. Bioelectromagnetics. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Generalized Beer-Lambert model for near-infrared light propagation in thick biological tissues

NASA Astrophysics Data System (ADS)

Bhatt, Manish; Ayyalasomayajula, Kalyan R.; Yalavarthy, Phaneendra K.

2016-07-01

The attenuation of near-infrared (NIR) light intensity as it propagates in a turbid medium like biological tissue is described by modified the Beer-Lambert law (MBLL). The MBLL is generally used to quantify the changes in tissue chromophore concentrations for NIR spectroscopic data analysis. Even though MBLL is effective in terms of providing qualitative comparison, it suffers from its applicability across tissue types and tissue dimensions. In this work, we introduce Lambert-W function-based modeling for light propagation in biological tissues, which is a generalized version of the Beer-Lambert model. The proposed modeling provides parametrization of tissue properties, which includes two attenuation coefficients μ0 and η. We validated our model against the Monte Carlo simulation, which is the gold standard for modeling NIR light propagation in biological tissue. We included numerous human and animal tissues to validate the proposed empirical model, including an inhomogeneous adult human head model. The proposed model, which has a closed form (analytical), is first of its kind in providing accurate modeling of NIR light propagation in biological tissues.

Unsteady wind loads for TMT: replacing parametric models with CFD

NASA Astrophysics Data System (ADS)

MacMartin, Douglas G.; Vogiatzis, Konstantinos

2014-08-01

Unsteady wind loads due to turbulence inside the telescope enclosure result in image jitter and higher-order image degradation due to M1 segment motion. Advances in computational fluid dynamics (CFD) allow unsteady simulations of the flow around realistic telescope geometry, in order to compute the unsteady forces due to wind turbulence. These simulations can then be used to understand the characteristics of the wind loads. Previous estimates used a parametric model based on a number of assumptions about the wind characteristics, such as a von Karman spectrum and frozen-flow turbulence across M1, and relied on CFD only to estimate parameters such as mean wind speed and turbulent kinetic energy. Using the CFD-computed forces avoids the need for assumptions regarding the flow. We discuss here both the loads on the telescope that lead to image jitter, and the spatially-varying force distribution across the primary mirror, using simulations with the Thirty Meter Telescope (TMT) geometry. The amplitude, temporal spectrum, and spatial distribution of wind disturbances are all estimated; these are then used to compute the resulting image motion and degradation. There are several key differences relative to our earlier parametric model. First, the TMT enclosure provides sufficient wind reduction at the top end (near M2) to render the larger cross-sectional structural areas further inside the enclosure (including M1) significant in determining the overall image jitter. Second, the temporal spectrum is not von Karman as the turbulence is not fully developed; this applies both in predicting image jitter and M1 segment motion. And third, for loads on M1, the spatial characteristics are not consistent with propagating a frozen-flow turbulence screen across the mirror: Frozen flow would result in a relationship between temporal frequency content and spatial frequency content that does not hold in the CFD predictions. Incorporating the new estimates of wind load characteristics into TMT response predictions leads to revised estimates of the response of TMT to wind turbulence, and validates the aerodynamic design of the enclosure.

Potential for Imaging Engineered Tissues with X-Ray Phase Contrast

PubMed Central

Appel, Alyssa; Anastasio, Mark A.

2011-01-01

As the field of tissue engineering advances, it is crucial to develop imaging methods capable of providing detailed three-dimensional information on tissue structure. X-ray imaging techniques based on phase-contrast (PC) have great potential for a number of biomedical applications due to their ability to provide information about soft tissue structure without exogenous contrast agents. X-ray PC techniques retain the excellent spatial resolution, tissue penetration, and calcified tissue contrast of conventional X-ray techniques while providing drastically improved imaging of soft tissue and biomaterials. This suggests that X-ray PC techniques are very promising for evaluation of engineered tissues. In this review, four different implementations of X-ray PC imaging are described and applications to tissues of relevance to tissue engineering reviewed. In addition, recent applications of X-ray PC to the evaluation of biomaterial scaffolds and engineered tissues are presented and areas for further development and application of these techniques are discussed. Imaging techniques based on X-ray PC have significant potential for improving our ability to image and characterize engineered tissues, and their continued development and optimization could have significant impact on the field of tissue engineering. PMID:21682604

Influence of ultrasound induced cavitation on magnetic resonance imaging contrast in the rat liver in the presence of macromolecular contrast agent.

PubMed

Frulio, Nora; Trillaud, Hervé; Deckers, Roel; Lepreux, Sébastien; Moonen, Chrit; Quesson, Bruno

2010-05-01

Local drug delivery by ultrasound (US)-induced cavitation is a promising strategy for increasing the drug concentration at the target location and for decreasing the systemic toxicity effects. The presence of microbubbles during sonication at the targeted location improves the likelihood for cavitation that can be exploited to increase the capillary permeability. The objective of this work was to evaluate the magnetic resonance imaging (MRI) contrast changes in hepatic tissue in vivo, induced by US-triggered cavitation and destruction of microbubbles (Sonovue), in the presence of a coinjected blood pool MRI contrast agent (Vistarem) used as a reporter macromolecule. The potential tissue damage induced by microbubbles destruction was also evaluated by histology. The change in the hepatic distribution of the macromolecular MRI contrast agent associated with cavitation was monitored at 1.5 T with a look-locker fast inversion recovery sequence to map the longitudinal relaxation rates, before and during 1 hour after intravenous administration of Vistarem and Sonovue. In 1 group of rats (n = 5), these microbubbles were immediately destroyed with a clinical echograph, using a high mechanical index (MI = 1.5) at low frequency (2 MHz). The control group (n = 7) received identical injections without application of US. The parametric relaxation rate images were computed, and the changes in time were analyzed to account for the potential effect of microbubble destruction by US on the permeability of the hepatic vessels. The animals were killed 1 day after the experiment for routine histology of the liver. For both groups of animals, after an initial increase, a transient decay of the longitudinal relaxation rate was observed, followed by a constant plateau after 20 minutes. The analysis of the mean relaxation rates in the liver showed significant (P < 0.01) higher values for the group with destruction of microbubbles as compared with the control group. The US-triggered cavitation and destruction of microbubble with the proposed protocol suggests an increased concentration of Vistarem of a factor 2 in the hepatic tissue. No tissue damage was observed at the microscopic analysis. The absence of tissue alterations indicates that the destruction of this US contrast agent could be safe in vivo under an appropriate choice of the sonication parameters. This approach opens new perspectives for translation toward clinical applications of local drug delivery. Ultrasound-mediated microbubble destruction may help in increasing the local concentration of a drug currently limited by the endothelial barrier. In addition, it may help in reducing the systemic toxicity to normal cells in standard chemotherapies, because the enhanced capillary permeability effect can be spatially adjusted by selecting the sonicated region.

Probabilistic images (PBIS): A concise image representation technique for multiple parameters

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

Wu, L.C.; Yeh, S.H.; Chen, Z.

1984-01-01

Based on m parametric images (PIs) derived from a dynamic series (DS), each pixel of DS is regarded as an m-dimensional vector. Given one set of normal samples (pixels) N and another of abnormal samples A, probability density functions (pdfs) of both sets are estimated. Any unknown sample is classified into N or A by calculating the probability of its being in the abnormal set using the Bayes' theorem. Instead of estimating the multivariate pdfs, a distance ratio transformation is introduced to map the m-dimensional sample space to one dimensional Euclidean space. Consequently, the image that localizes the regional abnormalitiesmore » is characterized by the probability of being abnormal. This leads to the new representation scheme of PBIs. Tc-99m HIDA study for detecting intrahepatic lithiasis (IL) was chosen as an example of constructing PBI from 3 parameters derived from DS and such a PBI was compared with those 3 PIs, namely, retention ratio image (RRI), peak time image (TNMAX) and excretion mean transit time image (EMTT). 32 normal subjects and 20 patients with proved IL were collected and analyzed. The resultant sensitivity and specificity of PBI were 97% and 98% respectively. They were superior to those of any of the 3 PIs: RRI (94/97), TMAX (86/88) and EMTT (94/97). Furthermore, the contrast of PBI was much better than that of any other image. This new image formation technique, based on multiple parameters, shows the functional abnormalities in a structural way. Its good contrast makes the interpretation easy. This technique is powerful compared to the existing parametric image method.« less

System and method for controlling depth of imaging in tissues using fluorescence microscopy under ultraviolet excitation following staining with fluorescing agents

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

Demos, Stavros; Levenson, Richard

The present disclosure relates to a method for analyzing tissue specimens. In one implementation the method involves obtaining a tissue sample and exposing the sample to one or more fluorophores as contrast agents to enhance contrast of subcellular compartments of the tissue sample. The tissue sample is illuminated by an ultraviolet (UV) light having a wavelength between about 200 nm to about 400 nm, with the wavelength being selected to result in penetration to only a specified depth below a surface of the tissue sample. Inter-image operations between images acquired under different imaging parameters allow for improvement of the imagemore » quality via removal of unwanted image components. A microscope may be used to image the tissue sample and provide the image to an image acquisition system that makes use of a camera. The image acquisition system may create a corresponding image that is transmitted to a display system for processing and display.« less

Automated movement correction for dynamic PET/CT images: evaluation with phantom and patient data.

PubMed

Ye, Hu; Wong, Koon-Pong; Wardak, Mirwais; Dahlbom, Magnus; Kepe, Vladimir; Barrio, Jorge R; Nelson, Linda D; Small, Gary W; Huang, Sung-Cheng

2014-01-01

Head movement during a dynamic brain PET/CT imaging results in mismatch between CT and dynamic PET images. It can cause artifacts in CT-based attenuation corrected PET images, thus affecting both the qualitative and quantitative aspects of the dynamic PET images and the derived parametric images. In this study, we developed an automated retrospective image-based movement correction (MC) procedure. The MC method first registered the CT image to each dynamic PET frames, then re-reconstructed the PET frames with CT-based attenuation correction, and finally re-aligned all the PET frames to the same position. We evaluated the MC method's performance on the Hoffman phantom and dynamic FDDNP and FDG PET/CT images of patients with neurodegenerative disease or with poor compliance. Dynamic FDDNP PET/CT images (65 min) were obtained from 12 patients and dynamic FDG PET/CT images (60 min) were obtained from 6 patients. Logan analysis with cerebellum as the reference region was used to generate regional distribution volume ratio (DVR) for FDDNP scan before and after MC. For FDG studies, the image derived input function was used to generate parametric image of FDG uptake constant (Ki) before and after MC. Phantom study showed high accuracy of registration between PET and CT and improved PET images after MC. In patient study, head movement was observed in all subjects, especially in late PET frames with an average displacement of 6.92 mm. The z-direction translation (average maximum = 5.32 mm) and x-axis rotation (average maximum = 5.19 degrees) occurred most frequently. Image artifacts were significantly diminished after MC. There were significant differences (P<0.05) in the FDDNP DVR and FDG Ki values in the parietal and temporal regions after MC. In conclusion, MC applied to dynamic brain FDDNP and FDG PET/CT scans could improve the qualitative and quantitative aspects of images of both tracers.

Automated Movement Correction for Dynamic PET/CT Images: Evaluation with Phantom and Patient Data

PubMed Central

Ye, Hu; Wong, Koon-Pong; Wardak, Mirwais; Dahlbom, Magnus; Kepe, Vladimir; Barrio, Jorge R.; Nelson, Linda D.; Small, Gary W.; Huang, Sung-Cheng

2014-01-01

Head movement during a dynamic brain PET/CT imaging results in mismatch between CT and dynamic PET images. It can cause artifacts in CT-based attenuation corrected PET images, thus affecting both the qualitative and quantitative aspects of the dynamic PET images and the derived parametric images. In this study, we developed an automated retrospective image-based movement correction (MC) procedure. The MC method first registered the CT image to each dynamic PET frames, then re-reconstructed the PET frames with CT-based attenuation correction, and finally re-aligned all the PET frames to the same position. We evaluated the MC method's performance on the Hoffman phantom and dynamic FDDNP and FDG PET/CT images of patients with neurodegenerative disease or with poor compliance. Dynamic FDDNP PET/CT images (65 min) were obtained from 12 patients and dynamic FDG PET/CT images (60 min) were obtained from 6 patients. Logan analysis with cerebellum as the reference region was used to generate regional distribution volume ratio (DVR) for FDDNP scan before and after MC. For FDG studies, the image derived input function was used to generate parametric image of FDG uptake constant (Ki) before and after MC. Phantom study showed high accuracy of registration between PET and CT and improved PET images after MC. In patient study, head movement was observed in all subjects, especially in late PET frames with an average displacement of 6.92 mm. The z-direction translation (average maximum = 5.32 mm) and x-axis rotation (average maximum = 5.19 degrees) occurred most frequently. Image artifacts were significantly diminished after MC. There were significant differences (P<0.05) in the FDDNP DVR and FDG Ki values in the parietal and temporal regions after MC. In conclusion, MC applied to dynamic brain FDDNP and FDG PET/CT scans could improve the qualitative and quantitative aspects of images of both tracers. PMID:25111700

Brain volumes in healthy adults aged 40 years and over: a voxel-based morphometry study.

PubMed

Riello, Roberta; Sabattoli, Francesca; Beltramello, Alberto; Bonetti, Matteo; Bono, Giorgio; Falini, Andrea; Magnani, Giuseppe; Minonzio, Giorgio; Piovan, Enrico; Alaimo, Giuseppina; Ettori, Monica; Galluzzi, Samantha; Locatelli, Enrico; Noiszewska, Malgorzata; Testa, Cristina; Frisoni, Giovanni B

2005-08-01

Gender and age effect on brain morphology have been extensively investigated. However, the great variety in methods applied to morphology partly explain the conflicting results of linear patterns of tissue changes and lateral asymmetry in men and women. The aim of the present study was to assess the effect of age, gender and laterality on the volumes of gray matter (GM) and white matter (WM) in a large group of healthy adults by means of voxel-based morphometry. This technique, based on observer-independent algorithms, automatically segments the 3 types of tissue and computes the amount of tissue in each single voxel. Subjects were 229 healthy subjects of 40 years of age or older, who underwent magnetic resonance (MR) for reasons other than cognitive impairment. MR images were reoriented following the AC-PC line and, after removing the voxels below the cerebellum, were processed by Statistical Parametric Mapping (SPM99). GM and WM volumes were normalized for intracranial volume. Women had more fractional GM and WM volumes than men. Age was negatively correlated with both fractional GM and WM, and a gender x age interaction effect was found for WM, men having greater WM loss with advancing age. Pairwise differences between left and right GM were negative (greater GM in right hemisphere) in men, and positive (greater GM in left hemisphere) in women (-0.56+/-4.2 vs 0.99+/-4.8; p=0.019). These results support side-specific accelerated WM loss in men, and may help our better understanding of changes in regional brain structures associated with pathological aging.

The development of computer-aided system for tissue scaffolds (CASTS) system for functionally graded tissue-engineering scaffolds.

PubMed

Sudarmadji, Novella; Chua, Chee Kai; Leong, Kah Fai

2012-01-01

Computer-aided system for tissue scaffolds (CASTS) is an in-house parametric library of polyhedral units that can be assembled into customized tissue scaffolds. Thirteen polyhedral configurations are available to select, depending on the biological and mechanical requirements of the target tissue/organ. Input parameters include the individual polyhedral units and overall scaffold block as well as the scaffold strut diameter. Taking advantage of its repeatability and reproducibility, the scaffold file is then converted into .STL file and fabricated using selective laser sintering, a rapid prototyping system. CASTS seeks to fulfill anatomical, biological, and mechanical requirements of the target tissue/organ. Customized anatomical scaffold shape is achieved through a Boolean operation between the scaffold block and the tissue defect image. Biological requirements, such as scaffold pore size and porosity, are unique for different type of cells. Matching mechanical properties, such as stiffness and strength, between the scaffold and target organ is very important, particularly in the regeneration of load-bearing organ, i.e., bone. This includes mimicking the compressive stiffness variation across the bone to prevent stress shielding and ensuring that the scaffold can withstand the load normally borne by the bone. The stiffness variation is tailored by adjusting the scaffold porosity based on the porosity-stiffness relationship of the CASTS scaffolds. Two types of functional gradients based on the gradient direction include radial and axial/linear gradient. Radial gradient is useful in the case of regenerating a section of long bones while the gradient in linear direction can be used in short or irregular bones. Stiffness gradient in the radial direction is achieved by using cylindrical unit cells arranged in a concentric manner, in which the porosity decreases from the center of the structure toward the outside radius, making the scaffold stiffer at the outer radius and more porous at the center of the scaffold. On the other hand, the linear gradient is accomplished by varying the strut diameter along the gradient direction. The parameters to vary in both gradient types are the strut diameter, the unit cell dimension, and the boundaries between two scaffold regions with different stiffness.

Magnetoacoustic imaging of human liver tumor with magnetic induction

NASA Astrophysics Data System (ADS)

Hu, Gang; Cressman, Erik; He, Bin

2011-01-01

Magnetoacoustic tomography with magnetic induction (MAT-MI) is an imaging technique under development to achieve imaging of electrical impedance contrast in biological tissues with spatial resolution close to ultrasound imaging. However, previously reported MAT-MI experimental results are obtained either from low salinity gel phantoms, or from normal animal tissue samples. In this study, we report the experimental study on the performance of the MAT-MI imaging method for imaging in vitro human liver tumor tissue. The present promising experimental results suggest the feasibility of MAT-MI to image electrical impedance contrast between the cancerous tissue and its surrounding normal tissues.

Objective breast tissue image classification using Quantitative Transmission ultrasound tomography

NASA Astrophysics Data System (ADS)

Malik, Bilal; Klock, John; Wiskin, James; Lenox, Mark

2016-12-01

Quantitative Transmission Ultrasound (QT) is a powerful and emerging imaging paradigm which has the potential to perform true three-dimensional image reconstruction of biological tissue. Breast imaging is an important application of QT and allows non-invasive, non-ionizing imaging of whole breasts in vivo. Here, we report the first demonstration of breast tissue image classification in QT imaging. We systematically assess the ability of the QT images’ features to differentiate between normal breast tissue types. The three QT features were used in Support Vector Machines (SVM) classifiers, and classification of breast tissue as either skin, fat, glands, ducts or connective tissue was demonstrated with an overall accuracy of greater than 90%. Finally, the classifier was validated on whole breast image volumes to provide a color-coded breast tissue volume. This study serves as a first step towards a computer-aided detection/diagnosis platform for QT.

Biomechanical assessment and monitoring of thermal ablation using Harmonic Motion Imaging for Focused Ultrasound (HMIFU)

NASA Astrophysics Data System (ADS)

Hou, Gary Yi

Cancer remains, one of the major public health problems in the United States as well as many other countries worldwide. According to According to the World Health Organization, cancer is currently the leading cause of death worldwide, accounting for 7.6 million deaths annually, and 25% of the annual death was due to Cancer during the year of 2011. In the long history of the cancer treatment field, many treatment options have been established up to date. Traditional procedures include surgical procedures as well as systemic therapies such as biologic therapy, chemotherapy, hormone therapy, and radiation therapy. Nevertheless, side-effects are often associated with such procedures due to the systemic delivery across the entire body. Recently technologies have been focused on localized therapy under minimally or noninvasive procedure with imaging-guidance, such as cryoablation, laser ablation, radio-frequency (RF) ablation, and High Intensity F-ocused Ultrasound (HIFU). HIFU is a non-invasive procedure aims to coagulate tissue thermally at a localized focal zone created with noninvasively emitting a set of focused ultrasound beams while the surrounding healthy tissues remain relatively untreated. Harmonic Motion Imaging for Focused Ultrasound (HMIFU) is a dynamic, radiation-force-based imaging technique, which utilizes a single HIFU transducer by emitting an Amplitude-modulated (AM) beam to both thermally ablate the tumor while inducing a stable oscillatory tissue displacement at its focal zone. The oscillatory response is then estimated by a cross-correlation based motion tracking technique on the signal collected by a confocally-aligned diagnostic transducer. HMIFU addresses the most critical aspect and one of the major unmet needs of HIFU treatment, which is the ability to perform real-time monitoring and mapping of tissue property change during the HIFU treatment. In this dissertation, both the assessment and monitoring aspects of HMIFU have been investigated fundamentally and experimentally through development of both a 1-D and 2-D based system. The performance assessment of HMIFU technique in depicting the lesion size increase as well as the lesion-to-background displacement contrast was first demonstrated using a 3D, FE-based interdisciplinary simulation framework. Through the development of 1-D HMIFU system, a multi-parametric monitoring approach was presented where presented where the focal HMI displacement, phase shift (Deltaφ), and correlation coefficients were monitored along with thermocouple and PCD under the HIFU treatment sequence with boiling and slow denaturation. For HIFU treatments with slow denaturation, consistent displacement increase-then-decrease trend was observed, indicating tissue softening-then-stiffening and phase shift increased with treatment time in agreement with mechanical testing outcomes. The correlation coefficient remained high throughout the entire treatment time under a minimized broadband energy and boiling mechanism. Contrarily, both displacement and phase shift changes lacked consistency under HIFU treatment sequences with boiling due to the presence of strong boiling mechanism confirmed by both PCD and thermocouple monitoring. In order to facilitate its clinical translation, a fully-integrated, clinically 2D real-time HMIFU system was also developed, which is capable of providing 2D real-time streaming during HIFU treatment up to 15 Hz without interruption. Reproducibility studies of the system showed consistent displacement estimation on tissue-mimicking phantoms as well as monitoring of tissue-softening-then-stiffening phase change across 16 out of 19 liver specimens (Increasing rate in phase shift (Deltaφ): 0.73+/-0.69 %/s, Decreasing rate in phase shift (Deltaφ): 0.60+/-0.19 %/s) along with thermocouple monitoring (Increasing: 0.84+/-1.15 %/ °C, Decreasing: 2.03+/- 0.93%/ °C) and validation of tissue stiffening using mechanical testing. In addition, the 2-D HMIFU system feasibility on preclinical pancreatic tumor mice model was also demonstrated in vivo, where HMI displacement decreases were observed across three of five treatment locations on the kP(f)c model at 20.8+/-6.84, 18.6+/-1.46, and 24.0+/-5.43%, as well as across four of the seven treatment locations on the KPC model at 39.5+/-2.98%, 34.5+/-21.5%, 16.0+/-3.05%, and 35.0+/-3.12% along with H&E histological confirmation. In order to improve the quantitative monitoring aspect of HMIFU, a novel, model-independent method for the estimating Young's modulus based on strain profile was also implemented, where 1-D HMIFU system showed feasibilities on polyacrylamide phantom (EHMI/E ≈ 2.3) and liver specimen (EHMI/E ≈ 8.1), and 2-D HMIFU system showed feasibilities on copolymer phantom(EHMI/E ≈ 30.4), liver specimen(E HMI/E ≈ 211.3), as well as HIFU treated liver specimen(EHMI,end /EHMI,beginning ≈ 5.96). In conclusion, the outcomes from the aforementioned studies successfully showed the feasibility of both HMIFU systems in multi-parametric monitoring of HIFU treatment with slow denaturation and boiling, which prepares its stage towards clinical translation.

Using Multivariate Adaptive Regression Spline and Artificial Neural Network to Simulate Urbanization in Mumbai, India

NASA Astrophysics Data System (ADS)

Ahmadlou, M.; Delavar, M. R.; Tayyebi, A.; Shafizadeh-Moghadam, H.

2015-12-01

Land use change (LUC) models used for modelling urban growth are different in structure and performance. Local models divide the data into separate subsets and fit distinct models on each of the subsets. Non-parametric models are data driven and usually do not have a fixed model structure or model structure is unknown before the modelling process. On the other hand, global models perform modelling using all the available data. In addition, parametric models have a fixed structure before the modelling process and they are model driven. Since few studies have compared local non-parametric models with global parametric models, this study compares a local non-parametric model called multivariate adaptive regression spline (MARS), and a global parametric model called artificial neural network (ANN) to simulate urbanization in Mumbai, India. Both models determine the relationship between a dependent variable and multiple independent variables. We used receiver operating characteristic (ROC) to compare the power of the both models for simulating urbanization. Landsat images of 1991 (TM) and 2010 (ETM+) were used for modelling the urbanization process. The drivers considered for urbanization in this area were distance to urban areas, urban density, distance to roads, distance to water, distance to forest, distance to railway, distance to central business district, number of agricultural cells in a 7 by 7 neighbourhoods, and slope in 1991. The results showed that the area under the ROC curve for MARS and ANN was 94.77% and 95.36%, respectively. Thus, ANN performed slightly better than MARS to simulate urban areas in Mumbai, India.

Statistical parametric mapping of stimuli-evoked changes in quantitative blood flow using extended-focus optical coherence microscopy (Conference Presentation)

NASA Astrophysics Data System (ADS)

Marchand, Paul J.; Bouwens, Arno; Shamaei, Vincent; Nguyen, David; Extermann, Jerome; Bolmont, Tristan; Lasser, Theo

2016-03-01

Magnetic Resonance Imaging has revolutionised our understanding of brain function through its ability to image human cerebral structures non-invasively over the entire brain. By exploiting the different magnetic properties of oxygenated and deoxygenated blood, functional MRI can indirectly map areas undergoing neural activation. Alongside the development of fMRI, powerful statistical tools have been developed in an effort to shed light on the neural pathways involved in processing of sensory and cognitive information. In spite of the major improvements made in fMRI technology, the obtained spatial resolution of hundreds of microns prevents MRI in resolving and monitoring processes occurring at the cellular level. In this regard, Optical Coherence Microscopy is an ideal instrumentation as it can image at high spatio-temporal resolution. Moreover, by measuring the mean and the width of the Doppler spectra of light scattered by moving particles, OCM allows extracting the axial and lateral velocity components of red blood cells. The ability to assess quantitatively total blood velocity, as opposed to classical axial velocity Doppler OCM, is of paramount importance in brain imaging as a large proportion of cortical vascular is oriented perpendicularly to the optical axis. We combine here quantitative blood flow imaging with extended-focus Optical Coherence Microscopy and Statistical Parametric Mapping tools to generate maps of stimuli-evoked cortical hemodynamics at the capillary level.

An SPM8-based Approach for Attenuation Correction Combining Segmentation and Non-rigid Template Formation: Application to Simultaneous PET/MR Brain Imaging

PubMed Central

Izquierdo-Garcia, David; Hansen, Adam E.; Förster, Stefan; Benoit, Didier; Schachoff, Sylvia; Fürst, Sebastian; Chen, Kevin T.; Chonde, Daniel B.; Catana, Ciprian

2014-01-01

We present an approach for head MR-based attenuation correction (MR-AC) based on the Statistical Parametric Mapping (SPM8) software that combines segmentation- and atlas-based features to provide a robust technique to generate attenuation maps (µ-maps) from MR data in integrated PET/MR scanners. Methods Coregistered anatomical MR and CT images acquired in 15 glioblastoma subjects were used to generate the templates. The MR images from these subjects were first segmented into 6 tissue classes (gray and white matter, cerebro-spinal fluid, bone and soft tissue, and air), which were then non-rigidly coregistered using a diffeomorphic approach. A similar procedure was used to coregister the anatomical MR data for a new subject to the template. Finally, the CT-like images obtained by applying the inverse transformations were converted to linear attenuation coefficients (LACs) to be used for AC of PET data. The method was validated on sixteen new subjects with brain tumors (N=12) or mild cognitive impairment (N=4) who underwent CT and PET/MR scans. The µ-maps and corresponding reconstructed PET images were compared to those obtained using the gold standard CT-based approach and the Dixon-based method available on the Siemens Biograph mMR scanner. Relative change (RC) images were generated in each case and voxel- and region of interest (ROI)-based analyses were performed. Results The leave-one-out cross-validation analysis of the data from the 15 atlas-generation subjects showed small errors in brain LACs (RC=1.38%±4.52%) compared to the gold standard. Similar results (RC=1.86±4.06%) were obtained from the analysis of the atlas-validation datasets. The voxel- and ROI-based analysis of the corresponding reconstructed PET images revealed quantification errors of 3.87±5.0% and 2.74±2.28%, respectively. The Dixon-based method performed substantially worse (the mean RC values were 13.0±10.25% and 9.38±4.97%, respectively). Areas closer to skull showed the largest improvement. Conclusion We have presented an SPM8-based approach for deriving the head µ-map from MR data to be used for PET AC in integrated PET/MR scanners. Its implementation is straightforward and only requires the morphological data acquired with a single MR sequence. The method is very accurate and robust, combining the strengths of both segmentation- and atlas-based approaches while minimizing their drawbacks. PMID:25278515

Fast elastic registration of soft tissues under large deformations.

PubMed

Peterlík, Igor; Courtecuisse, Hadrien; Rohling, Robert; Abolmaesumi, Purang; Nguan, Christopher; Cotin, Stéphane; Salcudean, Septimiu

2018-04-01

A fast and accurate fusion of intra-operative images with a pre-operative data is a key component of computer-aided interventions which aim at improving the outcomes of the intervention while reducing the patient's discomfort. In this paper, we focus on the problematic of the intra-operative navigation during abdominal surgery, which requires an accurate registration of tissues undergoing large deformations. Such a scenario occurs in the case of partial hepatectomy: to facilitate the access to the pathology, e.g. a tumor located in the posterior part of the right lobe, the surgery is performed on a patient in lateral position. Due to the change in patient's position, the resection plan based on the pre-operative CT scan acquired in the supine position must be updated to account for the deformations. We suppose that an imaging modality, such as the cone-beam CT, provides the information about the intra-operative shape of an organ, however, due to the reduced radiation dose and contrast, the actual locations of the internal structures necessary to update the planning are not available. To this end, we propose a method allowing for fast registration of the pre-operative data represented by a detailed 3D model of the liver and its internal structure and the actual configuration given by the organ surface extracted from the intra-operative image. The algorithm behind the method combines the iterative closest point technique with a biomechanical model based on a co-rotational formulation of linear elasticity which accounts for large deformations of the tissue. The performance, robustness and accuracy of the method is quantitatively assessed on a control semi-synthetic dataset with known ground truth and a real dataset composed of nine pairs of abdominal CT scans acquired in supine and flank positions. It is shown that the proposed surface-matching method is capable of reducing the target registration error evaluated of the internal structures of the organ from more than 40 mm to less then 10 mm. Moreover, the control data is used to demonstrate the compatibility of the method with intra-operative clinical scenario, while the real datasets are utilized to study the impact of parametrization on the accuracy of the method. The method is also compared to a state-of-the art intensity-based registration technique in terms of accuracy and performance. Copyright © 2017 Elsevier B.V. All rights reserved.

An augmented parametric response map with consideration of image registration error: towards guidance of locally adaptive radiotherapy

NASA Astrophysics Data System (ADS)

Lausch, Anthony; Chen, Jeff; Ward, Aaron D.; Gaede, Stewart; Lee, Ting-Yim; Wong, Eugene

2014-11-01

Parametric response map (PRM) analysis is a voxel-wise technique for predicting overall treatment outcome, which shows promise as a tool for guiding personalized locally adaptive radiotherapy (RT). However, image registration error (IRE) introduces uncertainty into this analysis which may limit its use for guiding RT. Here we extend the PRM method to include an IRE-related PRM analysis confidence interval and also incorporate multiple graded classification thresholds to facilitate visualization. A Gaussian IRE model was used to compute an expected value and confidence interval for PRM analysis. The augmented PRM (A-PRM) was evaluated using CT-perfusion functional image data from patients treated with RT for glioma and hepatocellular carcinoma. Known rigid IREs were simulated by applying one thousand different rigid transformations to each image set. PRM and A-PRM analyses of the transformed images were then compared to analyses of the original images (ground truth) in order to investigate the two methods in the presence of controlled IRE. The A-PRM was shown to help visualize and quantify IRE-related analysis uncertainty. The use of multiple graded classification thresholds also provided additional contextual information which could be useful for visually identifying adaptive RT targets (e.g. sub-volume boosts). The A-PRM should facilitate reliable PRM guided adaptive RT by allowing the user to identify if a patient’s unique IRE-related PRM analysis uncertainty has the potential to influence target delineation.

The Effect of Endogenous Adenosine on Neuronal Activity in Rats: An FDG PET Study

PubMed Central

Paul, Soumen; Zhang, Dali; Mzengeza, Shadreck; Ko, Ji Hyun

2016-01-01

ABSTRACT 2–18F‐fluorodeoxy‐D‐glucose (FDG) is a glucose analog that is taken up by cells and phosphorylated. The amount of FDG accumulated by cells is a measure of the rate of glycolysis, which reflects cellular activity. As the levels and actions of the neuromodulator adenosine are dynamically regulated by neuronal activity, this study was designed to test whether endogenous adenosine affects tissue accumulation of FDG as assessed by positron emission tomography (PET) or by postmortem analysis of tissue radioactivity. Rats were given an intraperitoneal injection of the adenosine A1 receptor antagonist 8‐cyclopentyl‐1,3‐dipropyl‐xanthine (DPCPX, 3 mg/kg), the adenosine kinase inhibitor ABT‐702 (3 mg/kg), or vehicle 10 minutes prior to an intravenous injection of FDG (15.4 ± 0.7 MBq per rat). Rats were then subjected to a 15 minute static PET scan. Reconstructed images were normalized to FDG PET template for rats and standard uptake values (SUVs) were calculated. To examine the regional effect of active treatment compared to vehicle, statistical parametric mapping analysis was performed. Whole‐brain FDG uptake was not affected by drug treatment. Significant regional hypometabolism was detected, particularly in cerebellum, of DPCPX‐ and ABT‐702 treated rats, relative to vehicle‐treated rats. Thus, endogenous adenosine can affect FDG accumulation although this effect is modest in quiescent rats. PMID:27082948

Parametric, bootstrap, and jackknife variance estimators for the k-Nearest Neighbors technique with illustrations using forest inventory and satellite image data

Treesearch

Ronald E. McRoberts; Steen Magnussen; Erkki O. Tomppo; Gherardo Chirici

2011-01-01

Nearest neighbors techniques have been shown to be useful for estimating forest attributes, particularly when used with forest inventory and satellite image data. Published reports of positive results have been truly international in scope. However, for these techniques to be more useful, they must be able to contribute to scientific inference which, for sample-based...

A generalized Benford's law for JPEG coefficients and its applications in image forensics

NASA Astrophysics Data System (ADS)

Fu, Dongdong; Shi, Yun Q.; Su, Wei

2007-02-01

In this paper, a novel statistical model based on Benford's law for the probability distributions of the first digits of the block-DCT and quantized JPEG coefficients is presented. A parametric logarithmic law, i.e., the generalized Benford's law, is formulated. Furthermore, some potential applications of this model in image forensics are discussed in this paper, which include the detection of JPEG compression for images in bitmap format, the estimation of JPEG compression Qfactor for JPEG compressed bitmap image, and the detection of double compressed JPEG image. The results of our extensive experiments demonstrate the effectiveness of the proposed statistical model.

Evaluation of sorafenib for advanced hepatocellular carcinoma with low α-fetoprotein in arrival time parametric imaging using contrast-enhanced ultrasonography.

PubMed

Shiozawa, Kazue; Watanabe, Manabu; Ikehara, Takashi; Shimizu, Ryo; Shinohara, Mie; Igarashi, Yoshinori; Sumino, Yasukiyo

2017-01-01

To determine the usefulness of arrival time parametric imaging (AtPI) using contrast-enhanced ultrasonography (CEUS) with Sonazoid in evaluating early response to sorafenib for hepatocellular carcinoma (HCC). Twenty-one advanced HCC patients with low α-fetoprotein (AFP) levels (≤35 ng/ml) who received sorafenib for at least 4 weeks were enrolled in this study. CEUS was performed before and 2 weeks after treatment, and the images of the target lesion in the arterial phase were analyzed by AtPI. In the color mapping images obtained by AtPI, the mean arrival time of the contrast agent in the target lesion from the reference point (mean time: MT) was calculated. In each patient, differences between MT before and MT 2 weeks after treatment were compared. MT (+) and MT (-) groups were defined as difference of 0 s or greater and less than 0 s, respectively. Overall survival was evaluated between the two groups. In the MT (+) (11 patients) and MT (-) (10 patients) groups, the median survival time was 792 and 403 days, respectively, which was statistically significant. The results suggested that AtPI was useful for evaluating early response to sorafenib for advanced HCC with low AFP level.

X-ray Phase Contrast Allows Three Dimensional, Quantitative Imaging of Hydrogel Implants

PubMed Central

Appel, Alyssa A.; Larson, Jeffery C.; Jiang, Bin; Zhong, Zhong; Anastasio, Mark A.; Brey, Eric M.

2015-01-01

Three dimensional imaging techniques are needed for the evaluation and assessment of biomaterials used for tissue engineering and drug delivery applications. Hydrogels are a particularly popular class of materials for medical applications but are difficult to image in tissue using most available imaging modalities. Imaging techniques based on X-ray Phase Contrast (XPC) have shown promise for tissue engineering applications due to their ability to provide image contrast based on multiple X-ray properties. In this manuscript, we investigate the use of XPC for imaging a model hydrogel and soft tissue structure. Porous fibrin loaded poly(ethylene glycol) hydrogels were synthesized and implanted in a rodent subcutaneous model. Samples were explanted and imaged with an analyzer-based XPC technique and processed and stained for histology for comparison. Both hydrogel and soft tissues structures could be identified in XPC images. Structure in skeletal muscle adjacent could be visualized and invading fibrovascular tissue could be quantified. There were no differences between invading tissue measurements from XPC and the gold-standard histology. These results provide evidence of the significant potential of techniques based on XPC for 3D imaging of hydrogel structure and local tissue response. PMID:26487123

TH-CD-206-01: Expectation-Maximization Algorithm-Based Tissue Mixture Quantification for Perfusion MRI

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

Han, H; Xing, L; Liang, Z

Purpose: To investigate the feasibility of estimating the tissue mixture perfusions and quantifying cerebral blood flow change in arterial spin labeled (ASL) perfusion MR images. Methods: The proposed perfusion MR image analysis framework consists of 5 steps: (1) Inhomogeneity correction was performed on the T1- and T2-weighted images, which are available for each studied perfusion MR dataset. (2) We used the publicly available FSL toolbox to strip off the non-brain structures from the T1- and T2-weighted MR images. (3) We applied a multi-spectral tissue-mixture segmentation algorithm on both T1- and T2-structural MR images to roughly estimate the fraction of eachmore » tissue type - white matter, grey matter and cerebral spinal fluid inside each image voxel. (4) The distributions of the three tissue types or tissue mixture across the structural image array are down-sampled and mapped onto the ASL voxel array via a co-registration operation. (5) The presented 4-dimensional expectation-maximization (4D-EM) algorithm takes the down-sampled three tissue type distributions on perfusion image data to generate the perfusion mean, variance and percentage images for each tissue type of interest. Results: Experimental results on three volunteer datasets demonstrated that the multi-spectral tissue-mixture segmentation algorithm was effective to initialize tissue mixtures from T1- and T2-weighted MR images. Compared with the conventional ASL image processing toolbox, the proposed 4D-EM algorithm not only generated comparable perfusion mean images, but also produced perfusion variance and percentage images, which the ASL toolbox cannot obtain. It is observed that the perfusion contribution percentages may not be the same as the corresponding tissue mixture volume fractions estimated in the structural images. Conclusion: A specific application to brain ASL images showed that the presented perfusion image analysis method is promising for detecting subtle changes in tissue perfusions, which is valuable for the early diagnosis of certain brain diseases, e.g. multiple sclerosis.« less

Rainbow correlation imaging with macroscopic twin beam

NASA Astrophysics Data System (ADS)

Allevi, Alessia; Bondani, Maria

2017-06-01

We present the implementation of a correlation-imaging protocol that exploits both the spatial and spectral correlations of macroscopic twin-beam states generated by parametric downconversion. In particular, the spectral resolution of an imaging spectrometer coupled to an EMCCD camera is used in a proof-of-principle experiment to encrypt and decrypt a simple code to be transmitted between two parties. In order to optimize the trade-off between visibility and resolution, we provide the characterization of the correlation images as a function of the spatio-spectral properties of twin beams generated at different pump power values.

3D on-chip microscopy of optically cleared tissue

NASA Astrophysics Data System (ADS)

Zhang, Yibo; Shin, Yoonjung; Sung, Kevin; Yang, Sam; Chen, Harrison; Wang, Hongda; Teng, Da; Rivenson, Yair; Kulkarni, Rajan P.; Ozcan, Aydogan

2018-02-01

Traditional pathology relies on tissue biopsy, micro-sectioning, immunohistochemistry and microscopic imaging, which are relatively expensive and labor-intensive, and therefore are less accessible in resource-limited areas. Low-cost tissue clearing techniques, such as the simplified CLARITY method (SCM), are promising to potentially reduce the cost of disease diagnosis by providing 3D imaging and phenotyping of thicker tissue samples with simpler preparation steps. However, the mainstream imaging approach for cleared tissue, fluorescence microscopy, suffers from high-cost, photobleaching and signal fading. As an alternative approach to fluorescence, here we demonstrate 3D imaging of SCMcleared tissue using on-chip holography, which is based on pixel-super-resolution and multi-height phase recovery algorithms to digitally compute the sample's amplitude and phase images at various z-slices/depths through the sample. The tissue clearing procedures and the lens-free imaging system were jointly optimized to find the best illumination wavelength, tissue thickness, staining solution pH, and the number of hologram heights to maximize the imaged tissue volume, minimize the amount of acquired data, while maintaining a high contrast-to-noise ratio for the imaged cells. After this optimization, we achieved 3D imaging of a 200-μm thick cleared mouse brain tissue over a field-of-view of <20mm2 , and the resulting 3D z-stack agrees well with the images acquired with a scanning lens-based microscope (20× 0.75NA). Moreover, the lens-free microscope achieves an order-of-magnitude better data efficiency compared to its lens-based counterparts for volumetric imaging of samples. The presented low-cost and high-throughput lens-free tissue imaging technique enabled by CLARITY can be used in various biomedical applications in low-resource-settings.

A polarization sensitive hyperspectral imaging system for detection of differences in tissue properties

NASA Astrophysics Data System (ADS)

Peller, Joseph A.; Ceja, Nancy K.; Wawak, Amanda J.; Trammell, Susan R.

2018-02-01

Polarized light imaging and optical spectroscopy can be used to distinguish between healthy and diseased tissue. In this study, the design and testing of a single-pixel hyperspectral imaging system that uses differences in the polarization of light reflected from tissue to differentiate between healthy and thermally damaged tissue is discussed. Thermal lesions were created in porcine skin (n = 8) samples using an IR laser. The damaged regions were clearly visible in the polarized light hyperspectral images. Reflectance hyperspectral and white light imaging was also obtained for all tissue samples. Sizes of the thermally damaged regions as measured via polarized light hyperspectral imaging are compared to sizes of these regions as measured in the reflectance hyperspectral images and white light images. Good agreement between the sizes measured by all three imaging modalities was found. Hyperspectral polarized light imaging can differentiate between healthy and damaged tissue. Possible applications of this imaging system include determination of tumor margins during cancer surgery or pre-surgical biopsy.

Activation of the Parieto-Premotor Network Is Associated with Vivid Motor Imagery—A Parametric fMRI Study

PubMed Central

Lorey, Britta; Pilgramm, Sebastian; Bischoff, Matthias; Stark, Rudolf; Vaitl, Dieter; Kindermann, Stefan; Munzert, Jörn; Zentgraf, Karen

2011-01-01

The present study examined the neural basis of vivid motor imagery with parametrical functional magnetic resonance imaging. 22 participants performed motor imagery (MI) of six different right-hand movements that differed in terms of pointing accuracy needs and object involvement, i.e., either none, two big or two small squares had to be pointed at in alternation either with or without an object grasped with the fingers. After each imagery trial, they rated the perceived vividness of motor imagery on a 7-point scale. Results showed that increased perceived imagery vividness was parametrically associated with increasing neural activation within the left putamen, the left premotor cortex (PMC), the posterior parietal cortex of the left hemisphere, the left primary motor cortex, the left somatosensory cortex, and the left cerebellum. Within the right hemisphere, activation was found within the right cerebellum, the right putamen, and the right PMC. It is concluded that the perceived vividness of MI is parametrically associated with neural activity within sensorimotor areas. The results corroborate the hypothesis that MI is an outcome of neural computations based on movement representations located within motor areas. PMID:21655298

Sgr A* Emission Parametrizations from GRMHD Simulations

NASA Astrophysics Data System (ADS)

Anantua, Richard; Ressler, Sean; Quataert, Eliot

2018-06-01

Galactic Center emission near the vicinity of the central black hole, Sagittarius (Sgr) A*, is modeled using parametrizations involving the electron temperature, which is found from general relativistic magnetohydrodynamic (GRMHD) simulations to be highest in the disk-outflow corona. Jet-motivated prescriptions generalizing equipartition of particle and magnetic energies, e.g., by scaling relativistic electron energy density to powers of the magnetic field strength, are also introduced. GRMHD jet (or outflow)/accretion disk/black hole (JAB) simulation postprocessing codes IBOTHROS and GRMONTY are employed in the calculation of images and spectra. Various parametric models reproduce spectral and morphological features, such as the sub-mm spectral bump in electron temperature models and asymmetric photon rings in equipartition-based models. The Event Horizon Telescope (EHT) will provide unprecedentedly high-resolution 230+ GHz observations of the "shadow" around Sgr A*'s supermassive black hole, which the synthetic models presented here will reverse-engineer. Both electron temperature and equipartition-based models can be constructed to be compatible with EHT size constraints for the emitting region of Sgr A*. This program sets the groundwork for devising a unified emission parametrization flexible enough to model disk, corona and outflow/jet regions with a small set of parameters including electron heating fraction and plasma beta.

"Facilitated" amino acid transport is upregulated in brain tumors.

PubMed

Miyagawa, T; Oku, T; Uehara, H; Desai, R; Beattie, B; Tjuvajev, J; Blasberg, R

1998-05-01

The goal of this study was to determine the magnitude of "facilitated" amino acid transport across tumor and brain capillaries and to evaluate whether amino acid transporter expression is "upregulated" in tumor vessels compared to capillaries in contralateral brain tissue. Aminocyclopentane carboxylic acid (ACPC), a non-metabolized [14C]-labeled amino acid, and a reference molecule for passive vascular permeability, [67Ga]-gallium-diethylenetriaminepentaacetic acid (Ga-DTPA), were used in these studies. Two experimental rat gliomas were studied (C6 and RG2). Brain tissue was rapidly processed for double label quantitative autoradiography 10 minutes after intravenous injection of ACPC and Ga-DTPA. Parametric images of blood-to-brain transport (K1ACPC and K1Ga-DTPA, microL/min/g) produced from the autoradiograms and the histology were obtained from the same tissue section. These three images were registered in an image array processor; regions of interest in tumor and contralateral brain were defined on morphologic criteria (histology) and were transferred to the autoradiographic images to obtain mean values. The facilitated component of ACPC transport (deltaK1ACPC) was calculated from the K1ACPC and K1Ga-DTPA data, and paired comparisons between tumor and contralateral brain were performed. ACPC flux, K1ACPC, across normal brain capillaries (22.6 +/- 8.1 microL/g/min) was >200-fold greater than that of Ga-DTPA (0.09 +/- 0.04 microL/g/min), and this difference was largely (approximately 90%) due to facilitated ACPC transport. Substantially higher K1ACPC values compared to corresponding K1DTPA values were also measured in C6 and RG2 gliomas. The deltaK1ACPC values for C6 glioma were more than twice that of contralateral brain cortex. K1ACPC and deltaK1ACPC values for RG2 gliomas was not significantly higher than that of contralateral cortex, although a approximately 2-fold difference in facilitated transport is obtained after normalization for differences in capillary surface area between RG2 tumors and contralateral cortex. K1ACPC, deltaK1ACPC, and K DTPA were directly related to tumor cell density, were higher in regions of "impending" necrosis, and the tumor/contralateral brain ACPC radio-activity ratios (0 to 10 minutes) were very similar to that obtained with 0 to 60 minutes experiments. These results indicate that facilitated transport of ACPC is upregulated across C6 and RG2 glioma capillaries, and that tumors can induce upregulation of amino acid transporter expression in their supporting vasculature. They also suggest that early imaging (e.g., 0 to 20 minutes) with radiolabeled amino acids in a clinical setting may be optimal for defining brain tumors.

Influence of signal intensity non-uniformity on brain volumetry using an atlas-based method.

PubMed

Goto, Masami; Abe, Osamu; Miyati, Tosiaki; Kabasawa, Hiroyuki; Takao, Hidemasa; Hayashi, Naoto; Kurosu, Tomomi; Iwatsubo, Takeshi; Yamashita, Fumio; Matsuda, Hiroshi; Mori, Harushi; Kunimatsu, Akira; Aoki, Shigeki; Ino, Kenji; Yano, Keiichi; Ohtomo, Kuni

2012-01-01

Many studies have reported pre-processing effects for brain volumetry; however, no study has investigated whether non-parametric non-uniform intensity normalization (N3) correction processing results in reduced system dependency when using an atlas-based method. To address this shortcoming, the present study assessed whether N3 correction processing provides reduced system dependency in atlas-based volumetry. Contiguous sagittal T1-weighted images of the brain were obtained from 21 healthy participants, by using five magnetic resonance protocols. After image preprocessing using the Statistical Parametric Mapping 5 software, we measured the structural volume of the segmented images with the WFU-PickAtlas software. We applied six different bias-correction levels (Regularization 10, Regularization 0.0001, Regularization 0, Regularization 10 with N3, Regularization 0.0001 with N3, and Regularization 0 with N3) to each set of images. The structural volume change ratio (%) was defined as the change ratio (%) = (100 × [measured volume - mean volume of five magnetic resonance protocols] / mean volume of five magnetic resonance protocols) for each bias-correction level. A low change ratio was synonymous with lower system dependency. The results showed that the images with the N3 correction had a lower change ratio compared with those without the N3 correction. The present study is the first atlas-based volumetry study to show that the precision of atlas-based volumetry improves when using N3-corrected images. Therefore, correction for signal intensity non-uniformity is strongly advised for multi-scanner or multi-site imaging trials.

Influence of Signal Intensity Non-Uniformity on Brain Volumetry Using an Atlas-Based Method

PubMed Central

Abe, Osamu; Miyati, Tosiaki; Kabasawa, Hiroyuki; Takao, Hidemasa; Hayashi, Naoto; Kurosu, Tomomi; Iwatsubo, Takeshi; Yamashita, Fumio; Matsuda, Hiroshi; Mori, Harushi; Kunimatsu, Akira; Aoki, Shigeki; Ino, Kenji; Yano, Keiichi; Ohtomo, Kuni

2012-01-01

Objective Many studies have reported pre-processing effects for brain volumetry; however, no study has investigated whether non-parametric non-uniform intensity normalization (N3) correction processing results in reduced system dependency when using an atlas-based method. To address this shortcoming, the present study assessed whether N3 correction processing provides reduced system dependency in atlas-based volumetry. Materials and Methods Contiguous sagittal T1-weighted images of the brain were obtained from 21 healthy participants, by using five magnetic resonance protocols. After image preprocessing using the Statistical Parametric Mapping 5 software, we measured the structural volume of the segmented images with the WFU-PickAtlas software. We applied six different bias-correction levels (Regularization 10, Regularization 0.0001, Regularization 0, Regularization 10 with N3, Regularization 0.0001 with N3, and Regularization 0 with N3) to each set of images. The structural volume change ratio (%) was defined as the change ratio (%) = (100 × [measured volume - mean volume of five magnetic resonance protocols] / mean volume of five magnetic resonance protocols) for each bias-correction level. Results A low change ratio was synonymous with lower system dependency. The results showed that the images with the N3 correction had a lower change ratio compared with those without the N3 correction. Conclusion The present study is the first atlas-based volumetry study to show that the precision of atlas-based volumetry improves when using N3-corrected images. Therefore, correction for signal intensity non-uniformity is strongly advised for multi-scanner or multi-site imaging trials. PMID:22778560

Photon Entanglement Through Brain Tissue.

PubMed

Shi, Lingyan; Galvez, Enrique J; Alfano, Robert R

2016-12-20

Photon entanglement, the cornerstone of quantum correlations, provides a level of coherence that is not present in classical correlations. Harnessing it by study of its passage through organic matter may offer new possibilities for medical diagnosis technique. In this work, we study the preservation of photon entanglement in polarization, created by spontaneous parametric down-conversion, after one entangled photon propagates through multiphoton-scattering brain tissue slices with different thickness. The Tangle-Entropy (TS) plots show the strong preservation of entanglement of photons propagating in brain tissue. By spatially filtering the ballistic scattering of an entangled photon, we find that its polarization entanglement is preserved and non-locally correlated with its twin in the TS plots. The degree of entanglement correlates better with structure and water content than with sample thickness.

Photon Entanglement Through Brain Tissue

NASA Astrophysics Data System (ADS)

Shi, Lingyan; Galvez, Enrique J.; Alfano, Robert R.

2016-12-01

Photon entanglement, the cornerstone of quantum correlations, provides a level of coherence that is not present in classical correlations. Harnessing it by study of its passage through organic matter may offer new possibilities for medical diagnosis technique. In this work, we study the preservation of photon entanglement in polarization, created by spontaneous parametric down-conversion, after one entangled photon propagates through multiphoton-scattering brain tissue slices with different thickness. The Tangle-Entropy (TS) plots show the strong preservation of entanglement of photons propagating in brain tissue. By spatially filtering the ballistic scattering of an entangled photon, we find that its polarization entanglement is preserved and non-locally correlated with its twin in the TS plots. The degree of entanglement correlates better with structure and water content than with sample thickness.

Imaging biological tissues with electrical conductivity contrast below 1 S m−1 by means of magnetoacoustic tomography with magnetic induction

PubMed Central

Hu, Gang; Li, Xu; He, Bin

2010-01-01

Magnetoacoustic tomography with magnetic induction (MAT-MI) is a recently introduced imaging modality for noninvasive electrical impedance imaging, with ultrasound imaging resolution and a contrast reflecting the electrical conductivity properties of tissues. However, previous MAT-MI systems can only image samples that are much more conductive than real human or animal tissues. To image real biological tissue samples, a large-current-carrying coil that can give stronger magnetic stimulations and stronger MAT-MI acoustic signals is employed in this study. The conductivity values of all the tissue samples employed in this study are also directly measured using a well calibrated four-electrode system. The experimental results demonstrated the feasibility to image biological tissues with electrical conductivity contrast below 1.0 S∕m using the MAT-MI technique with safe level of electromagnetic energy applied to tissue samples. PMID:20938494

Imaging biological tissues with electrical conductivity contrast below 1 S m-1 by means of magnetoacoustic tomography with magnetic induction

NASA Astrophysics Data System (ADS)

Hu, Gang; Li, Xu; He, Bin

2010-09-01

Magnetoacoustic tomography with magnetic induction (MAT-MI) is a recently introduced imaging modality for noninvasive electrical impedance imaging, with ultrasound imaging resolution and a contrast reflecting the electrical conductivity properties of tissues. However, previous MAT-MI systems can only image samples that are much more conductive than real human or animal tissues. To image real biological tissue samples, a large-current-carrying coil that can give stronger magnetic stimulations and stronger MAT-MI acoustic signals is employed in this study. The conductivity values of all the tissue samples employed in this study are also directly measured using a well calibrated four-electrode system. The experimental results demonstrated the feasibility to image biological tissues with electrical conductivity contrast below 1.0 S/m using the MAT-MI technique with safe level of electromagnetic energy applied to tissue samples.

SU-G-JeP2-02: A Unifying Multi-Atlas Approach to Electron Density Mapping Using Multi-Parametric MRI for Radiation Treatment Planning

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

Ren, S; Tianjin University, Tianjin; Hara, W

Purpose: MRI has a number of advantages over CT as a primary modality for radiation treatment planning (RTP). However, one key bottleneck problem still remains, which is the lack of electron density information in MRI. In the work, a reliable method to map electron density is developed by leveraging the differential contrast of multi-parametric MRI. Methods: We propose a probabilistic Bayesian approach for electron density mapping based on T1 and T2-weighted MRI, using multiple patients as atlases. For each voxel, we compute two conditional probabilities: (1) electron density given its image intensity on T1 and T2-weighted MR images, and (2)more » electron density given its geometric location in a reference anatomy. The two sources of information (image intensity and spatial location) are combined into a unifying posterior probability density function using the Bayesian formalism. The mean value of the posterior probability density function provides the estimated electron density. Results: We evaluated the method on 10 head and neck patients and performed leave-one-out cross validation (9 patients as atlases and remaining 1 as test). The proposed method significantly reduced the errors in electron density estimation, with a mean absolute HU error of 138, compared with 193 for the T1-weighted intensity approach and 261 without density correction. For bone detection (HU>200), the proposed method had an accuracy of 84% and a sensitivity of 73% at specificity of 90% (AUC = 87%). In comparison, the AUC for bone detection is 73% and 50% using the intensity approach and without density correction, respectively. Conclusion: The proposed unifying method provides accurate electron density estimation and bone detection based on multi-parametric MRI of the head with highly heterogeneous anatomy. This could allow for accurate dose calculation and reference image generation for patient setup in MRI-based radiation treatment planning.« less

High resolution microendoscopy with structured illumination and Lugol's iodine staining for evaluation of breast cancer architecture

NASA Astrophysics Data System (ADS)

Dobbs, Jessica; Kyrish, Matthew; Krishnamurthy, Savitri; Grant, Benjamin; Kuerer, Henry; Yang, Wei; Tkaczyk, Tomasz; Richards-Kortum, Rebecca

2016-03-01

Intraoperative margin assessment to evaluate resected tissue margins for neoplastic tissue is performed to prevent reoperations following breast-conserving surgery. High resolution microendoscopy (HRME) can rapidly acquire images of fresh tissue specimens, but is limited by low image contrast in tissues with high optical scattering. In this study we evaluated two techniques to reduce out-of-focus light: HRME image acquisition with structured illumination (SI-HRME) and topical application of Lugol's Iodine. Fresh breast tissue specimens from 19 patients were stained with proflavine alone or Lugol's Iodine and proflavine. Images of tissue specimens were acquired using a confocal microscope and an HRME system with and without structured illumination. Images were evaluated based on visual and quantitative assessment of image contrast. The highest mean contrast was measured in confocal images stained with proflavine. Contrast was significantly lower in HRME images stained with proflavine; however, incorporation of structured illumination significantly increased contrast in HRME images to levels comparable to that in confocal images. The addition of Lugol's Iodine did not increase mean contrast significantly for HRME or SI-HRME images. These findings suggest that structured illumination could potentially be used to increase contrast in HRME images of breast tissue for rapid image acquisition.

Deep Tissue Fluorescent Imaging in Scattering Specimens Using Confocal Microscopy

PubMed Central

Clendenon, Sherry G.; Young, Pamela A.; Ferkowicz, Michael; Phillips, Carrie; Dunn, Kenneth W.

2015-01-01

In scattering specimens, multiphoton excitation and nondescanned detection improve imaging depth by a factor of 2 or more over confocal microscopy; however, imaging depth is still limited by scattering. We applied the concept of clearing to deep tissue imaging of highly scattering specimens. Clearing is a remarkably effective approach to improving image quality at depth using either confocal or multiphoton microscopy. Tissue clearing appears to eliminate the need for multiphoton excitation for deep tissue imaging. PMID:21729357

Multiview hyperspectral topography of tissue structural and functional characteristics

NASA Astrophysics Data System (ADS)

Zhang, Shiwu; Liu, Peng; Huang, Jiwei; Xu, Ronald

2012-12-01

Accurate and in vivo characterization of structural, functional, and molecular characteristics of biological tissue will facilitate quantitative diagnosis, therapeutic guidance, and outcome assessment in many clinical applications, such as wound healing, cancer surgery, and organ transplantation. However, many clinical imaging systems have limitations and fail to provide noninvasive, real time, and quantitative assessment of biological tissue in an operation room. To overcome these limitations, we developed and tested a multiview hyperspectral imaging system. The multiview hyperspectral imaging system integrated the multiview and the hyperspectral imaging techniques in a single portable unit. Four plane mirrors are cohered together as a multiview reflective mirror set with a rectangular cross section. The multiview reflective mirror set was placed between a hyperspectral camera and the measured biological tissue. For a single image acquisition task, a hyperspectral data cube with five views was obtained. The five-view hyperspectral image consisted of a main objective image and four reflective images. Three-dimensional topography of the scene was achieved by correlating the matching pixels between the objective image and the reflective images. Three-dimensional mapping of tissue oxygenation was achieved using a hyperspectral oxygenation algorithm. The multiview hyperspectral imaging technique is currently under quantitative validation in a wound model, a tissue-simulating blood phantom, and an in vivo biological tissue model. The preliminary results have demonstrated the technical feasibility of using multiview hyperspectral imaging for three-dimensional topography of tissue functional properties.

Parametric Study of Wall Shear Stress in Idealized Avian Airways

NASA Astrophysics Data System (ADS)

Farnsworth, Michael S.; Riede, Tobias; Thomson, Scott L.

2017-11-01

Because wall shear stress (WSS) affects cell response, WSS patterns in avian respiratory airways may be related to the origin of the syrinx and corresponding voice-producing tissue structures (e.g., membranes or vocal folds) in birds. To explore possible linkages between WSS patterns and the locations of avian voice-producing structures, a computational model of flow through an idealized portion of the avian respiratory airway, including trachea and primary bronchi sections, has been developed. The flow is governed by the Navier-Stokes equations, with velocity boundary conditions derived from pressure-flow data in an adult zebra finch during quiet respiration. Geometric parameters such as tracheal/bronchial diameter and length, as well as bronchial branching angle, are parametrically varied based on data for different avian species. Simulation results predict elevated WSS in the vicinity of the tracheobronchial juncture, the location at which voice-producing tissues are found in avian species. In this presentation, the model will be described and spatial distributions of WSS during inspiration and expiration will be presented and compared for different geometric configurations and respiration rates and waveforms. Funding for this project from the Gordon and Betty Moore Foundation (Grant 4498) is gratefully acknowledged.

Differential diagnosis of normal pressure hydrocephalus by MRI mean diffusivity histogram analysis.

PubMed

Ivkovic, M; Liu, B; Ahmed, F; Moore, D; Huang, C; Raj, A; Kovanlikaya, I; Heier, L; Relkin, N

2013-01-01

Accurate diagnosis of normal pressure hydrocephalus is challenging because the clinical symptoms and radiographic appearance of NPH often overlap those of other conditions, including age-related neurodegenerative disorders such as Alzheimer and Parkinson diseases. We hypothesized that radiologic differences between NPH and AD/PD can be characterized by a robust and objective MR imaging DTI technique that does not require intersubject image registration or operator-defined regions of interest, thus avoiding many pitfalls common in DTI methods. We collected 3T DTI data from 15 patients with probable NPH and 25 controls with AD, PD, or dementia with Lewy bodies. We developed a parametric model for the shape of intracranial mean diffusivity histograms that separates brain and ventricular components from a third component composed mostly of partial volume voxels. To accurately fit the shape of the third component, we constructed a parametric function named the generalized Voss-Dyke function. We then examined the use of the fitting parameters for the differential diagnosis of NPH from AD, PD, and DLB. Using parameters for the MD histogram shape, we distinguished clinically probable NPH from the 3 other disorders with 86% sensitivity and 96% specificity. The technique yielded 86% sensitivity and 88% specificity when differentiating NPH from AD only. An adequate parametric model for the shape of intracranial MD histograms can distinguish NPH from AD, PD, or DLB with high sensitivity and specificity.

Accurate analysis and visualization of cardiac (11)C-PIB uptake in amyloidosis with semiautomatic software.

PubMed

Kero, Tanja; Lindsjö, Lars; Sörensen, Jens; Lubberink, Mark

2016-08-01

(11)C-PIB PET is a promising non-invasive diagnostic tool for cardiac amyloidosis. Semiautomatic analysis of PET data is now available but it is not known how accurate these methods are for amyloid imaging. The aim of this study was to evaluate the feasibility of one semiautomatic software tool for analysis and visualization of (11)C-PIB left ventricular retention index (RI) in cardiac amyloidosis. Patients with systemic amyloidosis and cardiac involvement (n = 10) and healthy controls (n = 5) were investigated with dynamic (11)C-PIB PET. Two observers analyzed the PET studies with semiautomatic software to calculate the left ventricular RI of (11)C-PIB and to create parametric images. The mean RI at 15-25 min from the semiautomatic analysis was compared with RI based on manual analysis and showed comparable values (0.056 vs 0.054 min(-1) for amyloidosis patients and 0.024 vs 0.025 min(-1) in healthy controls; P = .78) and the correlation was excellent (r = 0.98). Inter-reader reproducibility also was excellent (intraclass correlation coefficient, ICC > 0.98). Parametric polarmaps and histograms made visual separation of amyloidosis patients and healthy controls fast and simple. Accurate semiautomatic analysis of cardiac (11)C-PIB RI in amyloidosis patients is feasible. Parametric polarmaps and histograms make visual interpretation fast and simple.

Detection of the multiphoton signals in stained tissue using nonlinear optical microscopy

NASA Astrophysics Data System (ADS)

Zeng, Yaping; Xu, Jian; Kang, Deyong; Lin, Jiangbo; Chen, Jianxin

2016-10-01

Multiphoton microscopy (MPM) based on two-photon excited fluorescence (TPEF) and second harmonic generation (SHG) imaging, has become a powerful, important tool for tissue imaging at the molecular level. Recently, MPM is also used to image hematoxylin and eosin (H and E)-stained sections in cancer diagnostics. However, several studies have showed that the MPM images of tissue stained with H and E are significantly different from unstained tissue sections. Our aim was to detect of the multiphoton signals in stained tissue by using MPM. In this paper, MPM was used to image histological sections of esophageal invasive carcinoma tissues stained with H, E, H and E and fresh tissue. To detect of the multiphoton signals in stained tissue, the emission spectroscopic of tissue stained with H, E, H and E were obtained. For comparison, the fresh tissues were also investigated. Our results showed that the tissue stained with H, E, H and E could be detected by their TPEF signals. While the tissue stained with H and fresh tissue could be detected by their TPEF and SHG signals. In this work, we detect of the multiphoton signals in stained tissue. These findings will be useful for choosing suitable staining method so to improve the quality of MPM imaging in the future.

Application of hyperosmotic agent to determine gastric cancer with optical coherence tomography ex vivo in mice

NASA Astrophysics Data System (ADS)

Xiong, Honglian; Guo, Zhouyi; Zeng, Changchun; Wang, Like; He, Yonghong; Liu, Songhao

2009-03-01

Noninvasive tumor imaging could lead to the early detection and timely treatment of cancer. Optical coherence tomography (OCT) has been reported as an ideal diagnostic tool for distinguishing tumor tissues from normal tissues based on structural imaging. In this study, the capability of OCT for functional imaging of normal and tumor tissues based on time- and depth-resolved quantification of the permeability of biomolecules through these tissues is investigated. The orthotopic graft model of gastric cancer in nude mice is used, normal and tumor tissues from the gastric wall are imaged, and a diffusion of 20% aqueous solution of glucose in normal stomach tissues and gastric tumor tissues is monitored and quantified as a function of time and tissue depth by an OCT system. Our results show that the permeability coefficient is (0.94+/-0.04)×10-5 cm/s in stomach tissues and (5.32+/-0.17)×10-5 cm/s in tumor tissues, respectively, and that tumor tissues have a higher permeability coefficient compared to normal tissues in optical coherence tomographic images. From the results, it is found that the accurate and sensitive assessment of the permeability coefficients of normal and tumor tissues offers an effective OCT image method for detection of tumor tissues and clinical diagnosis.

Component pattern analysis of chemicals using multispectral THz imaging system

NASA Astrophysics Data System (ADS)

Kawase, Kodo; Ogawa, Yuichi; Watanabe, Yuki

2004-04-01

We have developed a novel basic technology for terahertz (THz) imaging, which allows detection and identification of chemicals by introducing the component spatial pattern analysis. The spatial distributions of the chemicals were obtained from terahertz multispectral transillumination images, using absorption spectra previously measured with a widely tunable THz-wave parametric oscillator. Further we have applied this technique to the detection and identification of illicit drugs concealed in envelopes. The samples we used were methamphetamine and MDMA, two of the most widely consumed illegal drugs in Japan, and aspirin as a reference.

Triaxial ellipsoid dimensions and rotational poles of seven asteroids from Lick Observatory adaptive optics images, and of Ceres

NASA Astrophysics Data System (ADS)

Drummond, Jack; Christou, Julian

2008-10-01

Seven main belt asteroids, 2 Pallas, 3 Juno, 4 Vesta, 16 Psyche, 87 Sylvia, 324 Bamberga, and 707 Interamnia, were imaged with the adaptive optics system on the 3 m Shane telescope at Lick Observatory in the near infrared, and their triaxial ellipsoid dimensions and rotational poles have been determined with parametric blind deconvolution. In addition, the dimensions and pole for 1 Ceres are derived from resolved images at multiple epochs, even though it is an oblate spheroid.

High Resolution X-ray Phase Contrast Imaging with Acoustic Tissue-Selective Contrast Enhancement

DTIC Science & Technology

2008-06-01

Imaging with Acoustic Tissue-Selective Contrast Enhancement PRINCIPAL INVESTIGATOR: Gerald J. Diebold, Ph.D. CONTRACTING... Contrast Imaging with Acoustic Tissue-Selective Contrast Enhancement 5b. GRANT NUMBER W81XWH-04-1-0481 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S...additional phase contrast features are visible at the interfaces of soft tissues as slight contrast enhancements . The image sequence in Fig. 2 shows an image

In vivo multiphoton tomography and fluorescence lifetime imaging of human brain tumor tissue.

PubMed

Kantelhardt, Sven R; Kalasauskas, Darius; König, Karsten; Kim, Ella; Weinigel, Martin; Uchugonova, Aisada; Giese, Alf

2016-05-01

High resolution multiphoton tomography and fluorescence lifetime imaging differentiates glioma from adjacent brain in native tissue samples ex vivo. Presently, multiphoton tomography is applied in clinical dermatology and experimentally. We here present the first application of multiphoton and fluorescence lifetime imaging for in vivo imaging on humans during a neurosurgical procedure. We used a MPTflex™ Multiphoton Laser Tomograph (JenLab, Germany). We examined cultured glioma cells in an orthotopic mouse tumor model and native human tissue samples. Finally the multiphoton tomograph was applied to provide optical biopsies during resection of a clinical case of glioblastoma. All tissues imaged by multiphoton tomography were sampled and processed for conventional histopathology. The multiphoton tomograph allowed fluorescence intensity- and fluorescence lifetime imaging with submicron spatial resolution and 200 picosecond temporal resolution. Morphological fluorescence intensity imaging and fluorescence lifetime imaging of tumor-bearing mouse brains and native human tissue samples clearly differentiated tumor and adjacent brain tissue. Intraoperative imaging was found to be technically feasible. Intraoperative image quality was comparable to ex vivo examinations. To our knowledge we here present the first intraoperative application of high resolution multiphoton tomography and fluorescence lifetime imaging of human brain tumors in situ. It allowed in vivo identification and determination of cell density of tumor tissue on a cellular and subcellular level within seconds. The technology shows the potential of rapid intraoperative identification of native glioma tissue without need for tissue processing or staining.

Acoustic Radiation Force Elasticity Imaging in Diagnostic Ultrasound

PubMed Central

Doherty, Joshua R.; Trahey, Gregg E.; Nightingale, Kathryn R.; Palmeri, Mark L.

2013-01-01

The development of ultrasound-based elasticity imaging methods has been the focus of intense research activity since the mid-1990s. In characterizing the mechanical properties of soft tissues, these techniques image an entirely new subset of tissue properties that cannot be derived with conventional ultrasound techniques. Clinically, tissue elasticity is known to be associated with pathological condition and with the ability to image these features in vivo, elasticity imaging methods may prove to be invaluable tools for the diagnosis and/or monitoring of disease. This review focuses on ultrasound-based elasticity imaging methods that generate an acoustic radiation force to induce tissue displacements. These methods can be performed non-invasively during routine exams to provide either qualitative or quantitative metrics of tissue elasticity. A brief overview of soft tissue mechanics relevant to elasticity imaging is provided, including a derivation of acoustic radiation force, and an overview of the various acoustic radiation force elasticity imaging methods. PMID:23549529

Acoustic radiation force elasticity imaging in diagnostic ultrasound.

PubMed

Doherty, Joshua R; Trahey, Gregg E; Nightingale, Kathryn R; Palmeri, Mark L

2013-04-01

The development of ultrasound-based elasticity imaging methods has been the focus of intense research activity since the mid-1990s. In characterizing the mechanical properties of soft tissues, these techniques image an entirely new subset of tissue properties that cannot be derived with conventional ultrasound techniques. Clinically, tissue elasticity is known to be associated with pathological condition and with the ability to image these features in vivo; elasticity imaging methods may prove to be invaluable tools for the diagnosis and/or monitoring of disease. This review focuses on ultrasound-based elasticity imaging methods that generate an acoustic radiation force to induce tissue displacements. These methods can be performed noninvasively during routine exams to provide either qualitative or quantitative metrics of tissue elasticity. A brief overview of soft tissue mechanics relevant to elasticity imaging is provided, including a derivation of acoustic radiation force, and an overview of the various acoustic radiation force elasticity imaging methods.

Intensity non-uniformity correction using N3 on 3-T scanners with multichannel phased array coils

PubMed Central

Boyes, Richard G.; Gunter, Jeff L.; Frost, Chris; Janke, Andrew L.; Yeatman, Thomas; Hill, Derek L.G.; Bernstein, Matt A.; Thompson, Paul M.; Weiner, Michael W.; Schuff, Norbert; Alexander, Gene E.; Killiany, Ronald J.; DeCarli, Charles; Jack, Clifford R.; Fox, Nick C.

2008-01-01

Measures of structural brain change based on longitudinal MR imaging are increasingly important but can be degraded by intensity non-uniformity. This non-uniformity can be more pronounced at higher field strengths, or when using multichannel receiver coils. We assessed the ability of the non-parametric non-uniform intensity normalization (N3) technique to correct non-uniformity in 72 volumetric brain MR scans from the preparatory phase of the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Normal elderly subjects (n = 18) were scanned on different 3-T scanners with a multichannel phased array receiver coil at baseline, using magnetization prepared rapid gradient echo (MP-RAGE) and spoiled gradient echo (SPGR) pulse sequences, and again 2 weeks later. When applying N3, we used five brain masks of varying accuracy and four spline smoothing distances (d = 50, 100, 150 and 200 mm) to ascertain which combination of parameters optimally reduces the non-uniformity. We used the normalized white matter intensity variance (standard deviation/mean) to ascertain quantitatively the correction for a single scan; we used the variance of the normalized difference image to assess quantitatively the consistency of the correction over time from registered scan pairs. Our results showed statistically significant (p < 0.01) improvement in uniformity for individual scans and reduction in the normalized difference image variance when using masks that identified distinct brain tissue classes, and when using smaller spline smoothing distances (e.g., 50-100 mm) for both MP-RAGE and SPGR pulse sequences. These optimized settings may assist future large-scale studies where 3-T scanners and phased array receiver coils are used, such as ADNI, so that intensity non-uniformity does not influence the power of MR imaging to detect disease progression and the factors that influence it. PMID:18063391

Phenotypic Characterization of Toxic Compound Effects on Liver Spheroids Derived from iPSC Using Confocal Imaging and Three-Dimensional Image Analysis.

PubMed

Sirenko, Oksana; Hancock, Michael K; Hesley, Jayne; Hong, Dihui; Cohen, Avrum; Gentry, Jason; Carlson, Coby B; Mann, David A

2016-09-01

Cell models are becoming more complex to better mimic the in vivo environment and provide greater predictivity for compound efficacy and toxicity. There is an increasing interest in exploring the use of three-dimensional (3D) spheroids for modeling developmental and tissue biology with the goal of accelerating translational research in these areas. Accordingly, the development of high-throughput quantitative assays using 3D cultures is an active area of investigation. In this study, we have developed and optimized methods for the formation of 3D liver spheroids derived from human iPS cells and used those for toxicity assessment. We used confocal imaging and 3D image analysis to characterize cellular information from a 3D matrix to enable a multi-parametric comparison of different spheroid phenotypes. The assay enables characterization of compound toxicities by spheroid size (volume) and shape, cell number and spatial distribution, nuclear characterization, number and distribution of cells expressing viability, apoptosis, mitochondrial potential, and viability marker intensities. In addition, changes in the content of live, dead, and apoptotic cells as a consequence of compound exposure were characterized. We tested 48 compounds and compared induced pluripotent stem cell (iPSC)-derived hepatocytes and HepG2 cells in both two-dimensional (2D) and 3D cultures. We observed significant differences in the pharmacological effects of compounds across the two cell types and between the different culture conditions. Our results indicate that a phenotypic assay using 3D model systems formed with human iPSC-derived hepatocytes is suitable for high-throughput screening and can be used for hepatotoxicity assessment in vitro.

Workflow for high-content, individual cell quantification of fluorescent markers from universal microscope data, supported by open source software.

PubMed

Stockwell, Simon R; Mittnacht, Sibylle

2014-12-16

Advances in understanding the control mechanisms governing the behavior of cells in adherent mammalian tissue culture models are becoming increasingly dependent on modes of single-cell analysis. Methods which deliver composite data reflecting the mean values of biomarkers from cell populations risk losing subpopulation dynamics that reflect the heterogeneity of the studied biological system. In keeping with this, traditional approaches are being replaced by, or supported with, more sophisticated forms of cellular assay developed to allow assessment by high-content microscopy. These assays potentially generate large numbers of images of fluorescent biomarkers, which enabled by accompanying proprietary software packages, allows for multi-parametric measurements per cell. However, the relatively high capital costs and overspecialization of many of these devices have prevented their accessibility to many investigators. Described here is a universally applicable workflow for the quantification of multiple fluorescent marker intensities from specific subcellular regions of individual cells suitable for use with images from most fluorescent microscopes. Key to this workflow is the implementation of the freely available Cell Profiler software(1) to distinguish individual cells in these images, segment them into defined subcellular regions and deliver fluorescence marker intensity values specific to these regions. The extraction of individual cell intensity values from image data is the central purpose of this workflow and will be illustrated with the analysis of control data from a siRNA screen for G1 checkpoint regulators in adherent human cells. However, the workflow presented here can be applied to analysis of data from other means of cell perturbation (e.g., compound screens) and other forms of fluorescence based cellular markers and thus should be useful for a wide range of laboratories.

Multimodal tissue perfusion imaging using multi-spectral and thermographic imaging systems applied on clinical data

NASA Astrophysics Data System (ADS)

Klaessens, John H. G. M.; Nelisse, Martin; Verdaasdonk, Rudolf M.; Noordmans, Herke Jan

2013-03-01

Clinical interventions can cause changes in tissue perfusion, oxygenation or temperature. Real-time imaging of these phenomena could be useful for surgical strategy or understanding of physiological regulation mechanisms. Two noncontact imaging techniques were applied for imaging of large tissue areas: LED based multispectral imaging (MSI, 17 different wavelengths 370 nm-880 nm) and thermal imaging (7.5 to 13.5 μm). Oxygenation concentration changes were calculated using different analyzing methods. The advantages of these methods are presented for stationary and dynamic applications. Concentration calculations of chromophores in tissue require right choices of wavelengths The effects of different wavelength choices for hemoglobin concentration calculations were studied in laboratory conditions and consequently applied in clinical studies. Corrections for interferences during the clinical registrations (ambient light fluctuations, tissue movements) were performed. The wavelength dependency of the algorithms were studied and wavelength sets with the best results will be presented. The multispectral and thermal imaging systems were applied during clinical intervention studies: reperfusion of tissue flap transplantation (ENT), effectiveness of local anesthetic block and during open brain surgery in patients with epileptic seizures. The LED multispectral imaging system successfully imaged the perfusion and oxygenation changes during clinical interventions. The thermal images show local heat distributions over tissue areas as a result of changes in tissue perfusion. Multispectral imaging and thermal imaging provide complementary information and are promising techniques for real-time diagnostics of physiological processes in medicine.

The use of algorithmic behavioural transfer functions in parametric EO system performance models

NASA Astrophysics Data System (ADS)

Hickman, Duncan L.; Smith, Moira I.

2015-10-01

The use of mathematical models to predict the overall performance of an electro-optic (EO) system is well-established as a methodology and is used widely to support requirements definition, system design, and produce performance predictions. Traditionally these models have been based upon cascades of transfer functions based on established physical theory, such as the calculation of signal levels from radiometry equations, as well as the use of statistical models. However, the performance of an EO system is increasing being dominated by the on-board processing of the image data and this automated interpretation of image content is complex in nature and presents significant modelling challenges. Models and simulations of EO systems tend to either involve processing of image data as part of a performance simulation (image-flow) or else a series of mathematical functions that attempt to define the overall system characteristics (parametric). The former approach is generally more accurate but statistically and theoretically weak in terms of specific operational scenarios, and is also time consuming. The latter approach is generally faster but is unable to provide accurate predictions of a system's performance under operational conditions. An alternative and novel architecture is presented in this paper which combines the processing speed attributes of parametric models with the accuracy of image-flow representations in a statistically valid framework. An additional dimension needed to create an effective simulation is a robust software design whose architecture reflects the structure of the EO System and its interfaces. As such, the design of the simulator can be viewed as a software prototype of a new EO System or an abstraction of an existing design. This new approach has been used successfully to model a number of complex military systems and has been shown to combine improved performance estimation with speed of computation. Within the paper details of the approach and architecture are described in detail, and example results based on a practical application are then given which illustrate the performance benefits. Finally, conclusions are drawn and comments given regarding the benefits and uses of the new approach.

Teledetection passive et processus decisionnel a reference spatiale: Application a l'aquaculture en milieu marin

NASA Astrophysics Data System (ADS)

Habbane, Mohamed

L'objectif de cette etude est d'elaborer un processus decisionnel a reference spatiale (PDRS) pour la mariculture. Le PDRS est applique aux eaux cotieres de la baie des Chaleurs, dans le golfe du Saint-Laurent (Canada). Une carte preliminaire regionale d'indices du potentiel maricole, d'une limite de resolution spatiale de 1 kmsp2, est produite avec des parametres du niveau 1. Ces parametres englobent la temperature de l'eau de surface, extraite des images AVHRR, la salinite, les courants ainsi que les pigments chlorophylliens, quantifies a l'aide de mesures in situ. Les images AVHRR, prises en 1994, ont ete utiliees comme reference primaire pour selectionner des aires pouvant supporter une activite maricole sur la cote nord de la baie des Chaleurs. La temperature de surface extraite de ces images permet une analyse mesoechelle a la fois qualitative et quantitative des processus cotiers observes pendant la periode d'acquisition des donnees. Les autres donnees, soit la salinite, les courants et les concentrations en pigments chlorophylliens, sont analysees de facon a identifier la variabilite spatio-temporelle des caracteristiques des eaux de surface. L'ensemble des informations permet de produire une carte preliminaire regionale d'indices du potentiel maricole de la partie centrale de la baie des Chaleurs. Selon cet indice (defini entre 0 et 1), le secteur de potentiel aquicole de 0,5 a 0,75 s'etend sur une superficie d'environ 300 kmsp2. La localisation de cette aire potentielle est en accord avec les fortes concentrations en pigments chlrophylliens, presentant des conditions environnementales ideales a une haute productivite biologique. Par la suite la carte preliminaire est modifiee en tenant compte des parametres du niveau 2. Ces parametres sont la geomorphologie littorale, la bathymetrie, les sediments en suspension, les vents, les vagues, le debit d'eau douce, la glace marine, le carbone organique dissous, les aires de peche et les sources de pollution. Ces parametres sont compares deux a deux par rapport a la carte preliminaire regionale d'indices du potentiel maricole pour determiner leur poids relatif. La carte finale produite avec ces parametres du niveau 2 presente un secteur ou les indices du potentiel maricole sont de 0,5 a 0,75. Ce secteur longe la cote et epouse les isobathes de 10 a 30 m de profondeur. L'effet de la profondeur d'eau semble avoir jouer un role important. Le secteur de potentiel maricole de 0,25 a 0,5 est toujours present et couvre une superficie d'environ 426 kmsp2. L'etude necessitera toujours un suivi des conditions environnementales prevalant dans la region. Ce suivi peut etre effectue a l'aide d'un outil de vision aerospatiale (capteurs de teledetection) et d'analyse spatio-temporelle (SIG-PDRS). (Abstract shortened by UMI.)

Fluid flow in porous media using image-based modelling to parametrize Richards' equation.

PubMed

Cooper, L J; Daly, K R; Hallett, P D; Naveed, M; Koebernick, N; Bengough, A G; George, T S; Roose, T

2017-11-01

The parameters in Richards' equation are usually calculated from experimentally measured values of the soil-water characteristic curve and saturated hydraulic conductivity. The complex pore structures that often occur in porous media complicate such parametrization due to hysteresis between wetting and drying and the effects of tortuosity. Rather than estimate the parameters in Richards' equation from these indirect measurements, image-based modelling is used to investigate the relationship between the pore structure and the parameters. A three-dimensional, X-ray computed tomography image stack of a soil sample with voxel resolution of 6 μm has been used to create a computational mesh. The Cahn-Hilliard-Stokes equations for two-fluid flow, in this case water and air, were applied to this mesh and solved using the finite-element method in COMSOL Multiphysics. The upscaled parameters in Richards' equation are then obtained via homogenization. The effect on the soil-water retention curve due to three different contact angles, 0°, 20° and 60°, was also investigated. The results show that the pore structure affects the properties of the flow on the large scale, and different contact angles can change the parameters for Richards' equation.

X-ray Phase Contrast Allows Three Dimensional, Quantitative Imaging of Hydrogel Implants

DOE PAGES

Appel, Alyssa A.; Larson, Jeffrey C.; Jiang, Bin; ...

2015-10-20

Three dimensional imaging techniques are needed for the evaluation and assessment of biomaterials used for tissue engineering and drug delivery applications. Hydrogels are a particularly popular class of materials for medical applications but are difficult to image in tissue using most available imaging modalities. Imaging techniques based on X-ray Phase Contrast (XPC) have shown promise for tissue engineering applications due to their ability to provide image contrast based on multiple X-ray properties. In this manuscript we describe results using XPC to image a model hydrogel and soft tissue structure. Porous fibrin loaded poly(ethylene glycol) hydrogels were synthesized and implanted inmore » a rodent subcutaneous model. Samples were explanted and imaged with an analyzer-based XPC technique and processed and stained for histology for comparison. Both hydrogel and soft tissues structures could be identified in XPC images. Structure in skeletal muscle adjacent could be visualized and invading fibrovascular tissue could be quantified. In quantitative results, there were no differences between XPC and the gold-standard histological measurements. These results provide evidence of the significant potential of techniques based on XPC for 3D imaging of hydrogel structure and local tissue response.« less

Multiview hyperspectral topography of tissue structural and functional characteristics

NASA Astrophysics Data System (ADS)

Liu, Peng; Huang, Jiwei; Zhang, Shiwu; Xu, Ronald X.

2016-01-01

Accurate and in vivo characterization of structural, functional, and molecular characteristics of biological tissue will facilitate quantitative diagnosis, therapeutic guidance, and outcome assessment in many clinical applications, such as wound healing, cancer surgery, and organ transplantation. We introduced and tested a multiview hyperspectral imaging technique for noninvasive topographic imaging of cutaneous wound oxygenation. The technique integrated a multiview module and a hyperspectral module in a single portable unit. Four plane mirrors were cohered to form a multiview reflective mirror set with a rectangular cross section. The mirror set was placed between a hyperspectral camera and the target biological tissue. For a single image acquisition task, a hyperspectral data cube with five views was obtained. The five-view hyperspectral image consisted of a main objective image and four reflective images. Three-dimensional (3-D) topography of the scene was achieved by correlating the matching pixels between the objective image and the reflective images. 3-D mapping of tissue oxygenation was achieved using a hyperspectral oxygenation algorithm. The multiview hyperspectral imaging technique was validated in a wound model, a tissue-simulating blood phantom, and in vivo biological tissue. The experimental results demonstrated the technical feasibility of using multiview hyperspectral imaging for 3-D topography of tissue functional properties.

Cloud GPU-based simulations for SQUAREMR.

PubMed

Kantasis, George; Xanthis, Christos G; Haris, Kostas; Heiberg, Einar; Aletras, Anthony H

2017-01-01

Quantitative Magnetic Resonance Imaging (MRI) is a research tool, used more and more in clinical practice, as it provides objective information with respect to the tissues being imaged. Pixel-wise T 1 quantification (T 1 mapping) of the myocardium is one such application with diagnostic significance. A number of mapping sequences have been developed for myocardial T 1 mapping with a wide range in terms of measurement accuracy and precision. Furthermore, measurement results obtained with these pulse sequences are affected by errors introduced by the particular acquisition parameters used. SQUAREMR is a new method which has the potential of improving the accuracy of these mapping sequences through the use of massively parallel simulations on Graphical Processing Units (GPUs) by taking into account different acquisition parameter sets. This method has been shown to be effective in myocardial T 1 mapping; however, execution times may exceed 30min which is prohibitively long for clinical applications. The purpose of this study was to accelerate the construction of SQUAREMR's multi-parametric database to more clinically acceptable levels. The aim of this study was to develop a cloud-based cluster in order to distribute the computational load to several GPU-enabled nodes and accelerate SQUAREMR. This would accommodate high demands for computational resources without the need for major upfront equipment investment. Moreover, the parameter space explored by the simulations was optimized in order to reduce the computational load without compromising the T 1 estimates compared to a non-optimized parameter space approach. A cloud-based cluster with 16 nodes resulted in a speedup of up to 13.5 times compared to a single-node execution. Finally, the optimized parameter set approach allowed for an execution time of 28s using the 16-node cluster, without compromising the T 1 estimates by more than 10ms. The developed cloud-based cluster and optimization of the parameter set reduced the execution time of the simulations involved in constructing the SQUAREMR multi-parametric database thus bringing SQUAREMR's applicability within time frames that would be likely acceptable in the clinic. Copyright © 2016 Elsevier Inc. All rights reserved.

Multiphoton Microscopy of Prostate and Periprostatic Neural Tissue: A Promising Imaging Technique for Improving Nerve-Sparing Prostatectomy

PubMed Central

Yadav, Rajiv; Mukherjee, Sushmita; Hermen, Michael; Tan, Gerald; Maxfield, Frederick R.; Webb, Watt W.

2009-01-01

Abstract Background and Purpose Various imaging modalities are under investigation for real-time tissue imaging of periprostatic nerves with the idea of improving the results of nerve-sparing radical prostatectomy. We explored multiphoton microscopy (MPM) for real-time tissue imaging of the prostate and periprostatic neural tissue in a male Sprague-Dawley rat model. The unique advantage of this technique is the acquisition of high-resolution images without necessitating any extrinsic labeling agent and with minimal phototoxic effect on tissue. Materials and Methods The prostate and cavernous nerves were surgically excised from male Sprague-Dawley rats. The imaging was carried out using intrinsic fluorescence and scattering properties of the tissues without any exogenous dye or contrast agent. A custom-built MPM, consisting of an Olympus BX61WI upright frame and a modified MRC 1024 scanhead, was used. A femtosecond pulsed titanium/sapphire laser at 780-nm wavelength was used to excite the tissue; laser power under the objective was modulated via a Pockels cell. Second harmonic generation (SHG) signals were collected at 390 (±35 nm), and broadband autofluorescence was collected at 380 to 530 nm. The images obtained from SHG and from tissue fluorescence were then merged and color coded during postprocessing for better appreciation of details. The corresponding tissues were subjected to hematoxylin and eosin staining for histologic confirmation of the structures. Results High-resolution images of the prostate capsule, underlying acini, and individual cells outlining the glands were obtained at varying magnifications. MPM images of adipose tissue and the neural tissues were also obtained. Histologic confirmation and correlation of the prostate gland, fat, cavernous nerve, and major pelvic ganglion validated the findings of MPM. Conclusion Real-time imaging and microscopic resolution of prostate and periprostatic neural tissue using MPM is feasible without the need for any extrinsic labeling agents. Integration of this imaging modality with operative technique has the potential to improve the precision of nerve-sparing prostatectomy. PMID:19425823

Parametric response mapping cut-off values that predict survival of hepatocellular carcinoma patients after TACE.

PubMed

Nörthen, Aventinus; Asendorf, Thomas; Shin, Hoen-Oh; Hinrichs, Jan B; Werncke, Thomas; Vogel, Arndt; Kirstein, Martha M; Wacker, Frank K; Rodt, Thomas

2018-04-21

Parametric response mapping (PRM) is a novel image-analysis technique applicable to assess tumor viability and predict intrahepatic recurrence of hepatocellular carcinoma (HCC) patients treated with transarterial chemoembolization (TACE). However, to date, the prognostic value of PRM for prediction of overall survival in HCC patients undergoing TACE is unclear. The objective of this explorative, single-center study was to identify cut-off values for voxel-specific PRM parameters that predict the post TACE overall survival in HCC patients. PRM was applied to biphasic CT data obtained at baseline and following 3 TACE treatments of 20 patients with HCC tumors ≥ 2 cm. The individual portal venous phases were registered to the arterial phases followed by segmentation of the largest lesion, i.e., the region of interest (ROI). Segmented voxels with their respective arterial and portal venous phase density values were displayed as a scatter plot. Voxel-specific PRM parameters were calculated and compared to patients' survival at 1, 2, and 3 years post treatment to identify the maximal predictive parameters. The hypervascularized tissue portion of the ROI was found to represent an independent predictor of the post TACE overall survival. For this parameter, cut-off values of 3650, 2057, and 2057 voxels, respectively, were determined to be optimal to predict overall survival at 1, 2, and 3 years after TACE. Using these cut points, patients were correctly classified as having died with a sensitivity of 80, 92, and 86% and as still being alive with a specificity of 60, 75, and 83%, respectively. The prognostic accuracy measured by area under the curve (AUC) values ranged from 0.73 to 0.87. PRM may have prognostic value to predict post TACE overall survival in HCC patients.

An extensive photometric catalogue of CALIFA galaxies

NASA Astrophysics Data System (ADS)

Gilhuly, Colleen; Courteau, Stéphane

2018-06-01

We present an extensive compendium of photometrically determined structural properties for all Calar Alto Legacy Integral Field spectroscopy Area (CALIFA) galaxies in the third data release (DR3). We exploit Sloan Digital Sky Survey (SDSS) images in order to extract one-dimensional (1D) gri surface brightness profiles for all CALIFA DR3 galaxies. We also derive a variety of non-parametric quantities and parametric models fitted to 1D i-band profiles. The galaxy images are decomposed using the 2D bulge-disc decomposition programs IMFIT and GALFIT. The relative performance and merit of our 1D and 2D modelling approaches are assessed. Where possible, we compare and augment our photometry with existing measurements from the literature. Close agreement is generally found with the studies of Walcher et al. and Méndez-Abreu et al., though some significant differences exist. Various structural metrics are also highlighted on account of their tight dispersion against an independent variable, such as the circular velocity.

Method to improve optical parametric oscillator beam quality

DOEpatents

Smith, Arlee V.; Alford, William J.; Bowers, Mark S.

2003-11-11

A method to improving optical parametric oscillator (OPO) beam quality having an optical pump, which generates a pump beam at a pump frequency greater than a desired signal frequency, a nonlinear optical medium oriented so that a signal wave at the desired signal frequency and a corresponding idler wave are produced when the pump beam (wave) propagates through the nonlinear optical medium, resulting in beam walk off of the signal and idler waves, and an optical cavity which directs the signal wave to repeatedly pass through the nonlinear optical medium, said optical cavity comprising an equivalently even number of non-planar mirrors that produce image rotation on each pass through the nonlinear optical medium. Utilizing beam walk off where the signal wave and said idler wave have nonparallel Poynting vectors in the nonlinear medium and image rotation, a correlation zone of distance equal to approximately .rho.L.sub.crystal is created which, through multiple passes through the nonlinear medium, improves the beam quality of the OPO output.

Optical parametric osicllators with improved beam quality

DOEpatents

Smith, Arlee V.; Alford, William J.

2003-11-11

An optical parametric oscillator (OPO) having an optical pump, which generates a pump beam at a pump frequency greater than a desired signal frequency, a nonlinear optical medium oriented so that a signal wave at the desired signal frequency and a corresponding idler wave are produced when the pump beam (wave) propagates through the nonlinear optical medium, resulting in beam walk off of the signal and idler waves, and an optical cavity which directs the signal wave to repeatedly pass through the nonlinear optical medium, said optical cavity comprising an equivalently even number of non-planar mirrors that produce image rotation on each pass through the nonlinear optical medium. Utilizing beam walk off where the signal wave and said idler wave have nonparallel Poynting vectors in the nonlinear medium and image rotation, a correlation zone of distance equal to approximately .rho.L.sub.crystal is created which, through multiple passes through the nonlinear medium, improves the beam quality of the OPO output.

The Relationship between MR Parameters and Biomechanical Quantities of Loaded Human Articular Cartilage in Osteoarthritis: An In-Vitro Study

NASA Astrophysics Data System (ADS)

Juráš, V.; Szomolányi, P.; Gäbler, S.; Frollo, I.; Trattnig, S.

2009-01-01

The aim of this study was to assess the changes in MRI parameters during applied load directly in MR scanner and correlate these changes with biomechanical parameters of human articular cartilage. Cartilage explants from patients who underwent total knee replacement were examined in the micro-imaging system in 3T scanner. Respective MRI parameters (T1 without- and T1 with contrast agent as a marker of proteoglycan content, T2 as a marker of collagen network anisotropy and ADC as a measure of diffusivity) were calculated in pre- and during compression state. Subsequently, these parameters were compared to the biomechanical properties of articular cartilage, instantaneous modulus (I), equilibrium modulus (Eq) and time of tissue relaxation (τ). Significant load-induced changes of T2 and ADC were recorded. High correlation between T1Gd and I (r = 0.6324), and between ADC and Eq (r = -0.4884) was found. Multi-parametric MRI may have great potential in analyzing static and dynamic biomechanical behavior of articular cartilage in early stages of osteoarthritis (OA).

Brown adipose and central nervous system glucose uptake is lower during cold exposure in older compared to young men: a preliminary PET study.

PubMed

Kindred, John H; Tuulari, Jetro J; Simon, Stacey; Luckasen, Gary J; Bell, Christopher; Rudroff, Thorsten

2016-06-01

The purpose of this study was to determine the activity of brown adipose tissue (BAT) and the central nervous system (CNS) during cold exposure in young and older men. Two young, 24 and 21 years, and two older, 76 and 74 years, men participated in the study. Positron emission tomography images showed cold-induced BAT activity was absent in older men but clearly present in the clavicular region of the young men (Standardized Uptake Value: SUVmean: 3.12 and 3.71). Statistical parametric mapping revealed cortical brain activity was lower in the older men within areas of the frontal, parietal, temporal, and occipital lobes, and the thalamus (peak-level p uncorr  < 0.036). Cervical spinal cord SUVmean values tended to be lower for older (SUVmean: 1.64 and 1.61) compared to young men (SUVmean: 1.91 and 1.71). These preliminary findings suggest lower BAT activity in older men may in part be due to lower CNS activity.

Thick tissue diffusion model with binding to optimize topical staining in fluorescence breast cancer margin imaging

NASA Astrophysics Data System (ADS)

Xu, Xiaochun; Kang, Soyoung; Navarro-Comes, Eric; Wang, Yu; Liu, Jonathan T. C.; Tichauer, Kenneth M.

2018-03-01

Intraoperative tumor/surgical margin assessment is required to achieve higher tumor resection rate in breast-conserving surgery. Though current histology provides incomparable accuracy in margin assessment, thin tissue sectioning and the limited field of view of microscopy makes histology too time-consuming for intraoperative applications. If thick tissue, wide-field imaging can provide an acceptable assessment of tumor cells at the surface of resected tissues, an intraoperative protocol can be developed to guide the surgery and provide immediate feedback for surgeons. Topical staining of margins with cancer-targeted molecular imaging agents has the potential to provide the sensitivity needed to see microscopic cancer on a wide-field image; however, diffusion and nonspecific retention of imaging agents in thick tissue can significantly diminish tumor contrast with conventional methods. Here, we present a mathematical model to accurately simulate nonspecific retention, binding, and diffusion of imaging agents in thick tissue topical staining to guide and optimize future thick tissue staining and imaging protocol. In order to verify the accuracy and applicability of the model, diffusion profiles of cancer targeted and untargeted (control) nanoparticles at different staining times in A431 tumor xenografts were acquired for model comparison and tuning. The initial findings suggest the existence of nonspecific retention in the tissue, especially at the tissue surface. The simulator can be used to compare the effect of nonspecific retention, receptor binding and diffusion under various conditions (tissue type, imaging agent) and provides optimal staining and imaging protocols for targeted and control imaging agent.

Functional and morphological ultrasonic biomicroscopy for tissue engineers

NASA Astrophysics Data System (ADS)

Mallidi, S.; Aglyamov, S. R.; Karpiouk, A. B.; Park, S.; Emelianov, S. Y.

2006-03-01

Tissue engineering is an interdisciplinary field that combines various aspects of engineering and life sciences and aims to develop biological substitutes to restore, repair or maintain tissue function. Currently, the ability to have quantitative functional assays of engineered tissues is limited to existing invasive methods like biopsy. Hence, an imaging tool for non-invasive and simultaneous evaluation of the anatomical and functional properties of the engineered tissue is needed. In this paper we present an advanced in-vivo imaging technology - ultrasound biomicroscopy combined with complementary photoacoustic and elasticity imaging techniques, capable of accurate visualization of both structural and functional changes in engineered tissues, sequential monitoring of tissue adaptation and/or regeneration, and possible assistance of drug delivery and treatment planning. The combined imaging at microscopic resolution was evaluated on tissue mimicking phantoms imaged with 25 MHz single element focused transducer. The results of our study demonstrate that the ultrasonic, photoacoustic and elasticity images synergistically complement each other in detecting features otherwise imperceptible using the individual techniques. Finally, we illustrate the feasibility of the combined ultrasound, photoacoustic and elasticity imaging techniques in accurately assessing the morphological and functional changes occurring in engineered tissue.

Multispectral fluorescence imaging of human ovarian and Fallopian tube tissue for early stage cancer detection

NASA Astrophysics Data System (ADS)

Tate, Tyler; Baggett, Brenda; Rice, Photini; Watson, Jennifer; Orsinger, Gabe; Nymeyer, Ariel C.; Welge, Weston A.; Keenan, Molly; Saboda, Kathylynn; Roe, Denise J.; Hatch, Kenneth; Chambers, Setsuko; Black, John; Utzinger, Urs; Barton, Jennifer

2015-03-01

With early detection, five year survival rates for ovarian cancer are over 90%, yet no effective early screening method exists. Emerging consensus suggests that perhaps over 50% of the most lethal form of the disease, high grade serous ovarian cancer, originates in the Fallopian tube. Cancer changes molecular concentrations of various endogenous fluorophores. Using specific excitation wavelengths and emissions bands on a Multispectral Fluorescence Imaging (MFI) system, spatial and spectral data over a wide field of view can be collected from endogenous fluorophores. Wavelength specific reflectance images provide additional information to normalize for tissue geometry and blood absorption. Ratiometric combination of the images may create high contrast between neighboring normal and abnormal tissue. Twenty-six women undergoing oophorectomy or debulking surgery consented the use of surgical discard tissue samples for MFI imaging. Forty-nine pieces of ovarian tissue and thirty-two pieces of Fallopian tube tissue were collected and imaged with excitation wavelengths between 280 nm and 550 nm. After imaging, each tissue sample was fixed, sectioned and HE stained for pathological evaluation. Comparison of mean intensity values between normal, benign, and cancerous tissue demonstrate a general trend of increased fluorescence of benign tissue and decreased fluorescence of cancerous tissue when compared to normal tissue. The predictive capabilities of the mean intensity measurements are tested using multinomial logistic regression and quadratic discriminant analysis. Adaption of the system for in vivo Fallopian tube and ovary endoscopic imaging is possible and is briefly described.

Limits on estimating the width of thin tubular structures in 3D images.

PubMed

Wörz, Stefan; Rohr, Karl

2006-01-01

This work studies limits on estimating the width of thin tubular structures in 3D images. Based on nonlinear estimation theory we analyze the minimal stochastic error of estimating the width. Given a 3D analytic model of the image intensities of tubular structures, we derive a closed-form expression for the Cramér-Rao bound of the width estimate under image noise. We use the derived lower bound as a benchmark and compare it with three previously proposed accuracy limits for vessel width estimation. Moreover, by experimental investigations we demonstrate that the derived lower bound can be achieved by fitting a 3D parametric intensity model directly to the image data.

Characterization of human oral tissues based on quantitative analysis of optical coherence tomography images

NASA Astrophysics Data System (ADS)

Salehi, Hassan S.; Kosa, Ali; Mahdian, Mina; Moslehpour, Saeid; Alnajjar, Hisham; Tadinada, Aditya

2017-02-01

In this paper, five types of tissues, human enamel, human cortical bone, human trabecular bone, muscular tissue, and fatty tissue were imaged ex vivo using optical coherence tomography (OCT). The specimens were prepared in blocks of 5 x 5 x 3 mm (width x length x height). The OCT imaging system was a swept source OCT system operating at wavelengths ranging between 1250 nm and 1360 nm with an average power of 18 mW and a scan rate of 50 to 100 kHz. The imaging probe was placed on top of a 2 x 2 cm stabilizing device to maintain a standard distance from the samples. Ten image samples from each type of tissue were obtained. To acquire images with minimum inhomogeneity, imaging was performed multiple times at different points. Based on the observed texture differences between OCT images of soft and hard tissues, spatial and spectral features were quantitatively extracted from the OCT images. The Radon transform from angles of 0 deg to 90 deg was computed, averaged over all the angles, normalized to peak at unity, and then fitted with Gaussian function. The mean absolute values of the spatial frequency components of the OCT image were considered as a feature, where 2-D fast Fourier transform (FFT) was done to OCT images. These OCT features can reliably differentiate between a range of hard and soft tissues, and could be extremely valuable in assisting dentists for in vivo evaluation of oral tissues and early detection of pathologic changes in tissues.

Anomalous Diffusion Measured by a Twice-Refocused Spin Echo Pulse Sequence: Analysis Using Fractional Order Calculus

PubMed Central

2011-01-01

Purpose To theoretically develop and experimentally validate a formulism based on a fractional order calculus (FC) diffusion model to characterize anomalous diffusion in brain tissues measured with a twice-refocused spin-echo (TRSE) pulse sequence. Materials and Methods The FC diffusion model is the fractional order generalization of the Bloch-Torrey equation. Using this model, an analytical expression was derived to describe the diffusion-induced signal attenuation in a TRSE pulse sequence. To experimentally validate this expression, a set of diffusion-weighted (DW) images was acquired at 3 Tesla from healthy human brains using a TRSE sequence with twelve b-values ranging from 0 to 2,600 s/mm2. For comparison, DW images were also acquired using a Stejskal-Tanner diffusion gradient in a single-shot spin-echo echo planar sequence. For both datasets, a Levenberg-Marquardt fitting algorithm was used to extract three parameters: diffusion coefficient D, fractional order derivative in space β, and a spatial parameter μ (in units of μm). Using adjusted R-squared values and standard deviations, D, β and μ values and the goodness-of-fit in three specific regions of interest (ROI) in white matter, gray matter, and cerebrospinal fluid were evaluated for each of the two datasets. In addition, spatially resolved parametric maps were assessed qualitatively. Results The analytical expression for the TRSE sequence, derived from the FC diffusion model, accurately characterized the diffusion-induced signal loss in brain tissues at high b-values. In the selected ROIs, the goodness-of-fit and standard deviations for the TRSE dataset were comparable with the results obtained from the Stejskal-Tanner dataset, demonstrating the robustness of the FC model across multiple data acquisition strategies. Qualitatively, the D, β, and μ maps from the TRSE dataset exhibited fewer artifacts, reflecting the improved immunity to eddy currents. Conclusion The diffusion-induced signal attenuation in a TRSE pulse sequence can be described by an FC diffusion model at high b-values. This model performs equally well for data acquired from the human brain tissues with a TRSE pulse sequence or a conventional Stejskal-Tanner sequence. PMID:21509877

Anomalous diffusion measured by a twice-refocused spin echo pulse sequence: analysis using fractional order calculus.

PubMed

Gao, Qing; Srinivasan, Girish; Magin, Richard L; Zhou, Xiaohong Joe

2011-05-01

To theoretically develop and experimentally validate a formulism based on a fractional order calculus (FC) diffusion model to characterize anomalous diffusion in brain tissues measured with a twice-refocused spin-echo (TRSE) pulse sequence. The FC diffusion model is the fractional order generalization of the Bloch-Torrey equation. Using this model, an analytical expression was derived to describe the diffusion-induced signal attenuation in a TRSE pulse sequence. To experimentally validate this expression, a set of diffusion-weighted (DW) images was acquired at 3 Tesla from healthy human brains using a TRSE sequence with twelve b-values ranging from 0 to 2600 s/mm(2). For comparison, DW images were also acquired using a Stejskal-Tanner diffusion gradient in a single-shot spin-echo echo planar sequence. For both datasets, a Levenberg-Marquardt fitting algorithm was used to extract three parameters: diffusion coefficient D, fractional order derivative in space β, and a spatial parameter μ (in units of μm). Using adjusted R-squared values and standard deviations, D, β, and μ values and the goodness-of-fit in three specific regions of interest (ROIs) in white matter, gray matter, and cerebrospinal fluid, respectively, were evaluated for each of the two datasets. In addition, spatially resolved parametric maps were assessed qualitatively. The analytical expression for the TRSE sequence, derived from the FC diffusion model, accurately characterized the diffusion-induced signal loss in brain tissues at high b-values. In the selected ROIs, the goodness-of-fit and standard deviations for the TRSE dataset were comparable with the results obtained from the Stejskal-Tanner dataset, demonstrating the robustness of the FC model across multiple data acquisition strategies. Qualitatively, the D, β, and μ maps from the TRSE dataset exhibited fewer artifacts, reflecting the improved immunity to eddy currents. The diffusion-induced signal attenuation in a TRSE pulse sequence can be described by an FC diffusion model at high b-values. This model performs equally well for data acquired from the human brain tissues with a TRSE pulse sequence or a conventional Stejskal-Tanner sequence. Copyright © 2011 Wiley-Liss, Inc.

Error analysis of speed of sound reconstruction in ultrasound limited angle transmission tomography.

PubMed

Jintamethasawat, Rungroj; Lee, Won-Mean; Carson, Paul L; Hooi, Fong Ming; Fowlkes, J Brian; Goodsitt, Mitchell M; Sampson, Richard; Wenisch, Thomas F; Wei, Siyuan; Zhou, Jian; Chakrabarti, Chaitali; Kripfgans, Oliver D

2018-04-07

We have investigated limited angle transmission tomography to estimate speed of sound (SOS) distributions for breast cancer detection. That requires both accurate delineations of major tissues, in this case by segmentation of prior B-mode images, and calibration of the relative positions of the opposed transducers. Experimental sensitivity evaluation of the reconstructions with respect to segmentation and calibration errors is difficult with our current system. Therefore, parametric studies of SOS errors in our bent-ray reconstructions were simulated. They included mis-segmentation of an object of interest or a nearby object, and miscalibration of relative transducer positions in 3D. Close correspondence of reconstruction accuracy was verified in the simplest case, a cylindrical object in homogeneous background with induced segmentation and calibration inaccuracies. Simulated mis-segmentation in object size and lateral location produced maximum SOS errors of 6.3% within 10 mm diameter change and 9.1% within 5 mm shift, respectively. Modest errors in assumed transducer separation produced the maximum SOS error from miscalibrations (57.3% within 5 mm shift), still, correction of this type of error can easily be achieved in the clinic. This study should aid in designing adequate transducer mounts and calibration procedures, and in specification of B-mode image quality and segmentation algorithms for limited angle transmission tomography relying on ray tracing algorithms. Copyright © 2018 Elsevier B.V. All rights reserved.

Imaging non-Gaussian output fields produced by Josephson parametric amplifiers: experiments

NASA Astrophysics Data System (ADS)

Toyli, D. M.; Venkatramani, A. V.; Boutin, S.; Eddins, A.; Didier, N.; Clerk, A. A.; Blais, A.; Siddiqi, I.

2015-03-01

In recent years, squeezed microwave states have become the focus of intense research motivated by applications in continuous-variables quantum computation and precision qubit measurement. Despite numerous demonstrations of vacuum squeezing with superconducting parametric amplifiers such as the Josephson parametric amplifier (JPA), most experiments have also suggested that the squeezed output field becomes non-ideal at the large (> 10dB) signal gains required for low-noise qubit measurement. Here we describe a systematic experimental study of JPA squeezing performance in this regime for varying lumped-element device designs and pumping methods. We reconstruct the JPA output fields through homodyne detection of the field moments and quantify the deviations from an ideal squeezed state using maximal entropy techniques. These methods provide a powerful diagnostic tool to understand how effects such as gain compression impact JPA squeezing. Our results highlight the importance of weak device nonlinearity for generating highly squeezed states. This work is supported by ARO and ONR.

A novel SURE-based criterion for parametric PSF estimation.

PubMed

Xue, Feng; Blu, Thierry

2015-02-01

We propose an unbiased estimate of a filtered version of the mean squared error--the blur-SURE (Stein's unbiased risk estimate)--as a novel criterion for estimating an unknown point spread function (PSF) from the degraded image only. The PSF is obtained by minimizing this new objective functional over a family of Wiener processings. Based on this estimated blur kernel, we then perform nonblind deconvolution using our recently developed algorithm. The SURE-based framework is exemplified with a number of parametric PSF, involving a scaling factor that controls the blur size. A typical example of such parametrization is the Gaussian kernel. The experimental results demonstrate that minimizing the blur-SURE yields highly accurate estimates of the PSF parameters, which also result in a restoration quality that is very similar to the one obtained with the exact PSF, when plugged into our recent multi-Wiener SURE-LET deconvolution algorithm. The highly competitive results obtained outline the great potential of developing more powerful blind deconvolution algorithms based on SURE-like estimates.

All-fiber optical parametric oscillator for bio-medical imaging applications

NASA Astrophysics Data System (ADS)

Gottschall, Thomas; Meyer, Tobias; Jauregui, Cesar; Just, Florian; Eidam, Tino; Schmitt, Michael; Popp, Jürgen; Limpert, Jens; Tünnermann, Andreas

2017-02-01

Among other modern imaging techniques, stimulated Raman Scattering (SRS) requires an extremely quiet, widely wavelength tunable laser, which, up to now, is unheard of in fiber laser systems. We present a compact all-fiber laser system, which features an optical parametric oscillator (OPO) based on degenerate four-wave mixing (FWM) in an endlessly single-mode photonic-crystal fiber. We employ an all-fiber frequency and repetition rate tunable laser in order to enable wideband conversion in the linear OPO cavity arrangement, the signal and idler radiation can be tuned between 764 and 960 nm and 1164 and 1552 nm at 9.5 MHz. Thus, all biochemically relevant Raman shifts between 922 and 3322 cm-1 may be addressed in combination with a secondary output, which is tunable between 1024 and 1052 nm. This ultra-low noise output emits synchronized pulses with twice the repetition rate to enable SRS imaging. We measure the relative intensity noise of this output beam at 9.5 MHz to be between -145 and -148 dBc, which is low enough to enable high-speed SRS imaging with a good signal-to-noise ratio. The laser system is computer controlled to access a certain energy differences within one second. Combining FWM based conversion, with all-fiber Yb-based fiber lasers enables the construction of the first automated, turn-key and widely tunable fiber laser. This laser concept could be the missing piece to establish CRS imaging as a reliable guiding tool for clinical diagnostics and surgical guidance.

3D imaging of optically cleared tissue using a simplified CLARITY method and on-chip microscopy

PubMed Central

Zhang, Yibo; Shin, Yoonjung; Sung, Kevin; Yang, Sam; Chen, Harrison; Wang, Hongda; Teng, Da; Rivenson, Yair; Kulkarni, Rajan P.; Ozcan, Aydogan

2017-01-01

High-throughput sectioning and optical imaging of tissue samples using traditional immunohistochemical techniques can be costly and inaccessible in resource-limited areas. We demonstrate three-dimensional (3D) imaging and phenotyping in optically transparent tissue using lens-free holographic on-chip microscopy as a low-cost, simple, and high-throughput alternative to conventional approaches. The tissue sample is passively cleared using a simplified CLARITY method and stained using 3,3′-diaminobenzidine to target cells of interest, enabling bright-field optical imaging and 3D sectioning of thick samples. The lens-free computational microscope uses pixel super-resolution and multi-height phase recovery algorithms to digitally refocus throughout the cleared tissue and obtain a 3D stack of complex-valued images of the sample, containing both phase and amplitude information. We optimized the tissue-clearing and imaging system by finding the optimal illumination wavelength, tissue thickness, sample preparation parameters, and the number of heights of the lens-free image acquisition and implemented a sparsity-based denoising algorithm to maximize the imaging volume and minimize the amount of the acquired data while also preserving the contrast-to-noise ratio of the reconstructed images. As a proof of concept, we achieved 3D imaging of neurons in a 200-μm-thick cleared mouse brain tissue over a wide field of view of 20.5 mm2. The lens-free microscope also achieved more than an order-of-magnitude reduction in raw data compared to a conventional scanning optical microscope imaging the same sample volume. Being low cost, simple, high-throughput, and data-efficient, we believe that this CLARITY-enabled computational tissue imaging technique could find numerous applications in biomedical diagnosis and research in low-resource settings. PMID:28819645

High-intensity-focused-ultrasound (HIFU) induced homeostasis and tissue ablation

NASA Astrophysics Data System (ADS)

Chauhan, Sunita; Michel, M. S.; Alken, Peter; Kohrmann, K. U.; Haecker, Axel

2003-06-01

At high intensity levels, ultrasound energy focused into remote tissue targets in human body has shown to produce thermal necrosis in circumscribed regions with sub-millimeter accuracy. The non-invasive modality known as HIFU has enormous potential for thermal ablation of cancers/tumors of the human body without any adverse effects in the surrounding normal tissue. In this paper, empirical results for parametric assessment and interdependence of several exposure variables are presented for producing thermal necrosis as well as hemostasis. Multiple HIFU transducers in selective spatial configuration have been deployed using a suitably designed experiemntal harness, with and without motorized jig scanning. The pre-planning and on-line procedure for treatment and specified instrumentation is described. Custom designed 25mm aperture HIFU probes resonating at 2 MHz focused at 64 and 80 mm are used. Results have been obtained in ex-vivo animal tissue and in vitro biological phantoms for hemostasis.

Next-Generation Pathology.

PubMed

Caie, Peter D; Harrison, David J

2016-01-01

The field of pathology is rapidly transforming from a semiquantitative and empirical science toward a big data discipline. Large data sets from across multiple omics fields may now be extracted from a patient's tissue sample. Tissue is, however, complex, heterogeneous, and prone to artifact. A reductionist view of tissue and disease progression, which does not take this complexity into account, may lead to single biomarkers failing in clinical trials. The integration of standardized multi-omics big data and the retention of valuable information on spatial heterogeneity are imperative to model complex disease mechanisms. Mathematical modeling through systems pathology approaches is the ideal medium to distill the significant information from these large, multi-parametric, and hierarchical data sets. Systems pathology may also predict the dynamical response of disease progression or response to therapy regimens from a static tissue sample. Next-generation pathology will incorporate big data with systems medicine in order to personalize clinical practice for both prognostic and predictive patient care.

Towards High-Resolution Tissue Imaging Using Nanospray Desorption Electrospray Ionization Mass Spectrometry Coupled to Shear Force Microscopy

NASA Astrophysics Data System (ADS)

Nguyen, Son N.; Sontag, Ryan L.; Carson, James P.; Corley, Richard A.; Ansong, Charles; Laskin, Julia

2018-02-01

Constant mode ambient mass spectrometry imaging (MSI) of tissue sections with high lateral resolution of better than 10 μm was performed by combining shear force microscopy with nanospray desorption electrospray ionization (nano-DESI). Shear force microscopy enabled precise control of the distance between the sample and nano-DESI probe during MSI experiments and provided information on sample topography. Proof-of-concept experiments were performed using lung and brain tissue sections representing spongy and dense tissues, respectively. Topography images obtained using shear force microscopy were comparable to the results obtained using contact profilometry over the same region of the tissue section. Variations in tissue height were found to be dependent on the tissue type and were in the range of 0-5 μm for lung tissue and 0-3 μm for brain tissue sections. Ion images of phospholipids obtained in this study are in good agreement with literature data. Normalization of nano-DESI MSI images to the signal of the internal standard added to the extraction solvent allowed us to construct high-resolution ion images free of matrix effects.

Hyperspectral imaging based on compressive sensing to determine cancer margins in human pancreatic tissue ex vivo

NASA Astrophysics Data System (ADS)

Peller, Joseph; Thompson, Kyle J.; Siddiqui, Imran; Martinie, John; Iannitti, David A.; Trammell, Susan R.

2017-02-01

Pancreatic cancer is the fourth leading cause of cancer death in the US. Currently, surgery is the only treatment that offers a chance of cure, however, accurately identifying tumor margins in real-time is difficult. Research has demonstrated that optical spectroscopy can be used to distinguish between healthy and diseased tissue. The design of a single-pixel imaging system for cancer detection is discussed. The system differentiates between healthy and diseased tissue based on differences in the optical reflectance spectra of these regions. In this study, pancreatic tissue samples from 6 patients undergoing Whipple procedures are imaged with the system (total number of tissue sample imaged was N=11). Regions of healthy and unhealthy tissue are determined based on SAM analysis of these spectral images. Hyperspectral imaging results are then compared to white light imaging and histological analysis. Cancerous regions were clearly visible in the hyperspectral images. Margins determined via spectral imaging were in good agreement with margins identified by histology, indicating that hyperspectral imaging system can differentiate between healthy and diseased tissue. After imaging the system was able to detect cancerous regions with a sensitivity of 74.50±5.89% and a specificity of 75.53±10.81%. Possible applications of this imaging system include determination of tumor margins during surgery/biopsy and assistance with cancer diagnosis and staging.

Tissue Equivalent Phantom Design for Characterization of a Coherent Scatter X-ray Imaging System

NASA Astrophysics Data System (ADS)

Albanese, Kathryn Elizabeth

Scatter in medical imaging is typically cast off as image-related noise that detracts from meaningful diagnosis. It is therefore typically rejected or removed from medical images. However, it has been found that every material, including cancerous tissue, has a unique X-ray coherent scatter signature that can be used to identify the material or tissue. Such scatter-based tissue-identification provides the advantage of locating and identifying particular materials over conventional anatomical imaging through X-ray radiography. A coded aperture X-ray coherent scatter spectral imaging system has been developed in our group to classify different tissue types based on their unique scatter signatures. Previous experiments using our prototype have demonstrated that the depth-resolved coherent scatter spectral imaging system (CACSSI) can discriminate healthy and cancerous tissue present in the path of a non-destructive x-ray beam. A key to the successful optimization of CACSSI as a clinical imaging method is to obtain anatomically accurate phantoms of the human body. This thesis describes the development and fabrication of 3D printed anatomical scatter phantoms of the breast and lung. The purpose of this work is to accurately model different breast geometries using a tissue equivalent phantom, and to classify these tissues in a coherent x-ray scatter imaging system. Tissue-equivalent anatomical phantoms were designed to assess the capability of the CACSSI system to classify different types of breast tissue (adipose, fibroglandular, malignant). These phantoms were 3D printed based on DICOM data obtained from CT scans of prone breasts. The phantoms were tested through comparison of measured scatter signatures with those of adipose and fibroglandular tissue from literature. Tumors in the phantom were modeled using a variety of biological tissue including actual surgically excised benign and malignant tissue specimens. Lung based phantoms have also been printed for future testing. Our imaging system has been able to define the location and composition of the various materials in the phantom. These phantoms were used to characterize the CACSSI system in terms of beam width and imaging technique. The result of this work showed accurate modeling and characterization of the phantoms through comparison of the tissue-equivalent form factors to those from literature. The physical construction of the phantoms, based on actual patient anatomy, was validated using mammography and computed tomography to visually compare the clinical images to those of actual patient anatomy.

Seeing through Musculoskeletal Tissues: Improving In Situ Imaging of Bone and the Lacunar Canalicular System through Optical Clearing

PubMed Central

Berke, Ian M.; Miola, Joseph P.; David, Michael A.; Smith, Melanie K.; Price, Christopher

2016-01-01

In situ, cells of the musculoskeletal system reside within complex and often interconnected 3-D environments. Key to better understanding how 3-D tissue and cellular environments regulate musculoskeletal physiology, homeostasis, and health is the use of robust methodologies for directly visualizing cell-cell and cell-matrix architecture in situ. However, the use of standard optical imaging techniques is often of limited utility in deep imaging of intact musculoskeletal tissues due to the highly scattering nature of biological tissues. Drawing inspiration from recent developments in the deep-tissue imaging field, we describe the application of immersion based optical clearing techniques, which utilize the principle of refractive index (RI) matching between the clearing/mounting media and tissue under observation, to improve the deep, in situ imaging of musculoskeletal tissues. To date, few optical clearing techniques have been applied specifically to musculoskeletal tissues, and a systematic comparison of the clearing ability of optical clearing agents in musculoskeletal tissues has yet to be fully demonstrated. In this study we tested the ability of eight different aqueous and non-aqueous clearing agents, with RIs ranging from 1.45 to 1.56, to optically clear murine knee joints and cortical bone. We demonstrated and quantified the ability of these optical clearing agents to clear musculoskeletal tissues and improve both macro- and micro-scale imaging of musculoskeletal tissue across several imaging modalities (stereomicroscopy, spectroscopy, and one-, and two-photon confocal microscopy) and investigational techniques (dynamic bone labeling and en bloc tissue staining). Based upon these findings we believe that optical clearing, in combination with advanced imaging techniques, has the potential to complement classical musculoskeletal analysis techniques; opening the door for improved in situ investigation and quantification of musculoskeletal tissues. PMID:26930293

Seeing through Musculoskeletal Tissues: Improving In Situ Imaging of Bone and the Lacunar Canalicular System through Optical Clearing.

PubMed

Berke, Ian M; Miola, Joseph P; David, Michael A; Smith, Melanie K; Price, Christopher

2016-01-01

In situ, cells of the musculoskeletal system reside within complex and often interconnected 3-D environments. Key to better understanding how 3-D tissue and cellular environments regulate musculoskeletal physiology, homeostasis, and health is the use of robust methodologies for directly visualizing cell-cell and cell-matrix architecture in situ. However, the use of standard optical imaging techniques is often of limited utility in deep imaging of intact musculoskeletal tissues due to the highly scattering nature of biological tissues. Drawing inspiration from recent developments in the deep-tissue imaging field, we describe the application of immersion based optical clearing techniques, which utilize the principle of refractive index (RI) matching between the clearing/mounting media and tissue under observation, to improve the deep, in situ imaging of musculoskeletal tissues. To date, few optical clearing techniques have been applied specifically to musculoskeletal tissues, and a systematic comparison of the clearing ability of optical clearing agents in musculoskeletal tissues has yet to be fully demonstrated. In this study we tested the ability of eight different aqueous and non-aqueous clearing agents, with RIs ranging from 1.45 to 1.56, to optically clear murine knee joints and cortical bone. We demonstrated and quantified the ability of these optical clearing agents to clear musculoskeletal tissues and improve both macro- and micro-scale imaging of musculoskeletal tissue across several imaging modalities (stereomicroscopy, spectroscopy, and one-, and two-photon confocal microscopy) and investigational techniques (dynamic bone labeling and en bloc tissue staining). Based upon these findings we believe that optical clearing, in combination with advanced imaging techniques, has the potential to complement classical musculoskeletal analysis techniques; opening the door for improved in situ investigation and quantification of musculoskeletal tissues.

Robust high-resolution quantification of time signals encoded by in vivo magnetic resonance spectroscopy

NASA Astrophysics Data System (ADS)

Belkić, Dževad; Belkić, Karen

2018-01-01

This paper on molecular imaging emphasizes improving specificity of magnetic resonance spectroscopy (MRS) for early cancer diagnostics by high-resolution data analysis. Sensitivity of magnetic resonance imaging (MRI) is excellent, but specificity is insufficient. Specificity is improved with MRS by going beyond morphology to assess the biochemical content of tissue. This is contingent upon accurate data quantification of diagnostically relevant biomolecules. Quantification is spectral analysis which reconstructs chemical shifts, amplitudes and relaxation times of metabolites. Chemical shifts inform on electronic shielding of resonating nuclei bound to different molecular compounds. Oscillation amplitudes in time signals retrieve the abundance of MR sensitive nuclei whose number is proportional to metabolite concentrations. Transverse relaxation times, the reciprocal of decay probabilities of resonances, arise from spin-spin coupling and reflect local field inhomogeneities. In MRS single voxels are used. For volumetric coverage, multi-voxels are employed within a hybrid of MRS and MRI called magnetic resonance spectroscopic imaging (MRSI). Common to MRS and MRSI is encoding of time signals and subsequent spectral analysis. Encoded data do not provide direct clinical information. Spectral analysis of time signals can yield the quantitative information, of which metabolite concentrations are the most clinically important. This information is equivocal with standard data analysis through the non-parametric, low-resolution fast Fourier transform and post-processing via fitting. By applying the fast Padé transform (FPT) with high-resolution, noise suppression and exact quantification via quantum mechanical signal processing, advances are made, presented herein, focusing on four areas of critical public health importance: brain, prostate, breast and ovarian cancers.

Periprosthetic Artifact Reduction Using Virtual Monochromatic Imaging Derived From Gemstone Dual-Energy Computed Tomography and Dedicated Software.

PubMed

Reynoso, Exequiel; Capunay, Carlos; Rasumoff, Alejandro; Vallejos, Javier; Carpio, Jimena; Lago, Karen; Carrascosa, Patricia

2016-01-01

The aim of this study was to explore the usefulness of combined virtual monochromatic imaging and metal artifact reduction software (MARS) for the evaluation of musculoskeletal periprosthetic tissue. Measurements were performed in periprosthetic and remote regions in 80 patients using a high-definition scanner. Polychromatic images with and without MARS and virtual monochromatic images were obtained. Periprosthetic polychromatic imaging (PI) showed significant differences compared with remote areas among the 3 tissues explored (P < 0.0001). No significant differences were observed between periprosthetic and remote tissues using monochromatic imaging with MARS (P = 0.053 bone, P = 0.32 soft tissue, and P = 0.13 fat). However, such differences were significant using PI with MARS among bone (P = 0.005) and fat (P = 0.02) tissues. All periprosthetic areas were noninterpretable using PI, compared with 11 (9%) using monochromatic imaging. The combined use of virtual monochromatic imaging and MARS reduced periprosthetic artifacts, achieving attenuation levels comparable to implant-free tissue.

Phase contrast imaging of buccal mucosa tissues-Feasibility study

NASA Astrophysics Data System (ADS)

Fatima, A.; Tripathi, S.; Shripathi, T.; Kulkarni, V. K.; Banda, N. R.; Agrawal, A. K.; Sarkar, P. S.; Kashyap, Y.; Sinha, A.

2015-06-01

Phase Contrast Imaging (PCI) technique has been used to interpret physical parameters obtained from the image taken on the normal buccal mucosa tissue extracted from cheek of a patient. The advantages of this method over the conventional imaging techniques are discussed. PCI technique uses the X-ray phase shift at the edges differentiated by very minute density differences and the edge enhanced high contrast images reveal details of soft tissues. The contrast in the images produced is related to changes in the X-ray refractive index of the tissues resulting in higher clarity compared with conventional absorption based X-ray imaging. The results show that this type of imaging has better ability to visualize microstructures of biological soft tissues with good contrast, which can lead to the diagnosis of lesions at an early stage of the diseases.

Stokes-polarimetry imaging of tissue

NASA Astrophysics Data System (ADS)

Wu, Paul J.

A novel Stokes-polarimetry imaging system and technique was developed to quantify fully the polarization properties of light remitted from tissue. The uniqueness of the system and technique is established in the incident polarization. Here, the diffuse illumination is varied and controlled with the intention to improve the visibility of tissue structures. Since light retains some polarization even after multiple-scattering events, the polarization of remitted light depends upon the interactions within the material. Differentiation between tissue structures is accomplished by two-dimensional mapping of the imaged area using metrics such as the degree of linear polarization, degree of circular polarization, ellipticity, and Stokes parameters. While Stokes-polarimetry imaging can be applied to a variety of tissues and conditions, this thesis focuses on tissue types associated with the disease endometriosis. The current standard in diagnosing endometriosis is visual laparoscopy with tissue biopsy. The documented correlation between laparoscopy inspection and histological confirmation of suspected lesions was at best 67%. Endometrial lesions vary greatly in their appearance and depth of infiltration. Although laparoscopy permits tissue to be assessed by color and texture, to advance beyond the state-of-the-art, a new imaging modality involving polarized light was investigated; in particular, Stokes-polarimetry imaging was used to determine the polarization signature of light that interacted with tissue. Basic science studies were conducted on rat tails embedded within turbid gelatin. The purpose of these experiments was to determine how identification of sub-surface structures could be improved. Experimental results indicate image contrast among various structures such as tendon, soft tissue and intervertebral discs. Stokes-polarimetry imaging experiments were performed on various tissues associated with endometriosis to obtain a baseline characterization for each tissue type. Structures such as birefringent collagen, smooth-muscle fiber-bundles, and nerve bundles were visualized that were otherwise not observable with unpolarized light imaging. Finally, a study of cutaneous scars indicated the feasibility of using Stokes-polarimetry imaging in the detection of atypical tissue. A relationship between incident linear polarization angle and skin anatomy was determined so as to obtain maximum contrast between scar tissue and normal skin.

Various clinical application of phase contrast X-ray

NASA Astrophysics Data System (ADS)

Oh, Chilhwan; Park, Sangyong; Ha, Seunghan; Park, Gyuman; Lee, Gunwoo; Lee, Onseok; Je, Jungho

2008-02-01

In biomedical application study using phase contrast X-ray, both sample thickness or density and absorption difference are very important factors in aspects of contrast enhancement. We present experimental evidence that synchrotron hard X-ray are suitable for radiological imaging of biological samples down to the cellular level. We investigated the potential of refractive index radiology using un-monochromatized synchrotron hard X-rays for the imaging of cell and tissue in various diseases. Material had been adopted various medical field, such as apoE knockout mouse in cardiologic field, specimen from renal and prostatic carcinoma patient in urology, basal cell epithelioma in dermatology, brain tissue from autosy sample of pakinson's disease, artificially induced artilrtis tissue from rabbits and extracted tooth from patients of crack tooth syndrome. Formalin and paraffin fixed tissue blocks were cut in 3 mm thickness for the X-ray radiographic imaging. From adjacent areas, 4 μm thickness sections were also prepared for hematoxylin-eosin staining. Radiographic images of dissected tissues were obtained using the hard X-rays from the 7B2 beamline of the Pohang Light Source (PLS). The technique used for the study was the phase contrast images were compared with the optical microscopic images of corresponding histological slides. Radiographic images of various diseased tissues showed clear histological details of organelles in normal tissues. Most of cancerous lesions were well differentiated from adjacent normal tissues and detailed histological features of each tumor were clearly identified. Also normal microstructures were identifiable by the phase contrast imaging. Tissue in cancer or other disease showed clearly different findings from those of surrounding normal tissue. For the first time we successfully demonstrated that synchrotron hard X-rays can be used for radiological imaging of relatively thick tissue samples with great histological details.

Photon Entanglement Through Brain Tissue

PubMed Central

Shi, Lingyan; Galvez, Enrique J.; Alfano, Robert R.

2016-01-01

Photon entanglement, the cornerstone of quantum correlations, provides a level of coherence that is not present in classical correlations. Harnessing it by study of its passage through organic matter may offer new possibilities for medical diagnosis technique. In this work, we study the preservation of photon entanglement in polarization, created by spontaneous parametric down-conversion, after one entangled photon propagates through multiphoton-scattering brain tissue slices with different thickness. The Tangle-Entropy (TS) plots show the strong preservation of entanglement of photons propagating in brain tissue. By spatially filtering the ballistic scattering of an entangled photon, we find that its polarization entanglement is preserved and non-locally correlated with its twin in the TS plots. The degree of entanglement correlates better with structure and water content than with sample thickness. PMID:27995952

Autofluorescence detection and imaging of bladder cancer realized through a cystoscope

DOEpatents

Demos, Stavros G [Livermore, CA; deVere White, Ralph W [Sacramento, CA

2007-08-14

Near infrared imaging using elastic light scattering and tissue autofluorescence and utilizing interior examination techniques and equipment are explored for medical applications. The approach involves imaging using cross-polarized elastic light scattering and/or tissue autofluorescence in the Near Infra-Red (NIR) coupled with image processing and inter-image operations to differentiate human tissue components.

Entropic Imaging of Cataract Lens: An In Vitro Study

PubMed Central

Shung, K. Kirk; Tsui, Po-Hsiang; Fang, Jui; Ma, Hsiang-Yang; Wu, Shuicai; Lin, Chung-Chih

2014-01-01

Phacoemulsification is a common surgical method for treating advanced cataracts. Determining the optimal phacoemulsification energy depends on the hardness of the lens involved. Previous studies have shown that it is possible to evaluate lens hardness via ultrasound parametric imaging based on statistical models that require data to follow a specific distribution. To make the method more system-adaptive, nonmodel-based imaging approach may be necessary in the visualization of lens hardness. This study investigated the feasibility of applying an information theory derived parameter – Shannon entropy from ultrasound backscatter to quantify lens hardness. To determine the physical significance of entropy, we performed computer simulations to investigate the relationship between the signal-to-noise ratio (SNR) based on the Rayleigh distribution and Shannon entropy. Young's modulus was measured in porcine lenses, in which cataracts had been artificially induced by the immersion in formalin solution in vitro. A 35-MHz ultrasound transducer was used to scan the cataract lenses for entropy imaging. The results showed that the entropy is 4.8 when the backscatter data form a Rayleigh distribution corresponding to an SNR of 1.91. The Young's modulus of the lens increased from approximately 8 to 100 kPa when we increased the immersion time from 40 to 160 min (correlation coefficient r = 0.99). Furthermore, the results indicated that entropy imaging seemed to facilitate visualizing different degrees of lens hardening. The mean entropy value increased from 2.7 to 4.0 as the Young's modulus increased from 8 to 100 kPa (r = 0.85), suggesting that entropy imaging may have greater potential than that of conventional statistical parametric imaging in determining the optimal energy to apply during phacoemulsification. PMID:24760103

The Impact of Sources of Variability on Parametric Response Mapping of Lung CT Scans

PubMed Central

Boes, Jennifer L.; Bule, Maria; Hoff, Benjamin A.; Chamberlain, Ryan; Lynch, David A.; Stojanovska, Jadranka; Martinez, Fernando J.; Han, Meilan K.; Kazerooni, Ella A.; Ross, Brian D.; Galbán, Craig J.

2015-01-01

Parametric response mapping (PRM) of inspiration and expiration computed tomography (CT) images improves the radiological phenotyping of chronic obstructive pulmonary disease (COPD). PRM classifies individual voxels of lung parenchyma as normal, emphysematous, or nonemphysematous air trapping. In this study, bias and noise characteristics of the PRM methodology to CT and clinical procedures were evaluated to determine best practices for this quantitative technique. Twenty patients of varying COPD status with paired volumetric inspiration and expiration CT scans of the lungs were identified from the baseline COPD-Gene cohort. The impact of CT scanner manufacturer and reconstruction kernels were evaluated as potential sources of variability in PRM measurements along with simulations to quantify the impact of inspiration/expiration lung volume levels, misregistration, and image spacing on PRM measurements. Negligible variation in PRM metrics was observed when CT scanner type and reconstruction were consistent and inspiration/expiration lung volume levels were near target volumes. CT scanner Hounsfield unit drift occurred but remained difficult to ameliorate. Increasing levels of image misregistration and CT slice spacing were found to have a minor effect on PRM measurements. PRM-derived values were found to be most sensitive to lung volume levels and mismatched reconstruction kernels. As with other quantitative imaging techniques, reliable PRM measurements are attainable when consistent clinical and CT protocols are implemented. PMID:26568983

Design of tissue engineering scaffolds based on hyperbolic surfaces: structural numerical evaluation.

PubMed

Almeida, Henrique A; Bártolo, Paulo J

2014-08-01

Tissue engineering represents a new field aiming at developing biological substitutes to restore, maintain, or improve tissue functions. In this approach, scaffolds provide a temporary mechanical and vascular support for tissue regeneration while tissue in-growth is being formed. These scaffolds must be biocompatible, biodegradable, with appropriate porosity, pore structure and distribution, and optimal vascularization with both surface and structural compatibility. The challenge is to establish a proper balance between porosity and mechanical performance of scaffolds. This work investigates the use of two different types of triple periodic minimal surfaces, Schwarz and Schoen, in order to design better biomimetic scaffolds with high surface-to-volume ratio, high porosity and good mechanical properties. The mechanical behaviour of these structures is assessed through the finite element method software Abaqus. The effect of two parametric parameters (thickness and surface radius) is also evaluated regarding its porosity and mechanical behaviour. Copyright © 2014 IPEM. Published by Elsevier Ltd. All rights reserved.

Evaluation of two outlier-detection-based methods for detecting tissue-selective genes from microarray data.

PubMed

Kadota, Koji; Konishi, Tomokazu; Shimizu, Kentaro

2007-05-01

Large-scale expression profiling using DNA microarrays enables identification of tissue-selective genes for which expression is considerably higher and/or lower in some tissues than in others. Among numerous possible methods, only two outlier-detection-based methods (an AIC-based method and Sprent's non-parametric method) can treat equally various types of selective patterns, but they produce substantially different results. We investigated the performance of these two methods for different parameter settings and for a reduced number of samples. We focused on their ability to detect selective expression patterns robustly. We applied them to public microarray data collected from 36 normal human tissue samples and analyzed the effects of both changing the parameter settings and reducing the number of samples. The AIC-based method was more robust in both cases. The findings confirm that the use of the AIC-based method in the recently proposed ROKU method for detecting tissue-selective expression patterns is correct and that Sprent's method is not suitable for ROKU.

Real-time three-dimensional optical coherence tomography image-guided core-needle biopsy system.

PubMed

Kuo, Wei-Cheng; Kim, Jongsik; Shemonski, Nathan D; Chaney, Eric J; Spillman, Darold R; Boppart, Stephen A

2012-06-01

Advances in optical imaging modalities, such as optical coherence tomography (OCT), enable us to observe tissue microstructure at high resolution and in real time. Currently, core-needle biopsies are guided by external imaging modalities such as ultrasound imaging and x-ray computed tomography (CT) for breast and lung masses, respectively. These image-guided procedures are frequently limited by spatial resolution when using ultrasound imaging, or by temporal resolution (rapid real-time feedback capabilities) when using x-ray CT. One feasible approach is to perform OCT within small gauge needles to optically image tissue microstructure. However, to date, no system or core-needle device has been developed that incorporates both three-dimensional OCT imaging and tissue biopsy within the same needle for true OCT-guided core-needle biopsy. We have developed and demonstrate an integrated core-needle biopsy system that utilizes catheter-based 3-D OCT for real-time image-guidance for target tissue localization, imaging of tissue immediately prior to physical biopsy, and subsequent OCT imaging of the biopsied specimen for immediate assessment at the point-of-care. OCT images of biopsied ex vivo tumor specimens acquired during core-needle placement are correlated with corresponding histology, and computational visualization of arbitrary planes within the 3-D OCT volumes enables feedback on specimen tissue type and biopsy quality. These results demonstrate the potential for using real-time 3-D OCT for needle biopsy guidance by imaging within the needle and tissue during biopsy procedures.

Time-dependent spatial intensity profiles of near-infrared idler pulses from nanosecond optical parametric oscillators

NASA Astrophysics Data System (ADS)

Olafsen, L. J.; Olafsen, J. S.; Eaves, I. K.

2018-06-01

We report on an experimental investigation of the time-dependent spatial intensity distribution of near-infrared idler pulses from an optical parametric oscillator measured using an infrared (IR) camera, in contrast to beam profiles obtained using traditional knife-edge techniques. Comparisons show the information gained by utilizing the thermal camera provides more detail than the spatially- or time-averaged measurements from a knife-edge profile. Synchronization, averaging, and thresholding techniques are applied to enhance the images acquired. The additional information obtained can improve the process by which semiconductor devices and other IR lasers are characterized for their beam quality and output response and thereby result in IR devices with higher performance.

Quantitative Segmentation of Fluorescence Microscopy Images of Heterogeneous Tissue: Application to the Detection of Residual Disease in Tumor Margins

PubMed Central

Mueller, Jenna L.; Harmany, Zachary T.; Mito, Jeffrey K.; Kennedy, Stephanie A.; Kim, Yongbaek; Dodd, Leslie; Geradts, Joseph; Kirsch, David G.; Willett, Rebecca M.; Brown, J. Quincy; Ramanujam, Nimmi

2013-01-01

Purpose To develop a robust tool for quantitative in situ pathology that allows visualization of heterogeneous tissue morphology and segmentation and quantification of image features. Materials and Methods Tissue excised from a genetically engineered mouse model of sarcoma was imaged using a subcellular resolution microendoscope after topical application of a fluorescent anatomical contrast agent: acriflavine. An algorithm based on sparse component analysis (SCA) and the circle transform (CT) was developed for image segmentation and quantification of distinct tissue types. The accuracy of our approach was quantified through simulations of tumor and muscle images. Specifically, tumor, muscle, and tumor+muscle tissue images were simulated because these tissue types were most commonly observed in sarcoma margins. Simulations were based on tissue characteristics observed in pathology slides. The potential clinical utility of our approach was evaluated by imaging excised margins and the tumor bed in a cohort of mice after surgical resection of sarcoma. Results Simulation experiments revealed that SCA+CT achieved the lowest errors for larger nuclear sizes and for higher contrast ratios (nuclei intensity/background intensity). For imaging of tumor margins, SCA+CT effectively isolated nuclei from tumor, muscle, adipose, and tumor+muscle tissue types. Differences in density were correctly identified with SCA+CT in a cohort of ex vivo and in vivo images, thus illustrating the diagnostic potential of our approach. Conclusion The combination of a subcellular-resolution microendoscope, acriflavine staining, and SCA+CT can be used to accurately isolate nuclei and quantify their density in anatomical images of heterogeneous tissue. PMID:23824589

Quantitative Segmentation of Fluorescence Microscopy Images of Heterogeneous Tissue: Application to the Detection of Residual Disease in Tumor Margins.

PubMed

Mueller, Jenna L; Harmany, Zachary T; Mito, Jeffrey K; Kennedy, Stephanie A; Kim, Yongbaek; Dodd, Leslie; Geradts, Joseph; Kirsch, David G; Willett, Rebecca M; Brown, J Quincy; Ramanujam, Nimmi

2013-01-01

To develop a robust tool for quantitative in situ pathology that allows visualization of heterogeneous tissue morphology and segmentation and quantification of image features. TISSUE EXCISED FROM A GENETICALLY ENGINEERED MOUSE MODEL OF SARCOMA WAS IMAGED USING A SUBCELLULAR RESOLUTION MICROENDOSCOPE AFTER TOPICAL APPLICATION OF A FLUORESCENT ANATOMICAL CONTRAST AGENT: acriflavine. An algorithm based on sparse component analysis (SCA) and the circle transform (CT) was developed for image segmentation and quantification of distinct tissue types. The accuracy of our approach was quantified through simulations of tumor and muscle images. Specifically, tumor, muscle, and tumor+muscle tissue images were simulated because these tissue types were most commonly observed in sarcoma margins. Simulations were based on tissue characteristics observed in pathology slides. The potential clinical utility of our approach was evaluated by imaging excised margins and the tumor bed in a cohort of mice after surgical resection of sarcoma. Simulation experiments revealed that SCA+CT achieved the lowest errors for larger nuclear sizes and for higher contrast ratios (nuclei intensity/background intensity). For imaging of tumor margins, SCA+CT effectively isolated nuclei from tumor, muscle, adipose, and tumor+muscle tissue types. Differences in density were correctly identified with SCA+CT in a cohort of ex vivo and in vivo images, thus illustrating the diagnostic potential of our approach. The combination of a subcellular-resolution microendoscope, acriflavine staining, and SCA+CT can be used to accurately isolate nuclei and quantify their density in anatomical images of heterogeneous tissue.

Functional imaging of small tissue volumes with diffuse optical tomography

NASA Astrophysics Data System (ADS)

Klose, Alexander D.; Hielscher, Andreas H.

2006-03-01

Imaging of dynamic changes in blood parameters, functional brain imaging, and tumor imaging are the most advanced application areas of diffuse optical tomography (DOT). When dealing with the image reconstruction problem one is faced with the fact that near-infrared photons, unlike X-rays, are highly scattered when they traverse biological tissue. Image reconstruction schemes are required that model the light propagation inside biological tissue and predict measurements on the tissue surface. By iteratively changing the tissue-parameters until the predictions agree with the real measurements, a spatial distribution of optical properties inside the tissue is found. The optical properties can be related to the tissue oxygenation, inflammation, or to the fluorophore concentration of a biochemical marker. If the model of light propagation is inaccurate, the reconstruction process will lead to an inaccurate result as well. Here, we focus on difficulties that are encountered when DOT is employed for functional imaging of small tissue volumes, for example, in cancer studies involving small animals, or human finger joints for early diagnosis of rheumatoid arthritis. Most of the currently employed image reconstruction methods rely on the diffusion theory that is an approximation to the equation of radiative transfer. But, in the cases of small tissue volumes and tissues that contain low scattering regions diffusion theory has been shown to be of limited applicability Therefore, we employ a light propagation model that is based on the equation of radiative transfer, which promises to overcome the limitations.

A Method to Prevent Protein Delocalization in Imaging Mass Spectrometry of Non-Adherent Tissues: Application to Small Vertebrate Lens Imaging

PubMed Central

Anderson, David M. G.; Floyd, Kyle A.; Barnes, Stephen; Clark, Judy M.; Clark, John I.; Mchaourab, Hassane; Schey, Kevin L.

2015-01-01

MALDI imaging requires careful sample preparation to obtain reliable, high quality images of small molecules, peptides, lipids, and proteins across tissue sections. Poor crystal formation, delocalization of analytes, and inadequate tissue adherence can affect the quality, reliability, and spatial resolution of MALDI images. We report a comparison of tissue mounting and washing methods that resulted in an optimized method using conductive carbon substrates that avoids thaw mounting or washing steps, minimizes protein delocalization, and prevents tissue detachment from the target surface. Application of this method to image ocular lens proteins of small vertebrate eyes demonstrates the improved methodology for imaging abundant crystallin protein products. This method was demonstrated for tissue sections from rat, mouse, and zebrafish lenses resulting in good quality MALDI images with little to no delocalization. The images indicate, for the first time in mouse and zebrafish, discrete localization of crystallin protein degradation products resulting in concentric rings of distinct protein contents that may be responsible for the refractive index gradient of vertebrate lenses. PMID:25665708

A high-resolution optical imaging system for obtaining the serial transverse section images of biologic tissue

NASA Astrophysics Data System (ADS)

Wu, Li; Zhang, Bin; Wu, Ping; Liu, Qian; Gong, Hui

2007-05-01

A high-resolution optical imaging system was designed and developed to obtain the serial transverse section images of the biologic tissue, such as the mouse brain, in which new knife-edge imaging technology, high-speed and high-sensitive line-scan CCD and linear air bearing stages were adopted and incorporated with an OLYMPUS microscope. The section images on the tip of the knife-edge were synchronously captured by the reflection imaging in the microscope while cutting the biologic tissue. The biologic tissue can be sectioned at interval of 250 nm with the same resolution of the transverse section images obtained in x and y plane. And the cutting job can be automatically finished based on the control program wrote specially in advance, so we save the mass labor of the registration of the vast images data. In addition, by using this system a larger sample can be cut than conventional ultramicrotome so as to avoid the loss of the tissue structure information because of splitting the tissue sample to meet the size request of the ultramicrotome.

An interventional multispectral photoacoustic imaging platform for the guidance of minimally invasive procedures

NASA Astrophysics Data System (ADS)

Xia, Wenfeng; Nikitichev, Daniil I.; Mari, Jean Martial; West, Simeon J.; Ourselin, Sebastien; Beard, Paul C.; Desjardins, Adrien E.

2015-07-01

Precise and efficient guidance of medical devices is of paramount importance for many minimally invasive procedures. These procedures include fetal interventions, tumor biopsies and treatments, central venous catheterisations and peripheral nerve blocks. Ultrasound imaging is commonly used for guidance, but it often provides insufficient contrast with which to identify soft tissue structures such as vessels, tumors, and nerves. In this study, a hybrid interventional imaging system that combines ultrasound imaging and multispectral photoacoustic imaging for guiding minimally invasive procedures was developed and characterized. The system provides both structural information from ultrasound imaging and molecular information from multispectral photoacoustic imaging. It uses a commercial linear-array ultrasound imaging probe as the ultrasound receiver, with a multimode optical fiber embedded in a needle to deliver pulsed excitation light to tissue. Co-registration of ultrasound and photoacoustic images is achieved with the use of the same ultrasound receiver for both modalities. Using tissue ex vivo, the system successfully discriminated deep-located fat tissue from the surrounding muscle tissue. The measured photoacoustic spectrum of the fat tissue had good agreement with the lipid spectrum in literature.

Mesoscopic Fluorescence Molecular Tomography for Evaluating Engineered Tissues.

PubMed

Ozturk, Mehmet S; Chen, Chao-Wei; Ji, Robin; Zhao, Lingling; Nguyen, Bao-Ngoc B; Fisher, John P; Chen, Yu; Intes, Xavier

2016-03-01

Optimization of regenerative medicine strategies includes the design of biomaterials, development of cell-seeding methods, and control of cell-biomaterial interactions within the engineered tissues. Among these steps, one paramount challenge is to non-destructively image the engineered tissues in their entirety to assess structure, function, and molecular expression. It is especially important to be able to enable cell phenotyping and monitor the distribution and migration of cells throughout the bulk scaffold. Advanced fluorescence microscopic techniques are commonly employed to perform such tasks; however, they are limited to superficial examination of tissue constructs. Therefore, the field of tissue engineering and regenerative medicine would greatly benefit from the development of molecular imaging techniques which are capable of non-destructive imaging of three-dimensional cellular distribution and maturation within a tissue-engineered scaffold beyond the limited depth of current microscopic techniques. In this review, we focus on an emerging depth-resolved optical mesoscopic imaging technique, termed laminar optical tomography (LOT) or mesoscopic fluorescence molecular tomography (MFMT), which enables longitudinal imaging of cellular distribution in thick tissue engineering constructs at depths of a few millimeters and with relatively high resolution. The physical principle, image formation, and instrumentation of LOT/MFMT systems are introduced. Representative applications in tissue engineering include imaging the distribution of human mesenchymal stem cells embedded in hydrogels, imaging of bio-printed tissues, and in vivo applications.

Cupping artifact correction and automated classification for high-resolution dedicated breast CT images.

PubMed

Yang, Xiaofeng; Wu, Shengyong; Sechopoulos, Ioannis; Fei, Baowei

2012-10-01

To develop and test an automated algorithm to classify the different tissues present in dedicated breast CT images. The original CT images are first corrected to overcome cupping artifacts, and then a multiscale bilateral filter is used to reduce noise while keeping edge information on the images. As skin and glandular tissues have similar CT values on breast CT images, morphologic processing is used to identify the skin mask based on its position information. A modified fuzzy C-means (FCM) classification method is then used to classify breast tissue as fat and glandular tissue. By combining the results of the skin mask with the FCM, the breast tissue is classified as skin, fat, and glandular tissue. To evaluate the authors' classification method, the authors use Dice overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on eight patient images. The correction method was able to correct the cupping artifacts and improve the quality of the breast CT images. For glandular tissue, the overlap ratios between the authors' automatic classification and manual segmentation were 91.6% ± 2.0%. A cupping artifact correction method and an automatic classification method were applied and evaluated for high-resolution dedicated breast CT images. Breast tissue classification can provide quantitative measurements regarding breast composition, density, and tissue distribution.

Cupping artifact correction and automated classification for high-resolution dedicated breast CT images

PubMed Central

Yang, Xiaofeng; Wu, Shengyong; Sechopoulos, Ioannis; Fei, Baowei

2012-01-01

Purpose: To develop and test an automated algorithm to classify the different tissues present in dedicated breast CT images. Methods: The original CT images are first corrected to overcome cupping artifacts, and then a multiscale bilateral filter is used to reduce noise while keeping edge information on the images. As skin and glandular tissues have similar CT values on breast CT images, morphologic processing is used to identify the skin mask based on its position information. A modified fuzzy C-means (FCM) classification method is then used to classify breast tissue as fat and glandular tissue. By combining the results of the skin mask with the FCM, the breast tissue is classified as skin, fat, and glandular tissue. To evaluate the authors’ classification method, the authors use Dice overlap ratios to compare the results of the automated classification to those obtained by manual segmentation on eight patient images. Results: The correction method was able to correct the cupping artifacts and improve the quality of the breast CT images. For glandular tissue, the overlap ratios between the authors’ automatic classification and manual segmentation were 91.6% ± 2.0%. Conclusions: A cupping artifact correction method and an automatic classification method were applied and evaluated for high-resolution dedicated breast CT images. Breast tissue classification can provide quantitative measurements regarding breast composition, density, and tissue distribution. PMID:23039675

A parametric ribcage geometry model accounting for variations among the adult population.

PubMed

Wang, Yulong; Cao, Libo; Bai, Zhonghao; Reed, Matthew P; Rupp, Jonathan D; Hoff, Carrie N; Hu, Jingwen

2016-09-06

The objective of this study is to develop a parametric ribcage model that can account for morphological variations among the adult population. Ribcage geometries, including 12 pair of ribs, sternum, and thoracic spine, were collected from CT scans of 101 adult subjects through image segmentation, landmark identification (1016 for each subject), symmetry adjustment, and template mesh mapping (26,180 elements for each subject). Generalized procrustes analysis (GPA), principal component analysis (PCA), and regression analysis were used to develop a parametric ribcage model, which can predict nodal locations of the template mesh according to age, sex, height, and body mass index (BMI). Two regression models, a quadratic model for estimating the ribcage size and a linear model for estimating the ribcage shape, were developed. The results showed that the ribcage size was dominated by the height (p=0.000) and age-sex-interaction (p=0.007) and the ribcage shape was significantly affected by the age (p=0.0005), sex (p=0.0002), height (p=0.0064) and BMI (p=0.0000). Along with proper assignment of cortical bone thickness, material properties and failure properties, this parametric ribcage model can directly serve as the mesh of finite element ribcage models for quantifying effects of human characteristics on thoracic injury risks. Copyright © 2016 Elsevier Ltd. All rights reserved.

Tissue oxygen monitoring by photoacoustic lifetime imaging (PALI) and its application to image-guided photodynamic therapy (PDT)

NASA Astrophysics Data System (ADS)

Shao, Qi; Morgounova, Ekaterina; Ashkenazi, Shai

2015-03-01

The oxygen partial pressure (pO2), which results from the balance between oxygen delivery and its consumption, is a key component of the physiological state of a tissue. Images of oxygen distribution can provide essential information for identifying hypoxic tissue and optimizing cancer treatment. Previously, we have reported a noninvasive in vivo imaging modality based on photoacoustic lifetime. The technique maps the excited triplet state of oxygen-sensitive dye, thus reflects the spatial and temporal distribution of tissue oxygen. We have applied PALI on tumor on small animals to identify hypoxia area. We also showed that PALI is able monitor changes of tissue oxygen, in an acute ischemia and breathing modulation model. Here we present our work on developing a treatment/imaging modality (PDT-PALI) that integrates PDT and a combined ultrasound/photoacoustic imaging system. The system provides real-time feedback of three essential parameters namely: tissue oxygen, light penetration in tumor location, and distribution of photosensitizer. Tissue oxygen imaging is performed by applying PALI, which relies on photoacoustic probing of oxygen-dependent, excitation lifetime of Methylene Blue (MB) photosensitizer. Lifetime information can also be used to generate image showing the distribution of photosensitizer. The level and penetration depth of PDT illumination can be deduced from photoacoustic imaging at the same wavelength. All images will be combined with ultrasound B-mode images for anatomical reference.

Hyperspectral Imaging and K-Means Classification for Histologic Evaluation of Ductal Carcinoma In Situ.

PubMed

Khouj, Yasser; Dawson, Jeremy; Coad, James; Vona-Davis, Linda

2018-01-01

Hyperspectral imaging (HSI) is a non-invasive optical imaging modality that shows the potential to aid pathologists in breast cancer diagnoses cases. In this study, breast cancer tissues from different patients were imaged by a hyperspectral system to detect spectral differences between normal and breast cancer tissues. Tissue samples mounted on slides were identified from 10 different patients. Samples from each patient included both normal and ductal carcinoma tissue, both stained with hematoxylin and eosin stain and unstained. Slides were imaged using a snapshot HSI system, and the spectral reflectance differences were evaluated. Analysis of the spectral reflectance values indicated that wavelengths near 550 nm showed the best differentiation between tissue types. This information was used to train image processing algorithms using supervised and unsupervised data. The K-means method was applied to the hyperspectral data cubes, and successfully detected spectral tissue differences with sensitivity of 85.45%, and specificity of 94.64% with true negative rate of 95.8%, and false positive rate of 4.2%. These results were verified by ground-truth marking of the tissue samples by a pathologist. In the hyperspectral image analysis, the image processing algorithm, K-means, shows the greatest potential for building a semi-automated system that could identify and sort between normal and ductal carcinoma in situ tissues.

Variance based joint sparsity reconstruction of synthetic aperture radar data for speckle reduction

NASA Astrophysics Data System (ADS)

Scarnati, Theresa; Gelb, Anne

2018-04-01

In observing multiple synthetic aperture radar (SAR) images of the same scene, it is apparent that the brightness distributions of the images are not smooth, but rather composed of complicated granular patterns of bright and dark spots. Further, these brightness distributions vary from image to image. This salt and pepper like feature of SAR images, called speckle, reduces the contrast in the images and negatively affects texture based image analysis. This investigation uses the variance based joint sparsity reconstruction method for forming SAR images from the multiple SAR images. In addition to reducing speckle, the method has the advantage of being non-parametric, and can therefore be used in a variety of autonomous applications. Numerical examples include reconstructions of both simulated phase history data that result in speckled images as well as the images from the MSTAR T-72 database.

Magnetoacoustic tomography with magnetic induction for high-resolution bioimepedance imaging through vector source reconstruction under the static field of MRI magnet.

PubMed

Mariappan, Leo; Hu, Gang; He, Bin

2014-02-01

Magnetoacoustic tomography with magnetic induction (MAT-MI) is an imaging modality to reconstruct the electrical conductivity of biological tissue based on the acoustic measurements of Lorentz force induced tissue vibration. This study presents the feasibility of the authors' new MAT-MI system and vector source imaging algorithm to perform a complete reconstruction of the conductivity distribution of real biological tissues with ultrasound spatial resolution. In the present study, using ultrasound beamformation, imaging point spread functions are designed to reconstruct the induced vector source in the object which is used to estimate the object conductivity distribution. Both numerical studies and phantom experiments are performed to demonstrate the merits of the proposed method. Also, through the numerical simulations, the full width half maximum of the imaging point spread function is calculated to estimate of the spatial resolution. The tissue phantom experiments are performed with a MAT-MI imaging system in the static field of a 9.4 T magnetic resonance imaging magnet. The image reconstruction through vector beamformation in the numerical and experimental studies gives a reliable estimate of the conductivity distribution in the object with a ∼ 1.5 mm spatial resolution corresponding to the imaging system frequency of 500 kHz ultrasound. In addition, the experiment results suggest that MAT-MI under high static magnetic field environment is able to reconstruct images of tissue-mimicking gel phantoms and real tissue samples with reliable conductivity contrast. The results demonstrate that MAT-MI is able to image the electrical conductivity properties of biological tissues with better than 2 mm spatial resolution at 500 kHz, and the imaging with MAT-MI under a high static magnetic field environment is able to provide improved imaging contrast for biological tissue conductivity reconstruction.

Design and implementation of coded aperture coherent scatter spectral imaging of cancerous and healthy breast tissue samples

PubMed Central

Lakshmanan, Manu N.; Greenberg, Joel A.; Samei, Ehsan; Kapadia, Anuj J.

2016-01-01

Abstract. A scatter imaging technique for the differentiation of cancerous and healthy breast tissue in a heterogeneous sample is introduced in this work. Such a technique has potential utility in intraoperative margin assessment during lumpectomy procedures. In this work, we investigate the feasibility of the imaging method for tumor classification using Monte Carlo simulations and physical experiments. The coded aperture coherent scatter spectral imaging technique was used to reconstruct three-dimensional (3-D) images of breast tissue samples acquired through a single-position snapshot acquisition, without rotation as is required in coherent scatter computed tomography. We perform a quantitative assessment of the accuracy of the cancerous voxel classification using Monte Carlo simulations of the imaging system; describe our experimental implementation of coded aperture scatter imaging; show the reconstructed images of the breast tissue samples; and present segmentations of the 3-D images in order to identify the cancerous and healthy tissue in the samples. From the Monte Carlo simulations, we find that coded aperture scatter imaging is able to reconstruct images of the samples and identify the distribution of cancerous and healthy tissues (i.e., fibroglandular, adipose, or a mix of the two) inside them with a cancerous voxel identification sensitivity, specificity, and accuracy of 92.4%, 91.9%, and 92.0%, respectively. From the experimental results, we find that the technique is able to identify cancerous and healthy tissue samples and reconstruct differential coherent scatter cross sections that are highly correlated with those measured by other groups using x-ray diffraction. Coded aperture scatter imaging has the potential to provide scatter images that automatically differentiate cancerous and healthy tissue inside samples within a time on the order of a minute per slice. PMID:26962543

Design and implementation of coded aperture coherent scatter spectral imaging of cancerous and healthy breast tissue samples.

PubMed

Lakshmanan, Manu N; Greenberg, Joel A; Samei, Ehsan; Kapadia, Anuj J

2016-01-01

A scatter imaging technique for the differentiation of cancerous and healthy breast tissue in a heterogeneous sample is introduced in this work. Such a technique has potential utility in intraoperative margin assessment during lumpectomy procedures. In this work, we investigate the feasibility of the imaging method for tumor classification using Monte Carlo simulations and physical experiments. The coded aperture coherent scatter spectral imaging technique was used to reconstruct three-dimensional (3-D) images of breast tissue samples acquired through a single-position snapshot acquisition, without rotation as is required in coherent scatter computed tomography. We perform a quantitative assessment of the accuracy of the cancerous voxel classification using Monte Carlo simulations of the imaging system; describe our experimental implementation of coded aperture scatter imaging; show the reconstructed images of the breast tissue samples; and present segmentations of the 3-D images in order to identify the cancerous and healthy tissue in the samples. From the Monte Carlo simulations, we find that coded aperture scatter imaging is able to reconstruct images of the samples and identify the distribution of cancerous and healthy tissues (i.e., fibroglandular, adipose, or a mix of the two) inside them with a cancerous voxel identification sensitivity, specificity, and accuracy of 92.4%, 91.9%, and 92.0%, respectively. From the experimental results, we find that the technique is able to identify cancerous and healthy tissue samples and reconstruct differential coherent scatter cross sections that are highly correlated with those measured by other groups using x-ray diffraction. Coded aperture scatter imaging has the potential to provide scatter images that automatically differentiate cancerous and healthy tissue inside samples within a time on the order of a minute per slice.

Simultaneous multiplane imaging of human ovarian cancer by volume holographic imaging

PubMed Central

Orsinger, Gabriel V.; Watson, Jennifer M.; Gordon, Michael; Nymeyer, Ariel C.; de Leon, Erich E.; Brownlee, Johnathan W.; Hatch, Kenneth D.; Chambers, Setsuko K.; Barton, Jennifer K.; Kostuk, Raymond K.; Romanowski, Marek

2014-01-01

Abstract. Ovarian cancer is the most deadly gynecologic cancer, a fact which is attributable to poor early detection and survival once the disease has reached advanced stages. Intraoperative laparoscopic volume holographic imaging has the potential to provide simultaneous visualization of surface and subsurface structures in ovarian tissues for improved assessment of developing ovarian cancer. In this ex vivo ovarian tissue study, we assembled a benchtop volume holographic imaging system (VHIS) to characterize the microarchitecture of 78 normal and 40 abnormal tissue specimens derived from ovarian, fallopian tube, uterine, and peritoneal tissues, collected from 26 patients aged 22 to 73 undergoing bilateral salpingo-oophorectomy, hysterectomy with bilateral salpingo-oophorectomy, or abdominal cytoreductive surgery. All tissues were successfully imaged with the VHIS in both reflectance- and fluorescence-modes revealing morphological features which can be used to distinguish between normal, benign abnormalities, and cancerous tissues. We present the development and successful application of VHIS for imaging human ovarian tissue. Comparison of VHIS images with corresponding histopathology allowed for qualitatively distinguishing microstructural features unique to the studied tissue type and disease state. These results motivate the development of a laparoscopic VHIS for evaluating the surface and subsurface morphological alterations in ovarian cancer pathogenesis. PMID:24676382

MO-F-CAMPUS-I-04: Magnetic Resonance Imaging of An in Vitro 3D Tumor Model

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

Veiga, C; Long, T; Siow, B

Purpose: To investigate the use of an in vitro 3D tumor model (tumoroid) as a bio-phantom for repetitive and sequential magnetic resonance imaging (MRI) studies. Methods: The tissue engineered tumoroid comprised an artificial cancer mass (ACM) containing 30 million HT29 cancer cells seeded in a collagen type I matrix, whose density was increased by plastic compression (dry/wet weight=40%). The ACM was embedded in an uncompressed collagen gel that mimicked the tumor stroma, and the tumoroid was incubated for 24h before imaging. Images were acquired using the 1T ICON™ (Bruker Corporation, Billerica, MA) MRI scanner. T1 maps were calculated using anmore » IR-RARE sequence (TE=12ms, TR=10000ms, 7 inversion times), while for T2 maps a MSME technique (TR=6000ms, 16 echoes) was used. T1 and T2 fittings were performed using a pixel-wise approach to produce relaxometric parametric maps. Results: The images acquired and corresponding T1 and T2 maps indicate contrast between the ACM and the stroma. T1 was 2500 and 2800ms, while T2 was 520 and 760ms, for the ACM and stroma respectively. The ACM construct was not homogenous and internal features were visible, which can be explained by local gradients of cell and/or collagen density. The viability of the cells was confirmed via confocal microscopy for several days after the imaging session, demonstrating the suitability of the tumoroid for sequential imaging studies. Conclusions: We have engineered a tumor model compatible with repetitive and sequential MRI. We found T1 and T2 contrast between the ACM and stroma using a pre-clinical MRI scanner. The model, which enables controllable cell and matrix densities, has potential for a wide range of applications in radiotherapy, such as to study tumor progression and to validate imaging biomarkers. Further work is necessary to understand the mechanisms behind the contrast achieved, and to correlate findings with biology and histology data.« less

Breast tissue classification in digital tomosynthesis images based on global gradient minimization and texture features

NASA Astrophysics Data System (ADS)

Qin, Xulei; Lu, Guolan; Sechopoulos, Ioannis; Fei, Baowei

2014-03-01

Digital breast tomosynthesis (DBT) is a pseudo-three-dimensional x-ray imaging modality proposed to decrease the effect of tissue superposition present in mammography, potentially resulting in an increase in clinical performance for the detection and diagnosis of breast cancer. Tissue classification in DBT images can be useful in risk assessment, computer-aided detection and radiation dosimetry, among other aspects. However, classifying breast tissue in DBT is a challenging problem because DBT images include complicated structures, image noise, and out-of-plane artifacts due to limited angular tomographic sampling. In this project, we propose an automatic method to classify fatty and glandular tissue in DBT images. First, the DBT images are pre-processed to enhance the tissue structures and to decrease image noise and artifacts. Second, a global smooth filter based on L0 gradient minimization is applied to eliminate detailed structures and enhance large-scale ones. Third, the similar structure regions are extracted and labeled by fuzzy C-means (FCM) classification. At the same time, the texture features are also calculated. Finally, each region is classified into different tissue types based on both intensity and texture features. The proposed method is validated using five patient DBT images using manual segmentation as the gold standard. The Dice scores and the confusion matrix are utilized to evaluate the classified results. The evaluation results demonstrated the feasibility of the proposed method for classifying breast glandular and fat tissue on DBT images.

A Dual-Modality System for Both Multi-Color Ultrasound-Switchable Fluorescence and Ultrasound Imaging

PubMed Central

Kandukuri, Jayanth; Yu, Shuai; Cheng, Bingbing; Bandi, Venugopal; D’Souza, Francis; Nguyen, Kytai T.; Hong, Yi; Yuan, Baohong

2017-01-01

Simultaneous imaging of multiple targets (SIMT) in opaque biological tissues is an important goal for molecular imaging in the future. Multi-color fluorescence imaging in deep tissues is a promising technology to reach this goal. In this work, we developed a dual-modality imaging system by combining our recently developed ultrasound-switchable fluorescence (USF) imaging technology with the conventional ultrasound (US) B-mode imaging. This dual-modality system can simultaneously image tissue acoustic structure information and multi-color fluorophores in centimeter-deep tissue with comparable spatial resolutions. To conduct USF imaging on the same plane (i.e., x-z plane) as US imaging, we adopted two 90°-crossed ultrasound transducers with an overlapped focal region, while the US transducer (the third one) was positioned at the center of these two USF transducers. Thus, the axial resolution of USF is close to the lateral resolution, which allows a point-by-point USF scanning on the same plane as the US imaging. Both multi-color USF and ultrasound imaging of a tissue phantom were demonstrated. PMID:28165390

Magneto-photo-acoustic imaging

PubMed Central

Qu, Min; Mallidi, Srivalleesha; Mehrmohammadi, Mohammad; Truby, Ryan; Homan, Kimberly; Joshi, Pratixa; Chen, Yun-Sheng; Sokolov, Konstantin; Emelianov, Stanislav

2011-01-01

Magneto-photo-acoustic imaging, a technique based on the synergy of magneto-motive ultrasound, photoacoustic and ultrasound imaging, is introduced. Hybrid nanoconstructs, liposomes encapsulating gold nanorods and iron oxide nanoparticles, were used as a dual-contrast agent for magneto-photo-acoustic imaging. Tissue-mimicking phantom and macrophage cells embedded in ex vivo porcine tissue were used to demonstrate that magneto-photo-acoustic imaging is capable of visualizing the location of cells or tissues labeled with dual-contrast nanoparticles with sufficient contrast, excellent contrast resolution and high spatial resolution in the context of the anatomical structure of the surrounding tissues. Therefore, magneto-photo-acoustic imaging is capable of identifying the nanoparticle-labeled pathological regions from the normal tissue, providing a promising platform to noninvasively diagnose and characterize pathologies. PMID:21339883

Regional metabolic liver function measured in patients with cirrhosis by 2-[¹⁸F]fluoro-2-deoxy-D-galactose PET/CT.

PubMed

Sørensen, Michael; Mikkelsen, Kasper S; Frisch, Kim; Villadsen, Gerda E; Keiding, Susanne

2013-06-01

There is a clinical need for methods that can quantify regional hepatic function non-invasively in patients with cirrhosis. Here we validate the use of 2-[(18)F]fluoro-2-deoxy-d-galactose (FDGal) PET/CT for measuring regional metabolic function to this purpose, and apply the method to test the hypothesis of increased intrahepatic metabolic heterogeneity in cirrhosis. Nine cirrhotic patients underwent dynamic liver FDGal PET/CT with blood samples from a radial artery and a liver vein. Hepatic blood flow was measured by indocyanine green infusion/Fick's principle. From blood measurements, hepatic systemic clearance (Ksyst, Lblood/min) and hepatic intrinsic clearance (Vmax/Km, Lblood/min) of FDGal were calculated. From PET data, hepatic systemic clearance of FDGal in liver parenchyma (Kmet, mL blood/mL liver tissue/min) was calculated. Intrahepatic metabolic heterogeneity was evaluated in terms of coefficient-of-variation (CoV, %) using parametric images of Kmet. Mean approximation of Ksyst to Vmax/Km was 86% which validates the use of FDGal as PET tracer of hepatic metabolic function. Mean Kmet was 0.157 mL blood/mL liver tissue/min, which was lower than 0.274 mL blood/mL liver tissue/min, previously found in healthy subjects (p<0.001), in accordance with decreased metabolic function in cirrhotic livers. Mean CoV for Kmet in liver tissue was 24.4% in patients and 14.4% in healthy subjects (p<0.0001). The degree of intrahepatic metabolic heterogeneity correlated positively with HVPG (p<0.05). A 20-min dynamic FDGal PET/CT with arterial sampling provides an accurate measure of regional hepatic metabolic function in patients with cirrhosis. This is likely to have clinical implications for the assessment of patients with liver disease as well as treatment planning and monitoring. Copyright © 2013 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.