Remote-sensing data processing with the multivariate regression analysis method for iron mineral resource potential mapping: a case study in the Sarvian area, central Iran

NASA Astrophysics Data System (ADS)

Mansouri, Edris; Feizi, Faranak; Jafari Rad, Alireza; Arian, Mehran

2018-03-01

This paper uses multivariate regression to create a mathematical model for iron skarn exploration in the Sarvian area, central Iran, using multivariate regression for mineral prospectivity mapping (MPM). The main target of this paper is to apply multivariate regression analysis (as an MPM method) to map iron outcrops in the northeastern part of the study area in order to discover new iron deposits in other parts of the study area. Two types of multivariate regression models using two linear equations were employed to discover new mineral deposits. This method is one of the reliable methods for processing satellite images. ASTER satellite images (14 bands) were used as unique independent variables (UIVs), and iron outcrops were mapped as dependent variables for MPM. According to the results of the probability value (p value), coefficient of determination value (R2) and adjusted determination coefficient (Radj2), the second regression model (which consistent of multiple UIVs) fitted better than other models. The accuracy of the model was confirmed by iron outcrops map and geological observation. Based on field observation, iron mineralization occurs at the contact of limestone and intrusive rocks (skarn type).

Voxelwise multivariate analysis of multimodality magnetic resonance imaging

PubMed Central

Naylor, Melissa G.; Cardenas, Valerie A.; Tosun, Duygu; Schuff, Norbert; Weiner, Michael; Schwartzman, Armin

2015-01-01

Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remains a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available. PMID:23408378

Multivariate evaluation of Thyroid Imaging Reporting and Data System (TI-RADS) in diagnosis malignant thyroid nodule: application to PCA and PLS-DA analysis.

PubMed

Zhang, Tan; Li, Fangxuan; Mu, Jiali; Liu, Juntian; Zhang, Sheng

2017-06-01

To explore the significance of ultrasonic features in differential diagnosis of thyroid nodules via combining the thyroid imaging reporting and data system (TI-RADS) and multivariate statistical analysis. Patients who received surgical treatment and was diagnosed with single thyroid nodule by postoperative pathology and preoperative ultrasound were enrolled in this study. Multivariate analysis was applied to assess the significant ultrasonic features which correlated with identifying benign or malignance and grading the TI-RADS classification of thyroid nodule. There were significant differences in the nodule size, aspect ratio, internal, echogenicity, boundary, presence or absence of calcifications, calcification type and CDFI between benign and malignant thyroid nodules. Multivariate analysis showed clear-cut distinction both between benign and malignance and among different TI-RADS categories of malignancy nodules. The shape and calcification of the nodule were important factors for distinguish the benign and malignance. Height of the nodule, aspect and calcification was important factors for grading TI-RADS categories of malignancy thyroid nodules. Ill-defined boundary, irregular shape and presence of calcification related with highly malignant risk for thyroid nodule. The larger height and aspect and presence of calcification related with higher TI-RADS classification of malignancy thyroid nodule.

A comparison of hyperspectral reflectance and fluorescence imaging techniques for detection of contaminants on leafy greens

USDA-ARS?s Scientific Manuscript database

Ensuring the supply of safe, contaminant free fresh fruit and vegetables is of importance to consumers, suppliers and governments worldwide. In this study, three hyperspectral imaging (HSI) configurations coupled with two multivariate image analysis techniques are compared for detection of fecal con...

A Versatile Cell Death Screening Assay Using Dye-Stained Cells and Multivariate Image Analysis.

PubMed

Collins, Tony J; Ylanko, Jarkko; Geng, Fei; Andrews, David W

2015-11-01

A novel dye-based method for measuring cell death in image-based screens is presented. Unlike conventional high- and medium-throughput cell death assays that measure only one form of cell death accurately, using multivariate analysis of micrographs of cells stained with the inexpensive mix, red dye nonyl acridine orange, and a nuclear stain, it was possible to quantify cell death induced by a variety of different agonists even without a positive control. Surprisingly, using a single known cytotoxic agent as a positive control for training a multivariate classifier allowed accurate quantification of cytotoxicity for mechanistically unrelated compounds enabling generation of dose-response curves. Comparison with low throughput biochemical methods suggested that cell death was accurately distinguished from cell stress induced by low concentrations of the bioactive compounds Tunicamycin and Brefeldin A. High-throughput image-based format analyses of more than 300 kinase inhibitors correctly identified 11 as cytotoxic with only 1 false positive. The simplicity and robustness of this dye-based assay makes it particularly suited to live cell screening for toxic compounds.

A Versatile Cell Death Screening Assay Using Dye-Stained Cells and Multivariate Image Analysis

PubMed Central

Collins, Tony J.; Ylanko, Jarkko; Geng, Fei

2015-01-01

Abstract A novel dye-based method for measuring cell death in image-based screens is presented. Unlike conventional high- and medium-throughput cell death assays that measure only one form of cell death accurately, using multivariate analysis of micrographs of cells stained with the inexpensive mix, red dye nonyl acridine orange, and a nuclear stain, it was possible to quantify cell death induced by a variety of different agonists even without a positive control. Surprisingly, using a single known cytotoxic agent as a positive control for training a multivariate classifier allowed accurate quantification of cytotoxicity for mechanistically unrelated compounds enabling generation of dose–response curves. Comparison with low throughput biochemical methods suggested that cell death was accurately distinguished from cell stress induced by low concentrations of the bioactive compounds Tunicamycin and Brefeldin A. High-throughput image-based format analyses of more than 300 kinase inhibitors correctly identified 11 as cytotoxic with only 1 false positive. The simplicity and robustness of this dye-based assay makes it particularly suited to live cell screening for toxic compounds. PMID:26422066

Comparative study of different approaches for multivariate image analysis in HPTLC fingerprinting of natural products such as plant resin.

PubMed

Ristivojević, Petar; Trifković, Jelena; Vovk, Irena; Milojković-Opsenica, Dušanka

2017-01-01

Considering the introduction of phytochemical fingerprint analysis, as a method of screening the complex natural products for the presence of most bioactive compounds, use of chemometric classification methods, application of powerful scanning and image capturing and processing devices and algorithms, advancement in development of novel stationary phases as well as various separation modalities, high-performance thin-layer chromatography (HPTLC) fingerprinting is becoming attractive and fruitful field of separation science. Multivariate image analysis is crucial in the light of proper data acquisition. In a current study, different image processing procedures were studied and compared in detail on the example of HPTLC chromatograms of plant resins. In that sense, obtained variables such as gray intensities of pixels along the solvent front, peak area and mean values of peak were used as input data and compared to obtained best classification models. Important steps in image analysis, baseline removal, denoising, target peak alignment and normalization were pointed out. Numerical data set based on mean value of selected bands and intensities of pixels along the solvent front proved to be the most convenient for planar-chromatographic profiling, although required at least the basic knowledge on image processing methodology, and could be proposed for further investigation in HPLTC fingerprinting. Copyright © 2016 Elsevier B.V. All rights reserved.

Multispectral UV imaging for surface analysis of MUPS tablets with special focus on the pellet distribution.

PubMed

Novikova, Anna; Carstensen, Jens M; Rades, Thomas; Leopold, Prof Dr Claudia S

2016-12-30

In the present study the applicability of multispectral UV imaging in combination with multivariate image analysis for surface evaluation of MUPS tablets was investigated with respect to the differentiation of the API pellets from the excipients matrix, estimation of the drug content as well as pellet distribution, and influence of the coating material and tablet thickness on the predictive model. Different formulations consisting of coated drug pellets with two coating polymers (Aquacoat ® ECD and Eudragit ® NE 30 D) at three coating levels each were compressed to MUPS tablets with various amounts of coated pellets and different tablet thicknesses. The coated drug pellets were clearly distinguishable from the excipients matrix using a partial least squares approach regardless of the coating layer thickness and coating material used. Furthermore, the number of the detected drug pellets on the tablet surface allowed an estimation of the true drug content in the respective MUPS tablet. In addition, the pellet distribution in the MUPS formulations could be estimated by UV image analysis of the tablet surface. In conclusion, this study revealed that UV imaging in combination with multivariate image analysis is a promising approach for the automatic quality control of MUPS tablets during the manufacturing process. Copyright © 2016 Elsevier B.V. All rights reserved.

Multivariate statistical characterization of charged and uncharged domain walls in multiferroic hexagonal YMnO3 single crystal visualized by a spherical aberration-corrected STEM.

PubMed

Matsumoto, Takao; Ishikawa, Ryo; Tohei, Tetsuya; Kimura, Hideo; Yao, Qiwen; Zhao, Hongyang; Wang, Xiaolin; Chen, Dapeng; Cheng, Zhenxiang; Shibata, Naoya; Ikuhara, Yuichi

2013-10-09

A state-of-the-art spherical aberration-corrected STEM was fully utilized to directly visualize the multiferroic domain structure in a hexagonal YMnO3 single crystal at atomic scale. With the aid of multivariate statistical analysis (MSA), we obtained unbiased and quantitative maps of ferroelectric domain structures with atomic resolution. Such a statistical image analysis of the transition region between opposite polarizations has confirmed atomically sharp transitions of ferroelectric polarization both in antiparallel (uncharged) and tail-to-tail 180° (charged) domain boundaries. Through the analysis, a correlated subatomic image shift of Mn-O layers with that of Y layers, exhibiting a double-arc shape of reversed curvatures, have been elucidated. The amount of image shift in Mn-O layers along the c-axis is statistically significant as small as 0.016 nm, roughly one-third of the evident image shift of 0.048 nm in Y layers. Interestingly, a careful analysis has shown that such a subatomic image shift in Mn-O layers vanishes at the tail-to-tail 180° domain boundaries. Furthermore, taking advantage of the annular bright field (ABF) imaging technique combined with MSA, the tilting of MnO5 bipyramids, the very core mechanism of multiferroicity of the material, is evaluated.

Methods for spectral image analysis by exploiting spatial simplicity

DOEpatents

Keenan, Michael R.

2010-05-25

Several full-spectrum imaging techniques have been introduced in recent years that promise to provide rapid and comprehensive chemical characterization of complex samples. One of the remaining obstacles to adopting these techniques for routine use is the difficulty of reducing the vast quantities of raw spectral data to meaningful chemical information. Multivariate factor analysis techniques, such as Principal Component Analysis and Alternating Least Squares-based Multivariate Curve Resolution, have proven effective for extracting the essential chemical information from high dimensional spectral image data sets into a limited number of components that describe the spectral characteristics and spatial distributions of the chemical species comprising the sample. There are many cases, however, in which those constraints are not effective and where alternative approaches may provide new analytical insights. For many cases of practical importance, imaged samples are "simple" in the sense that they consist of relatively discrete chemical phases. That is, at any given location, only one or a few of the chemical species comprising the entire sample have non-zero concentrations. The methods of spectral image analysis of the present invention exploit this simplicity in the spatial domain to make the resulting factor models more realistic. Therefore, more physically accurate and interpretable spectral and abundance components can be extracted from spectral images that have spatially simple structure.

Methods for spectral image analysis by exploiting spatial simplicity

DOEpatents

Keenan, Michael R.

2010-11-23

Several full-spectrum imaging techniques have been introduced in recent years that promise to provide rapid and comprehensive chemical characterization of complex samples. One of the remaining obstacles to adopting these techniques for routine use is the difficulty of reducing the vast quantities of raw spectral data to meaningful chemical information. Multivariate factor analysis techniques, such as Principal Component Analysis and Alternating Least Squares-based Multivariate Curve Resolution, have proven effective for extracting the essential chemical information from high dimensional spectral image data sets into a limited number of components that describe the spectral characteristics and spatial distributions of the chemical species comprising the sample. There are many cases, however, in which those constraints are not effective and where alternative approaches may provide new analytical insights. For many cases of practical importance, imaged samples are "simple" in the sense that they consist of relatively discrete chemical phases. That is, at any given location, only one or a few of the chemical species comprising the entire sample have non-zero concentrations. The methods of spectral image analysis of the present invention exploit this simplicity in the spatial domain to make the resulting factor models more realistic. Therefore, more physically accurate and interpretable spectral and abundance components can be extracted from spectral images that have spatially simple structure.

Multivariate image analysis of laser-induced photothermal imaging used for detection of caries tooth

NASA Astrophysics Data System (ADS)

El-Sherif, Ashraf F.; Abdel Aziz, Wessam M.; El-Sharkawy, Yasser H.

2010-08-01

Time-resolved photothermal imaging has been investigated to characterize tooth for the purpose of discriminating between normal and caries areas of the hard tissue using thermal camera. Ultrasonic thermoelastic waves were generated in hard tissue by the absorption of fiber-coupled Q-switched Nd:YAG laser pulses operating at 1064 nm in conjunction with a laser-induced photothermal technique used to detect the thermal radiation waves for diagnosis of human tooth. The concepts behind the use of photo-thermal techniques for off-line detection of caries tooth features were presented by our group in earlier work. This paper illustrates the application of multivariate image analysis (MIA) techniques to detect the presence of caries tooth. MIA is used to rapidly detect the presence and quantity of common caries tooth features as they scanned by the high resolution color (RGB) thermal cameras. Multivariate principal component analysis is used to decompose the acquired three-channel tooth images into a two dimensional principal components (PC) space. Masking score point clusters in the score space and highlighting corresponding pixels in the image space of the two dominant PCs enables isolation of caries defect pixels based on contrast and color information. The technique provides a qualitative result that can be used for early stage caries tooth detection. The proposed technique can potentially be used on-line or real-time resolved to prescreen the existence of caries through vision based systems like real-time thermal camera. Experimental results on the large number of extracted teeth as well as one of the thermal image panoramas of the human teeth voltanteer are investigated and presented.

Hyperspectral fluorescence imaging using violet LEDs as excitation sources for fecal matter contaminate identification on spinach leaves

USDA-ARS?s Scientific Manuscript database

Food safety in the production of fresh produce for human consumption is a worldwide issue and needs to be addressed to decrease foodborne illnesses and resulting costs. Hyperspectral fluorescence imaging coupled with multivariate image analysis techniques for detection of fecal contaminates on spina...

Comparison of multivariate analysis methods for extracting the paraffin component from the paraffin-embedded cancer tissue spectra for Raman imaging

NASA Astrophysics Data System (ADS)

Meksiarun, Phiranuphon; Ishigaki, Mika; Huck-Pezzei, Verena A. C.; Huck, Christian W.; Wongravee, Kanet; Sato, Hidetoshi; Ozaki, Yukihiro

2017-03-01

This study aimed to extract the paraffin component from paraffin-embedded oral cancer tissue spectra using three multivariate analysis (MVA) methods; Independent Component Analysis (ICA), Partial Least Squares (PLS) and Independent Component - Partial Least Square (IC-PLS). The estimated paraffin components were used for removing the contribution of paraffin from the tissue spectra. These three methods were compared in terms of the efficiency of paraffin removal and the ability to retain the tissue information. It was found that ICA, PLS and IC-PLS could remove the paraffin component from the spectra at almost the same level while Principal Component Analysis (PCA) was incapable. In terms of retaining cancer tissue spectral integrity, effects of PLS and IC-PLS on the non-paraffin region were significantly less than that of ICA where cancer tissue spectral areas were deteriorated. The paraffin-removed spectra were used for constructing Raman images of oral cancer tissue and compared with Hematoxylin and Eosin (H&E) stained tissues for verification. This study has demonstrated the capability of Raman spectroscopy together with multivariate analysis methods as a diagnostic tool for the paraffin-embedded tissue section.

Quantitative fibrosis parameters highly predict esophageal-gastro varices in primary biliary cirrhosis.

PubMed

Wu, Q-M; Zhao, X-Y; You, H

2016-01-01

Esophageal-gastro Varices (EGV) may develop in any histological stages of primary biliary cirrhosis (PBC). We aim to establish and validate quantitative fibrosis (qFibrosis) parameters in portal, septal and fibrillar areas as ideal predictors of EGV in PBC patients. PBC patients with liver biopsy, esophagogastroscopy and Second Harmonic Generation (SHG)/Two-photon Excited Fluorescence (TPEF) microscopy images were retrospectively enrolled in this study. qFibrosis parameters in portal, septal and fibrillar areas were acquired by computer-assisted SHG/TPEF imaging system. Independent predictor was identified using multivariate logistic regression analysis. PBC patients with liver biopsy, esophagogastroscopy and Second Harmonic Generation (SHG)/Two-photon Excited Fluorescence (TPEF) microscopy images were retrospectively enrolled in this study. qFibrosis parameters in portal, septal and fibrillar areas were acquired by computer-assisted SHG/TPEF imaging system. Independent predictor was identified using multivariate logistic regression analysis. Among the forty-nine PBC patients with qFibrosis images, twenty-nine PBC patients with both esophagogastroscopy data and qFibrosis data were selected out for EGV prognosis analysis and 44.8% (13/29) of them had EGV. The qFibrosis parameters of collagen percentage and number of crosslink in fibrillar area, short/long/thin strings number and length/width of the strings in septa area were associated with EGV (p < 0.05). Multivariate logistic analysis showed that the collagen percentage in fibrillar area ≥ 3.6% was an independent factor to predict EGV (odds ratio 6.9; 95% confidence interval 1.6-27.4). The area under receiver operating characteristic (ROC), diagnostic sensitivity and specificity was 0.9, 100% and 75% respectively. Collagen percentage in Collagen percentage in the fibrillar area as an independent predictor can highly predict EGV in PBC patients.

Voxelwise multivariate analysis of multimodality magnetic resonance imaging.

PubMed

Naylor, Melissa G; Cardenas, Valerie A; Tosun, Duygu; Schuff, Norbert; Weiner, Michael; Schwartzman, Armin

2014-03-01

Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remain a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available. Copyright © 2013 Wiley Periodicals, Inc.

Passive Fully Polarimetric W-Band Millimeter-Wave Imaging

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

Bernacki, Bruce E.; Kelly, James F.; Sheen, David M.

2012-04-01

We present the theory, design, and experimental results obtained from a scanning passive W-band fully polarimetric imager. Passive millimeter-wave imaging offers persistent day/nighttime imaging and the ability to penetrate dust, clouds and other obscurants, including clothing and dry soil. The single-pixel scanning imager includes both far-field and near-field fore-optics for investigation of polarization phenomena. Using both fore-optics, a variety of scenes including natural and man-made objects was imaged and these results are presented showing the utility of polarimetric imaging for anomaly detection. Analysis includes conventional Stokes-parameter based approaches as well as multivariate image analysis methods.

Value of dual contrast liver MRI at 3.0 T in differentiating well-differentiated hepatocellular carcinomas from dysplastic nodules: preliminary results of multivariate analysis.

PubMed

Yoon, Min A; Kim, Se Hyung; Park, Hee Sun; Lee, Dong Ho; Lee, Jae Young; Han, Joon Koo; Choi, Byung Ihn

2009-10-01

To assess the diagnostic value of dual contrast magnetic resonance imaging (DC-MRI) in the differentiation of well-differentiated hepatocellular carcinomas (WD-HCCs) from dysplastic nodules (DNs) and to determine the significant MRI predictors using univariate and multivariate analyses. Thirty-two WD-HCCs and 33 DNs in 28 patients who underwent liver transplantation with available histopathology as a gold standard were enrolled in this study. All patients underwent DC-MRI using superparamagnetic iron oxide (SPIO) and gadolinium (Gd) agents on a 3 T MRI unit. For all patients, precontrast T1- and T2-weighted (T2W) images as well as post-SPIO T2- and T2*W images were obtained. Then, for dynamic MRI, arterial (AP), portal, and equilibrium images were also obtained. Two radiologists reviewed the MR images for analyzing signal intensity on the all MR sequences in consensus. On AP images, the degree of enhancement was subjectively categorized into 4 groups: no, minimal, moderate, and strong enhancement. For quantitative analysis, relative arterial enhancement ratio was calculated by averaging 3 regions of interest values of each nodule on pre-Gd T1W and AP images. Each variable was initially evaluated using univariate analyses to assess statistically significant MRI findings differentiating HCCs and DNs, then with multivariate logistic regression analysis to find the most predictable MRI findings. Twenty WD-HCCs showed iso- or high SI on precontrast T2W images, whereas 23 DNs showed low SI (P = 0.003). Most DNs showed low SI on post-SPIO T2W (30/33) and T2*W (25/33) images, whereas HCCs tended to show heterogeneous high or high SI (16/32 and 19/32) (P < 0.012). On post-SPIO and pre-Gd T1W GRE images, 28 WD-HCCs showed iso- or high SI, whereas 24 DNs showed low SI (P < 0.001). On AP images, 20 HCCs revealed more than minimal degree of enhancement, whereas 32 DNs did not show any enhancement (P < 0.001). Mean relative arterial enhancement ratio of HCCs (39.4%) was also significantly larger than that of DNs (15.6%) (P = 0.001). On portal images, WD-HCCs tended to show iso- or high SI (n = 21), whereas DNs showed low SI (n = 29) (P < 0.001). Multivariate analysis revealed that a subjective degree of enhancement on AP images and SI on post-SPIO and pre-Gd T1W GRE images were the 2 variables that independently differentiated WD-HCCs from DNs. The use of DC-MRI is helpful in the differentiation of WD HCCs and DNs. More specifically, a subjective degree of enhancement on AP images and SI on post-SPIO and pre-Gd T1W GRE images are the 2 variables that independently differentiate WD-HCCs from DNs.

Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer's disease and mild cognitive impairment.

PubMed

Westman, Eric; Aguilar, Carlos; Muehlboeck, J-Sebastian; Simmons, Andrew

2013-01-01

Automated structural magnetic resonance imaging (MRI) processing pipelines are gaining popularity for Alzheimer's disease (AD) research. They generate regional volumes, cortical thickness measures and other measures, which can be used as input for multivariate analysis. It is not clear which combination of measures and normalization approach are most useful for AD classification and to predict mild cognitive impairment (MCI) conversion. The current study includes MRI scans from 699 subjects [AD, MCI and controls (CTL)] from the Alzheimer's disease Neuroimaging Initiative (ADNI). The Freesurfer pipeline was used to generate regional volume, cortical thickness, gray matter volume, surface area, mean curvature, gaussian curvature, folding index and curvature index measures. 259 variables were used for orthogonal partial least square to latent structures (OPLS) multivariate analysis. Normalisation approaches were explored and the optimal combination of measures determined. Results indicate that cortical thickness measures should not be normalized, while volumes should probably be normalized by intracranial volume (ICV). Combining regional cortical thickness measures (not normalized) with cortical and subcortical volumes (normalized with ICV) using OPLS gave a prediction accuracy of 91.5 % when distinguishing AD versus CTL. This model prospectively predicted future decline from MCI to AD with 75.9 % of converters correctly classified. Normalization strategy did not have a significant effect on the accuracies of multivariate models containing multiple MRI measures for this large dataset. The appropriate choice of input for multivariate analysis in AD and MCI is of great importance. The results support the use of un-normalised cortical thickness measures and volumes normalised by ICV.

Image-based compound profiling reveals a dual inhibitor of tyrosine kinase and microtubule polymerization.

PubMed

Tanabe, Kenji

2016-04-27

Small-molecule compounds are widely used as biological research tools and therapeutic drugs. Therefore, uncovering novel targets of these compounds should provide insights that are valuable in both basic and clinical studies. I developed a method for image-based compound profiling by quantitating the effects of compounds on signal transduction and vesicle trafficking of epidermal growth factor receptor (EGFR). Using six signal transduction molecules and two markers of vesicle trafficking, 570 image features were obtained and subjected to multivariate analysis. Fourteen compounds that affected EGFR or its pathways were classified into four clusters, based on their phenotypic features. Surprisingly, one EGFR inhibitor (CAS 879127-07-8) was classified into the same cluster as nocodazole, a microtubule depolymerizer. In fact, this compound directly depolymerized microtubules. These results indicate that CAS 879127-07-8 could be used as a chemical probe to investigate both the EGFR pathway and microtubule dynamics. The image-based multivariate analysis developed herein has potential as a powerful tool for discovering unexpected drug properties.

Extending Inferential Group Analysis in Type 2 Diabetic Patients with Multivariate GLM Implemented in SPM8.

PubMed

Ferreira, Fábio S; Pereira, João M S; Duarte, João V; Castelo-Branco, Miguel

2017-01-01

Although voxel based morphometry studies are still the standard for analyzing brain structure, their dependence on massive univariate inferential methods is a limiting factor. A better understanding of brain pathologies can be achieved by applying inferential multivariate methods, which allow the study of multiple dependent variables, e.g. different imaging modalities of the same subject. Given the widespread use of SPM software in the brain imaging community, the main aim of this work is the implementation of massive multivariate inferential analysis as a toolbox in this software package. applied to the use of T1 and T2 structural data from diabetic patients and controls. This implementation was compared with the traditional ANCOVA in SPM and a similar multivariate GLM toolbox (MRM). We implemented the new toolbox and tested it by investigating brain alterations on a cohort of twenty-eight type 2 diabetes patients and twenty-six matched healthy controls, using information from both T1 and T2 weighted structural MRI scans, both separately - using standard univariate VBM - and simultaneously, with multivariate analyses. Univariate VBM replicated predominantly bilateral changes in basal ganglia and insular regions in type 2 diabetes patients. On the other hand, multivariate analyses replicated key findings of univariate results, while also revealing the thalami as additional foci of pathology. While the presented algorithm must be further optimized, the proposed toolbox is the first implementation of multivariate statistics in SPM8 as a user-friendly toolbox, which shows great potential and is ready to be validated in other clinical cohorts and modalities.

Extending Inferential Group Analysis in Type 2 Diabetic Patients with Multivariate GLM Implemented in SPM8

PubMed Central

Ferreira, Fábio S.; Pereira, João M.S.; Duarte, João V.; Castelo-Branco, Miguel

2017-01-01

Background: Although voxel based morphometry studies are still the standard for analyzing brain structure, their dependence on massive univariate inferential methods is a limiting factor. A better understanding of brain pathologies can be achieved by applying inferential multivariate methods, which allow the study of multiple dependent variables, e.g. different imaging modalities of the same subject. Objective: Given the widespread use of SPM software in the brain imaging community, the main aim of this work is the implementation of massive multivariate inferential analysis as a toolbox in this software package. applied to the use of T1 and T2 structural data from diabetic patients and controls. This implementation was compared with the traditional ANCOVA in SPM and a similar multivariate GLM toolbox (MRM). Method: We implemented the new toolbox and tested it by investigating brain alterations on a cohort of twenty-eight type 2 diabetes patients and twenty-six matched healthy controls, using information from both T1 and T2 weighted structural MRI scans, both separately – using standard univariate VBM - and simultaneously, with multivariate analyses. Results: Univariate VBM replicated predominantly bilateral changes in basal ganglia and insular regions in type 2 diabetes patients. On the other hand, multivariate analyses replicated key findings of univariate results, while also revealing the thalami as additional foci of pathology. Conclusion: While the presented algorithm must be further optimized, the proposed toolbox is the first implementation of multivariate statistics in SPM8 as a user-friendly toolbox, which shows great potential and is ready to be validated in other clinical cohorts and modalities. PMID:28761571

Physical vs. photolithographic patterning of plasma polymers: an investigation by ToF-SSIMS and multivariate analysis

PubMed Central

Mishra, Gautam; Easton, Christopher D.; McArthur, Sally L.

2009-01-01

Physical and photolithographic techniques are commonly used to create chemical patterns for a range of technologies including cell culture studies, bioarrays and other biomedical applications. In this paper, we describe the fabrication of chemical micropatterns from commonly used plasma polymers. Atomic force microcopy (AFM) imaging, Time-of-Flight Static Secondary Ion Mass Spectrometry (ToF-SSIMS) imaging and multivariate analysis have been employed to visualize the chemical boundaries created by these patterning techniques and assess the spatial and chemical resolution of the patterns. ToF-SSIMS analysis demonstrated that well defined chemical and spatial boundaries were obtained from photolithographic patterning, while the resolution of physical patterning via a transmission electron microscopy (TEM) grid varied depending on the properties of the plasma system including the substrate material. In general, physical masking allowed diffusion of the plasma species below the mask and bleeding of the surface chemistries. Multivariate analysis techniques including Principal Component Analysis (PCA) and Region of Interest (ROI) assessment were used to investigate the ToF-SSIMS images of a range of different plasma polymer patterns. In the most challenging case, where two strongly reacting polymers, allylamine and acrylic acid were deposited, PCA confirmed the fabrication of micropatterns with defined spatial resolution. ROI analysis allowed for the identification of an interface between the two plasma polymers for patterns fabricated using the photolithographic technique which has been previously overlooked. This study clearly demonstrated the versatility of photolithographic patterning for the production of multichemistry plasma polymer arrays and highlighted the need for complimentary characterization and analytical techniques during the fabrication plasma polymer micropatterns. PMID:19950941

Edge-Preserving Image Smoothing Constraint in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) of Hyperspectral Data.

PubMed

Hugelier, Siewert; Vitale, Raffaele; Ruckebusch, Cyril

2018-03-01

This article explores smoothing with edge-preserving properties as a spatial constraint for the resolution of hyperspectral images with multivariate curve resolution-alternating least squares (MCR-ALS). For each constrained component image (distribution map), irrelevant spatial details and noise are smoothed applying an L 1 - or L 0 -norm penalized least squares regression, highlighting in this way big changes in intensity of adjacent pixels. The feasibility of the constraint is demonstrated on three different case studies, in which the objects under investigation are spatially clearly defined, but have significant spectral overlap. This spectral overlap is detrimental for obtaining a good resolution and additional spatial information should be provided. The final results show that the spatial constraint enables better image (map) abstraction, artifact removal, and better interpretation of the results obtained, compared to a classical MCR-ALS analysis of hyperspectral images.

Anima: Modular Workflow System for Comprehensive Image Data Analysis

PubMed Central

Rantanen, Ville; Valori, Miko; Hautaniemi, Sampsa

2014-01-01

Modern microscopes produce vast amounts of image data, and computational methods are needed to analyze and interpret these data. Furthermore, a single image analysis project may require tens or hundreds of analysis steps starting from data import and pre-processing to segmentation and statistical analysis; and ending with visualization and reporting. To manage such large-scale image data analysis projects, we present here a modular workflow system called Anima. Anima is designed for comprehensive and efficient image data analysis development, and it contains several features that are crucial in high-throughput image data analysis: programing language independence, batch processing, easily customized data processing, interoperability with other software via application programing interfaces, and advanced multivariate statistical analysis. The utility of Anima is shown with two case studies focusing on testing different algorithms developed in different imaging platforms and an automated prediction of alive/dead C. elegans worms by integrating several analysis environments. Anima is a fully open source and available with documentation at www.anduril.org/anima. PMID:25126541

Exploring image data assimilation in the prospect of high-resolution satellite oceanic observations

NASA Astrophysics Data System (ADS)

Durán Moro, Marina; Brankart, Jean-Michel; Brasseur, Pierre; Verron, Jacques

2017-07-01

Satellite sensors increasingly provide high-resolution (HR) observations of the ocean. They supply observations of sea surface height (SSH) and of tracers of the dynamics such as sea surface salinity (SSS) and sea surface temperature (SST). In particular, the Surface Water Ocean Topography (SWOT) mission will provide measurements of the surface ocean topography at very high-resolution (HR) delivering unprecedented information on the meso-scale and submeso-scale dynamics. This study investigates the feasibility to use these measurements to reconstruct meso-scale features simulated by numerical models, in particular on the vertical dimension. A methodology to reconstruct three-dimensional (3D) multivariate meso-scale scenes is developed by using a HR numerical model of the Solomon Sea region. An inverse problem is defined in the framework of a twin experiment where synthetic observations are used. A true state is chosen among the 3D multivariate states which is considered as a reference state. In order to correct a first guess of this true state, a two-step analysis is carried out. A probability distribution of the first guess is defined and updated at each step of the analysis: (i) the first step applies the analysis scheme of a reduced-order Kalman filter to update the first guess probability distribution using SSH observation; (ii) the second step minimizes a cost function using observations of HR image structure and a new probability distribution is estimated. The analysis is extended to the vertical dimension using 3D multivariate empirical orthogonal functions (EOFs) and the probabilistic approach allows the update of the probability distribution through the two-step analysis. Experiments show that the proposed technique succeeds in correcting a multivariate state using meso-scale and submeso-scale information contained in HR SSH and image structure observations. It also demonstrates how the surface information can be used to reconstruct the ocean state below the surface.

Hierarchical multivariate covariance analysis of metabolic connectivity.

PubMed

Carbonell, Felix; Charil, Arnaud; Zijdenbos, Alex P; Evans, Alan C; Bedell, Barry J

2014-12-01

Conventional brain connectivity analysis is typically based on the assessment of interregional correlations. Given that correlation coefficients are derived from both covariance and variance, group differences in covariance may be obscured by differences in the variance terms. To facilitate a comprehensive assessment of connectivity, we propose a unified statistical framework that interrogates the individual terms of the correlation coefficient. We have evaluated the utility of this method for metabolic connectivity analysis using [18F]2-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. As an illustrative example of the utility of this approach, we examined metabolic connectivity in angular gyrus and precuneus seed regions of mild cognitive impairment (MCI) subjects with low and high β-amyloid burdens. This new multivariate method allowed us to identify alterations in the metabolic connectome, which would not have been detected using classic seed-based correlation analysis. Ultimately, this novel approach should be extensible to brain network analysis and broadly applicable to other imaging modalities, such as functional magnetic resonance imaging (MRI).

Quantitative image processing in fluid mechanics

NASA Technical Reports Server (NTRS)

Hesselink, Lambertus; Helman, James; Ning, Paul

1992-01-01

The current status of digital image processing in fluid flow research is reviewed. In particular, attention is given to a comprehensive approach to the extraction of quantitative data from multivariate databases and examples of recent developments. The discussion covers numerical simulations and experiments, data processing, generation and dissemination of knowledge, traditional image processing, hybrid processing, fluid flow vector field topology, and isosurface analysis using Marching Cubes.

Multivariate statistical model for 3D image segmentation with application to medical images.

PubMed

John, Nigel M; Kabuka, Mansur R; Ibrahim, Mohamed O

2003-12-01

In this article we describe a statistical model that was developed to segment brain magnetic resonance images. The statistical segmentation algorithm was applied after a pre-processing stage involving the use of a 3D anisotropic filter along with histogram equalization techniques. The segmentation algorithm makes use of prior knowledge and a probability-based multivariate model designed to semi-automate the process of segmentation. The algorithm was applied to images obtained from the Center for Morphometric Analysis at Massachusetts General Hospital as part of the Internet Brain Segmentation Repository (IBSR). The developed algorithm showed improved accuracy over the k-means, adaptive Maximum Apriori Probability (MAP), biased MAP, and other algorithms. Experimental results showing the segmentation and the results of comparisons with other algorithms are provided. Results are based on an overlap criterion against expertly segmented images from the IBSR. The algorithm produced average results of approximately 80% overlap with the expertly segmented images (compared with 85% for manual segmentation and 55% for other algorithms).

Rapid and early detection of salmonella serotypes with hyperspectral microscope and multivariate data analysis

USDA-ARS?s Scientific Manuscript database

This study was designed to evaluate hyperspectral microscope images for early and rapid detection of Salmonella serotypes: S. Enteritidis, S. Heidelberg, S. Infantis, S. Kentucky, and S. Typhimurium at incubation times of 6, 8, 10, 12, and 24 hours. Images were collected by an acousto-optical tunab...

Multivariate statistical analysis of diffusion imaging parameters using partial least squares: Application to white matter variations in Alzheimer's disease.

PubMed

Konukoglu, Ender; Coutu, Jean-Philippe; Salat, David H; Fischl, Bruce

2016-07-01

Diffusion magnetic resonance imaging (dMRI) is a unique technology that allows the noninvasive quantification of microstructural tissue properties of the human brain in healthy subjects as well as the probing of disease-induced variations. Population studies of dMRI data have been essential in identifying pathological structural changes in various conditions, such as Alzheimer's and Huntington's diseases (Salat et al., 2010; Rosas et al., 2006). The most common form of dMRI involves fitting a tensor to the underlying imaging data (known as diffusion tensor imaging, or DTI), then deriving parametric maps, each quantifying a different aspect of the underlying microstructure, e.g. fractional anisotropy and mean diffusivity. To date, the statistical methods utilized in most DTI population studies either analyzed only one such map or analyzed several of them, each in isolation. However, it is most likely that variations in the microstructure due to pathology or normal variability would affect several parameters simultaneously, with differing variations modulating the various parameters to differing degrees. Therefore, joint analysis of the available diffusion maps can be more powerful in characterizing histopathology and distinguishing between conditions than the widely used univariate analysis. In this article, we propose a multivariate approach for statistical analysis of diffusion parameters that uses partial least squares correlation (PLSC) analysis and permutation testing as building blocks in a voxel-wise fashion. Stemming from the common formulation, we present three different multivariate procedures for group analysis, regressing-out nuisance parameters and comparing effects of different conditions. We used the proposed procedures to study the effects of non-demented aging, Alzheimer's disease and mild cognitive impairment on the white matter. Here, we present results demonstrating that the proposed PLSC-based approach can differentiate between effects of different conditions in the same region as well as uncover spatial variations of effects across the white matter. The proposed procedures were able to answer questions on structural variations such as: "are there regions in the white matter where Alzheimer's disease has a different effect than aging or similar effect as aging?" and "are there regions in the white matter that are affected by both mild cognitive impairment and Alzheimer's disease but with differing multivariate effects?" Copyright © 2016 Elsevier Inc. All rights reserved.

Multivariate Statistical Analysis of Diffusion Imaging Parameters using Partial Least Squares: Application to White Matter Variations in Alzheimer’s Disease

PubMed Central

Konukoglu, Ender; Coutu, Jean-Philippe; Salat, David H.; Fischl, Bruce

2016-01-01

Diffusion magnetic resonance imaging (dMRI) is a unique technology that allows the noninvasive quantification of microstructural tissue properties of the human brain in healthy subjects as well as the probing of disease-induced variations. Population studies of dMRI data have been essential in identifying pathological structural changes in various conditions, such as Alzheimer’s and Huntington’s diseases1,2. The most common form of dMRI involves fitting a tensor to the underlying imaging data (known as Diffusion Tensor Imaging, or DTI), then deriving parametric maps, each quantifying a different aspect of the underlying microstructure, e.g. fractional anisotropy and mean diffusivity. To date, the statistical methods utilized in most DTI population studies either analyzed only one such map or analyzed several of them, each in isolation. However, it is most likely that variations in the microstructure due to pathology or normal variability would affect several parameters simultaneously, with differing variations modulating the various parameters to differing degrees. Therefore, joint analysis of the available diffusion maps can be more powerful in characterizing histopathology and distinguishing between conditions than the widely used univariate analysis. In this article, we propose a multivariate approach for statistical analysis of diffusion parameters that uses partial least squares correlation (PLSC) analysis and permutation testing as building blocks in a voxel-wise fashion. Stemming from the common formulation, we present three different multivariate procedures for group analysis, regressing-out nuisance parameters and comparing effects of different conditions. We used the proposed procedures to study the effects of non-demented aging, Alzheimer’s disease and mild cognitive impairment on the white matter. Here, we present results demonstrating that the proposed PLSC-based approach can differentiate between effects of different conditions in the same region as well as uncover spatial variations of effects across the white matter. The proposed procedures were able to answer questions on structural variations such as: “are there regions in the white matter where Alzheimer’s disease has a different effect than aging or similar effect as aging?” and “are there regions in the white matter that are affected by both mild cognitive impairment and Alzheimer’s disease but with differing multivariate effects?” PMID:27103138

Handling Different Spatial Resolutions in Image Fusion by Multivariate Curve Resolution-Alternating Least Squares for Incomplete Image Multisets.

PubMed

Piqueras, Sara; Bedia, Carmen; Beleites, Claudia; Krafft, Christoph; Popp, Jürgen; Maeder, Marcel; Tauler, Romà; de Juan, Anna

2018-06-05

Data fusion of different imaging techniques allows a comprehensive description of chemical and biological systems. Yet, joining images acquired with different spectroscopic platforms is complex because of the different sample orientation and image spatial resolution. Whereas matching sample orientation is often solved by performing suitable affine transformations of rotation, translation, and scaling among images, the main difficulty in image fusion is preserving the spatial detail of the highest spatial resolution image during multitechnique image analysis. In this work, a special variant of the unmixing algorithm Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) for incomplete multisets is proposed to provide a solution for this kind of problem. This algorithm allows analyzing simultaneously images collected with different spectroscopic platforms without losing spatial resolution and ensuring spatial coherence among the images treated. The incomplete multiset structure concatenates images of the two platforms at the lowest spatial resolution with the image acquired with the highest spatial resolution. As a result, the constituents of the sample analyzed are defined by a single set of distribution maps, common to all platforms used and with the highest spatial resolution, and their related extended spectral signatures, covering the signals provided by each of the fused techniques. We demonstrate the potential of the new variant of MCR-ALS for multitechnique analysis on three case studies: (i) a model example of MIR and Raman images of pharmaceutical mixture, (ii) FT-IR and Raman images of palatine tonsil tissue, and (iii) mass spectrometry and Raman images of bean tissue.

Recent applications of multivariate data analysis methods in the authentication of rice and the most analyzed parameters: A review.

PubMed

Maione, Camila; Barbosa, Rommel Melgaço

2018-01-24

Rice is one of the most important staple foods around the world. Authentication of rice is one of the most addressed concerns in the present literature, which includes recognition of its geographical origin and variety, certification of organic rice and many other issues. Good results have been achieved by multivariate data analysis and data mining techniques when combined with specific parameters for ascertaining authenticity and many other useful characteristics of rice, such as quality, yield and others. This paper brings a review of the recent research projects on discrimination and authentication of rice using multivariate data analysis and data mining techniques. We found that data obtained from image processing, molecular and atomic spectroscopy, elemental fingerprinting, genetic markers, molecular content and others are promising sources of information regarding geographical origin, variety and other aspects of rice, being widely used combined with multivariate data analysis techniques. Principal component analysis and linear discriminant analysis are the preferred methods, but several other data classification techniques such as support vector machines, artificial neural networks and others are also frequently present in some studies and show high performance for discrimination of rice.

Utility of Intermediate-Delay Washout CT Images for Differentiation of Malignant and Benign Adrenal Lesions: A Multivariate Analysis.

PubMed

Ng, Chaan S; Altinmakas, Emre; Wei, Wei; Ghosh, Payel; Li, Xiao; Grubbs, Elizabeth G; Perrier, Nancy D; Lee, Jeffrey E; Prieto, Victor G; Hobbs, Brian P

2018-06-27

The objective of this study was to identify features that impact the diagnostic performance of intermediate-delay washout CT for distinguishing malignant from benign adrenal lesions. This retrospective study evaluated 127 pathologically proven adrenal lesions (82 malignant, 45 benign) in 126 patients who had undergone portal venous phase and intermediate-delay washout CT (1-3 minutes after portal venous phase) with or without unenhanced images. Unenhanced images were available for 103 lesions. Quantitatively, lesion CT attenuation on unenhanced (UA) and delayed (DL) images, absolute and relative percentage of enhancement washout (APEW and RPEW, respectively), descriptive CT features (lesion size, margin characteristics, heterogeneity or homogeneity, fat, calcification), patient demographics, and medical history were evaluated for association with lesion status using multiple logistic regression with stepwise model selection. Area under the ROC curve (A z ) was calculated from both univariate and multivariate analyses. The predictive diagnostic performance of multivariate evaluations was ascertained through cross-validation. A z for DL, APEW, RPEW, and UA was 0.751, 0.795, 0.829, and 0.839, respectively. Multivariate analyses yielded the following significant CT quantitative features and associated A z when combined: RPEW and DL (A z = 0.861) when unenhanced images were not available and APEW and UA (A z = 0.889) when unenhanced images were available. Patient demographics and presence of a prior malignancy were additional significant factors, increasing A z to 0.903 and 0.927, respectively. The combined predictive classifier, without and with UA available, yielded 85.7% and 87.3% accuracies with cross-validation, respectively. When appropriately combined with other CT features, washout derived from intermediate-delay CT with or without additional clinical data has potential utility in differentiating malignant from benign adrenal lesions.

The Assessment of Neurological Systems with Functional Imaging

ERIC Educational Resources Information Center

Eidelberg, David

2007-01-01

In recent years a number of multivariate approaches have been introduced to map neural systems in health and disease. In this review, we focus on spatial covariance methods applied to functional imaging data to identify patterns of regional activity associated with behavior. In the rest state, this form of network analysis can be used to detect…

Potential use of MCR-ALS for the identification of coeliac-related biochemical changes in hyperspectral Raman maps from pediatric intestinal biopsies.

PubMed

Fornasaro, Stefano; Vicario, Annalisa; De Leo, Luigina; Bonifacio, Alois; Not, Tarcisio; Sergo, Valter

2018-05-14

Raman hyperspectral imaging is an emerging practice in biological and biomedical research for label free analysis of tissues and cells. Using this method, both spatial distribution and spectral information of analyzed samples can be obtained. The current study reports the first Raman microspectroscopic characterisation of colon tissues from patients with Coeliac Disease (CD). The aim was to assess if Raman imaging coupled with hyperspectral multivariate image analysis is capable of detecting the alterations in the biochemical composition of intestinal tissues associated with CD. The analytical approach was based on a multi-step methodology: duodenal biopsies from healthy and coeliac patients were measured and processed with Multivariate Curve Resolution Alternating Least Squares (MCR-ALS). Based on the distribution maps and the pure spectra of the image constituents obtained from MCR-ALS, interesting biochemical differences between healthy and coeliac patients has been derived. Noticeably, a reduced distribution of complex lipids in the pericryptic space, and a different distribution and abundance of proteins rich in beta-sheet structures was found in CD patients. The output of the MCR-ALS analysis was then used as a starting point for two clustering algorithms (k-means clustering and hierarchical clustering methods). Both methods converged with similar results providing precise segmentation over multiple Raman images of studied tissues.

Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables

NASA Astrophysics Data System (ADS)

Cannon, Alex J.

2018-01-01

Most bias correction algorithms used in climatology, for example quantile mapping, are applied to univariate time series. They neglect the dependence between different variables. Those that are multivariate often correct only limited measures of joint dependence, such as Pearson or Spearman rank correlation. Here, an image processing technique designed to transfer colour information from one image to another—the N-dimensional probability density function transform—is adapted for use as a multivariate bias correction algorithm (MBCn) for climate model projections/predictions of multiple climate variables. MBCn is a multivariate generalization of quantile mapping that transfers all aspects of an observed continuous multivariate distribution to the corresponding multivariate distribution of variables from a climate model. When applied to climate model projections, changes in quantiles of each variable between the historical and projection period are also preserved. The MBCn algorithm is demonstrated on three case studies. First, the method is applied to an image processing example with characteristics that mimic a climate projection problem. Second, MBCn is used to correct a suite of 3-hourly surface meteorological variables from the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) across a North American domain. Components of the Canadian Forest Fire Weather Index (FWI) System, a complicated set of multivariate indices that characterizes the risk of wildfire, are then calculated and verified against observed values. Third, MBCn is used to correct biases in the spatial dependence structure of CanRCM4 precipitation fields. Results are compared against a univariate quantile mapping algorithm, which neglects the dependence between variables, and two multivariate bias correction algorithms, each of which corrects a different form of inter-variable correlation structure. MBCn outperforms these alternatives, often by a large margin, particularly for annual maxima of the FWI distribution and spatiotemporal autocorrelation of precipitation fields.

MToS: A Tree of Shapes for Multivariate Images.

PubMed

Carlinet, Edwin; Géraud, Thierry

2015-12-01

The topographic map of a gray-level image, also called tree of shapes, provides a high-level hierarchical representation of the image contents. This representation, invariant to contrast changes and to contrast inversion, has been proved very useful to achieve many image processing and pattern recognition tasks. Its definition relies on the total ordering of pixel values, so this representation does not exist for color images, or more generally, multivariate images. Common workarounds, such as marginal processing, or imposing a total order on data, are not satisfactory and yield many problems. This paper presents a method to build a tree-based representation of multivariate images, which features marginally the same properties of the gray-level tree of shapes. Briefly put, we do not impose an arbitrary ordering on values, but we only rely on the inclusion relationship between shapes in the image definition domain. The interest of having a contrast invariant and self-dual representation of multivariate image is illustrated through several applications (filtering, segmentation, and object recognition) on different types of data: color natural images, document images, satellite hyperspectral imaging, multimodal medical imaging, and videos.

Hierarchical multivariate covariance analysis of metabolic connectivity

PubMed Central

Carbonell, Felix; Charil, Arnaud; Zijdenbos, Alex P; Evans, Alan C; Bedell, Barry J

2014-01-01

Conventional brain connectivity analysis is typically based on the assessment of interregional correlations. Given that correlation coefficients are derived from both covariance and variance, group differences in covariance may be obscured by differences in the variance terms. To facilitate a comprehensive assessment of connectivity, we propose a unified statistical framework that interrogates the individual terms of the correlation coefficient. We have evaluated the utility of this method for metabolic connectivity analysis using [18F]2-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. As an illustrative example of the utility of this approach, we examined metabolic connectivity in angular gyrus and precuneus seed regions of mild cognitive impairment (MCI) subjects with low and high β-amyloid burdens. This new multivariate method allowed us to identify alterations in the metabolic connectome, which would not have been detected using classic seed-based correlation analysis. Ultimately, this novel approach should be extensible to brain network analysis and broadly applicable to other imaging modalities, such as functional magnetic resonance imaging (MRI). PMID:25294129

Quantitative Analysis of {sup 18}F-Fluorodeoxyglucose Positron Emission Tomography Identifies Novel Prognostic Imaging Biomarkers in Locally Advanced Pancreatic Cancer Patients Treated With Stereotactic Body Radiation Therapy

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

Cui, Yi; Global Institution for Collaborative Research and Education, Hokkaido University, Sapporo; Song, Jie

Purpose: To identify prognostic biomarkers in pancreatic cancer using high-throughput quantitative image analysis. Methods and Materials: In this institutional review board–approved study, we retrospectively analyzed images and outcomes for 139 locally advanced pancreatic cancer patients treated with stereotactic body radiation therapy (SBRT). The overall population was split into a training cohort (n=90) and a validation cohort (n=49) according to the time of treatment. We extracted quantitative imaging characteristics from pre-SBRT {sup 18}F-fluorodeoxyglucose positron emission tomography, including statistical, morphologic, and texture features. A Cox proportional hazard regression model was built to predict overall survival (OS) in the training cohort using 162more » robust image features. To avoid over-fitting, we applied the elastic net to obtain a sparse set of image features, whose linear combination constitutes a prognostic imaging signature. Univariate and multivariate Cox regression analyses were used to evaluate the association with OS, and concordance index (CI) was used to evaluate the survival prediction accuracy. Results: The prognostic imaging signature included 7 features characterizing different tumor phenotypes, including shape, intensity, and texture. On the validation cohort, univariate analysis showed that this prognostic signature was significantly associated with OS (P=.002, hazard ratio 2.74), which improved upon conventional imaging predictors including tumor volume, maximum standardized uptake value, and total legion glycolysis (P=.018-.028, hazard ratio 1.51-1.57). On multivariate analysis, the proposed signature was the only significant prognostic index (P=.037, hazard ratio 3.72) when adjusted for conventional imaging and clinical factors (P=.123-.870, hazard ratio 0.53-1.30). In terms of CI, the proposed signature scored 0.66 and was significantly better than competing prognostic indices (CI 0.48-0.64, Wilcoxon rank sum test P<1e-6). Conclusion: Quantitative analysis identified novel {sup 18}F-fluorodeoxyglucose positron emission tomography image features that showed improved prognostic value over conventional imaging metrics. If validated in large, prospective cohorts, the new prognostic signature might be used to identify patients for individualized risk-adaptive therapy.« less

Analysis of Multilayer Devices for Superconducting Electronics by High-Resolution Scanning Transmission Electron Microscopy and Energy Dispersive Spectroscopy

DOE PAGES

Missert, Nancy; Kotula, Paul G.; Rye, Michael; ...

2017-02-15

We used a focused ion beam to obtain cross-sectional specimens from both magnetic multilayer and Nb/Al-AlOx/Nb Josephson junction devices for characterization by scanning transmission electron microscopy (STEM) and energy dispersive X-ray spectroscopy (EDX). An automated multivariate statistical analysis of the EDX spectral images produced chemically unique component images of individual layers within the multilayer structures. STEM imaging elucidated distinct variations in film morphology, interface quality, and/or etch artifacts that could be correlated to magnetic and/or electrical properties measured on the same devices.

A cross-species socio-emotional behaviour development revealed by a multivariate analysis.

PubMed

Koshiba, Mamiko; Senoo, Aya; Mimura, Koki; Shirakawa, Yuka; Karino, Genta; Obara, Saya; Ozawa, Shinpei; Sekihara, Hitomi; Fukushima, Yuta; Ueda, Toyotoshi; Kishino, Hirohisa; Tanaka, Toshihisa; Ishibashi, Hidetoshi; Yamanouchi, Hideo; Yui, Kunio; Nakamura, Shun

2013-01-01

Recent progress in affective neuroscience and social neurobiology has been propelled by neuro-imaging technology and epigenetic approach in neurobiology of animal behaviour. However, quantitative measurements of socio-emotional development remains lacking, though sensory-motor development has been extensively studied in terms of digitised imaging analysis. Here, we developed a method for socio-emotional behaviour measurement that is based on the video recordings under well-defined social context using animal models with variously social sensory interaction during development. The behaviour features digitized from the video recordings were visualised in a multivariate statistic space using principal component analysis. The clustering of the behaviour parameters suggested the existence of species- and stage-specific as well as cross-species behaviour modules. These modules were used to characterise the behaviour of children with or without autism spectrum disorders (ASDs). We found that socio-emotional behaviour is highly dependent on social context and the cross-species behaviour modules may predict neurobiological basis of ASDs.

Computational Approaches to Image Understanding.

DTIC Science & Technology

1981-10-01

represnting points, edges, surfaces, and volumes to facilitate display. The geometry or perspective and parailcl (or orthographic) projection has...of making the image forming process explicit. This in turn leads to a concern with geometry , such as the properties f the gradient, stereographic, and...dual spaces. Combining geometry and smoothness leads naturally to multi-variate vector analysis, and to differential geometry . For the most part, a

A review of multivariate methods in brain imaging data fusion

NASA Astrophysics Data System (ADS)

Sui, Jing; Adali, Tülay; Li, Yi-Ou; Yang, Honghui; Calhoun, Vince D.

2010-03-01

On joint analysis of multi-task brain imaging data sets, a variety of multivariate methods have shown their strengths and been applied to achieve different purposes based on their respective assumptions. In this paper, we provide a comprehensive review on optimization assumptions of six data fusion models, including 1) four blind methods: joint independent component analysis (jICA), multimodal canonical correlation analysis (mCCA), CCA on blind source separation (sCCA) and partial least squares (PLS); 2) two semi-blind methods: parallel ICA and coefficient-constrained ICA (CC-ICA). We also propose a novel model for joint blind source separation (BSS) of two datasets using a combination of sCCA and jICA, i.e., 'CCA+ICA', which, compared with other joint BSS methods, can achieve higher decomposition accuracy as well as the correct automatic source link. Applications of the proposed model to real multitask fMRI data are compared to joint ICA and mCCA; CCA+ICA further shows its advantages in capturing both shared and distinct information, differentiating groups, and interpreting duration of illness in schizophrenia patients, hence promising applicability to a wide variety of medical imaging problems.

Using Copula Distributions to Support More Accurate Imaging-Based Diagnostic Classifiers for Neuropsychiatric Disorders

PubMed Central

Bansal, Ravi; Hao, Xuejun; Liu, Jun; Peterson, Bradley S.

2014-01-01

Many investigators have tried to apply machine learning techniques to magnetic resonance images (MRIs) of the brain in order to diagnose neuropsychiatric disorders. Usually the number of brain imaging measures (such as measures of cortical thickness and measures of local surface morphology) derived from the MRIs (i.e., their dimensionality) has been large (e.g. >10) relative to the number of participants who provide the MRI data (<100). Sparse data in a high dimensional space increases the variability of the classification rules that machine learning algorithms generate, thereby limiting the validity, reproducibility, and generalizability of those classifiers. The accuracy and stability of the classifiers can improve significantly if the multivariate distributions of the imaging measures can be estimated accurately. To accurately estimate the multivariate distributions using sparse data, we propose to estimate first the univariate distributions of imaging data and then combine them using a Copula to generate more accurate estimates of their multivariate distributions. We then sample the estimated Copula distributions to generate dense sets of imaging measures and use those measures to train classifiers. We hypothesize that the dense sets of brain imaging measures will generate classifiers that are stable to variations in brain imaging measures, thereby improving the reproducibility, validity, and generalizability of diagnostic classification algorithms in imaging datasets from clinical populations. In our experiments, we used both computer-generated and real-world brain imaging datasets to assess the accuracy of multivariate Copula distributions in estimating the corresponding multivariate distributions of real-world imaging data. Our experiments showed that diagnostic classifiers generated using imaging measures sampled from the Copula were significantly more accurate and more reproducible than were the classifiers generated using either the real-world imaging measures or their multivariate Gaussian distributions. Thus, our findings demonstrate that estimated multivariate Copula distributions can generate dense sets of brain imaging measures that can in turn be used to train classifiers, and those classifiers are significantly more accurate and more reproducible than are those generated using real-world imaging measures alone. PMID:25093634

Atomic-scale phase composition through multivariate statistical analysis of atom probe tomography data.

PubMed

Keenan, Michael R; Smentkowski, Vincent S; Ulfig, Robert M; Oltman, Edward; Larson, David J; Kelly, Thomas F

2011-06-01

We demonstrate for the first time that multivariate statistical analysis techniques can be applied to atom probe tomography data to estimate the chemical composition of a sample at the full spatial resolution of the atom probe in three dimensions. Whereas the raw atom probe data provide the specific identity of an atom at a precise location, the multivariate results can be interpreted in terms of the probabilities that an atom representing a particular chemical phase is situated there. When aggregated to the size scale of a single atom (∼0.2 nm), atom probe spectral-image datasets are huge and extremely sparse. In fact, the average spectrum will have somewhat less than one total count per spectrum due to imperfect detection efficiency. These conditions, under which the variance in the data is completely dominated by counting noise, test the limits of multivariate analysis, and an extensive discussion of how to extract the chemical information is presented. Efficient numerical approaches to performing principal component analysis (PCA) on these datasets, which may number hundreds of millions of individual spectra, are put forward, and it is shown that PCA can be computed in a few seconds on a typical laptop computer.

Cross multivariate correlation coefficients as screening tool for analysis of concurrent EEG-fMRI recordings.

PubMed

Ji, Hong; Petro, Nathan M; Chen, Badong; Yuan, Zejian; Wang, Jianji; Zheng, Nanning; Keil, Andreas

2018-02-06

Over the past decade, the simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) data has garnered growing interest because it may provide an avenue towards combining the strengths of both imaging modalities. Given their pronounced differences in temporal and spatial statistics, the combination of EEG and fMRI data is however methodologically challenging. Here, we propose a novel screening approach that relies on a Cross Multivariate Correlation Coefficient (xMCC) framework. This approach accomplishes three tasks: (1) It provides a measure for testing multivariate correlation and multivariate uncorrelation of the two modalities; (2) it provides criterion for the selection of EEG features; (3) it performs a screening of relevant EEG information by grouping the EEG channels into clusters to improve efficiency and to reduce computational load when searching for the best predictors of the BOLD signal. The present report applies this approach to a data set with concurrent recordings of steady-state-visual evoked potentials (ssVEPs) and fMRI, recorded while observers viewed phase-reversing Gabor patches. We test the hypothesis that fluctuations in visuo-cortical mass potentials systematically covary with BOLD fluctuations not only in visual cortical, but also in anterior temporal and prefrontal areas. Results supported the hypothesis and showed that the xMCC-based analysis provides straightforward identification of neurophysiological plausible brain regions with EEG-fMRI covariance. Furthermore xMCC converged with other extant methods for EEG-fMRI analysis. © 2018 The Authors Journal of Neuroscience Research Published by Wiley Periodicals, Inc.

Hidden Process Models

DTIC Science & Technology

2009-12-18

cannot be detected with univariate techniques, but require multivariate analysis instead (Kamitani and Tong [2005]). Two other time series analysis ...learning for time series analysis . The historical record of DBNs can be traced back to Dean and Kanazawa [1988] and Dean and Wellman [1991], with...Rev. 8-98) Prescribed by ANSI Std Z39-18 Keywords: Hidden Process Models, probabilistic time series modeling, functional Magnetic Resonance Imaging

Magnetic resonance analysis of malignant transformation in recurrent glioma.

PubMed

Jalbert, Llewellyn E; Neill, Evan; Phillips, Joanna J; Lupo, Janine M; Olson, Marram P; Molinaro, Annette M; Berger, Mitchel S; Chang, Susan M; Nelson, Sarah J

2016-08-01

Patients with low-grade glioma (LGG) have a relatively long survival, and a balance is often struck between treating the tumor and impacting quality of life. While lesions may remain stable for many years, they may also undergo malignant transformation (MT) at the time of recurrence and require more aggressive intervention. Here we report on a state-of-the-art multiparametric MRI study of patients with recurrent LGG. One hundred and eleven patients previously diagnosed with LGG were scanned at either 1.5 T or 3 T MR at the time of recurrence. Volumetric and intensity parameters were estimated from anatomic, diffusion, perfusion, and metabolic MR data. Direct comparisons of histopathological markers from image-guided tissue samples with metrics derived from the corresponding locations on the in vivo images were made. A bioinformatics approach was applied to visualize and interpret these results, which included imaging heatmaps and network analysis. Multivariate linear-regression modeling was utilized for predicting transformation. Many advanced imaging parameters were found to be significantly different for patients with tumors that had undergone MT versus those that had not. Imaging metrics calculated at the tissue sample locations highlighted the distinct biological significance of the imaging and the heterogeneity present in recurrent LGG, while multivariate modeling yielded a 76.04% accuracy in predicting MT. The acquisition and quantitative analysis of such multiparametric MR data may ultimately allow for improved clinical assessment and treatment stratification for patients with recurrent LGG. © The Author(s) 2016. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.

Robust tumor morphometry in multispectral fluorescence microscopy

NASA Astrophysics Data System (ADS)

Tabesh, Ali; Vengrenyuk, Yevgen; Teverovskiy, Mikhail; Khan, Faisal M.; Sapir, Marina; Powell, Douglas; Mesa-Tejada, Ricardo; Donovan, Michael J.; Fernandez, Gerardo

2009-02-01

Morphological and architectural characteristics of primary tissue compartments, such as epithelial nuclei (EN) and cytoplasm, provide important cues for cancer diagnosis, prognosis, and therapeutic response prediction. We propose two feature sets for the robust quantification of these characteristics in multiplex immunofluorescence (IF) microscopy images of prostate biopsy specimens. To enable feature extraction, EN and cytoplasm regions were first segmented from the IF images. Then, feature sets consisting of the characteristics of the minimum spanning tree (MST) connecting the EN and the fractal dimension (FD) of gland boundaries were obtained from the segmented compartments. We demonstrated the utility of the proposed features in prostate cancer recurrence prediction on a multi-institution cohort of 1027 patients. Univariate analysis revealed that both FD and one of the MST features were highly effective for predicting cancer recurrence (p <= 0.0001). In multivariate analysis, an MST feature was selected for a model incorporating clinical and image features. The model achieved a concordance index (CI) of 0.73 on the validation set, which was significantly higher than the CI of 0.69 for the standard multivariate model based solely on clinical features currently used in clinical practice (p < 0.0001). The contributions of this work are twofold. First, it is the first demonstration of the utility of the proposed features in morphometric analysis of IF images. Second, this is the largest scale study of the efficacy and robustness of the proposed features in prostate cancer prognosis.

Elimination of RF inhomogeneity effects in segmentation.

PubMed

Agus, Onur; Ozkan, Mehmed; Aydin, Kubilay

2007-01-01

There are various methods proposed for the segmentation and analysis of MR images. However the efficiency of these techniques is effected by various artifacts that occur in the imaging system. One of the most encountered problems is the intensity variation across an image. To overcome this problem different methods are used. In this paper we propose a method for the elimination of intensity artifacts in segmentation of MRI images. Inter imager variations are also minimized to produce the same tissue segmentation for the same patient. A well-known multivariate classification algorithm, maximum likelihood is employed to illustrate the enhancement in segmentation.

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging

PubMed Central

Gaonkar, Bilwaj; Shinohara, Russell T; Davatzikos, Christos

2015-01-01

Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier’s decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification. PMID:26210913

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

Missert, Nancy; Kotula, Paul G.; Rye, Michael

We used a focused ion beam to obtain cross-sectional specimens from both magnetic multilayer and Nb/Al-AlOx/Nb Josephson junction devices for characterization by scanning transmission electron microscopy (STEM) and energy dispersive X-ray spectroscopy (EDX). An automated multivariate statistical analysis of the EDX spectral images produced chemically unique component images of individual layers within the multilayer structures. STEM imaging elucidated distinct variations in film morphology, interface quality, and/or etch artifacts that could be correlated to magnetic and/or electrical properties measured on the same devices.

Efficient principal component analysis for multivariate 3D voxel-based mapping of brain functional imaging data sets as applied to FDG-PET and normal aging.

PubMed

Zuendorf, Gerhard; Kerrouche, Nacer; Herholz, Karl; Baron, Jean-Claude

2003-01-01

Principal component analysis (PCA) is a well-known technique for reduction of dimensionality of functional imaging data. PCA can be looked at as the projection of the original images onto a new orthogonal coordinate system with lower dimensions. The new axes explain the variance in the images in decreasing order of importance, showing correlations between brain regions. We used an efficient, stable and analytical method to work out the PCA of Positron Emission Tomography (PET) images of 74 normal subjects using [(18)F]fluoro-2-deoxy-D-glucose (FDG) as a tracer. Principal components (PCs) and their relation to age effects were investigated. Correlations between the projections of the images on the new axes and the age of the subjects were carried out. The first two PCs could be identified as being the only PCs significantly correlated to age. The first principal component, which explained 10% of the data set variance, was reduced only in subjects of age 55 or older and was related to loss of signal in and adjacent to ventricles and basal cisterns, reflecting expected age-related brain atrophy with enlarging CSF spaces. The second principal component, which accounted for 8% of the total variance, had high loadings from prefrontal, posterior parietal and posterior cingulate cortices and showed the strongest correlation with age (r = -0.56), entirely consistent with previously documented age-related declines in brain glucose utilization. Thus, our method showed that the effect of aging on brain metabolism has at least two independent dimensions. This method should have widespread applications in multivariate analysis of brain functional images. Copyright 2002 Wiley-Liss, Inc.

Differentiation between borderline and benign ovarian tumors: combined analysis of MRI with tumor markers for large cystic masses (≥5 cm).

PubMed

Park, Sung Yoon; Oh, Young Taik; Jung, Dae Chul

2016-05-01

There is overlap in imaging features between borderline and benign ovarian tumors. To analyze diagnostic performance of magnetic resonance imaging (MRI) combined with tumor markers for differentiating borderline from benign ovarian tumor. Ninety-nine patient with MRI and surgically confirmed ovarian tumors 5 cm or larger (borderline, n = 37; benign, n = 62) were included. On MRI, tumor size, septal number (0; 1-4; 5 or more), and presence of solid portion such as papillary projection or septal thickening 0.5 cm or larger were investigated. Serum tumor markers (carbohydrate antigen 125 [CA 125] and CA 19-9) were recorded. Multivariate analysis was conducted for assessing whether combined MRI with tumor markers could differentiate borderline from benign tumor. The diagnostic performance was also analyzed. Incidence of solid portion was 67.6% (25/37) in borderline and 3.2% (2/62) in benign tumors (P < 0.05). In all patients, without combined analysis of MRI with tumor markers, multivariate analysis revealed solid portion (P < 0.001) and CA 125 (P = 0.039) were significant for predicting borderline tumors. When combined analysis of MRI with CA 125 ((i) the presence of solid portion or (ii) CA 125 > 44.1 U/mL with septal number ≥5 for borderline tumor) is incorporated to multivariate analysis, it was only significant (P = 0.001). The sensitivity, specificity, PPV, NPV, and accuracy of combined analysis of MRI with CA 125 were 89.1%, 91.9%, 86.8%, 93.4, and 90.9%, respectively. Combined analysis of MRI with CA 125 may allow better differentiation between borderline and benign ovarian tumor compared with MRI alone. © The Foundation Acta Radiologica 2015.

Multivariate analysis of the influences of oceanic and meteorological processes on suspended particulate matter distributions in Mississippi coastal waters

NASA Astrophysics Data System (ADS)

O'Brien, S. J.; Fitzpatrick, P. J.; Dzwonkowski, B.; Dykstra, S. L.; Wallace, D. J.; Church, I.; Wiggert, J. D.

2016-02-01

The Mississippi Sound is influenced by a high volume of sediment discharge from the Biloxi River, Mobile Bay via Pas aux Herons, Pascagoula River, Pearl River, Wolf River, and Lake Pontchartrain through the Rigolets. The river discharge, variable wind speed, wind direction and tides have a significant impact on the turbidity and transport of sediments in the Sound. Level 1 Moderate Resolution Imaging Spectroradiometer (MODIS) data is processed to extract the remote sensing reflectance at the wavelength of 645 nm and binned into an 8-day composite at a resolution of 500 m. The study uses a regional ocean color algorithm to compute suspended particulate matter (SPM) concentration based on these 8-day composite images. Multivariate analysis is applied between the SPM and time series of tides, wind, turbidity and river discharge measured at federal and academic institutions' stations and moorings. The multivariate analysis also includes in situ measurements of suspended sediment concentration and advective exchanges through the Mississippi Sound's tidal inlets between the coastal shelf and the nearshore estuarine waters. Mechanisms underlying the observed spatiotemporal distribution of SPM, including material exchange between the Sound and adjacent shelf waters, will be explored. The results of this study will contribute to current understanding of exchange mechanisms and pathways with the Mississippi Bight via the Mississippi Sound's tidal inlets.

An explorative chemometric approach applied to hyperspectral images for the study of illuminated manuscripts

NASA Astrophysics Data System (ADS)

Catelli, Emilio; Randeberg, Lise Lyngsnes; Alsberg, Bjørn Kåre; Gebremariam, Kidane Fanta; Bracci, Silvano

2017-04-01

Hyperspectral imaging (HSI) is a fast non-invasive imaging technology recently applied in the field of art conservation. With the help of chemometrics, important information about the spectral properties and spatial distribution of pigments can be extracted from HSI data. With the intent of expanding the applications of chemometrics to the interpretation of hyperspectral images of historical documents, and, at the same time, to study the colorants and their spatial distribution on ancient illuminated manuscripts, an explorative chemometric approach is here presented. The method makes use of chemometric tools for spectral de-noising (minimum noise fraction (MNF)) and image analysis (multivariate image analysis (MIA) and iterative key set factor analysis (IKSFA)/spectral angle mapper (SAM)) which have given an efficient separation, classification and mapping of colorants from visible-near-infrared (VNIR) hyperspectral images of an ancient illuminated fragment. The identification of colorants was achieved by extracting and interpreting the VNIR spectra as well as by using a portable X-ray fluorescence (XRF) spectrometer.

Cell nuclei and cytoplasm joint segmentation using the sliding band filter.

PubMed

Quelhas, Pedro; Marcuzzo, Monica; Mendonça, Ana Maria; Campilho, Aurélio

2010-08-01

Microscopy cell image analysis is a fundamental tool for biological research. In particular, multivariate fluorescence microscopy is used to observe different aspects of cells in cultures. It is still common practice to perform analysis tasks by visual inspection of individual cells which is time consuming, exhausting and prone to induce subjective bias. This makes automatic cell image analysis essential for large scale, objective studies of cell cultures. Traditionally the task of automatic cell analysis is approached through the use of image segmentation methods for extraction of cells' locations and shapes. Image segmentation, although fundamental, is neither an easy task in computer vision nor is it robust to image quality changes. This makes image segmentation for cell detection semi-automated requiring frequent tuning of parameters. We introduce a new approach for cell detection and shape estimation in multivariate images based on the sliding band filter (SBF). This filter's design makes it adequate to detect overall convex shapes and as such it performs well for cell detection. Furthermore, the parameters involved are intuitive as they are directly related to the expected cell size. Using the SBF filter we detect cells' nucleus and cytoplasm location and shapes. Based on the assumption that each cell has the same approximate shape center in both nuclei and cytoplasm fluorescence channels, we guide cytoplasm shape estimation by the nuclear detections improving performance and reducing errors. Then we validate cell detection by gathering evidence from nuclei and cytoplasm channels. Additionally, we include overlap correction and shape regularization steps which further improve the estimated cell shapes. The approach is evaluated using two datasets with different types of data: a 20 images benchmark set of simulated cell culture images, containing 1000 simulated cells; a 16 images Drosophila melanogaster Kc167 dataset containing 1255 cells, stained for DNA and actin. Both image datasets present a difficult problem due to the high variability of cell shapes and frequent cluster overlap between cells. On the Drosophila dataset our approach achieved a precision/recall of 95%/69% and 82%/90% for nuclei and cytoplasm detection respectively and an overall accuracy of 76%.

Multi-scale pixel-based image fusion using multivariate empirical mode decomposition.

PubMed

Rehman, Naveed ur; Ehsan, Shoaib; Abdullah, Syed Muhammad Umer; Akhtar, Muhammad Jehanzaib; Mandic, Danilo P; McDonald-Maier, Klaus D

2015-05-08

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences.

Multi-Scale Pixel-Based Image Fusion Using Multivariate Empirical Mode Decomposition

PubMed Central

Rehman, Naveed ur; Ehsan, Shoaib; Abdullah, Syed Muhammad Umer; Akhtar, Muhammad Jehanzaib; Mandic, Danilo P.; McDonald-Maier, Klaus D.

2015-01-01

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences. PMID:26007714

FTIR Imaging Coupled with Multivariate Analysis for Study of Initial Diffusion of Different Solvents in Cellulose Acetate Butyrate Films

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

Lindblad, M.S.; Keyes, B.; Gedvilas, L.

Fourier transform infrared (FTIR) spectroscopic imaging was used to study the initial diffusion of different solvents in cellulose acetate butyrate (CAB) films containing different amounts of acetyl and butyryl substituents. Different solvents and solvent/non-solvent mixtures were also studied. The FTIR imaging system allowed acquisition of sequential images of the CAB films as solvent penetration proceeded without disturbing the system. The interface between the non-swollen polymer and the initial swelling front could be identified using multivariate data analysis tools. For a series of ketone solvents the initial diffusion coefficients and diffusion rates could be quantified and were found to be relatedmore » to the polar and hydrogen interaction parameters in the Hansen solubility parameters of the solvents. For the solvent/non-solvent system the initial diffusion rate decreased less than linearly with the weight-percent of non-solvent present in the solution, which probably was due to the swelling characteristic of the non-solvent. For a given solvent, increasing the butyryl content of the CAB increased the initial diffusion rate. Increasing the butyryl content from 17 wt.% butyryl to 37 wt.% butyryl produced a considerably larger increase in initial diffusion rate compared to an increase in butyryl content from 37 wt.% to 50 wt.% butyryl.« less

Neuropsychological Testing Predicts Cerebrospinal Fluid Aβ in Mild Cognitive Impairment (MCI)

PubMed Central

Kandel, Benjamin M.; Avants, Brian B.; Gee, James C.; Arnold, Steven E.; Wolk, David A.

2015-01-01

Background Psychometric tests predict conversion of Mild Cognitive Impairment (MCI) to probable Alzheimer's Disease (AD). Because the definition of clinical AD relies on those same psychometric tests, the ability of these tests to identify underlying AD pathology remains unclear. Objective To determine the degree to which psychometric testing predicts molecular evidence of AD amyloid pathology, as indicated by CSF Aβ1–42, in patients with MCI, as compared to neuroimaging biomarkers. Methods We identified 408 MCI subjects with CSF Aβ levels, psychometric test data, FDG-PET scans, and acceptable volumetric MR scans from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We used psychometric tests and imaging biomarkers in univariate and multivariate models to predict Aβ status. Results The 30-minute delayed recall score of the Rey Auditory Verbal Learning Test (AVLT) was the best predictor of Aβ status among the psychometric tests, achieving an AUC of 0.67±0.02 and odds ratio of 2.5±0.4. FDG-PET was the best imaging-based biomarker (AUC 0.67±0.03, OR 3.2±1.2), followed by hippocampal volume (AUC 0.64±0.02,,OR 2.4±0.3). A multivariate analysis based on the psychometric tests improved on the univariate predictors, achieving an AUC of 0.68±0.03 (OR 3.38±1.2). Adding imaging biomarkers to the multivariate analysis did not improve the AUC. Conclusion Psychometric tests perform as well as imaging biomarkers to predict presence of molecular markers of AD pathology in MCI patients and should be considered in the determination of the likelihood that MCI is due to AD. PMID:25881908

Characterization of cytochrome c as marker for retinal cell degeneration by uv/vis spectroscopic imaging

NASA Astrophysics Data System (ADS)

Hollmach, Julia; Schweizer, Julia; Steiner, Gerald; Knels, Lilla; Funk, Richard H. W.; Thalheim, Silko; Koch, Edmund

2011-07-01

Retinal diseases like age-related macular degeneration have become an important cause of visual loss depending on increasing life expectancy and lifestyle habits. Due to the fact that no satisfying treatment exists, early diagnosis and prevention are the only possibilities to stop the degeneration. The protein cytochrome c (cyt c) is a suitable marker for degeneration processes and apoptosis because it is a part of the respiratory chain and involved in the apoptotic pathway. The determination of the local distribution and oxidative state of cyt c in living cells allows the characterization of cell degeneration processes. Since cyt c exhibits characteristic absorption bands between 400 and 650 nm wavelength, uv/vis in situ spectroscopic imaging was used for its characterization in retinal ganglion cells. The large amount of data, consisting of spatial and spectral information, was processed by multivariate data analysis. The challenge consists in the identification of the molecular information of cyt c. Baseline correction, principle component analysis (PCA) and cluster analysis (CA) were performed in order to identify cyt c within the spectral dataset. The combination of PCA and CA reveals cyt c and its oxidative state. The results demonstrate that uv/vis spectroscopic imaging in conjunction with sophisticated multivariate methods is a suitable tool to characterize cyt c under in situ conditions.

Imaging mass spectrometry data reduction: automated feature identification and extraction.

PubMed

McDonnell, Liam A; van Remoortere, Alexandra; de Velde, Nico; van Zeijl, René J M; Deelder, André M

2010-12-01

Imaging MS now enables the parallel analysis of hundreds of biomolecules, spanning multiple molecular classes, which allows tissues to be described by their molecular content and distribution. When combined with advanced data analysis routines, tissues can be analyzed and classified based solely on their molecular content. Such molecular histology techniques have been used to distinguish regions with differential molecular signatures that could not be distinguished using established histologic tools. However, its potential to provide an independent, complementary analysis of clinical tissues has been limited by the very large file sizes and large number of discrete variables associated with imaging MS experiments. Here we demonstrate data reduction tools, based on automated feature identification and extraction, for peptide, protein, and lipid imaging MS, using multiple imaging MS technologies, that reduce data loads and the number of variables by >100×, and that highlight highly-localized features that can be missed using standard data analysis strategies. It is then demonstrated how these capabilities enable multivariate analysis on large imaging MS datasets spanning multiple tissues. Copyright © 2010 American Society for Mass Spectrometry. Published by Elsevier Inc. All rights reserved.

A tool for classifying individuals with chronic back pain: using multivariate pattern analysis with functional magnetic resonance imaging data.

PubMed

Callan, Daniel; Mills, Lloyd; Nott, Connie; England, Robert; England, Shaun

2014-01-01

Chronic pain is one of the most prevalent health problems in the world today, yet neurological markers, critical to diagnosis of chronic pain, are still largely unknown. The ability to objectively identify individuals with chronic pain using functional magnetic resonance imaging (fMRI) data is important for the advancement of diagnosis, treatment, and theoretical knowledge of brain processes associated with chronic pain. The purpose of our research is to investigate specific neurological markers that could be used to diagnose individuals experiencing chronic pain by using multivariate pattern analysis with fMRI data. We hypothesize that individuals with chronic pain have different patterns of brain activity in response to induced pain. This pattern can be used to classify the presence or absence of chronic pain. The fMRI experiment consisted of alternating 14 seconds of painful electric stimulation (applied to the lower back) with 14 seconds of rest. We analyzed contrast fMRI images in stimulation versus rest in pain-related brain regions to distinguish between the groups of participants: 1) chronic pain and 2) normal controls. We employed supervised machine learning techniques, specifically sparse logistic regression, to train a classifier based on these contrast images using a leave-one-out cross-validation procedure. We correctly classified 92.3% of the chronic pain group (N = 13) and 92.3% of the normal control group (N = 13) by recognizing multivariate patterns of activity in the somatosensory and inferior parietal cortex. This technique demonstrates that differences in the pattern of brain activity to induced pain can be used as a neurological marker to distinguish between individuals with and without chronic pain. Medical, legal and business professionals have recognized the importance of this research topic and of developing objective measures of chronic pain. This method of data analysis was very successful in correctly classifying each of the two groups.

A Tool for Classifying Individuals with Chronic Back Pain: Using Multivariate Pattern Analysis with Functional Magnetic Resonance Imaging Data

PubMed Central

Callan, Daniel; Mills, Lloyd; Nott, Connie; England, Robert; England, Shaun

2014-01-01

Chronic pain is one of the most prevalent health problems in the world today, yet neurological markers, critical to diagnosis of chronic pain, are still largely unknown. The ability to objectively identify individuals with chronic pain using functional magnetic resonance imaging (fMRI) data is important for the advancement of diagnosis, treatment, and theoretical knowledge of brain processes associated with chronic pain. The purpose of our research is to investigate specific neurological markers that could be used to diagnose individuals experiencing chronic pain by using multivariate pattern analysis with fMRI data. We hypothesize that individuals with chronic pain have different patterns of brain activity in response to induced pain. This pattern can be used to classify the presence or absence of chronic pain. The fMRI experiment consisted of alternating 14 seconds of painful electric stimulation (applied to the lower back) with 14 seconds of rest. We analyzed contrast fMRI images in stimulation versus rest in pain-related brain regions to distinguish between the groups of participants: 1) chronic pain and 2) normal controls. We employed supervised machine learning techniques, specifically sparse logistic regression, to train a classifier based on these contrast images using a leave-one-out cross-validation procedure. We correctly classified 92.3% of the chronic pain group (N = 13) and 92.3% of the normal control group (N = 13) by recognizing multivariate patterns of activity in the somatosensory and inferior parietal cortex. This technique demonstrates that differences in the pattern of brain activity to induced pain can be used as a neurological marker to distinguish between individuals with and without chronic pain. Medical, legal and business professionals have recognized the importance of this research topic and of developing objective measures of chronic pain. This method of data analysis was very successful in correctly classifying each of the two groups. PMID:24905072

Label-free Chemical Imaging of Fungal Spore Walls by Raman Microscopy and Multivariate Curve Resolution Analysis

PubMed Central

Noothalapati, Hemanth; Sasaki, Takahiro; Kaino, Tomohiro; Kawamukai, Makoto; Ando, Masahiro; Hamaguchi, Hiro-o; Yamamoto, Tatsuyuki

2016-01-01

Fungal cell walls are medically important since they represent a drug target site for antifungal medication. So far there is no method to directly visualize structurally similar cell wall components such as α-glucan, β-glucan and mannan with high specificity, especially in a label-free manner. In this study, we have developed a Raman spectroscopy based molecular imaging method and combined multivariate curve resolution analysis to enable detection and visualization of multiple polysaccharide components simultaneously at the single cell level. Our results show that vegetative cell and ascus walls are made up of both α- and β-glucans while spore wall is exclusively made of α-glucan. Co-localization studies reveal the absence of mannans in ascus wall but are distributed primarily in spores. Such detailed picture is believed to further enhance our understanding of the dynamic spore wall architecture, eventually leading to advancements in drug discovery and development in the near future. PMID:27278218

Multivariate pattern analysis of MEG and EEG: A comparison of representational structure in time and space.

PubMed

Cichy, Radoslaw Martin; Pantazis, Dimitrios

2017-09-01

Multivariate pattern analysis of magnetoencephalography (MEG) and electroencephalography (EEG) data can reveal the rapid neural dynamics underlying cognition. However, MEG and EEG have systematic differences in sampling neural activity. This poses the question to which degree such measurement differences consistently bias the results of multivariate analysis applied to MEG and EEG activation patterns. To investigate, we conducted a concurrent MEG/EEG study while participants viewed images of everyday objects. We applied multivariate classification analyses to MEG and EEG data, and compared the resulting time courses to each other, and to fMRI data for an independent evaluation in space. We found that both MEG and EEG revealed the millisecond spatio-temporal dynamics of visual processing with largely equivalent results. Beyond yielding convergent results, we found that MEG and EEG also captured partly unique aspects of visual representations. Those unique components emerged earlier in time for MEG than for EEG. Identifying the sources of those unique components with fMRI, we found the locus for both MEG and EEG in high-level visual cortex, and in addition for MEG in low-level visual cortex. Together, our results show that multivariate analyses of MEG and EEG data offer a convergent and complimentary view on neural processing, and motivate the wider adoption of these methods in both MEG and EEG research. Copyright © 2017 Elsevier Inc. All rights reserved.

Quality of Acute Care for Patients With Urinary Stones in the United States.

PubMed

Scales, Charles D; Bergman, Jonathan; Carter, Stacey; Jack, Gregory; Saigal, Christopher S; Litwin, Mark S

2015-11-01

To describe guideline adherence for patients with suspected upper tract stones. We performed a cross-sectional analysis of visits recorded by the National Hospital Ambulatory Medical Care Survey (emergency department [ED] component) in 2007-2010 (most recent data). We assessed adherence to clinical guidelines for diagnostic laboratory testing, imaging, and pharmacologic therapy. Multivariable regression models controlled for important covariates. An estimated 4,956,444 ED visits for patients with suspected kidney stones occurred during the study period. Guideline adherence was highest for diagnostic imaging, with 3,122,229 (63%) visits providing optimal imaging. Complete guideline-based laboratory testing occurred in only 2 of every 5 visits. Pharmacologic therapy to facilitate stone passage was prescribed during only 17% of eligible visits. In multivariable analysis of guideline adherence, we found little variation by patient, provider, or facility characteristics. Guideline-recommended care was absent from a substantial proportion of acute care visits for patients with suspected kidney stones. These failures of care delivery likely increase costs and temporary disability. Targeted interventions to improve guideline adherence should be designed and evaluated to improve care for patients with symptomatic kidney stones. Published by Elsevier Inc.

Quality of Acute Care for Patients with Urinary Stones in the United States

PubMed Central

Scales, Charles D.; Bergman, Jonathan; Carter, Stacey; Jack, Gregory; Saigal, Christopher S.; Litwin, Mark S.

2015-01-01

Objective To describe guideline adherence for patients with suspected upper tract stones. Methods We performed a cross-sectional analysis of visits recorded by the National Hospital Ambulatory Medical Care Survey (ED component) in 2007–2010 (most recent data). We assessed adherence to clinical guidelines for diagnostic laboratory testing, imaging, and pharmacologic therapy. Multivariable regression models controlled for important covariates. Results An estimated 4,956,444 ED visits for patients with suspected kidney stones occurred during the study period. Guideline adherence was highest for diagnostic imaging, with 3,122,229 (63%) visits providing optimal imaging. Complete guideline-based laboratory testing occurred in only 2 of every 5 visits. Pharmacologic therapy to facilitate stone passage was prescribed during only 17% of eligible visits. In multivariable analysis of guideline adherence, we found little variation by patient, provider or facility characteristics. Conclusions Guideline-recommended care was absent from a substantial proportion of acute care visits for patients with suspected kidney stones. These failures of care delivery likely increase costs and temporary disability. Targeted interventions to improve guideline adherence should be designed and evaluated to improve care for patients with symptomatic kidney stones. PMID:26335495

A Review of Multivariate Methods for Multimodal Fusion of Brain Imaging Data

PubMed Central

Adali, Tülay; Yu, Qingbao; Calhoun, Vince D.

2011-01-01

The development of various neuroimaging techniques is rapidly improving the measurements of brain function/structure. However, despite improvements in individual modalities, it is becoming increasingly clear that the most effective research approaches will utilize multi-modal fusion, which takes advantage of the fact that each modality provides a limited view of the brain. The goal of multimodal fusion is to capitalize on the strength of each modality in a joint analysis, rather than a separate analysis of each. This is a more complicated endeavor that must be approached more carefully and efficient methods should be developed to draw generalized and valid conclusions from high dimensional data with a limited number of subjects. Numerous research efforts have been reported in the field based on various statistical approaches, e.g. independent component analysis (ICA), canonical correlation analysis (CCA) and partial least squares (PLS). In this review paper, we survey a number of multivariate methods appearing in previous reports, which are performed with or without prior information and may have utility for identifying potential brain illness biomarkers. We also discuss the possible strengths and limitations of each method, and review their applications to brain imaging data. PMID:22108139

Morphologic Features of Magnetic Resonance Imaging as a Surrogate of Capsular Contracture in Breast Cancer Patients With Implant-based Reconstructions.

PubMed

Tyagi, Neelam; Sutton, Elizabeth; Hunt, Margie; Zhang, Jing; Oh, Jung Hun; Apte, Aditya; Mechalakos, James; Wilgucki, Molly; Gelb, Emily; Mehrara, Babak; Matros, Evan; Ho, Alice

2017-02-01

Capsular contracture (CC) is a serious complication in patients receiving implant-based reconstruction for breast cancer. Currently, no objective methods are available for assessing CC. The goal of the present study was to identify image-based surrogates of CC using magnetic resonance imaging (MRI). We analyzed a retrospective data set of 50 patients who had undergone both a diagnostic MRI scan and a plastic surgeon's evaluation of the CC score (Baker's score) within a 6-month period after mastectomy and reconstructive surgery. The MRI scans were assessed for morphologic shape features of the implant and histogram features of the pectoralis muscle. The shape features, such as roundness, eccentricity, solidity, extent, and ratio length for the implant, were compared with the Baker score. For the pectoralis muscle, the muscle width and median, skewness, and kurtosis of the intensity were compared with the Baker score. Univariate analysis (UVA) using a Wilcoxon rank-sum test and multivariate analysis with the least absolute shrinkage and selection operator logistic regression was performed to determine significant differences in these features between the patient groups categorized according to their Baker's scores. UVA showed statistically significant differences between grade 1 and grade ≥2 for morphologic shape features and histogram features, except for volume and skewness. Only eccentricity, ratio length, and volume were borderline significant in differentiating grade ≤2 and grade ≥3. Features with P<.1 on UVA were used in the multivariate least absolute shrinkage and selection operator logistic regression analysis. Multivariate analysis showed a good level of predictive power for grade 1 versus grade ≥2 CC (area under the receiver operating characteristic curve 0.78, sensitivity 0.78, and specificity 0.82) and for grade ≤2 versus grade ≥3 CC (area under the receiver operating characteristic curve 0.75, sensitivity 0.75, and specificity 0.79). The morphologic shape features described on MR images were associated with the severity of CC. MRI has the potential to further improve the diagnostic ability of the Baker score in breast cancer patients who undergo implant reconstruction. Copyright © 2016 Elsevier Inc. All rights reserved.

Fabric pilling measurement using three-dimensional image

NASA Astrophysics Data System (ADS)

Ouyang, Wenbin; Wang, Rongwu; Xu, Bugao

2013-10-01

We introduce a stereovision system and the three-dimensional (3-D) image analysis algorithms for fabric pilling measurement. Based on the depth information available in the 3-D image, the pilling detection process starts from the seed searching at local depth maxima to the region growing around the selected seeds using both depth and distance criteria. After the pilling detection, the density, height, and area of individual pills in the image can be extracted to describe the pilling appearance. According to the multivariate regression analysis on the 3-D images of 30 cotton fabrics treated by the random-tumble and home-laundering machines, the pilling grade is highly correlated with the pilling density (R=0.923) but does not consistently change with the pilling height and area. The pilling densities measured from the 3-D images also correlate well with those counted manually from the samples (R=0.985).

Study on the application of MRF and the D-S theory to image segmentation of the human brain and quantitative analysis of the brain tissue

NASA Astrophysics Data System (ADS)

Guan, Yihong; Luo, Yatao; Yang, Tao; Qiu, Lei; Li, Junchang

2012-01-01

The features of the spatial information of Markov random field image was used in image segmentation. It can effectively remove the noise, and get a more accurate segmentation results. Based on the fuzziness and clustering of pixel grayscale information, we find clustering center of the medical image different organizations and background through Fuzzy cmeans clustering method. Then we find each threshold point of multi-threshold segmentation through two dimensional histogram method, and segment it. The features of fusing multivariate information based on the Dempster-Shafer evidence theory, getting image fusion and segmentation. This paper will adopt the above three theories to propose a new human brain image segmentation method. Experimental result shows that the segmentation result is more in line with human vision, and is of vital significance to accurate analysis and application of tissues.

An efficient approach to integrated MeV ion imaging.

PubMed

Nikbakht, T; Kakuee, O; Solé, V A; Vosuoghi, Y; Lamehi-Rachti, M

2018-03-01

An ionoluminescence (IL) spectral imaging system, besides the common MeV ion imaging facilities such as µ-PIXE and µ-RBS, is implemented at the Van de Graaff laboratory of Tehran. A versatile processing software is required to handle the large amount of data concurrently collected in µ-IL and common MeV ion imaging measurements through the respective methodologies. The open-source freeware PyMca, with image processing and multivariate analysis capabilities, is employed to simultaneously process common MeV ion imaging and µ-IL data. Herein, the program was adapted to support the OM_DAQ listmode data format. The appropriate performance of the µ-IL data acquisition system is confirmed through a case study. Moreover, the capabilities of the software for simultaneous analysis of µ-PIXE and µ-RBS experimental data are presented. Copyright © 2017 Elsevier B.V. All rights reserved.

Spatial Elucidation of Spinal Cord Lipid- and Metabolite- Regulations in Amyotrophic Lateral Sclerosis

NASA Astrophysics Data System (ADS)

Hanrieder, Jörg; Ewing, Andrew G.

2014-06-01

Amyotrophic lateral sclerosis (ALS) is a devastating, rapidly progressing disease of the central nervous system that is characterized by motor neuron degeneration in the brain stem and the spinal cord. We employed time of flight secondary ion mass spectrometry (ToF-SIMS) to profile spatial lipid- and metabolite- regulations in post mortem human spinal cord tissue from ALS patients to investigate chemical markers of ALS pathogenesis. ToF-SIMS scans and multivariate analysis of image and spectral data were performed on thoracic human spinal cord sections. Multivariate statistics of the image data allowed delineation of anatomical regions of interest based on their chemical identity. Spectral data extracted from these regions were compared using two different approaches for multivariate statistics, for investigating ALS related lipid and metabolite changes. The results show a significant decrease for cholesterol, triglycerides, and vitamin E in the ventral horn of ALS samples, which is presumably a consequence of motor neuron degeneration. Conversely, the biogenic mediator lipid lysophosphatidylcholine and its fragments were increased in ALS ventral spinal cord, pointing towards neuroinflammatory mechanisms associated with neuronal cell death. ToF-SIMS imaging is a promising approach for chemical histology and pathology for investigating the subcellular mechanisms underlying motor neuron degeneration in amyotrophic lateral sclerosis.

Machine learning for neuroimaging with scikit-learn.

PubMed

Abraham, Alexandre; Pedregosa, Fabian; Eickenberg, Michael; Gervais, Philippe; Mueller, Andreas; Kossaifi, Jean; Gramfort, Alexandre; Thirion, Bertrand; Varoquaux, Gaël

2014-01-01

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.

Machine learning for neuroimaging with scikit-learn

PubMed Central

Abraham, Alexandre; Pedregosa, Fabian; Eickenberg, Michael; Gervais, Philippe; Mueller, Andreas; Kossaifi, Jean; Gramfort, Alexandre; Thirion, Bertrand; Varoquaux, Gaël

2014-01-01

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain. PMID:24600388

WHIDE—a web tool for visual data mining colocation patterns in multivariate bioimages

PubMed Central

Kölling, Jan; Langenkämper, Daniel; Abouna, Sylvie; Khan, Michael; Nattkemper, Tim W.

2012-01-01

Motivation: Bioimaging techniques rapidly develop toward higher resolution and dimension. The increase in dimension is achieved by different techniques such as multitag fluorescence imaging, Matrix Assisted Laser Desorption / Ionization (MALDI) imaging or Raman imaging, which record for each pixel an N-dimensional intensity array, representing local abundances of molecules, residues or interaction patterns. The analysis of such multivariate bioimages (MBIs) calls for new approaches to support users in the analysis of both feature domains: space (i.e. sample morphology) and molecular colocation or interaction. In this article, we present our approach WHIDE (Web-based Hyperbolic Image Data Explorer) that combines principles from computational learning, dimension reduction and visualization in a free web application. Results: We applied WHIDE to a set of MBI recorded using the multitag fluorescence imaging Toponome Imaging System. The MBI show field of view in tissue sections from a colon cancer study and we compare tissue from normal/healthy colon with tissue classified as tumor. Our results show, that WHIDE efficiently reduces the complexity of the data by mapping each of the pixels to a cluster, referred to as Molecular Co-Expression Phenotypes and provides a structural basis for a sophisticated multimodal visualization, which combines topology preserving pseudocoloring with information visualization. The wide range of WHIDE's applicability is demonstrated with examples from toponome imaging, high content screens and MALDI imaging (shown in the Supplementary Material). Availability and implementation: The WHIDE tool can be accessed via the BioIMAX website http://ani.cebitec.uni-bielefeld.de/BioIMAX/; Login: whidetestuser; Password: whidetest. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: tim.nattkemper@uni-bielefeld.de PMID:22390938

Identifying prognostic intratumor heterogeneity using pre- and post-radiotherapy 18F-FDG PET images for pancreatic cancer patients.

PubMed

Yue, Yong; Osipov, Arsen; Fraass, Benedick; Sandler, Howard; Zhang, Xiao; Nissen, Nicholas; Hendifar, Andrew; Tuli, Richard

2017-02-01

To stratify risks of pancreatic adenocarcinoma (PA) patients using pre- and post-radiotherapy (RT) PET/CT images, and to assess the prognostic value of texture variations in predicting therapy response of patients. Twenty-six PA patients treated with RT from 2011-2013 with pre- and post-treatment 18F-FDG-PET/CT scans were identified. Tumor locoregional texture was calculated using 3D kernel-based approach, and texture variations were identified by fitting discrepancies of texture maps of pre- and post-treatment images. A total of 48 texture and clinical variables were identified and evaluated for association with overall survival (OS). The prognostic heterogeneity features were selected using lasso/elastic net regression, and further were evaluated by multivariate Cox analysis. Median age was 69 y (range, 46-86 y). The texture map and temporal variations between pre- and post-treatment were well characterized by histograms and statistical fitting. The lasso analysis identified seven predictors (age, node stage, post-RT SUVmax, variations of homogeneity, variance, sum mean, and cluster tendency). The multivariate Cox analysis identified five significant variables: age, node stage, variations of homogeneity, variance, and cluster tendency (with P=0.020, 0.040, 0.065, 0.078, and 0.081, respectively). The patients were stratified into two groups based on the risk score of multivariate analysis with log-rank P=0.001: a low risk group (n=11) with a longer mean OS (29.3 months) and higher texture variation (>30%), and a high risk group (n=15) with a shorter mean OS (17.7 months) and lower texture variation (<15%). Locoregional metabolic texture response provides a feasible approach for evaluating and predicting clinical outcomes following treatment of PA with RT. The proposed method can be used to stratify patient risk and help select appropriate treatment strategies for individual patients toward implementing response-driven adaptive RT.

Brain regions with abnormal network properties in severe epilepsy of Lennox-Gastaut phenotype: Multivariate analysis of task-free fMRI.

PubMed

Pedersen, Mangor; Curwood, Evan K; Archer, John S; Abbott, David F; Jackson, Graeme D

2015-11-01

Lennox-Gastaut syndrome, and the similar but less tightly defined Lennox-Gastaut phenotype, describe patients with severe epilepsy, generalized epileptic discharges, and variable intellectual disability. Our previous functional neuroimaging studies suggest that abnormal diffuse association network activity underlies the epileptic discharges of this clinical phenotype. Herein we use a data-driven multivariate approach to determine the spatial changes in local and global networks of patients with severe epilepsy of the Lennox-Gastaut phenotype. We studied 9 adult patients and 14 controls. In 20 min of task-free blood oxygen level-dependent functional magnetic resonance imaging data, two metrics of functional connectivity were studied: Regional homogeneity or local connectivity, a measure of concordance between each voxel to a focal cluster of adjacent voxels; and eigenvector centrality, a global connectivity estimate designed to detect important neural hubs. Multivariate pattern analysis of these data in a machine-learning framework was used to identify spatial features that classified disease subjects. Multivariate pattern analysis was 95.7% accurate in classifying subjects for both local and global connectivity measures (22/23 subjects correctly classified). Maximal discriminating features were the following: increased local connectivity in frontoinsular and intraparietal areas; increased global connectivity in posterior association areas; decreased local connectivity in sensory (visual and auditory) and medial frontal cortices; and decreased global connectivity in the cingulate cortex, striatum, hippocampus, and pons. Using a data-driven analysis method in task-free functional magnetic resonance imaging, we show increased connectivity in critical areas of association cortex and decreased connectivity in primary cortex. This supports previous findings of a critical role for these association cortical regions as a final common pathway in generating the Lennox-Gastaut phenotype. Abnormal function of these areas is likely to be important in explaining the intellectual problems characteristic of this disorder. Wiley Periodicals, Inc. © 2015 International League Against Epilepsy.

NCA-LDAS land analysis: Development and performance of a multisensory, multivariate land data assimilation for the National Climate Assessment

NASA Astrophysics Data System (ADS)

Kumar, S.; Jasinski, M. F.; Mocko, D. M.; Rodell, M.; Borak, J.; Li, B.; Beaudoing, H. K.; Peters-Lidard, C. D.

2017-12-01

This presentation will describe one of the first successful examples of multisensor, multivariate land data assimilation, encompassing a large suite of soil moisture, snow depth, snow cover and irrigation intensity environmental data records (EDRs) from Scanning Multi-channel Microwave Radiometer (SMMR), the Special Sensor Microwave Imager (SSM/I), the Advanced Scatterometer (ASCAT), the Moderate-Resolution Imaging Spectroradiometer (MODIS), the Advanced Microwave Scanning Radiometer (AMSR-E and AMSR2), the Soil Moisture Ocean Salinity (SMOS) mission and the Soil Moisture Active Passive (SMAP) mission. The analysis is performed using the NASA Land Information System (LIS) as an enabling tool for the U.S. National Climate Assessment (NCA). The performance of NCA Land Data Assimilation System (NCA-LDAS) is evaluated by comparing to a number of hydrological reference data products. Results indicate that multivariate assimilation provides systematic improvements in simulated soil moisture and snow depth, with marginal effects on the accuracy of simulated streamflow and ET. An important conclusion is that across all evaluated variables, assimilation of data from increasingly more modern sensors (e.g. SMOS, SMAP, AMSR2, ASCAT) produces more skillful results than assimilation of data from older sensors (e.g. SMMR, SSM/I, AMSR-E). The evaluation also indicates high skill of NCA-LDAS when compared with other land analysis products. Further, drought indicators based on NCA-LDAS output suggest a trend of longer and more severe droughts over parts of Western U.S. during 1979-2015, particularly in the Southwestern U.S.

Large Uptake of Titania and Iron Oxide Nanoparticles in the Nucleus of Lung Epithelial Cells as Measured by Raman Imaging and Multivariate Classification

PubMed Central

Ahlinder, Linnea; Ekstrand-Hammarström, Barbro; Geladi, Paul; Österlund, Lars

2013-01-01

It is a challenging task to characterize the biodistribution of nanoparticles in cells and tissue on a subcellular level. Conventional methods to study the interaction of nanoparticles with living cells rely on labeling techniques that either selectively stain the particles or selectively tag them with tracer molecules. In this work, Raman imaging, a label-free technique that requires no extensive sample preparation, was combined with multivariate classification to quantify the spatial distribution of oxide nanoparticles inside living lung epithelial cells (A549). Cells were exposed to TiO2 (titania) and/or α-FeO(OH) (goethite) nanoparticles at various incubation times (4 or 48 h). Using multivariate classification of hyperspectral Raman data with partial least-squares discriminant analysis, we show that a surprisingly large fraction of spectra, classified as belonging to the cell nucleus, show Raman bands associated with nanoparticles. Up to 40% of spectra from the cell nucleus show Raman bands associated with nanoparticles. Complementary transmission electron microscopy data for thin cell sections qualitatively support the conclusions. PMID:23870252

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

Li, B; Fujita, A; Buch, K

Purpose: To investigate the correlation between texture analysis-based model observer and human observer in the task of diagnosis of ischemic infarct in non-contrast head CT of adults. Methods: Non-contrast head CTs of five patients (2 M, 3 F; 58–83 y) with ischemic infarcts were retro-reconstructed using FBP and Adaptive Statistical Iterative Reconstruction (ASIR) of various levels (10–100%). Six neuro -radiologists reviewed each image and scored image quality for diagnosing acute infarcts by a 9-point Likert scale in a blinded test. These scores were averaged across the observers to produce the average human observer responses. The chief neuro-radiologist placed multiple ROIsmore » over the infarcts. These ROIs were entered into a texture analysis software package. Forty-two features per image, including 11 GLRL, 5 GLCM, 4 GLGM, 9 Laws, and 13 2-D features, were computed and averaged over the images per dataset. The Fisher-coefficient (ratio of between-class variance to in-class variance) was calculated for each feature to identify the most discriminating features from each matrix that separate the different confidence scores most efficiently. The 15 features with the highest Fisher -coefficient were entered into linear multivariate regression for iterative modeling. Results: Multivariate regression analysis resulted in the best prediction model of the confidence scores after three iterations (df=11, F=11.7, p-value<0.0001). The model predicted scores and human observers were highly correlated (R=0.88, R-sq=0.77). The root-mean-square and maximal residual were 0.21 and 0.44, respectively. The residual scatter plot appeared random, symmetric, and unbiased. Conclusion: For diagnosis of ischemic infarct in non-contrast head CT in adults, the predicted image quality scores from texture analysis-based model observer was highly correlated with that of human observers for various noise levels. Texture-based model observer can characterize image quality of low contrast, subtle texture changes in addition to human observers.« less

A Test Strategy for High Resolution Image Scanners.

DTIC Science & Technology

1983-10-01

for multivariate analysis. Holt, Richart and Winston, Inc., New York. Graybill , F.A., 1961: An introduction to linear statistical models . SVolume I...i , j i -(7) 02 1 )2 y 4n .i ij 13 The linear estimation model for the polynomial coefficients can be set up as - =; =(8) with T = ( x’ . . X-nn "X...Resolution Image Scanner MTF Geometrical and radiometric performance Dynamic range, linearity , noise - Dynamic scanning errors Response uniformity Skewness of

Differentiation of nonneoplastic and neoplastic gallbladder polyps 1 cm or bigger with multi-detector row computed tomography.

PubMed

Park, Ko Woon; Kim, Seong Hyun; Choi, Seong Ho; Lee, Won Jae

2010-01-01

To evaluate useful computed tomographic features to differentiate nonneoplastic and neoplastic gallbladder polyps 1 cm or bigger. Thirty-one patients with 32 nonneoplastic polyps and 67 patients with 73 neoplastic polyps 1 cm or bigger underwent unenhanced and dual-phase (arterial and portal venous phases) multi-detector row computed tomography. Gallbladder polyps were diagnosed by cholecystectomy. Computed tomographic features including size (1.5 cm), surface (smooth or irregular), shape (pedunculated or sessile), accompanying wall thickening, basal indentation, perception on unenhanced images, and enhancement pattern between 2 groups were compared using univariate and multivariate analyses. On univariate analysis, age 55 years or older (P = 0.0019), size bigger than 1.5 cm (P < 0.0001), irregular surface (P = 0.0033), sessile shape (P = 0.0016), accompanying wall thickening (P = 0.0056), basal indentation (P = 0.0236), and perception on unenhanced images (P < 0.0001) were significantly more frequent in neoplastic polyps as compared with nonneoplastic polyps. On multivariate analysis, size bigger than 1.5 cm (P = 0.0260), sessile shape (P = 0.0397), and perception on unenhanced images (P < 0.0001) were statistically significant. Size bigger than 1.5 cm, sessile shape, and perception on unenhanced images are the main factors that differentiate neoplastic from nonneoplastic gallbladder polyps 1 cm or bigger.

Magnetic resonance imaging and magnetic resonance spectroscopy for detection of early Alzheimer's disease.

PubMed

Westman, Eric; Wahlund, Lars-Olof; Foy, Catherine; Poppe, Michaela; Cooper, Allison; Murphy, Declan; Spenger, Christian; Lovestone, Simon; Simmons, Andrew

2011-01-01

Alzheimer's disease is the most common form of neurodegenerative disorder and early detection is of great importance if new therapies are to be effectively administered. We have investigated whether the discrimination between early Alzheimer's disease (AD) and elderly healthy control subjects can be improved by adding magnetic resonance spectroscopy (MRS) measures to magnetic resonance imaging (MRI) measures. In this study 30 AD patients and 36 control subjects were included. High resolution T1-weighted axial magnetic resonance images were obtained from each subject. Automated regional volume segmentation and cortical thickness measures were determined for the images. 1H MRS was acquired from the hippocampus and LCModel was used for metabolic quantification. Altogether, this yielded 58 different volumetric, cortical thickness and metabolite ratio variables which were used for multivariate analysis to distinguish between subjects with AD and Healthy controls. Combining MRI and MRS measures resulted in a sensitivity of 97% and a specificity of 94% compared to using MRI or MRS measures alone (sensitivity: 87%, 76%, specificity: 86%, 83% respectively). Adding the MRS measures to the MRI measures more than doubled the positive likelihood ratio from 6 to 17. Adding MRS measures to a multivariate analysis of MRI measures resulted in significantly better classification than using MRI measures alone. The method shows strong potential for discriminating between Alzheimer's disease and controls.

Conventional MRI features for predicting the clinical outcome of patients with invasive placenta

PubMed Central

Chen, Ting; Xu, Xiao-Quan; Shi, Hai-Bin; Yang, Zheng-Qiang; Zhou, Xin; Pan, Yi

2017-01-01

PURPOSE We aimed to evaluate whether morphologic magnetic resonance imaging (MRI) features could help to predict the maternal outcome after uterine artery embolization (UAE)-assisted cesarean section (CS) in patients with invasive placenta previa. METHODS We retrospectively reviewed the MRI data of 40 pregnant women who have undergone UAE-assisted cesarean section due to suspected high risk of massive hemorrhage caused by invasive placenta previa. Patients were divided into two groups based on the maternal outcome (good-outcome group: minor hemorrhage and uterus preserved; poor-outcome group: significant hemorrhage or emergency hysterectomy). Morphologic MRI features were compared between the two groups. Multivariate logistic regression analysis was used to identify the most valuable variables, and predictive value of the identified risk factor was determined. RESULTS Low signal intensity bands on T2-weighted imaging (P < 0.001), placenta percreta (P = 0.011), and placental cervical protrusion sign (P = 0.002) were more frequently observed in patients with poor outcome. Low signal intensity bands on T2-weighted imaging was the only significant predictor of poor maternal outcome in multivariate analysis (P = 0.020; odds ratio, 14.79), with 81.3% sensitivity and 84.3% specificity. CONCLUSION Low signal intensity bands on T2-weighted imaging might be a predictor of poor maternal outcome after UAE-assisted cesarean section in patients with invasive placenta previa. PMID:28345524

TOF-SIMS imaging technique with information entropy

NASA Astrophysics Data System (ADS)

Aoyagi, Satoka; Kawashima, Y.; Kudo, Masahiro

2005-05-01

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is capable of chemical imaging of proteins on insulated samples in principal. However, selection of specific peaks related to a particular protein, which are necessary for chemical imaging, out of numerous candidates had been difficult without an appropriate spectrum analysis technique. Therefore multivariate analysis techniques, such as principal component analysis (PCA), and analysis with mutual information defined by information theory, have been applied to interpret SIMS spectra of protein samples. In this study mutual information was applied to select specific peaks related to proteins in order to obtain chemical images. Proteins on insulated materials were measured with TOF-SIMS and then SIMS spectra were analyzed by means of the analysis method based on the comparison using mutual information. Chemical mapping of each protein was obtained using specific peaks related to each protein selected based on values of mutual information. The results of TOF-SIMS images of proteins on the materials provide some useful information on properties of protein adsorption, optimality of immobilization processes and reaction between proteins. Thus chemical images of proteins by TOF-SIMS contribute to understand interactions between material surfaces and proteins and to develop sophisticated biomaterials.

Police witness identification images: a geometric morphometric analysis.

PubMed

Hayes, Susan; Tullberg, Cameron

2012-11-01

Research into witness identification images typically occurs within the laboratory and involves subjective likeness and recognizability judgments. This study analyzed whether actual witness identification images systematically alter the facial shapes of the suspects described. The shape analysis tool, geometric morphometrics, was applied to 46 homologous facial landmarks displayed on 50 witness identification images and their corresponding arrest photographs, using principal component analysis and multivariate regressions. The results indicate that compared with arrest photographs, witness identification images systematically depict suspects with lowered and medially located eyebrows (p = <0.000001). This was found to occur independently of the Police Artist, and did not occur with composites produced under laboratory conditions. There are several possible explanations for this finding, including any, or all, of the following: The suspect was frowning at the time of the incident, the witness had negative feelings toward the suspect, this is an effect of unfamiliar face processing, the suspect displayed fear at the time of their arrest photograph. © 2012 American Academy of Forensic Sciences.

Astronomical data analysis software and systems I; Proceedings of the 1st Annual Conference, Tucson, AZ, Nov. 6-8, 1991

NASA Technical Reports Server (NTRS)

Worrall, Diana M. (Editor); Biemesderfer, Chris (Editor); Barnes, Jeannette (Editor)

1992-01-01

Consideration is given to a definition of a distribution format for X-ray data, the Einstein on-line system, the NASA/IPAC extragalactic database, COBE astronomical databases, Cosmic Background Explorer astronomical databases, the ADAM software environment, the Groningen Image Processing System, search for a common data model for astronomical data analysis systems, deconvolution for real and synthetic apertures, pitfalls in image reconstruction, a direct method for spectral and image restoration, and a discription of a Poisson imagery super resolution algorithm. Also discussed are multivariate statistics on HI and IRAS images, a faint object classification using neural networks, a matched filter for improving SNR of radio maps, automated aperture photometry of CCD images, interactive graphics interpreter, the ROSAT extreme ultra-violet sky survey, a quantitative study of optimal extraction, an automated analysis of spectra, applications of synthetic photometry, an algorithm for extra-solar planet system detection and data reduction facilities for the William Herschel telescope.

Diagnostic performance of conventional MRI parameters and apparent diffusion coefficient values in differentiating between benign and malignant soft-tissue tumours.

PubMed

Song, Y; Yoon, Y C; Chong, Y; Seo, S W; Choi, Y-L; Sohn, I; Kim, M-J

2017-08-01

To compare the abilities of conventional magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) in differentiating between benign and malignant soft-tissue tumours (STT). A total of 123 patients with STT who underwent 3 T MRI, including diffusion-weighted imaging (DWI), were retrospectively analysed using variate conventional MRI parameters, ADC mean and ADC min . For the all-STT group, the correlation between the malignant STT conventional MRI parameters, except deep compartment involvement, compared to those of benign STT were statistically significant with univariate analysis. Maximum diameter of the tumour (p=0.001; odds ratio [OR], 8.97) and ADC mean (p=0.020; OR, 4.30) were independent factors with multivariate analysis. For the non-myxoid non-haemosiderin STT group, signal heterogeneity on axial T1-weighted imaging (T1WI; p=0.017), ADC mean , and ADC min (p=0.001, p=0.001), showed significant differences with univariate analysis between malignancy and benignity. Signal heterogeneity in axial T1WI (p=0.025; OR, 12.64) and ADC mean (p=0.004; OR, 33.15) were independent factors with multivariate analysis. ADC values as well as conventional MRI parameters were useful in differentiating between benign and malignant STT. The ADC mean was the most powerful diagnostic parameter in non-myxoid non-haemosiderin STT. Copyright © 2017 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) with Raman Imaging Applied to Lunar Meteorites.

PubMed

Smith, Joseph P; Smith, Frank C; Booksh, Karl S

2018-03-01

Lunar meteorites provide a more random sampling of the surface of the Moon than do the returned lunar samples, and they provide valuable information to help estimate the chemical composition of the lunar crust, the lunar mantle, and the bulk Moon. As of July 2014, ∼96 lunar meteorites had been documented and ten of these are unbrecciated mare basalts. Using Raman imaging with multivariate curve resolution-alternating least squares (MCR-ALS), we investigated portions of polished thin sections of paired, unbrecciated, mare-basalt lunar meteorites that had been collected from the LaPaz Icefield (LAP) of Antarctica-LAP 02205 and LAP 04841. Polarized light microscopy displays that both meteorites are heterogeneous and consist of polydispersed sized and shaped particles of varying chemical composition. For two distinct probed areas within each meteorite, the individual chemical species and associated chemical maps were elucidated using MCR-ALS applied to Raman hyperspectral images. For LAP 02205, spatially and spectrally resolved clinopyroxene, ilmenite, substrate-adhesive epoxy, and diamond polish were observed within the probed areas. Similarly, for LAP 04841, spatially resolved chemical images with corresponding resolved Raman spectra of clinopyroxene, troilite, a high-temperature polymorph of anorthite, substrate-adhesive epoxy, and diamond polish were generated. In both LAP 02205 and LAP 04841, substrate-adhesive epoxy and diamond polish were more readily observed within fractures/veinlet features. Spectrally diverse clinopyroxenes were resolved in LAP 04841. Factors that allow these resolved clinopyroxenes to be differentiated include crystal orientation, spatially distinct chemical zoning of pyroxene crystals, and/or chemical and molecular composition. The minerals identified using this analytical methodology-clinopyroxene, anorthite, ilmenite, and troilite-are consistent with the results of previous studies of the two meteorites using electron microprobe analysis. To our knowledge, this is the first report of MCR-ALS with Raman imaging used for the investigation of both lunar and other types of meteorites. We have demonstrated the use of multivariate analysis methods, namely MCR-ALS, with Raman imaging to investigate heterogeneous lunar meteorites. Our analytical methodology can be used to elucidate the chemical, molecular, and structural characteristics of phases in a host of complex, heterogeneous geological, geochemical, and extraterrestrial materials.

Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

NASA Astrophysics Data System (ADS)

Belianinov, Alex; Ganesh, Panchapakesan; Lin, Wenzhi; Sales, Brian C.; Sefat, Athena S.; Jesse, Stephen; Pan, Minghu; Kalinin, Sergei V.

2014-12-01

Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe0.55Se0.45 (Tc = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe1-xSex structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified by their electronic signature and differentiated by their local chemical environment. This approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces.

Measuring changes in chemistry, composition, and molecular structure within hair fibers by infrared and Raman spectroscopic imaging.

PubMed

Zhang, Guojin; Senak, Laurence; Moore, David J

2011-05-01

Spatially resolved infrared (IR) and Raman images are acquired from human hair cross sections or intact hair fibers. The full informational content of these spectra are spatially correlated to hair chemistry, anatomy, and structural organization through univariate and multivariate data analysis. Specific IR and Raman images from untreated human hair describing the spatial dependence of lipid and protein distribution, protein secondary structure, lipid chain conformational order, and distribution of disulfide cross-links in hair protein are presented in this study. Factor analysis of the image plane acquired with IR microscopy in hair sections, permits delineation of specific micro-regions within the hair. These data indicate that both IR and Raman imaging of molecular structural changes in a specific region of hair will prove to be valuable tools in the understanding of hair structure, physiology, and the effect of various stresses upon its integrity.

Predictors of underestimation of malignancy after image-guided core needle biopsy diagnosis of flat epithelial atypia or atypical ductal hyperplasia.

PubMed

Yu, Chi-Chang; Ueng, Shir-Hwa; Cheung, Yun-Chung; Shen, Shih-Che; Kuo, Wen-Lin; Tsai, Hsiu-Pei; Lo, Yung-Feng; Chen, Shin-Cheh

2015-01-01

Flat epithelial atypia (FEA) and atypical ductal hyperplasia (ADH) are precursors of breast malignancy. Management of FEA or ADH after image-guided core needle biopsy (CNB) remains controversial. The aim of this study was to evaluate malignancy underestimation rates after FEA or ADH diagnosis using image-guided CNB and to identify clinical characteristics and imaging features associated with malignancy as well as identify cases with low underestimation rates that may be treatable by observation only. We retrospectively reviewed 2,875 consecutive image-guided CNBs recorded in an electronic data base from January 2010 to December 2011 and identified 128 (4.5%) FEA and 83 (2.9%) ADH diagnoses (211 total cases). Of these, 64 (30.3%) were echo-guided CNB procedures and 147 (69.7%) mammography-guided CNBs. Twenty patients (9.5%) were upgraded to malignancy. Multivariate analysis indicated that age (OR = 1.123, p = 0.002, increase of 1 year), mass-type lesion with calcifications (OR = 8.213, p = 0.006), and ADH in CNB specimens (OR = 8.071, p = 0.003) were independent predictors of underestimation. In univariate analysis of echo-guided CNB (n = 64), mass with calcifications had the highest underestimation rate (p < 0.001). Multivariate analysis of 147 mammography-guided CNBs revealed that age (OR = 1.122, p = 0.040, increase of 1 year) and calcification distribution were significant independent predictors of underestimation. No FEA case in which, complete calcification retrieval was recorded after CNB was upgraded to malignancy. Older age at diagnosis on image-guided CNB was a predictor of malignancy underestimation. Mass with calcifications was more likely to be associated with malignancy, and in cases presenting as calcifications only, segmental distribution or linear shapes were significantly associated with upgrading. Excision after FEA or ADH diagnosis by image-guided CNB is warranted except for FEA diagnosed using mammography-guided CNB with complete calcification retrieval. © 2015 Wiley Periodicals, Inc.

Using time-of-flight secondary ion mass spectrometry and multivariate statistical analysis to detect and image octabenzyl-polyhedral oligomeric silsesquioxane in polycarbonate

NASA Astrophysics Data System (ADS)

Smentkowski, V. S.; Duong, H. M.; Tamaki, R.; Keenan, M. R.; Ohlhausen, J. A. Tony; Kotula, P. G.

2006-11-01

Silsesquioxane, with an empirical formula of RSiO3/2, has the potential to combine the mechanical properties of plastics with the oxidative stability of ceramics in one material [D.W. Scott, J. Am. Chem. Soc. 68 (1946) 356; K.J. Shea, D.A. Loy, Acc. Chem. Res. 34 (2001) 707; K.-M. Kim, D.-K. Keum, Y. Chujo, Macromolecules 36 (2003) 867; M.J. Abad, L. Barral, D.P. Fasce, R.J.J. William, Macromolecules 36 (2003) 3128]. The high sensitivity, surface specificity, and ability to detect and image high mass additives make time-of-flight secondary ion mass spectrometry (ToF-SIMS) a powerful surface analytical instrument for the characterization of polymer composite surfaces in an analytical laboratory [J.C. Vickerman, D. Briggs (Eds.), ToF-SIMS Surface Analysis by Mass Spectrometry, Surface Spectra/IMPublications, UK, 2001; X. Vanden Eynde, P. Bertand, Surf. Interface Anal. 27 (1999) 157; P.M. Thompson, Anal. Chem. 63 (1991) 2447; S.J. Simko, S.R. Bryan, D.P. Griffis, R.W. Murray, R.W. Linton, Anal. Chem. 57 (1985) 1198; S. Affrossman, S.A. O'Neill, M. Stamm, Macromolecules 31 (1998) 6280]. In this paper, we compare ToF-SIMS spectra of control samples with spectra generated from polymer nano-composites based on octabenzyl-polyhedral oligomeric silsesquioxane (BnPOSS) as well as spectra (and images) generated from multivariate statistical analysis (MVSA) of the entire spectral image. We will demonstrate that ToF-SIMS is able to detect and image low concentrations of BnPOSS in polycarbonate. We emphasize the use of MVSA tools for converting the massive amount of data contained in a ToF-SIMS spectral image into a smaller number of useful chemical components (spectra and images) that fully describe the ToF-SIMS measurement.

Secondary ion mass spectrometry imaging and multivariate data analysis reveal co-aggregation patterns of Populus trichocarpa leaf surface compounds on a micrometer scale.

PubMed

Kulkarni, Purva; Dost, Mina; Bulut, Özgül Demir; Welle, Alexander; Böcker, Sebastian; Boland, Wilhelm; Svatoš, Aleš

2018-01-01

Spatially resolved analysis of a multitude of compound classes has become feasible with the rapid advancement in mass spectrometry imaging strategies. In this study, we present a protocol that combines high lateral resolution time-of-flight secondary ion mass spectrometry (TOF-SIMS) imaging with a multivariate data analysis (MVA) approach to probe the complex leaf surface chemistry of Populus trichocarpa. Here, epicuticular waxes (EWs) found on the adaxial leaf surface of P. trichocarpa were blotted on silicon wafers and imaged using TOF-SIMS at 10 μm and 1 μm lateral resolution. Intense M +● and M -● molecular ions were clearly visible, which made it possible to resolve the individual compound classes present in EWs. Series of long-chain aliphatic saturated alcohols (C 21 -C 30 ), hydrocarbons (C 25 -C 33 ) and wax esters (WEs; C 44 -C 48 ) were clearly observed. These data correlated with the 7 Li-chelation matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis, which yielded mostly molecular adduct ions of the analyzed compounds. Subsequently, MVA was used to interrogate the TOF-SIMS dataset for identifying hidden patterns on the leaf's surface based on its chemical profile. After the application of principal component analysis (PCA), a small number of principal components (PCs) were found to be sufficient to explain maximum variance in the data. To further confirm the contributions from pure components, a five-factor multivariate curve resolution (MCR) model was applied. Two distinct patterns of small islets, here termed 'crystals', were apparent from the resulting score plots. Based on PCA and MCR results, the crystals were found to be formed by C 23 or C 29 alcohols. Other less obvious patterns observed in the PCs revealed that the adaxial leaf surface is coated with a relatively homogenous layer of alcohols, hydrocarbons and WEs. The ultra-high-resolution TOF-SIMS imaging combined with the MVA approach helped to highlight the diverse patterns underlying the leaf's surface. Currently, the methods available to analyze the surface chemistry of waxes in conjunction with the spatial information related to the distribution of compounds are limited. This study uses tools that may provide important biological insights into the composition of the wax layer, how this layer is repaired after mechanical damage or insect feeding, and which transport mechanisms are involved in deploying wax constituents to specific regions on the leaf surface. © 2017 The Authors The Plant Journal © 2017 John Wiley & Sons Ltd.

Batch settling curve registration via image data modeling.

PubMed

Derlon, Nicolas; Thürlimann, Christian; Dürrenmatt, David; Villez, Kris

2017-05-01

To this day, obtaining reliable characterization of sludge settling properties remains a challenging and time-consuming task. Without such assessments however, optimal design and operation of secondary settling tanks is challenging and conservative approaches will remain necessary. With this study, we show that automated sludge blanket height registration and zone settling velocity estimation is possible thanks to analysis of images taken during batch settling experiments. The experimental setup is particularly interesting for practical applications as it consists of off-the-shelf components only, no moving parts are required, and the software is released publicly. Furthermore, the proposed multivariate shape constrained spline model for image analysis appears to be a promising method for reliable sludge blanket height profile registration. Copyright © 2017 Elsevier Ltd. All rights reserved.

An image analysis of TLC patterns for quality control of saffron based on soil salinity effect: A strategy for data (pre)-processing.

PubMed

Sereshti, Hassan; Poursorkh, Zahra; Aliakbarzadeh, Ghazaleh; Zarre, Shahin; Ataolahi, Sahar

2018-01-15

Quality of saffron, a valuable food additive, could considerably affect the consumers' health. In this work, a novel preprocessing strategy for image analysis of saffron thin layer chromatographic (TLC) patterns was introduced. This includes performing a series of image pre-processing techniques on TLC images such as compression, inversion, elimination of general baseline (using asymmetric least squares (AsLS)), removing spots shift and concavity (by correlation optimization warping (COW)), and finally conversion to RGB chromatograms. Subsequently, an unsupervised multivariate data analysis including principal component analysis (PCA) and k-means clustering was utilized to investigate the soil salinity effect, as a cultivation parameter, on saffron TLC patterns. This method was used as a rapid and simple technique to obtain the chemical fingerprints of saffron TLC images. Finally, the separated TLC spots were chemically identified using high-performance liquid chromatography-diode array detection (HPLC-DAD). Accordingly, the saffron quality from different areas of Iran was evaluated and classified. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prognostic molecular markers with no impact on decision-making: the paradox of gliomas based on a prospective study.

PubMed

Wager, M; Menei, P; Guilhot, J; Levillain, P; Michalak, S; Bataille, B; Blanc, J-L; Lapierre, F; Rigoard, P; Milin, S; Duthe, F; Bonneau, D; Larsen, C-J; Karayan-Tapon, L

2008-06-03

This study assessed the prognostic value of several markers involved in gliomagenesis, and compared it with that of other clinical and imaging markers already used. Four-hundred and sixteen adult patients with newly diagnosed glioma were included over a 3-year period and tumour suppressor genes, oncogenes, MGMT and hTERT expressions, losses of heterozygosity, as well as relevant clinical and imaging information were recorded. This prospective study was based on all adult gliomas. Analyses were performed on patient groups selected according to World Health Organization histoprognostic criteria and on the entire cohort. The endpoint was overall survival, estimated by the Kaplan-Meier method. Univariate analysis was followed by multivariate analysis according to a Cox model. p14(ARF), p16(INK4A) and PTEN expressions, and 10p 10q23, 10q26 and 13q LOH for the entire cohort, hTERT expression for high-grade tumours, EGFR for glioblastomas, 10q26 LOH for grade III tumours and anaplastic oligodendrogliomas were found to be correlated with overall survival on univariate analysis and age and grade on multivariate analysis only. This study confirms the prognostic value of several markers. However, the scattering of the values explained by tumour heterogeneity prevents their use in individual decision-making.

Multivariate Strategies in Functional Magnetic Resonance Imaging

ERIC Educational Resources Information Center

Hansen, Lars Kai

2007-01-01

We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a "mind reading" predictive multivariate fMRI model.

The Statistical Consulting Center for Astronomy (SCCA)

NASA Technical Reports Server (NTRS)

Akritas, Michael

2001-01-01

The process by which raw astronomical data acquisition is transformed into scientifically meaningful results and interpretation typically involves many statistical steps. Traditional astronomy limits itself to a narrow range of old and familiar statistical methods: means and standard deviations; least-squares methods like chi(sup 2) minimization; and simple nonparametric procedures such as the Kolmogorov-Smirnov tests. These tools are often inadequate for the complex problems and datasets under investigations, and recent years have witnessed an increased usage of maximum-likelihood, survival analysis, multivariate analysis, wavelet and advanced time-series methods. The Statistical Consulting Center for Astronomy (SCCA) assisted astronomers with the use of sophisticated tools, and to match these tools with specific problems. The SCCA operated with two professors of statistics and a professor of astronomy working together. Questions were received by e-mail, and were discussed in detail with the questioner. Summaries of those questions and answers leading to new approaches were posted on the Web (www.state.psu.edu/ mga/SCCA). In addition to serving individual astronomers, the SCCA established a Web site for general use that provides hypertext links to selected on-line public-domain statistical software and services. The StatCodes site (www.astro.psu.edu/statcodes) provides over 200 links in the areas of: Bayesian statistics; censored and truncated data; correlation and regression, density estimation and smoothing, general statistics packages and information; image analysis; interactive Web tools; multivariate analysis; multivariate clustering and classification; nonparametric analysis; software written by astronomers; spatial statistics; statistical distributions; time series analysis; and visualization tools. StatCodes has received a remarkable high and constant hit rate of 250 hits/week (over 10,000/year) since its inception in mid-1997. It is of interest to scientists both within and outside of astronomy. The most popular sections are multivariate techniques, image analysis, and time series analysis. Hundreds of copies of the ASURV, SLOPES and CENS-TAU codes developed by SCCA scientists were also downloaded from the StatCodes site. In addition to formal SCCA duties, SCCA scientists continued a variety of related activities in astrostatistics, including refereeing of statistically oriented papers submitted to the Astrophysical Journal, talks in meetings including Feigelson's talk to science journalists entitled "The reemergence of astrostatistics" at the American Association for the Advancement of Science meeting, and published papers of astrostatistical content.

Beauty or Brains: Which Image for Your Mate.

ERIC Educational Resources Information Center

Meiners, Mary L.; Sheposh, John P.

Male and female subjects evaluated a male after seeing a videotape of him with his girlfriend. The attractiveness and intelligence of the girlfriend was varied. A multivariate analysis of variance on 10 dependent measures showed the male to be evaluated more favorably when his partner was more attractive or more intelligent. Univariate analysis…

Discrete Fourier Transform-Based Multivariate Image Analysis: Application to Modeling of Aromatase Inhibitory Activity.

PubMed

Barigye, Stephen J; Freitas, Matheus P; Ausina, Priscila; Zancan, Patricia; Sola-Penna, Mauro; Castillo-Garit, Juan A

2018-02-12

We recently generalized the formerly alignment-dependent multivariate image analysis applied to quantitative structure-activity relationships (MIA-QSAR) method through the application of the discrete Fourier transform (DFT), allowing for its application to noncongruent and structurally diverse chemical compound data sets. Here we report the first practical application of this method in the screening of molecular entities of therapeutic interest, with human aromatase inhibitory activity as the case study. We developed an ensemble classification model based on the two-dimensional (2D) DFT MIA-QSAR descriptors, with which we screened the NCI Diversity Set V (1593 compounds) and obtained 34 chemical compounds with possible aromatase inhibitory activity. These compounds were docked into the aromatase active site, and the 10 most promising compounds were selected for in vitro experimental validation. Of these compounds, 7419 (nonsteroidal) and 89 201 (steroidal) demonstrated satisfactory antiproliferative and aromatase inhibitory activities. The obtained results suggest that the 2D-DFT MIA-QSAR method may be useful in ligand-based virtual screening of new molecular entities of therapeutic utility.

Raman Imaging of Plant Cell Walls in Sections of Cucumis sativus

PubMed Central

Zeise, Ingrid; Heiner, Zsuzsanna; Holz, Sabine; Joester, Maike; Büttner, Carmen

2018-01-01

Raman microspectra combine information on chemical composition of plant tissues with spatial information. The contributions from the building blocks of the cell walls in the Raman spectra of plant tissues can vary in the microscopic sub-structures of the tissue. Here, we discuss the analysis of 55 Raman maps of root, stem, and leaf tissues of Cucumis sativus, using different spectral contributions from cellulose and lignin in both univariate and multivariate imaging methods. Imaging based on hierarchical cluster analysis (HCA) and principal component analysis (PCA) indicates different substructures in the xylem cell walls of the different tissues. Using specific signals from the cell wall spectra, analysis of the whole set of different tissue sections based on the Raman images reveals differences in xylem tissue morphology. Due to the specifics of excitation of the Raman spectra in the visible wavelength range (532 nm), which is, e.g., in resonance with carotenoid species, effects of photobleaching and the possibility of exploiting depletion difference spectra for molecular characterization in Raman imaging of plants are discussed. The reported results provide both, specific information on the molecular composition of cucumber tissue Raman spectra, and general directions for future imaging studies in plant tissues. PMID:29370089

Raman Imaging of Plant Cell Walls in Sections of Cucumis sativus.

PubMed

Zeise, Ingrid; Heiner, Zsuzsanna; Holz, Sabine; Joester, Maike; Büttner, Carmen; Kneipp, Janina

2018-01-25

Raman microspectra combine information on chemical composition of plant tissues with spatial information. The contributions from the building blocks of the cell walls in the Raman spectra of plant tissues can vary in the microscopic sub-structures of the tissue. Here, we discuss the analysis of 55 Raman maps of root, stem, and leaf tissues of Cucumis sativus , using different spectral contributions from cellulose and lignin in both univariate and multivariate imaging methods. Imaging based on hierarchical cluster analysis (HCA) and principal component analysis (PCA) indicates different substructures in the xylem cell walls of the different tissues. Using specific signals from the cell wall spectra, analysis of the whole set of different tissue sections based on the Raman images reveals differences in xylem tissue morphology. Due to the specifics of excitation of the Raman spectra in the visible wavelength range (532 nm), which is, e.g., in resonance with carotenoid species, effects of photobleaching and the possibility of exploiting depletion difference spectra for molecular characterization in Raman imaging of plants are discussed. The reported results provide both, specific information on the molecular composition of cucumber tissue Raman spectra, and general directions for future imaging studies in plant tissues.

Development of Raman microspectroscopy for automated detection and imaging of basal cell carcinoma

NASA Astrophysics Data System (ADS)

Larraona-Puy, Marta; Ghita, Adrian; Zoladek, Alina; Perkins, William; Varma, Sandeep; Leach, Iain H.; Koloydenko, Alexey A.; Williams, Hywel; Notingher, Ioan

2009-09-01

We investigate the potential of Raman microspectroscopy (RMS) for automated evaluation of excised skin tissue during Mohs micrographic surgery (MMS). The main aim is to develop an automated method for imaging and diagnosis of basal cell carcinoma (BCC) regions. Selected Raman bands responsible for the largest spectral differences between BCC and normal skin regions and linear discriminant analysis (LDA) are used to build a multivariate supervised classification model. The model is based on 329 Raman spectra measured on skin tissue obtained from 20 patients. BCC is discriminated from healthy tissue with 90+/-9% sensitivity and 85+/-9% specificity in a 70% to 30% split cross-validation algorithm. This multivariate model is then applied on tissue sections from new patients to image tumor regions. The RMS images show excellent correlation with the gold standard of histopathology sections, BCC being detected in all positive sections. We demonstrate the potential of RMS as an automated objective method for tumor evaluation during MMS. The replacement of current histopathology during MMS by a ``generalization'' of the proposed technique may improve the feasibility and efficacy of MMS, leading to a wider use according to clinical need.

Digital image processing for information extraction.

NASA Technical Reports Server (NTRS)

Billingsley, F. C.

1973-01-01

The modern digital computer has made practical image processing techniques for handling nonlinear operations in both the geometrical and the intensity domains, various types of nonuniform noise cleanup, and the numerical analysis of pictures. An initial requirement is that a number of anomalies caused by the camera (e.g., geometric distortion, MTF roll-off, vignetting, and nonuniform intensity response) must be taken into account or removed to avoid their interference with the information extraction process. Examples illustrating these operations are discussed along with computer techniques used to emphasize details, perform analyses, classify materials by multivariate analysis, detect temporal differences, and aid in human interpretation of photos.

PyMVPA: A python toolbox for multivariate pattern analysis of fMRI data.

PubMed

Hanke, Michael; Halchenko, Yaroslav O; Sederberg, Per B; Hanson, Stephen José; Haxby, James V; Pollmann, Stefan

2009-01-01

Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability.

PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data

PubMed Central

Hanke, Michael; Halchenko, Yaroslav O.; Sederberg, Per B.; Hanson, Stephen José; Haxby, James V.; Pollmann, Stefan

2009-01-01

Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine-learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability. PMID:19184561

Multivariate Associations Among Behavioral, Clinical, and Multimodal Imaging Phenotypes in Patients With Psychosis.

PubMed

Moser, Dominik A; Doucet, Gaelle E; Lee, Won Hee; Rasgon, Alexander; Krinsky, Hannah; Leibu, Evan; Ing, Alex; Schumann, Gunter; Rasgon, Natalie; Frangou, Sophia

2018-04-01

Alterations in multiple neuroimaging phenotypes have been reported in psychotic disorders. However, neuroimaging measures can be influenced by factors that are not directly related to psychosis and may confound the interpretation of case-control differences. Therefore, a detailed characterization of the contribution of these factors to neuroimaging phenotypes in psychosis is warranted. To quantify the association between neuroimaging measures and behavioral, health, and demographic variables in psychosis using an integrated multivariate approach. This imaging study was conducted at a university research hospital from June 26, 2014, to March 9, 2017. High-resolution multimodal magnetic resonance imaging data were obtained from 100 patients with schizophrenia, 40 patients with bipolar disorder, and 50 healthy volunteers; computed were cortical thickness, subcortical volumes, white matter fractional anisotropy, task-related brain activation (during working memory and emotional recognition), and resting-state functional connectivity. Ascertained in all participants were nonimaging measures pertaining to clinical features, cognition, substance use, psychological trauma, physical activity, and body mass index. The association between imaging and nonimaging measures was modeled using sparse canonical correlation analysis with robust reliability testing. Multivariate patterns of the association between nonimaging and neuroimaging measures in patients with psychosis and healthy volunteers. The analyses were performed in 92 patients with schizophrenia (23 female [25.0%]; mean [SD] age, 27.0 [7.6] years), 37 patients with bipolar disorder (12 female [32.4%]; mean [SD] age, 27.5 [8.1] years), and 48 healthy volunteers (20 female [41.7%]; mean [SD] age, 29.8 [8.5] years). The imaging and nonimaging data sets showed significant covariation (r = 0.63, P < .001), which was independent of diagnosis. Among the nonimaging variables examined, age (r = -0.53), IQ (r = 0.36), and body mass index (r = -0.25) were associated with multiple imaging phenotypes; cannabis use (r = 0.23) and other substance use (r = 0.33) were associated with subcortical volumes, and alcohol use was associated with white matter integrity (r = -0.15). Within the multivariate models, positive symptoms retained associations with the global neuroimaging (r = -0.13), the cortical thickness (r = -0.22), and the task-related activation variates (r = -0.18); negative symptoms were mostly associated with measures of subcortical volume (r = 0.23), and depression/anxiety was associated with measures of white matter integrity (r = 0.12). Multivariate analyses provide a more accurate characterization of the association between brain alterations and psychosis because they enable the modeling of other key factors that influence neuroimaging phenotypes.

Single Particle Analysis by Combined Chemical Imaging to Study Episodic Air Pollution Events in Vienna

NASA Astrophysics Data System (ADS)

Ofner, Johannes; Eitenberger, Elisabeth; Friedbacher, Gernot; Brenner, Florian; Hutter, Herbert; Schauer, Gerhard; Kistler, Magdalena; Greilinger, Marion; Lohninger, Hans; Lendl, Bernhard; Kasper-Giebl, Anne

2017-04-01

The aerosol composition of a city like Vienna is characterized by a complex interaction of local emissions and atmospheric input on a regional and continental scale. The identification of major aerosol constituents for basic source appointment and air quality issues needs a high analytical effort. Exceptional episodic air pollution events strongly change the typical aerosol composition of a city like Vienna on a time-scale of few hours to several days. Analyzing the chemistry of particulate matter from these events is often hampered by the sampling time and related sample amount necessary to apply the full range of bulk analytical methods needed for chemical characterization. Additionally, morphological and single particle features are hardly accessible. Chemical Imaging evolved to a powerful tool for image-based chemical analysis of complex samples. As a complementary technique to bulk analytical methods, chemical imaging can address a new access to study air pollution events by obtaining major aerosol constituents with single particle features at high temporal resolutions and small sample volumes. The analysis of the chemical imaging datasets is assisted by multivariate statistics with the benefit of image-based chemical structure determination for direct aerosol source appointment. A novel approach in chemical imaging is combined chemical imaging or so-called multisensor hyperspectral imaging, involving elemental imaging (electron microscopy-based energy dispersive X-ray imaging), vibrational imaging (Raman micro-spectroscopy) and mass spectrometric imaging (Time-of-Flight Secondary Ion Mass Spectrometry) with subsequent combined multivariate analytics. Combined chemical imaging of precipitated aerosol particles will be demonstrated by the following examples of air pollution events in Vienna: Exceptional episodic events like the transformation of Saharan dust by the impact of the city of Vienna will be discussed and compared to samples obtained at a high alpine background site (Sonnblick Observatory, Saharan Dust Event from April 2016). Further, chemical imaging of biological aerosol constituents of an autumnal pollen breakout in Vienna, with background samples from nearby locations from November 2016 will demonstrate the advantages of the chemical imaging approach. Additionally, the chemical fingerprint of an exceptional air pollution event from a local emission source, caused by the pull down process of a building in Vienna will unravel the needs for multisensor imaging, especially the combinational access. Obtained chemical images will be correlated to bulk analytical results. Benefits of the overall methodical access by combining bulk analytics and combined chemical imaging of exceptional episodic air pollution events will be discussed.

A CCA+ICA based model for multi-task brain imaging data fusion and its application to schizophrenia.

PubMed

Sui, Jing; Adali, Tülay; Pearlson, Godfrey; Yang, Honghui; Sponheim, Scott R; White, Tonya; Calhoun, Vince D

2010-05-15

Collection of multiple-task brain imaging data from the same subject has now become common practice in medical imaging studies. In this paper, we propose a simple yet effective model, "CCA+ICA", as a powerful tool for multi-task data fusion. This joint blind source separation (BSS) model takes advantage of two multivariate methods: canonical correlation analysis and independent component analysis, to achieve both high estimation accuracy and to provide the correct connection between two datasets in which sources can have either common or distinct between-dataset correlation. In both simulated and real fMRI applications, we compare the proposed scheme with other joint BSS models and examine the different modeling assumptions. The contrast images of two tasks: sensorimotor (SM) and Sternberg working memory (SB), derived from a general linear model (GLM), were chosen to contribute real multi-task fMRI data, both of which were collected from 50 schizophrenia patients and 50 healthy controls. When examining the relationship with duration of illness, CCA+ICA revealed a significant negative correlation with temporal lobe activation. Furthermore, CCA+ICA located sensorimotor cortex as the group-discriminative regions for both tasks and identified the superior temporal gyrus in SM and prefrontal cortex in SB as task-specific group-discriminative brain networks. In summary, we compared the new approach to some competitive methods with different assumptions, and found consistent results regarding each of their hypotheses on connecting the two tasks. Such an approach fills a gap in existing multivariate methods for identifying biomarkers from brain imaging data.

Development of a prediction model for residual disease in newly diagnosed advanced ovarian cancer.

PubMed

Janco, Jo Marie Tran; Glaser, Gretchen; Kim, Bohyun; McGree, Michaela E; Weaver, Amy L; Cliby, William A; Dowdy, Sean C; Bakkum-Gamez, Jamie N

2015-07-01

To construct a tool, using computed tomography (CT) imaging and preoperative clinical variables, to estimate successful primary cytoreduction for advanced epithelial ovarian cancer (EOC). Women who underwent primary cytoreductive surgery for stage IIIC/IV EOC at Mayo Clinic between 1/2/2003 and 12/30/2011 and had preoperative CT images of the abdomen and pelvis within 90days prior to their surgery available for review were included. CT images were reviewed for large-volume ascites, diffuse peritoneal thickening (DPT), omental cake, lymphadenopathy (LP), and spleen or liver involvement. Preoperative factors included age, body mass index (BMI), Eastern Cooperative Oncology Group performance status (ECOG PS), American Society of Anesthesiologists (ASA) score, albumin, CA-125, and thrombocytosis. Two prediction models were developed to estimate the probability of (i) complete and (ii) suboptimal cytoreduction (residual disease (RD) >1cm) using multivariable logistic analysis with backward and stepwise variable selection methods. Internal validation was assessed using bootstrap resampling to derive an optimism-corrected estimate of the c-index. 279 patients met inclusion criteria: 143 had complete cytoreduction, 26 had suboptimal cytoreduction (RD>1cm), and 110 had measurable RD ≤1cm. On multivariable analysis, age, absence of ascites, omental cake, and DPT on CT imaging independently predicted complete cytoreduction (c-index=0.748). Conversely, predictors of suboptimal cytoreduction were ECOG PS, DPT, and LP on preoperative CT imaging (c-index=0.685). The generated models serve as preoperative evaluation tools that may improve counseling and selection for primary surgery, but need to be externally validated. Copyright © 2015 Elsevier Inc. All rights reserved.

Landsat TM inventory and assessment of waterbird habitat in the southern altiplano of South America

USGS Publications Warehouse

Boyle, T.P.; Caziani, S.M.; Waltermire, R.G.

2004-01-01

The diverse set of wetlands in southern altiplano of South America supports a number of endemic and migratory waterbirds. These species include endangered endemic flamingos and shorebirds that nest in North America and winter in the altiplano. This research developed maps from nine Landsat Thematic Mapper (TM) images (254,300 km2) to provide an inventory of aquatic waterbird habitats. Image processing software was used to produce a map with a classification of wetlands according to the habitat requirements of different types of waterbirds. A hierarchical procedure was used to, first, isolate the bodies of water within the TM image; second, execute an unsupervised classification on the subsetted image to produce 300 signatures of cover types, which were further subdivided as necessary. Third, each of the classifications was examined in the light of field data and personal experience for relevance to the determination of the various habitat types. Finally, the signatures were applied to the entire image and other adjacent images to yield a map depicting the location of the various waterbird habitats in the southern altiplano. The data sets referenced with a global positioning system receiver were used to test the classification system. Multivariate analysis of the bird communities censused at each lake by individual habitats indicated a salinity gradient, and then the depth of the water separated the birds. Multivariate analysis of the chemical and physical data from the lakes showed that the variation in lakes were significantly associated with difference in depth, transparency, latitude, elevation, and pH. The presence of gravel bottoms was also one of the qualities distinguishing a group of lakes. This information will be directly useful to the Flamingo Census Project and serve as an element for risk assessment for future development.

Impact of Delayed Time to Advanced Imaging on Missed Appointments Across Different Demographic and Socioeconomic Factors.

PubMed

Daye, Dania; Carrodeguas, Emmanuel; Glover, McKinley; Guerrier, Claude Emmanuel; Harvey, H Benjamin; Flores, Efrén J

2018-05-01

The aim of this study was to investigate the impact of wait days (WDs) on missed outpatient MRI appointments across different demographic and socioeconomic factors. An institutional review board-approved retrospective study was conducted among adult patients scheduled for outpatient MRI during a 12-month period. Scheduling data and demographic information were obtained. Imaging missed appointments were defined as missed scheduled imaging encounters. WDs were defined as the number of days from study order to appointment. Multivariate logistic regression was applied to assess the contribution of race and socioeconomic factors to missed appointments. Linear regression was performed to assess the relationship between missed appointment rates and WDs stratified by race, income, and patient insurance groups with analysis of covariance statistics. A total of 42,727 patients met the inclusion criteria. Mean WDs were 7.95 days. Multivariate regression showed increased odds ratio for missed appointments for patients with increased WDs (7-21 days: odds ratio [OR], 1.39; >21 days: OR, 1.77), African American patients (OR, 1.71), Hispanic patients (OR, 1.30), patients with noncommercial insurance (OR, 2.00-2.55), and those with imaging performed at the main hospital campus (OR, 1.51). Missed appointment rate linearly increased with WDs, with analysis of covariance revealing underrepresented minorities and Medicaid insurance as significant effect modifiers. Increased WDs for advanced imaging significantly increases the likelihood of missed appointments. This effect is most pronounced among underrepresented minorities and patients with lower socioeconomic status. Efforts to reduce WDs may improve equity in access to and utilization of advanced diagnostic imaging for all patients. Copyright © 2018. Published by Elsevier Inc.

Computer program documentation for the patch subsampling processor

NASA Technical Reports Server (NTRS)

Nieves, M. J.; Obrien, S. O.; Oney, J. K. (Principal Investigator)

1981-01-01

The programs presented are intended to provide a way to extract a sample from a full-frame scene and summarize it in a useful way. The sample in each case was chosen to fill a 512-by-512 pixel (sample-by-line) image since this is the largest image that can be displayed on the Integrated Multivariant Data Analysis and Classification System. This sample size provides one megabyte of data for manipulation and storage and contains about 3% of the full-frame data. A patch image processor computes means for 256 32-by-32 pixel squares which constitute the 512-by-512 pixel image. Thus, 256 measurements are available for 8 vegetation indexes over a 100-mile square.

Detecting spatio-temporal modes in multivariate data by entropy field decomposition

NASA Astrophysics Data System (ADS)

Frank, Lawrence R.; Galinsky, Vitaly L.

2016-09-01

A new data analysis method that addresses a general problem of detecting spatio-temporal variations in multivariate data is presented. The method utilizes two recent and complimentary general approaches to data analysis, information field theory (IFT) and entropy spectrum pathways (ESPs). Both methods reformulate and incorporate Bayesian theory, thus use prior information to uncover underlying structure of the unknown signal. Unification of ESP and IFT creates an approach that is non-Gaussian and nonlinear by construction and is found to produce unique spatio-temporal modes of signal behavior that can be ranked according to their significance, from which space-time trajectories of parameter variations can be constructed and quantified. Two brief examples of real world applications of the theory to the analysis of data bearing completely different, unrelated nature, lacking any underlying similarity, are also presented. The first example provides an analysis of resting state functional magnetic resonance imaging data that allowed us to create an efficient and accurate computational method for assessing and categorizing brain activity. The second example demonstrates the potential of the method in the application to the analysis of a strong atmospheric storm circulation system during the complicated stage of tornado development and formation using data recorded by a mobile Doppler radar. Reference implementation of the method will be made available as a part of the QUEST toolkit that is currently under development at the Center for Scientific Computation in Imaging.

Cross-cultural relationships between self-concept and body image in high school-age boys.

PubMed

Austin, J K; Champion, V L; Tzeng, O C

1989-08-01

The relationship between self-concept and body image was investigated through a secondary analysis of data from a sample of 1,200 high school male students from 30 language/culture communities (Osgood, May, & Myron, 1975). Subjects rated adjectives pertaining to self-concept and body image using 7-step semantic differential bipolar scales. Adjectives were related to the dimensions of Evaluation, Potency, and Activity. Correlation, factor analysis, and multiple regression were utilized to examine multivariate relationships among self-concept dimensions and body-image dimensions. Significant positive correlations were found between self-concept and body image. In addition, significant positive relationships were found when self-concept factors were regressed on the body-image factor (R2 = .49 to .57, p less than or equal to .001) for Activity and Potency. Results support the existence of a strong positive relationship between self-concept and body image across the 30 cultures involved. Findings have important implications for nursing in assessment and interventions with clients who have deficits in either self-concept or body image.

Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer.

PubMed

Tamez-Peña, Jose-Gerardo; Rodriguez-Rojas, Juan-Andrés; Gomez-Rueda, Hugo; Celaya-Padilla, Jose-Maria; Rivera-Prieto, Roxana-Alicia; Palacios-Corona, Rebeca; Garza-Montemayor, Margarita; Cardona-Huerta, Servando; Treviño, Victor

2018-01-01

In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with breast cancer that underwent pre-treatment digital mammograms and tumor biopsies. From digital mammograms, a semi-automated radiogenomics analysis generated 1,078 features describing the shape, signal distribution, and texture of tumors along their contralateral image used as control. From tumor biopsy, we estimated the OncotypeDX and PAM50 recurrence scores using gene expression microarrays. Then, we used multivariate analysis under stringent cross-validation to train models predicting recurrence scores. Few univariate features reached Spearman correlation coefficients above 0.4. Nevertheless, multivariate analysis yielded significantly correlated models for both signatures (correlation of OncotypeDX = 0.49 ± 0.07 and PAM50 = 0.32 ± 0.10 in stringent cross-validation and OncotypeDX = 0.83 and PAM50 = 0.78 for a unique model). Equivalent models trained from the unaffected contralateral breast were not correlated suggesting that the image signatures were tumor-specific and that overfitting was not a considerable issue. We also noted that models were improved by combining clinical information (triple negative status and progesterone receptor). The models used mostly wavelets and fractal features suggesting their importance to capture tumor information. Our results suggest that molecular-based recurrence risk and breast cancer subtypes have observable radiographic phenotypes. To our knowledge, this is the first study associating mammographic information to gene expression recurrence signatures.

Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer

PubMed Central

Tamez-Peña, Jose-Gerardo; Rodriguez-Rojas, Juan-Andrés; Gomez-Rueda, Hugo; Celaya-Padilla, Jose-Maria; Rivera-Prieto, Roxana-Alicia; Palacios-Corona, Rebeca; Garza-Montemayor, Margarita; Cardona-Huerta, Servando

2018-01-01

In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with breast cancer that underwent pre-treatment digital mammograms and tumor biopsies. From digital mammograms, a semi-automated radiogenomics analysis generated 1,078 features describing the shape, signal distribution, and texture of tumors along their contralateral image used as control. From tumor biopsy, we estimated the OncotypeDX and PAM50 recurrence scores using gene expression microarrays. Then, we used multivariate analysis under stringent cross-validation to train models predicting recurrence scores. Few univariate features reached Spearman correlation coefficients above 0.4. Nevertheless, multivariate analysis yielded significantly correlated models for both signatures (correlation of OncotypeDX = 0.49 ± 0.07 and PAM50 = 0.32 ± 0.10 in stringent cross-validation and OncotypeDX = 0.83 and PAM50 = 0.78 for a unique model). Equivalent models trained from the unaffected contralateral breast were not correlated suggesting that the image signatures were tumor-specific and that overfitting was not a considerable issue. We also noted that models were improved by combining clinical information (triple negative status and progesterone receptor). The models used mostly wavelets and fractal features suggesting their importance to capture tumor information. Our results suggest that molecular-based recurrence risk and breast cancer subtypes have observable radiographic phenotypes. To our knowledge, this is the first study associating mammographic information to gene expression recurrence signatures. PMID:29596496

Optimisation of colour schemes to accurately display mass spectrometry imaging data based on human colour perception.

PubMed

Race, Alan M; Bunch, Josephine

2015-03-01

The choice of colour scheme used to present data can have a dramatic effect on the perceived structure present within the data. This is of particular significance in mass spectrometry imaging (MSI), where ion images that provide 2D distributions of a wide range of analytes are used to draw conclusions about the observed system. Commonly employed colour schemes are generally suboptimal for providing an accurate representation of the maximum amount of data. Rainbow-based colour schemes are extremely popular within the community, but they introduce well-documented artefacts which can be actively misleading in the interpretation of the data. In this article, we consider the suitability of colour schemes and composite image formation found in MSI literature in the context of human colour perception. We also discuss recommendations of rules for colour scheme selection for ion composites and multivariate analysis techniques such as principal component analysis (PCA).

Identifying image preferences based on demographic attributes

NASA Astrophysics Data System (ADS)

Fedorovskaya, Elena A.; Lawrence, Daniel R.

2014-02-01

The intent of this study is to determine what sorts of images are considered more interesting by which demographic groups. Specifically, we attempt to identify images whose interestingness ratings are influenced by the demographic attribute of the viewer's gender. To that end, we use the data from an experiment where 18 participants (9 women and 9 men) rated several hundred images based on "visual interest" or preferences in viewing images. The images were selected to represent the consumer "photo-space" - typical categories of subject matter found in consumer photo collections. They were annotated using perceptual and semantic descriptors. In analyzing the image interestingness ratings, we apply a multivariate procedure known as forced classification, a feature of dual scaling, a discrete analogue of principal components analysis (similar to correspondence analysis). This particular analysis of ratings (i.e., ordered-choice or Likert) data enables the investigator to emphasize the effect of a specific item or collection of items. We focus on the influence of the demographic item of gender on the analysis, so that the solutions are essentially confined to subspaces spanned by the emphasized item. Using this technique, we can know definitively which images' ratings have been influenced by the demographic item of choice. Subsequently, images can be evaluated and linked, on one hand, to their perceptual and semantic descriptors, and, on the other hand, to the preferences associated with viewers' demographic attributes.

Near-earth orbital guidance and remote sensing

NASA Technical Reports Server (NTRS)

Powers, W. F.

1972-01-01

The curriculum of a short course in remote sensing and parameter optimization is presented. The subjects discussed are: (1) basics of remote sensing and the user community, (2) multivariant spectral analysis, (3) advanced mathematics and physics of remote sensing, (4) the atmospheric environment, (5) imaging sensing, and (6)nonimaging sensing. Mathematical models of optimization techniques are developed.

Multimodal image analysis of clinical influences on preterm brain development

PubMed Central

Ball, Gareth; Aljabar, Paul; Nongena, Phumza; Kennea, Nigel; Gonzalez‐Cinca, Nuria; Falconer, Shona; Chew, Andrew T.M.; Harper, Nicholas; Wurie, Julia; Rutherford, Mary A.; Edwards, A. David

2017-01-01

Objective Premature birth is associated with numerous complex abnormalities of white and gray matter and a high incidence of long‐term neurocognitive impairment. An integrated understanding of these abnormalities and their association with clinical events is lacking. The aim of this study was to identify specific patterns of abnormal cerebral development and their antenatal and postnatal antecedents. Methods In a prospective cohort of 449 infants (226 male), we performed a multivariate and data‐driven analysis combining multiple imaging modalities. Using canonical correlation analysis, we sought separable multimodal imaging markers associated with specific clinical and environmental factors and correlated to neurodevelopmental outcome at 2 years. Results We found five independent patterns of neuroanatomical variation that related to clinical factors including age, prematurity, sex, intrauterine complications, and postnatal adversity. We also confirmed the association between imaging markers of neuroanatomical abnormality and poor cognitive and motor outcomes at 2 years. Interpretation This data‐driven approach defined novel and clinically relevant imaging markers of cerebral maldevelopment, which offer new insights into the nature of preterm brain injury. Ann Neurol 2017;82:233–246 PMID:28719076

Image analysis-based modelling for flower number estimation in grapevine.

PubMed

Millan, Borja; Aquino, Arturo; Diago, Maria P; Tardaguila, Javier

2017-02-01

Grapevine flower number per inflorescence provides valuable information that can be used for assessing yield. Considerable research has been conducted at developing a technological tool, based on image analysis and predictive modelling. However, the behaviour of variety-independent predictive models and yield prediction capabilities on a wide set of varieties has never been evaluated. Inflorescence images from 11 grapevine Vitis vinifera L. varieties were acquired under field conditions. The flower number per inflorescence and the flower number visible in the images were calculated manually, and automatically using an image analysis algorithm. These datasets were used to calibrate and evaluate the behaviour of two linear (single-variable and multivariable) and a nonlinear variety-independent model. As a result, the integrated tool composed of the image analysis algorithm and the nonlinear approach showed the highest performance and robustness (RPD = 8.32, RMSE = 37.1). The yield estimation capabilities of the flower number in conjunction with fruit set rate (R 2  = 0.79) and average berry weight (R 2  = 0.91) were also tested. This study proves the accuracy of flower number per inflorescence estimation using an image analysis algorithm and a nonlinear model that is generally applicable to different grapevine varieties. This provides a fast, non-invasive and reliable tool for estimation of yield at harvest. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

FGWAS: Functional genome wide association analysis.

PubMed

Huang, Chao; Thompson, Paul; Wang, Yalin; Yu, Yang; Zhang, Jingwen; Kong, Dehan; Colen, Rivka R; Knickmeyer, Rebecca C; Zhu, Hongtu

2017-10-01

Functional phenotypes (e.g., subcortical surface representation), which commonly arise in imaging genetic studies, have been used to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. However, existing statistical methods largely ignore the functional features (e.g., functional smoothness and correlation). The aim of this paper is to develop a functional genome-wide association analysis (FGWAS) framework to efficiently carry out whole-genome analyses of functional phenotypes. FGWAS consists of three components: a multivariate varying coefficient model, a global sure independence screening procedure, and a test procedure. Compared with the standard multivariate regression model, the multivariate varying coefficient model explicitly models the functional features of functional phenotypes through the integration of smooth coefficient functions and functional principal component analysis. Statistically, compared with existing methods for genome-wide association studies (GWAS), FGWAS can substantially boost the detection power for discovering important genetic variants influencing brain structure and function. Simulation studies show that FGWAS outperforms existing GWAS methods for searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. We have successfully applied FGWAS to large-scale analysis of data from the Alzheimer's Disease Neuroimaging Initiative for 708 subjects, 30,000 vertices on the left and right hippocampal surfaces, and 501,584 SNPs. Copyright © 2017 Elsevier Inc. All rights reserved.

Insight into plant cell wall chemistry and structure by combination of multiphoton microscopy with Raman imaging.

PubMed

Heiner, Zsuzsanna; Zeise, Ingrid; Elbaum, Rivka; Kneipp, Janina

2018-04-01

Spontaneous Raman scattering microspectroscopy, second harmonic generation (SHG) and 2-photon excited fluorescence (2PF) were used in combination to characterize the morphology together with the chemical composition of the cell wall in native plant tissues. As the data obtained with unstained sections of Sorghum bicolor root and leaf tissues illustrate, nonresonant as well as pre-resonant Raman microscopy in combination with hyperspectral analysis reveals details about the distribution and composition of the major cell wall constituents. Multivariate analysis of the Raman data allows separation of different tissue regions, specifically the endodermis, xylem and lumen. The orientation of cellulose microfibrils is obtained from polarization-resolved SHG signals. Furthermore, 2-photon autofluorescence images can be used to image lignification. The combined compositional, morphological and orientational information in the proposed coupling of SHG, Raman imaging and 2PF presents an extension of existing vibrational microspectroscopic imaging and multiphoton microscopic approaches not only for plant tissues. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

DOE PAGES

Belianinov, Alex; Panchapakesan, G.; Lin, Wenzhi; ...

2014-12-02

Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe0.55Se0.45 (Tc = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe1 x Sex structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified by their electronic signaturemore » and differentiated by their local chemical environment. This approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces.« less

Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

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

Belianinov, Alex, E-mail: belianinova@ornl.gov; Ganesh, Panchapakesan; Lin, Wenzhi

2014-12-01

Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe{sub 0.55}Se{sub 0.45} (T{sub c} = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe{sub 1−x}Se{sub x} structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified bymore » their electronic signature and differentiated by their local chemical environment. This approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces.« less

US characteristics for the prediction of neoplasm in gallbladder polyps 10 mm or larger.

PubMed

Kim, Jin Sil; Lee, Jeong Kyong; Kim, Yookyung; Lee, Sang Min

2016-04-01

To evaluate the characteristics of gallbladder polyps 10 mm or larger to predict a neoplasm in US examinations. Fifty-three patients with gallbladder polyps ≥ 10 mm with follow-up images or pathologic diagnosis were included in the retrospective study. All images and reports were reviewed to determine the imaging characteristics of gallbladder polyps. Univariate and multivariate analyses were used to evaluate predictors for a neoplastic polyp. A neoplastic polyp was verified in 12 of 53 patients and the mean size was 13.9 mm. The univariate analysis revealed that adjacent gallbladder wall thickening, larger size (≥15 mm), older age (≥57 years), absence of hyperechoic foci in a polyp, CT visibility, sessile shape, a solitary polyp, and an irregular surface were significant predictors for a neoplastic polyp. In the multivariate analysis, larger size (≥15 mm) was a significant predictor for a neoplastic polyp. A polyp size ≥15 mm was the strongest predictor for a neoplastic polyp with US. The hyperechoic foci in a polyp and CT visibility would be useful indicators for the differentiation of a neoplastic polyp, in addition to the established predictors. • A polyp size ≥15 mm is the strongest predictor for a neoplastic polyp with US. • Hyperechoic foci in a polyp and CT visibility are new predictors. • The rate of malignancy is low in polyps even 10 mm or larger (15.1 %).

Diagnostic value of history and physical examination in patients suspected of lumbosacral nerve root compression

PubMed Central

Vroomen, P; de Krom, M C T F M; Wilmink, J; Kester, A; Knottnerus, J

2002-01-01

Objective: To evaluate patient characteristics, symptoms, and examination findings in the clinical diagnosis of lumbosacral nerve root compression causing sciatica. Methods: The study involved 274 patients with pain radiating into the leg. All had a standardised clinical assessment and magnetic resonance (MR) imaging. The associations between patient characteristics, clinical findings, and lumbosacral nerve root compression on MR imaging were analysed. Results: Nerve root compression was associated with three patient characteristics, three symptoms, and four physical examination findings (paresis, absence of tendon reflexes, a positive straight leg raising test, and increased finger-floor distance). Multivariate analysis, analysing the independent diagnostic value of the tests, showed that nerve root compression was predicted by two patient characteristics, four symptoms, and two signs (increased finger-floor distance and paresis). The straight leg raise test was not predictive. The area under the curve of the receiver-operating characteristic was 0.80 for the history items. It increased to 0.83 when the physical examination items were added. Conclusions: Various clinical findings were found to be associated with nerve root compression on MR imaging. While this set of findings agrees well with those commonly used in daily practice, the tests tended to have lower sensitivity and specificity than previously reported. Stepwise multivariate analysis showed that most of the diagnostic information revealed by physical examination findings had already been revealed by the history items. PMID:11971050

Mapping as a visual health communication tool: promises and dilemmas.

PubMed

Parrott, Roxanne; Hopfer, Suellen; Ghetian, Christie; Lengerich, Eugene

2007-01-01

In the era of evidence-based public health promotion and planning, the use of maps as a form of evidence to communicate about the multiple determinants of cancer is on the rise. Geographic information systems and mapping technologies make future proliferation of this strategy likely. Yet disease maps as a communication form remain largely unexamined. This content analysis considers the presence of multivariate information, credibility cues, and the communication function of publicly accessible maps for cancer control activities. Thirty-six state comprehensive cancer control plans were publicly available in July 2005 and were reviewed for the presence of maps. Fourteen of the 36 state cancer plans (39%) contained map images (N = 59 static maps). A continuum of map inter activity was observed, with 10 states having interactive mapping tools available to query and map cancer information. Four states had both cancer plans with map images and interactive mapping tools available to the public on their Web sites. Of the 14 state cancer plans that depicted map images, two displayed multivariate data in a single map. Nine of the 10 states with interactive mapping capability offered the option to display multivariate health risk messages. The most frequent content category mapped was cancer incidence and mortality, with stage at diagnosis infrequently available. The most frequent communication function served by the maps reviewed was redundancy, as maps repeated information contained in textual forms. The social and ethical implications for communicating about cancer through the use of visual geographic representations are discussed.

Meteor localization via statistical analysis of spatially temporal fluctuations in image sequences

NASA Astrophysics Data System (ADS)

Kukal, Jaromír.; Klimt, Martin; Šihlík, Jan; Fliegel, Karel

2015-09-01

Meteor detection is one of the most important procedures in astronomical imaging. Meteor path in Earth's atmosphere is traditionally reconstructed from double station video observation system generating 2D image sequences. However, the atmospheric turbulence and other factors cause spatially-temporal fluctuations of image background, which makes the localization of meteor path more difficult. Our approach is based on nonlinear preprocessing of image intensity using Box-Cox and logarithmic transform as its particular case. The transformed image sequences are then differentiated along discrete coordinates to obtain statistical description of sky background fluctuations, which can be modeled by multivariate normal distribution. After verification and hypothesis testing, we use the statistical model for outlier detection. Meanwhile the isolated outlier points are ignored, the compact cluster of outliers indicates the presence of meteoroids after ignition.

Discerning mild cognitive impairment and Alzheimer Disease from normal aging: morphologic characterization based on univariate and multivariate models.

PubMed

Liao, Weiqi; Long, Xiaojing; Jiang, Chunxiang; Diao, Yanjun; Liu, Xin; Zheng, Hairong; Zhang, Lijuan

2014-05-01

Differentiating mild cognitive impairment (MCI) and Alzheimer Disease (AD) from healthy aging remains challenging. This study aimed to explore the cerebral structural alterations of subjects with MCI or AD as compared to healthy elderly based on the individual and collective effects of cerebral morphologic indices using univariate and multivariate analyses. T1-weighted images (T1WIs) were retrieved from Alzheimer Disease Neuroimaging Initiative database for 116 subjects who were categorized into groups of healthy aging, MCI, and AD. Analysis of covariance (ANCOVA) and multivariate analysis of covariance (MANCOVA) were performed to explore the intergroup morphologic alterations indexed by surface area, curvature index, cortical thickness, and subjacent white matter volume with age and sex controlled as covariates, in 34 parcellated gyri regions of interest (ROIs) for both cerebral hemispheres based on the T1WI. Statistical parameters were mapped on the anatomic images to facilitate visual inspection. Global rather than region-specific structural alterations were revealed in groups of MCI and AD relative to healthy elderly using MANCOVA. ANCOVA revealed that the cortical thickness decreased more prominently in entorhinal, temporal, and cingulate cortices and was positively correlated with patients' cognitive performance in AD group but not in MCI. The temporal lobe features marked atrophy of white matter during the disease dynamics. Significant intercorrelations were observed among the morphologic indices with univariate analysis for given ROIs. Significant global structural alterations were identified in MCI and AD based on MANCOVA model with improved sensitivity. The intercorrelation among the morphologic indices may dampen the use of individual morphological parameter in featuring cerebral structural alterations. Decrease in cortical thickness is not reflective of the cognitive performance at the early stage of AD. Copyright © 2014 AUR. Published by Elsevier Inc. All rights reserved.

Angular reconstitution-based 3D reconstructions of nanomolecular structures from superresolution light-microscopy images

PubMed Central

Salas, Desirée; Le Gall, Antoine; Fiche, Jean-Bernard; Valeri, Alessandro; Ke, Yonggang; Bron, Patrick; Bellot, Gaetan

2017-01-01

Superresolution light microscopy allows the imaging of labeled supramolecular assemblies at a resolution surpassing the classical diffraction limit. A serious limitation of the superresolution approach is sample heterogeneity and the stochastic character of the labeling procedure. To increase the reproducibility and the resolution of the superresolution results, we apply multivariate statistical analysis methods and 3D reconstruction approaches originally developed for cryogenic electron microscopy of single particles. These methods allow for the reference-free 3D reconstruction of nanomolecular structures from two-dimensional superresolution projection images. Since these 2D projection images all show the structure in high-resolution directions of the optical microscope, the resulting 3D reconstructions have the best possible isotropic resolution in all directions. PMID:28811371

Contrast medium at the site of the anastomosis is crucial in detecting anastomotic leakage with CT imaging after colorectal surgery.

PubMed

Huiberts, Astrid A M; Dijksman, Lea M; Boer, Simone A; Krul, Eveline J T; Peringa, Jan; Donkervoort, Sandra C

2015-06-01

The use of computed tomography (CT) to detect anastomotic leakage (AL) is becoming the standard of care. Accurate detection of AL is crucial. The aim of this study was to define CT criteria that are most predictive for AL. From January 2006 to December 2012, all consecutive patients who had undergone CT imaging because of clinical suspicion of anastomotic leakage after colorectal surgery were analysed. All CT scans were re-evaluated by two independent abdominal radiologists blinded for clinical outcome. The images were scored with a set of criteria and a conclusion whether or not AL was present was drawn. Each separate criterion was analysed for its value in predicting AL by uni- and multivariable logistic regression Of 668 patients with colorectal surgery, 108 had undergone CT imaging within 16 days postoperatively. According to our standard of reference, 34 (31%) of the patients had AL. Univariable analysis showed that "fluid near anastomosis" (radiologist 1 (rad 1), p < 0.001; radiologist 2 (rad 2), p < 0.001) and "air near anastomosis" (rad 1, p < 0.001; rad 2, p < 0.001), "air intra-abdominally" (rad 1, p = 0.019; rad 2, p = 0.004) and "contrast leakage" (rad 1, p < 0.001; rad 2, p < 0.001) were associated with AL. Contrast leakage was the only independent predictor for AL in multivariable analysis for both radiologists (rad 1, OR 5.43 (95% CI 1.18-25.02); rad 2, OR 8.51 (95% CI 2.21-32.83)). The only independent variable predicting AL is leakage of contrast medium. To improve the accuracy of CT imaging, optimal contrast administration near the anastomosis appears to be crucial.

Localization of polyhydroxybutyrate in sugarcane using Fourier-transform infrared microspectroscopy and multivariate imaging

DOE PAGES

Lupoi, Jason S.; Smith-Moritz, Andreia; Singh, Seema; ...

2015-07-10

Background: Slow-degrading, fossil fuel-derived plastics can have deleterious effects on the environment, especially marine ecosystems. The production of bio-based, biodegradable plastics from or in plants can assist in supplanting those manufactured using fossil fuels. Polyhydroxybutyrate (PHB) is one such biodegradable polyester that has been evaluated as a possible candidate for relinquishing the use of environmentally harmful plastics. Results: PHB, possessing similar properties to polyesters produced from non-renewable sources, has been previously engineered in sugarcane, thereby creating a high-value co-product in addition to the high biomass yield. This manuscript illustrates the coupling of a Fourier-transform infrared microspectrometer, equipped with a focalmore » plane array (FPA) detector, with multivariate imaging to successfully identify and localize PHB aggregates. Principal component analysis imaging facilitated the mining of the abundant quantity of spectral data acquired using the FPA for distinct PHB vibrational modes. PHB was measured in the chloroplasts of mesophyll and bundle sheath cells, acquiescent with previously evaluated plant samples. Conclusion: This study demonstrates the power of IR microspectroscopy to rapidly image plant sections to provide a snapshot of the chemical composition of the cell. While PHB was localized in sugarcane, this method is readily transferable to other value-added co-products in different plants.« less

Localization of polyhydroxybutyrate in sugarcane using Fourier-transform infrared microspectroscopy and multivariate imaging

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

Lupoi, Jason S.; Smith-Moritz, Andreia; Singh, Seema

Background: Slow-degrading, fossil fuel-derived plastics can have deleterious effects on the environment, especially marine ecosystems. The production of bio-based, biodegradable plastics from or in plants can assist in supplanting those manufactured using fossil fuels. Polyhydroxybutyrate (PHB) is one such biodegradable polyester that has been evaluated as a possible candidate for relinquishing the use of environmentally harmful plastics. Results: PHB, possessing similar properties to polyesters produced from non-renewable sources, has been previously engineered in sugarcane, thereby creating a high-value co-product in addition to the high biomass yield. This manuscript illustrates the coupling of a Fourier-transform infrared microspectrometer, equipped with a focalmore » plane array (FPA) detector, with multivariate imaging to successfully identify and localize PHB aggregates. Principal component analysis imaging facilitated the mining of the abundant quantity of spectral data acquired using the FPA for distinct PHB vibrational modes. PHB was measured in the chloroplasts of mesophyll and bundle sheath cells, acquiescent with previously evaluated plant samples. Conclusion: This study demonstrates the power of IR microspectroscopy to rapidly image plant sections to provide a snapshot of the chemical composition of the cell. While PHB was localized in sugarcane, this method is readily transferable to other value-added co-products in different plants.« less

Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network.

PubMed

Zafar, Raheel; Kamel, Nidal; Naufal, Mohamad; Malik, Aamir Saeed; Dass, Sarat C; Ahmad, Rana Fayyaz; Abdullah, Jafri M; Reza, Faruque

2017-01-01

Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).

Multivariate functional response regression, with application to fluorescence spectroscopy in a cervical pre-cancer study.

PubMed

Zhu, Hongxiao; Morris, Jeffrey S; Wei, Fengrong; Cox, Dennis D

2017-07-01

Many scientific studies measure different types of high-dimensional signals or images from the same subject, producing multivariate functional data. These functional measurements carry different types of information about the scientific process, and a joint analysis that integrates information across them may provide new insights into the underlying mechanism for the phenomenon under study. Motivated by fluorescence spectroscopy data in a cervical pre-cancer study, a multivariate functional response regression model is proposed, which treats multivariate functional observations as responses and a common set of covariates as predictors. This novel modeling framework simultaneously accounts for correlations between functional variables and potential multi-level structures in data that are induced by experimental design. The model is fitted by performing a two-stage linear transformation-a basis expansion to each functional variable followed by principal component analysis for the concatenated basis coefficients. This transformation effectively reduces the intra-and inter-function correlations and facilitates fast and convenient calculation. A fully Bayesian approach is adopted to sample the model parameters in the transformed space, and posterior inference is performed after inverse-transforming the regression coefficients back to the original data domain. The proposed approach produces functional tests that flag local regions on the functional effects, while controlling the overall experiment-wise error rate or false discovery rate. It also enables functional discriminant analysis through posterior predictive calculation. Analysis of the fluorescence spectroscopy data reveals local regions with differential expressions across the pre-cancer and normal samples. These regions may serve as biomarkers for prognosis and disease assessment.

Detecting Spatio-Temporal Modes in Multivariate Data by Entropy Field Decomposition

PubMed Central

Frank, Lawrence R.; Galinsky, Vitaly L.

2016-01-01

A new data analysis method that addresses a general problem of detecting spatio-temporal variations in multivariate data is presented. The method utilizes two recent and complimentary general approaches to data analysis, information field theory (IFT) and entropy spectrum pathways (ESP). Both methods reformulate and incorporate Bayesian theory, thus use prior information to uncover underlying structure of the unknown signal. Unification of ESP and IFT creates an approach that is non-Gaussian and non-linear by construction and is found to produce unique spatio-temporal modes of signal behavior that can be ranked according to their significance, from which space-time trajectories of parameter variations can be constructed and quantified. Two brief examples of real world applications of the theory to the analysis of data bearing completely different, unrelated nature, lacking any underlying similarity, are also presented. The first example provides an analysis of resting state functional magnetic resonance imaging (rsFMRI) data that allowed us to create an efficient and accurate computational method for assessing and categorizing brain activity. The second example demonstrates the potential of the method in the application to the analysis of a strong atmospheric storm circulation system during the complicated stage of tornado development and formation using data recorded by a mobile Doppler radar. Reference implementation of the method will be made available as a part of the QUEST toolkit that is currently under development at the Center for Scientific Computation in Imaging. PMID:27695512

Big-Data RHEED analysis for understanding epitaxial film growth processes

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

Vasudevan, Rama K; Tselev, Alexander; Baddorf, Arthur P

Reflection high energy electron diffraction (RHEED) has by now become a standard tool for in-situ monitoring of film growth by pulsed laser deposition and molecular beam epitaxy. Yet despite the widespread adoption and wealth of information in RHEED image, most applications are limited to observing intensity oscillations of the specular spot, and much additional information on growth is discarded. With ease of data acquisition and increased computation speeds, statistical methods to rapidly mine the dataset are now feasible. Here, we develop such an approach to the analysis of the fundamental growth processes through multivariate statistical analysis of RHEED image sequence.more » This approach is illustrated for growth of LaxCa1-xMnO3 films grown on etched (001) SrTiO3 substrates, but is universal. The multivariate methods including principal component analysis and k-means clustering provide insight into the relevant behaviors, the timing and nature of a disordered to ordered growth change, and highlight statistically significant patterns. Fourier analysis yields the harmonic components of the signal and allows separation of the relevant components and baselines, isolating the assymetric nature of the step density function and the transmission spots from the imperfect layer-by-layer (LBL) growth. These studies show the promise of big data approaches to obtaining more insight into film properties during and after epitaxial film growth. Furthermore, these studies open the pathway to use forward prediction methods to potentially allow significantly more control over growth process and hence final film quality.« less

Pathological and 3 Tesla Volumetric Magnetic Resonance Imaging Predictors of Biochemical Recurrence after Robotic Assisted Radical Prostatectomy: Correlation with Whole Mount Histopathology.

PubMed

Tan, Nelly; Shen, Luyao; Khoshnoodi, Pooria; Alcalá, Héctor E; Yu, Weixia; Hsu, William; Reiter, Robert E; Lu, David Y; Raman, Steven S

2018-05-01

We sought to identify the clinical and magnetic resonance imaging variables predictive of biochemical recurrence after robotic assisted radical prostatectomy in patients who underwent multiparametric 3 Tesla prostate magnetic resonance imaging. We performed an institutional review board approved, HIPAA (Health Insurance Portability and Accountability Act) compliant, single arm observational study of 3 Tesla multiparametric magnetic resonance imaging prior to robotic assisted radical prostatectomy from December 2009 to March 2016. Clinical, magnetic resonance imaging and pathological information, and clinical outcomes were compiled. Biochemical recurrence was defined as prostate specific antigen 0.2 ng/cc or greater. Univariate and multivariate regression analysis was performed. Biochemical recurrence had developed in 62 of the 255 men (24.3%) included in the study at a median followup of 23.5 months. Compared to the subcohort without biochemical recurrence the subcohort with biochemical recurrence had a greater proportion of patients with a high grade biopsy Gleason score, higher preoperative prostate specific antigen (7.4 vs 5.6 ng/ml), intermediate and high D'Amico classifications, larger tumor volume on magnetic resonance imaging (0.66 vs 0.30 ml), higher PI-RADS® (Prostate Imaging-Reporting and Data System) version 2 category lesions, a greater proportion of intermediate and high grade radical prostatectomy Gleason score lesions, higher pathological T3 stage (all p <0.01) and a higher positive surgical margin rate (19.3% vs 7.8%, p = 0.016). On multivariable analysis only tumor volume on magnetic resonance imaging (adjusted OR 1.57, p = 0.016), pathological T stage (adjusted OR 2.26, p = 0.02), positive surgical margin (adjusted OR 5.0, p = 0.004) and radical prostatectomy Gleason score (adjusted OR 2.29, p = 0.004) predicted biochemical recurrence. In this cohort tumor volume on magnetic resonance imaging and pathological variables, including Gleason score, staging and positive surgical margins, significantly predicted biochemical recurrence. This suggests an important new imaging biomarker. Copyright © 2018 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Cumulative total effective whole-body radiation dose in critically ill patients.

PubMed

Rohner, Deborah J; Bennett, Suzanne; Samaratunga, Chandrasiri; Jewell, Elizabeth S; Smith, Jeffrey P; Gaskill-Shipley, Mary; Lisco, Steven J

2013-11-01

Uncertainty exists about a safe dose limit to minimize radiation-induced cancer. Maximum occupational exposure is 20 mSv/y averaged over 5 years with no more than 50 mSv in any single year. Radiation exposure to the general population is less, but the average dose in the United States has doubled in the past 30 years, largely from medical radiation exposure. We hypothesized that patients in a mixed-use surgical ICU (SICU) approach or exceed this limit and that trauma patients were more likely to exceed 50 mSv because of frequent diagnostic imaging. Patients admitted into 15 predesignated SICU beds in a level I trauma center during a 30-day consecutive period were prospectively observed. Effective dose was determined using Huda's method for all radiography, CT imaging, and fluoroscopic examinations. Univariate and multivariable linear regressions were used to analyze the relationships between observed values and outcomes. Five of 74 patients (6.8%) exceeded exposures of 50 mSv. Univariate analysis showed trauma designation, length of stay, number of CT scans, fluoroscopy minutes, and number of general radiographs were all associated with increased doses, leading to exceeding occupational exposure limits. In a multivariable analysis, only the number of CT scans and fluoroscopy minutes remained significantly associated with increased whole-body radiation dose. Radiation levels frequently exceeded occupational exposure standards. CT imaging contributed the most exposure. Health-care providers must practice efficient stewardship of radiologic imaging in all critically ill and injured patients. Diagnostic benefit must always be weighed against the risk of cumulative radiation dose.

Applying quantitative adiposity feature analysis models to predict benefit of bevacizumab-based chemotherapy in ovarian cancer patients

NASA Astrophysics Data System (ADS)

Wang, Yunzhi; Qiu, Yuchen; Thai, Theresa; More, Kathleen; Ding, Kai; Liu, Hong; Zheng, Bin

2016-03-01

How to rationally identify epithelial ovarian cancer (EOC) patients who will benefit from bevacizumab or other antiangiogenic therapies is a critical issue in EOC treatments. The motivation of this study is to quantitatively measure adiposity features from CT images and investigate the feasibility of predicting potential benefit of EOC patients with or without receiving bevacizumab-based chemotherapy treatment using multivariate statistical models built based on quantitative adiposity image features. A dataset involving CT images from 59 advanced EOC patients were included. Among them, 32 patients received maintenance bevacizumab after primary chemotherapy and the remaining 27 patients did not. We developed a computer-aided detection (CAD) scheme to automatically segment subcutaneous fat areas (VFA) and visceral fat areas (SFA) and then extracted 7 adiposity-related quantitative features. Three multivariate data analysis models (linear regression, logistic regression and Cox proportional hazards regression) were performed respectively to investigate the potential association between the model-generated prediction results and the patients' progression-free survival (PFS) and overall survival (OS). The results show that using all 3 statistical models, a statistically significant association was detected between the model-generated results and both of the two clinical outcomes in the group of patients receiving maintenance bevacizumab (p<0.01), while there were no significant association for both PFS and OS in the group of patients without receiving maintenance bevacizumab. Therefore, this study demonstrated the feasibility of using quantitative adiposity-related CT image features based statistical prediction models to generate a new clinical marker and predict the clinical outcome of EOC patients receiving maintenance bevacizumab-based chemotherapy.

Quasi-biennial (QBO), annual (AO), and semi-annual oscillation (SAO) in stratospheric SCIAMACHY O3, NO2, and BrO limb data using a multivariate least squares approach

NASA Astrophysics Data System (ADS)

Dikty, Sebastian; von Savigny, Christian; Sinnhuber, Bjoern-Martin; Rozanov, Alexej; Weber, Mark; Burrows, John P.

We use SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartog-raphY) ozone, nitrogen dioxide and bromine oxide profiles (20-50 km altitude, 2003-2008) to quantify the amplitudes of QBO, AO, and SAO signals with the help of a simple multivariate regression model. The analysis is being carried out with SCIAMACHY data covering all lat-itudes with the exception of polar nights, when measurements are not available. The overall global yield is approximately 10,000 profiles per month, which are binned into 10-steps with one zonal mean profile being calculated per day and per latitude bin.

Does placental inflammation relate to brain lesions and volume in preterm infants?

PubMed

Reiman, Milla; Kujari, Harry; Maunu, Jonna; Parkkola, Riitta; Rikalainen, Hellevi; Lapinleimu, Helena; Lehtonen, Liisa; Haataja, Leena

2008-05-01

To evaluate the association between histologic inflammation of placenta and brain findings in ultrasound examinations and regional brain volumes in magnetic resonance imaging in very-low-birth-weight (VLBW) or in very preterm infants. VLBW or very preterm infants (n = 121) were categorized into 3 groups according to the most pathologic brain finding on ultrasound examinations until term. The brain magnetic resonance imaging performed at term was analyzed for regional brain volumes. The placentas were analyzed for histologic inflammatory findings. Histologic chorioamnionitis on the fetal side correlated to brain lesions in univariate but not in multivariate analyses. Low gestational age was the only significant risk factor for brain lesions in multivariate analysis (P < .0001). Histologic chorioamnionitis was not associated with brain volumes in multivariate analyses. Female sex, low gestational age, and low birth weight z score correlated to smaller volumes in total brain tissue (P = .001, P = .0002, P < .0001, respectively) and cerebellum (P = .047, P = .003, P = .001, respectively). In addition, low gestational age and low-birth-weight z score correlated to a smaller combined volume of basal ganglia and thalami (P = .0002). Placental inflammation does not appear to correlate to brain lesions or smaller regional brain volumes in VLBW or in very preterm infants at term age.

Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome.

PubMed

Davatzikos, Christos; Rathore, Saima; Bakas, Spyridon; Pati, Sarthak; Bergman, Mark; Kalarot, Ratheesh; Sridharan, Patmaa; Gastounioti, Aimilia; Jahani, Nariman; Cohen, Eric; Akbari, Hamed; Tunc, Birkan; Doshi, Jimit; Parker, Drew; Hsieh, Michael; Sotiras, Aristeidis; Li, Hongming; Ou, Yangming; Doot, Robert K; Bilello, Michel; Fan, Yong; Shinohara, Russell T; Yushkevich, Paul; Verma, Ragini; Kontos, Despina

2018-01-01

The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.

Testing the performance of pure spectrum resolution from Raman hyperspectral images of differently manufactured pharmaceutical tablets.

PubMed

Vajna, Balázs; Farkas, Attila; Pataki, Hajnalka; Zsigmond, Zsolt; Igricz, Tamás; Marosi, György

2012-01-27

Chemical imaging is a rapidly emerging analytical method in pharmaceutical technology. Due to the numerous chemometric solutions available, characterization of pharmaceutical samples with unknown components present has also become possible. This study compares the performance of current state-of-the-art curve resolution methods (multivariate curve resolution-alternating least squares, positive matrix factorization, simplex identification via split augmented Lagrangian and self-modelling mixture analysis) in the estimation of pure component spectra from Raman maps of differently manufactured pharmaceutical tablets. The batches of different technologies differ in the homogeneity level of the active ingredient, thus, the curve resolution methods are tested under different conditions. An empirical approach is shown to determine the number of components present in a sample. The chemometric algorithms are compared regarding the number of detected components, the quality of the resolved spectra and the accuracy of scores (spectral concentrations) compared to those calculated with classical least squares, using the true pure component (reference) spectra. It is demonstrated that using appropriate multivariate methods, Raman chemical imaging can be a useful tool in the non-invasive characterization of unknown (e.g. illegal or counterfeit) pharmaceutical products. Copyright © 2011 Elsevier B.V. All rights reserved.

Multispectral UV imaging for fast and non-destructive quality control of chemical and physical tablet attributes.

PubMed

Klukkert, Marten; Wu, Jian X; Rantanen, Jukka; Carstensen, Jens M; Rades, Thomas; Leopold, Claudia S

2016-07-30

Monitoring of tablet quality attributes in direct vicinity of the production process requires analytical techniques that allow fast, non-destructive, and accurate tablet characterization. The overall objective of this study was to investigate the applicability of multispectral UV imaging as a reliable, rapid technique for estimation of the tablet API content and tablet hardness, as well as determination of tablet intactness and the tablet surface density profile. One of the aims was to establish an image analysis approach based on multivariate image analysis and pattern recognition to evaluate the potential of UV imaging for automatized quality control of tablets with respect to their intactness and surface density profile. Various tablets of different composition and different quality regarding their API content, radial tensile strength, intactness, and surface density profile were prepared using an eccentric as well as a rotary tablet press at compression pressures from 20MPa up to 410MPa. It was found, that UV imaging can provide both, relevant information on chemical and physical tablet attributes. The tablet API content and radial tensile strength could be estimated by UV imaging combined with partial least squares analysis. Furthermore, an image analysis routine was developed and successfully applied to the UV images that provided qualitative information on physical tablet surface properties such as intactness and surface density profiles, as well as quantitative information on variations in the surface density. In conclusion, this study demonstrates that UV imaging combined with image analysis is an effective and non-destructive method to determine chemical and physical quality attributes of tablets and is a promising approach for (near) real-time monitoring of the tablet compaction process and formulation optimization purposes. Copyright © 2015 Elsevier B.V. All rights reserved.

Analysis and compensation for the effect of the catheter position on image intensities in intravascular optical coherence tomography

NASA Astrophysics Data System (ADS)

Liu, Shengnan; Eggermont, Jeroen; Wolterbeek, Ron; Broersen, Alexander; Busk, Carol A. G. R.; Precht, Helle; Lelieveldt, Boudewijn P. F.; Dijkstra, Jouke

2016-12-01

Intravascular optical coherence tomography (IVOCT) is an imaging technique that is used to analyze the underlying cause of cardiovascular disease. Because a catheter is used during imaging, the intensities can be affected by the catheter position. This work aims to analyze the effect of the catheter position on IVOCT image intensities and to propose a compensation method to minimize this effect in order to improve the visualization and the automatic analysis of IVOCT images. The effect of catheter position is modeled with respect to the distance between the catheter and the arterial wall (distance-dependent factor) and the incident angle onto the arterial wall (angle-dependent factor). A light transmission model incorporating both factors is introduced. On the basis of this model, the interaction effect of both factors is estimated with a hierarchical multivariant linear regression model. Statistical analysis shows that IVOCT intensities are significantly affected by both factors with p<0.001, as either aspect increases the intensity decreases. This effect differs for different pullbacks. The regression results were used to compensate for this effect. Experiments show that the proposed compensation method can improve the performance of the automatic bioresorbable vascular scaffold strut detection.

A novel structure-aware sparse learning algorithm for brain imaging genetics.

PubMed

Du, Lei; Jingwen, Yan; Kim, Sungeun; Risacher, Shannon L; Huang, Heng; Inlow, Mark; Moore, Jason H; Saykin, Andrew J; Shen, Li

2014-01-01

Brain imaging genetics is an emergent research field where the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is evaluated. Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. Most existing SCCA algorithms are designed using the soft threshold strategy, which assumes that the features in the data are independent from each other. This independence assumption usually does not hold in imaging genetic data, and thus inevitably limits the capability of yielding optimal solutions. We propose a novel structure-aware SCCA (denoted as S2CCA) algorithm to not only eliminate the independence assumption for the input data, but also incorporate group-like structure in the model. Empirical comparison with a widely used SCCA implementation, on both simulated and real imaging genetic data, demonstrated that S2CCA could yield improved prediction performance and biologically meaningful findings.

Quantitative skeletal maturation estimation using cone-beam computed tomography-generated cervical vertebral images: a pilot study in 5- to 18-year-old Japanese children.

PubMed

Byun, Bo-Ram; Kim, Yong-Il; Yamaguchi, Tetsutaro; Maki, Koutaro; Ko, Ching-Chang; Hwang, Dea-Seok; Park, Soo-Byung; Son, Woo-Sung

2015-11-01

The purpose of this study was to establish multivariable regression models for the estimation of skeletal maturation status in Japanese boys and girls using the cone-beam computed tomography (CBCT)-based cervical vertebral maturation (CVM) assessment method and hand-wrist radiography. The analyzed sample consisted of hand-wrist radiographs and CBCT images from 47 boys and 57 girls. To quantitatively evaluate the correlation between the skeletal maturation status and measurement ratios, a CBCT-based CVM assessment method was applied to the second, third, and fourth cervical vertebrae. Pearson's correlation coefficient analysis and multivariable regression analysis were used to determine the ratios for each of the cervical vertebrae (p < 0.05). Four characteristic parameters ((OH2 + PH2)/W2, (OH2 + AH2)/W2, D2, AH3/W3), as independent variables, were used to build the multivariable regression models: for the Japanese boys, the skeletal maturation status according to the CBCT-based quantitative cervical vertebral maturation (QCVM) assessment was 5.90 + 99.11 × AH3/W3 - 14.88 × (OH2 + AH2)/W2 + 13.24 × D2; for the Japanese girls, it was 41.39 + 59.52 × AH3/W3 - 15.88 × (OH2 + PH2)/W2 + 10.93 × D2. The CBCT-generated CVM images proved very useful to the definition of the cervical vertebral body and the odontoid process. The newly developed CBCT-based QCVM assessment method showed a high correlation between the derived ratios from the second cervical vertebral body and odontoid process. There are high correlations between the skeletal maturation status and the ratios of the second cervical vertebra based on the remnant of dentocentral synchondrosis.

Ability of preoperative 3.0-Tesla magnetic resonance imaging to predict the absence of side-specific extracapsular extension of prostate cancer.

PubMed

Hara, Tomohiko; Nakanishi, Hiroyuki; Nakagawa, Tohru; Komiyama, Motokiyo; Kawahara, Takashi; Manabe, Tomoko; Miyake, Mototaka; Arai, Eri; Kanai, Yae; Fujimoto, Hiroyuki

2013-10-01

Recent studies have shown an improvement in prostate cancer diagnosis with the use of 3.0-Tesla magnetic resonance imaging. We retrospectively assessed the ability of this imaging technique to predict side-specific extracapsular extension of prostate cancer. From October 2007 to August 2011, prostatectomy was carried out in 396 patients after preoperative 3.0-Tesla magnetic resonance imaging. Among these, 132 (primary sample) and 134 patients (validation sample) underwent 12-core prostate biopsy at the National Cancer Center Hospital of Tokyo, Japan, and at other institutions, respectively. In the primary dataset, univariate and multivariate analyses were carried out to predict side-specific extracapsular extension using variables determined preoperatively, including 3.0-Tesla magnetic resonance imaging findings (T2-weighted and diffusion-weighted imaging). A prediction model was then constructed and applied to the validation study sample. Multivariate analysis identified four significant independent predictors (P < 0.05), including a biopsy Gleason score of ≥8, positive 3.0-Tesla diffusion-weighted magnetic resonance imaging findings, ≥2 positive biopsy cores on each side and a maximum percentage of positive cores ≥31% on each side. The negative predictive value was 93.9% in the combination model with these four predictors, meanwhile the positive predictive value was 33.8%. Good reproducibility of these four significant predictors and the combination model was observed in the validation study sample. The side-specific extracapsular extension prediction by the biopsy Gleason score and factors associated with tumor location, including a positive 3.0-Tesla diffusion-weighted magnetic resonance imaging finding, have a high negative predictive value, but a low positive predictive value. © 2013 The Japanese Urological Association.

Multivariate information-theoretic measures reveal directed information structure and task relevant changes in fMRI connectivity.

PubMed

Lizier, Joseph T; Heinzle, Jakob; Horstmann, Annette; Haynes, John-Dylan; Prokopenko, Mikhail

2011-02-01

The human brain undertakes highly sophisticated information processing facilitated by the interaction between its sub-regions. We present a novel method for interregional connectivity analysis, using multivariate extensions to the mutual information and transfer entropy. The method allows us to identify the underlying directed information structure between brain regions, and how that structure changes according to behavioral conditions. This method is distinguished in using asymmetric, multivariate, information-theoretical analysis, which captures not only directional and non-linear relationships, but also collective interactions. Importantly, the method is able to estimate multivariate information measures with only relatively little data. We demonstrate the method to analyze functional magnetic resonance imaging time series to establish the directed information structure between brain regions involved in a visuo-motor tracking task. Importantly, this results in a tiered structure, with known movement planning regions driving visual and motor control regions. Also, we examine the changes in this structure as the difficulty of the tracking task is increased. We find that task difficulty modulates the coupling strength between regions of a cortical network involved in movement planning and between motor cortex and the cerebellum which is involved in the fine-tuning of motor control. It is likely these methods will find utility in identifying interregional structure (and experimentally induced changes in this structure) in other cognitive tasks and data modalities.

Low enhancement on multiphase contrast-enhanced CT images: an independent predictor of the presence of high tumor grade of clear cell renal cell carcinoma.

PubMed

Zhu, Ye-Hua; Wang, Xun; Zhang, Jin; Chen, Yong-Hui; Kong, Wen; Huang, Yi-Ran

2014-09-01

The purpose of this study was to assess the relation between tumor enhancement on multiphase contrast-enhanced CT images and Fuhrman grade of clear cell renal cell carcinoma. A single-institution retrospective review was conducted on the records of 255 patients who underwent radical or partial nephrectomy and received a histologic diagnosis of clear cell renal cell carcinoma. Two radiologists recorded the radiographic features of each patient, including the attenuation value of the lesion, lesion size, calcification within the lesion, cystic versus solid appearance, and margin regularity. Parameters representing the extent of tumor enhancement were defined and calculated. The association between tumor enhancement and Fuhrman grade was analyzed, and multivariate analysis was performed to find independent predictors of high tumor grade. Significant differences existed in tumor enhancement among different Fuhrman grades (p < 0.001). High-grade tumors had significantly lower enhancement (p < 0.001). The enhancement parameter had a sensitivity of 0.84 and specificity of 0.93 in prediction of high tumor grade. In the multivariate analysis, more advanced age, irregular margin, and low tumor enhancement were the three independent predictors of high tumor grade. Tumor enhancement of clear cell renal cell carcinoma on multiphase contrast-enhanced CT images is associated with Fuhrman grade. Low tumor enhancement in the corticomedullary phase is an independent predictor of high tumor grade. This system may be helpful in clinical decision making about the care of patients treated by nonsurgical approaches.

Robot-assisted radical prostatectomy: Multiparametric MR imaging-directed intraoperative frozen-section analysis to reduce the rate of positive surgical margins.

PubMed

Petralia, Giuseppe; Musi, Gennaro; Padhani, Anwar R; Summers, Paul; Renne, Giuseppe; Alessi, Sarah; Raimondi, Sara; Matei, Deliu V; Renne, Salvatore L; Jereczek-Fossa, Barbara A; De Cobelli, Ottavio; Bellomi, Massimo

2015-02-01

To investigate whether use of multiparametric magnetic resonance (MR) imaging-directed intraoperative frozen-section (IFS) analysis during nerve-sparing robot-assisted radical prostatectomy reduces the rate of positive surgical margins. This retrospective analysis of prospectively acquired data was approved by an institutional ethics committee, and the requirement for informed consent was waived. Data were reviewed for 134 patients who underwent preoperative multiparametric MR imaging (T2 weighted, diffusion weighted, and dynamic contrast-material enhanced) and nerve-sparing robot-assisted radical prostatectomy, during which IFS analysis was used, and secondary resections were performed when IFS results were positive for cancer. Control patients (n = 134) matched for age, prostate-specific antigen level, and stage were selected from a pool of 322 patients who underwent nerve-sparing robot-assisted radical prostatectomy without multiparametric MR imaging and IFS analysis. Rates of positive surgical margins were compared by means of the McNemar test, and a multivariate conditional logistic regression model was used to estimate the odds ratio of positive surgical margins for patients who underwent MR imaging and IFS analysis compared with control subjects. Eighteen patients who underwent MR imaging and IFS analysis underwent secondary resections, and 13 of these patients were found to have negative surgical margins at final pathologic examination. Positive surgical margins were found less frequently in the patients who underwent MR imaging and IFS analysis than in control patients (7.5% vs 18.7%, P = .01). When the differences in risk factors are taken into account, patients who underwent MR imaging and IFS had one-seventh the risk of having positive surgical margins relative to control patients (adjusted odds ratio: 0.15; 95% confidence interval: 0.04, 0.61). The significantly lower rate of positive surgical margins compared with that in control patients provides preliminary evidence of the positive clinical effect of multiparametric MR imaging-directed IFS analysis for patients who undergo prostatectomy. © RSNA, 2014.

Stroke Severity Affects Timing: Time From Stroke Code Activation to Initial Imaging is Longer in Patients With Milder Strokes.

PubMed

Kwei, Kimberly T; Liang, John; Wilson, Natalie; Tuhrim, Stanley; Dhamoon, Mandip

2018-05-01

Optimizing the time it takes to get a potential stroke patient to imaging is essential in a rapid stroke response. At our hospital, door-to-imaging time is comprised of 2 time periods: the time before a stroke is recognized, followed by the period after the stroke code is called during which the stroke team assesses and brings the patient to the computed tomography scanner. To control for delays due to triage, we isolated the time period after a potential stroke has been recognized, as few studies have examined the biases of stroke code responders. This "code-to-imaging time" (CIT) encompassed the time from stroke code activation to initial imaging, and we hypothesized that perception of stroke severity would affect how quickly stroke code responders act. In consecutively admitted ischemic stroke patients at The Mount Sinai Hospital emergency department, we tested associations between National Institutes of Health Stroke Scale scores (NIHSS), continuously and at different cutoffs, and CIT using spline regression, t tests for univariate analysis, and multivariable linear regression adjusting for age, sex, and race/ethnicity. In our study population, mean CIT was 26 minutes, and mean presentation NIHSS was 8. In univariate and multivariate analyses comparing CIT between mild and severe strokes, stroke scale scores <4 were associated with longer response times. Milder strokes are associated with a longer CIT with a threshold effect at a NIHSS of 4.

Face recognition using tridiagonal matrix enhanced multivariance products representation

NASA Astrophysics Data System (ADS)

Ã-zay, Evrim Korkmaz

2017-01-01

This study aims to retrieve face images from a database according to a target face image. For this purpose, Tridiagonal Matrix Enhanced Multivariance Products Representation (TMEMPR) is taken into consideration. TMEMPR is a recursive algorithm based on Enhanced Multivariance Products Representation (EMPR). TMEMPR decomposes a matrix into three components which are a matrix of left support terms, a tridiagonal matrix of weight parameters for each recursion, and a matrix of right support terms, respectively. In this sense, there is an analogy between Singular Value Decomposition (SVD) and TMEMPR. However TMEMPR is a more flexible algorithm since its initial support terms (or vectors) can be chosen as desired. Low computational complexity is another advantage of TMEMPR because the algorithm has been constructed with recursions of certain arithmetic operations without requiring any iteration. The algorithm has been trained and tested with ORL face image database with 400 different grayscale images of 40 different people. TMEMPR's performance has been compared with SVD's performance as a result.

Improvement in toxicity in high risk prostate cancer patients treated with image-guided intensity-modulated radiotherapy compared to 3D conformal radiotherapy without daily image guidance.

PubMed

Sveistrup, Joen; af Rosenschöld, Per Munck; Deasy, Joseph O; Oh, Jung Hun; Pommer, Tobias; Petersen, Peter Meidahl; Engelholm, Svend Aage

2014-02-04

Image-guided radiotherapy (IGRT) facilitates the delivery of a very precise radiation dose. In this study we compare the toxicity and biochemical progression-free survival between patients treated with daily image-guided intensity-modulated radiotherapy (IG-IMRT) and 3D conformal radiotherapy (3DCRT) without daily image guidance for high risk prostate cancer (PCa). A total of 503 high risk PCa patients treated with radiotherapy (RT) and endocrine treatment between 2000 and 2010 were retrospectively reviewed. 115 patients were treated with 3DCRT, and 388 patients were treated with IG-IMRT. 3DCRT patients were treated to 76 Gy and without daily image guidance and with 1-2 cm PTV margins. IG-IMRT patients were treated to 78 Gy based on daily image guidance of fiducial markers, and the PTV margins were 5-7 mm. Furthermore, the dose-volume constraints to both the rectum and bladder were changed with the introduction of IG-IMRT. The 2-year actuarial likelihood of developing grade > = 2 GI toxicity following RT was 57.3% in 3DCRT patients and 5.8% in IG-IMRT patients (p < 0.001). For GU toxicity the numbers were 41.8% and 29.7%, respectively (p = 0.011). On multivariate analysis, 3DCRT was associated with a significantly increased risk of developing grade > = 2 GI toxicity compared to IG-IMRT (p < 0.001, HR = 11.59 [CI: 6.67-20.14]). 3DCRT was also associated with an increased risk of developing GU toxicity compared to IG-IMRT.The 3-year actuarial biochemical progression-free survival probability was 86.0% for 3DCRT and 90.3% for IG-IMRT (p = 0.386). On multivariate analysis there was no difference in biochemical progression-free survival between 3DCRT and IG-IMRT. The difference in toxicity can be attributed to the combination of the IMRT technique with reduced dose to organs-at-risk, daily image guidance and margin reduction.

Multilingualism and fMRI: Longitudinal Study of Second Language Acquisition

PubMed Central

Andrews, Edna; Frigau, Luca; Voyvodic-Casabo, Clara; Voyvodic, James; Wright, John

2013-01-01

BOLD fMRI is often used for the study of human language. However, there are still very few attempts to conduct longitudinal fMRI studies in the study of language acquisition by measuring auditory comprehension and reading. The following paper is the first in a series concerning a unique longitudinal study devoted to the analysis of bi- and multilingual subjects who are: (1) already proficient in at least two languages; or (2) are acquiring Russian as a second/third language. The focus of the current analysis is to present data from the auditory sections of a set of three scans acquired from April, 2011 through April, 2012 on a five-person subject pool who are learning Russian during the study. All subjects were scanned using the same protocol for auditory comprehension on the same General Electric LX 3T Signa scanner in Duke University Hospital. Using a multivariate analysis of covariance (MANCOVA) for statistical analysis, proficiency measurements are shown to correlate significantly with scan results in the Russian conditions over time. The importance of both the left and right hemispheres in language processing is discussed. Special attention is devoted to the importance of contextualizing imaging data with corresponding behavioral and empirical testing data using a multivariate analysis of variance. This is the only study to date that includes: (1) longitudinal fMRI data with subject-based proficiency and behavioral data acquired in the same time frame; and (2) statistical modeling that demonstrates the importance of covariate language proficiency data for understanding imaging results of language acquisition. PMID:24961428

Big-data reflection high energy electron diffraction analysis for understanding epitaxial film growth processes.

PubMed

Vasudevan, Rama K; Tselev, Alexander; Baddorf, Arthur P; Kalinin, Sergei V

2014-10-28

Reflection high energy electron diffraction (RHEED) has by now become a standard tool for in situ monitoring of film growth by pulsed laser deposition and molecular beam epitaxy. Yet despite the widespread adoption and wealth of information in RHEED images, most applications are limited to observing intensity oscillations of the specular spot, and much additional information on growth is discarded. With ease of data acquisition and increased computation speeds, statistical methods to rapidly mine the data set are now feasible. Here, we develop such an approach to the analysis of the fundamental growth processes through multivariate statistical analysis of a RHEED image sequence. This approach is illustrated for growth of La(x)Ca(1-x)MnO(3) films grown on etched (001) SrTiO(3) substrates, but is universal. The multivariate methods including principal component analysis and k-means clustering provide insight into the relevant behaviors, the timing and nature of a disordered to ordered growth change, and highlight statistically significant patterns. Fourier analysis yields the harmonic components of the signal and allows separation of the relevant components and baselines, isolating the asymmetric nature of the step density function and the transmission spots from the imperfect layer-by-layer (LBL) growth. These studies show the promise of big data approaches to obtaining more insight into film properties during and after epitaxial film growth. Furthermore, these studies open the pathway to use forward prediction methods to potentially allow significantly more control over growth process and hence final film quality.

Multilingualism and fMRI: Longitudinal Study of Second Language Acquisition.

PubMed

Andrews, Edna; Frigau, Luca; Voyvodic-Casabo, Clara; Voyvodic, James; Wright, John

2013-05-28

BOLD fMRI is often used for the study of human language. However, there are still very few attempts to conduct longitudinal fMRI studies in the study of language acquisition by measuring auditory comprehension and reading. The following paper is the first in a series concerning a unique longitudinal study devoted to the analysis of bi- and multilingual subjects who are: (1) already proficient in at least two languages; or (2) are acquiring Russian as a second/third language. The focus of the current analysis is to present data from the auditory sections of a set of three scans acquired from April, 2011 through April, 2012 on a five-person subject pool who are learning Russian during the study. All subjects were scanned using the same protocol for auditory comprehension on the same General Electric LX 3T Signa scanner in Duke University Hospital. Using a multivariate analysis of covariance (MANCOVA) for statistical analysis, proficiency measurements are shown to correlate significantly with scan results in the Russian conditions over time. The importance of both the left and right hemispheres in language processing is discussed. Special attention is devoted to the importance of contextualizing imaging data with corresponding behavioral and empirical testing data using a multivariate analysis of variance. This is the only study to date that includes: (1) longitudinal fMRI data with subject-based proficiency and behavioral data acquired in the same time frame; and (2) statistical modeling that demonstrates the importance of covariate language proficiency data for understanding imaging results of language acquisition.

Cross-Modal Multivariate Pattern Analysis

PubMed Central

Meyer, Kaspar; Kaplan, Jonas T.

2011-01-01

Multivariate pattern analysis (MVPA) is an increasingly popular method of analyzing functional magnetic resonance imaging (fMRI) data1-4. Typically, the method is used to identify a subject's perceptual experience from neural activity in certain regions of the brain. For instance, it has been employed to predict the orientation of visual gratings a subject perceives from activity in early visual cortices5 or, analogously, the content of speech from activity in early auditory cortices6. Here, we present an extension of the classical MVPA paradigm, according to which perceptual stimuli are not predicted within, but across sensory systems. Specifically, the method we describe addresses the question of whether stimuli that evoke memory associations in modalities other than the one through which they are presented induce content-specific activity patterns in the sensory cortices of those other modalities. For instance, seeing a muted video clip of a glass vase shattering on the ground automatically triggers in most observers an auditory image of the associated sound; is the experience of this image in the "mind's ear" correlated with a specific neural activity pattern in early auditory cortices? Furthermore, is this activity pattern distinct from the pattern that could be observed if the subject were, instead, watching a video clip of a howling dog? In two previous studies7,8, we were able to predict sound- and touch-implying video clips based on neural activity in early auditory and somatosensory cortices, respectively. Our results are in line with a neuroarchitectural framework proposed by Damasio9,10, according to which the experience of mental images that are based on memories - such as hearing the shattering sound of a vase in the "mind's ear" upon seeing the corresponding video clip - is supported by the re-construction of content-specific neural activity patterns in early sensory cortices. PMID:22105246

Symposium on Machine Processing of Remotely Sensed Data, Purdue University, West Lafayette, Ind., June 29-July 1, 1976, Proceedings

NASA Technical Reports Server (NTRS)

1976-01-01

Papers are presented on the applicability of Landsat data to water management and control needs, IBIS, a geographic information system based on digital image processing and image raster datatype, and the Image Data Access Method (IDAM) for the Earth Resources Interactive Processing System. Attention is also given to the Prototype Classification and Mensuration System (PROCAMS) applied to agricultural data, the use of Landsat for water quality monitoring in North Carolina, and the analysis of geophysical remote sensing data using multivariate pattern recognition. The Illinois crop-acreage estimation experiment, the Pacific Northwest Resources Inventory Demonstration, and the effects of spatial misregistration on multispectral recognition are also considered. Individual items are announced in this issue.

New breast cancer prognostic factors identified by computer-aided image analysis of HE stained histopathology images

PubMed Central

Chen, Jia-Mei; Qu, Ai-Ping; Wang, Lin-Wei; Yuan, Jing-Ping; Yang, Fang; Xiang, Qing-Ming; Maskey, Ninu; Yang, Gui-Fang; Liu, Juan; Li, Yan

2015-01-01

Computer-aided image analysis (CAI) can help objectively quantify morphologic features of hematoxylin-eosin (HE) histopathology images and provide potentially useful prognostic information on breast cancer. We performed a CAI workflow on 1,150 HE images from 230 patients with invasive ductal carcinoma (IDC) of the breast. We used a pixel-wise support vector machine classifier for tumor nests (TNs)-stroma segmentation, and a marker-controlled watershed algorithm for nuclei segmentation. 730 morphologic parameters were extracted after segmentation, and 12 parameters identified by Kaplan-Meier analysis were significantly associated with 8-year disease free survival (P < 0.05 for all). Moreover, four image features including TNs feature (HR 1.327, 95%CI [1.001 - 1.759], P = 0.049), TNs cell nuclei feature (HR 0.729, 95%CI [0.537 - 0.989], P = 0.042), TNs cell density (HR 1.625, 95%CI [1.177 - 2.244], P = 0.003), and stromal cell structure feature (HR 1.596, 95%CI [1.142 - 2.229], P = 0.006) were identified by multivariate Cox proportional hazards model to be new independent prognostic factors. The results indicated that CAI can assist the pathologist in extracting prognostic information from HE histopathology images for IDC. The TNs feature, TNs cell nuclei feature, TNs cell density, and stromal cell structure feature could be new prognostic factors. PMID:26022540

A whole brain morphometric analysis of changes associated with pre-term birth

NASA Astrophysics Data System (ADS)

Thomaz, C. E.; Boardman, J. P.; Counsell, S.; Hill, D. L. G.; Hajnal, J. V.; Edwards, A. D.; Rutherford, M. A.; Gillies, D. F.; Rueckert, D.

2006-03-01

Pre-term birth is strongly associated with subsequent neuropsychiatric impairment. To identify structural differences in preterm infants we have examined a dataset of magnetic resonance (MR) images containing 88 preterm infants and 19 term born controls. We have analyzed these images by combining image registration, deformation based morphometry (DBM), multivariate statistics, and effect size maps (ESM). The methodology described has been performed directly on the MR intensity images rather than on segmented versions of the images. The results indicate that the approach described makes clear the statistical differences between the control and preterm samples, showing a leave-one-out classification accuracy of 94.74% and 95.45% respectively. In addition, finding the most discriminant direction between the groups and using DBM features and ESM we are able to identify not only what are the changes between preterm and term groups but also how relatively relevant they are in terms of volume expansion and contraction.

Lumbar subcutaneous edema and degenerative spinal disease in patients with low back pain: a retrospective MRI study.

PubMed

Quattrocchi, C C; Giona, A; Di Martino, A; Gaudino, F; Mallio, C A; Errante, Y; Occhicone, F; Vitali, M A; Zobel, B B; Denaro, V

2015-08-01

This study was designed to determine the association between LSE, spondylolisthesis, facet arthropathy, lumbar canal stenosis, BMI, radiculopathy and bone marrow edema at conventional lumbar spine MR imaging. This is a retrospective radiological study; 441 consecutive patients with low back pain (224 men and 217 women; mean age 57.3 years; mean BMI 26) underwent conventional lumbar MRI using a 1.5-T magnet (Avanto, Siemens). Lumbar MR images were reviewed by consensus for the presence of LSE, spondylolisthesis, facet arthropathy, lumbar canal stenosis, radiculopathy and bone marrow edema. Descriptive statistics and association studies were conducted using STATA software 11.0. Association studies have been performed using linear univariate regression analysis and multivariate regression analysis, considering LSE as response variable. The overall prevalence of LSE was 40%; spondylolisthesis (p = 0.01), facet arthropathy (p < 0.001), BMI (p = 0.008) and lumbar canal stenosis (p < 0.001) were included in the multivariate regression model, whereas bone marrow edema, radiculopathy and age were not. LSE is highly associated with spondylolisthesis, facet arthropathy and BMI, suggesting underestimation of its clinical impact as an integral component in chronic lumbar back pain. Longitudinal simultaneous X-ray/MRI studies should be conducted to test the relationship of LSE with lumbar spinal instability and low back pain.

Does the nephrostomy tract length impact the outcomes of percutaneous nephrolithotomy (PNL)?

PubMed

Astroza, Gaston M; Neisius, Andreas; Tsivian, Matvey; Wang, Agnes J; Preminger, Glenn M; Lipkin, Michael E

2014-12-01

Different factors can determine the outcomes of percutaneous nephrolithotomy (PNL). We analyzed the effect of tract length (TL) on outcomes after PNL. We performed a retrospective review of patients undergoing PNL between 2006 and 2011. Patients with preoperative computed tomography (CT), one percutaneous access tract and follow-up imaging within 3 months were included. TL was defined as distance between the skin to the calyx of puncture as measured on preoperative CT. Measurements were independently performed by two urologists and the average was used for analysis. Stone-free rate (SFR) was defined as zero fragments on follow-up imaging. Factors independently associated with the likelihood of being stone-free after PNL were determined using multivariable analysis adjusted for TL, location of access, the presence of incomplete or complete staghorn calculi and type of follow-up imaging. Complications (Clavien score) were independently assessed. A total of 222 patients were included. Median stone burden and body mass index (BMI) was 239.4 mm(2) and 30.5 [interquartile range (IQR): 25.7-36.2]. The median TL was 85.0 mm (IQR: 70.3-100.0) and highly correlated with BMI (ρ = 0.66, p < 0.001). A total of 101 patients (45.5 %) were stone-free. TL was not associated with SFR (p = 0.53). Clavien 1 and 2 complications occurred in 38 (17 %) while Clavien 3 and 4 complications occurred in 17 (8 %) patients. Multivariable analysis revealed no association between complications and TL even when adjusted for gender. Percutaneous TL is not associated with outcomes of PNL. PNL is a safe and effective treatment for stones in patients with differing body habitus.

Accuracy of MRI for the diagnosis of metastatic cervical lymphadenopathy in patients with thyroid cancer.

PubMed

Chen, Qinghua; Raghavan, Prashant; Mukherjee, Sugoto; Jameson, Mark J; Patrie, James; Xin, Wenjun; Xian, Junfang; Wang, Zhenchang; Levine, Paul A; Wintermark, Max

2015-10-01

The aim of this study was to systematically compare a comprehensive array of magnetic resonance (MR) imaging features in terms of their sensitivity and specificity to diagnose cervical lymph node metastases in patients with thyroid cancer. The study included 41 patients with thyroid malignancy who underwent surgical excision of cervical lymph nodes and had preoperative MR imaging ≤4weeks prior to surgery. Three head and neck neuroradiologists independently evaluated all the MR images. Using the pathology results as reference, the sensitivity, specificity and interobserver agreement of each MR imaging characteristic were calculated. On multivariate analysis, no single imaging feature was significantly correlated with metastasis. In general, imaging features demonstrated high specificity, but poor sensitivity and moderate interobserver agreement at best. Commonly used MR imaging features have limited sensitivity at correctly identifying cervical lymph node metastases in patients with thyroid cancer. A negative neck MR scan should not dissuade a surgeon from performing a neck dissection in patients with thyroid carcinomas.

Imaging nanoscale lattice variations by machine learning of x-ray diffraction microscopy data

DOE PAGES

Laanait, Nouamane; Zhang, Zhan; Schlepütz, Christian M.

2016-08-09

In this paper, we present a novel methodology based on machine learning to extract lattice variations in crystalline materials, at the nanoscale, from an x-ray Bragg diffraction-based imaging technique. By employing a full-field microscopy setup, we capture real space images of materials, with imaging contrast determined solely by the x-ray diffracted signal. The data sets that emanate from this imaging technique are a hybrid of real space information (image spatial support) and reciprocal lattice space information (image contrast), and are intrinsically multidimensional (5D). By a judicious application of established unsupervised machine learning techniques and multivariate analysis to this multidimensional datamore » cube, we show how to extract features that can be ascribed physical interpretations in terms of common structural distortions, such as lattice tilts and dislocation arrays. Finally, we demonstrate this 'big data' approach to x-ray diffraction microscopy by identifying structural defects present in an epitaxial ferroelectric thin-film of lead zirconate titanate.« less

Imaging nanoscale lattice variations by machine learning of x-ray diffraction microscopy data

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

Laanait, Nouamane; Zhang, Zhan; Schlepütz, Christian M.

In this paper, we present a novel methodology based on machine learning to extract lattice variations in crystalline materials, at the nanoscale, from an x-ray Bragg diffraction-based imaging technique. By employing a full-field microscopy setup, we capture real space images of materials, with imaging contrast determined solely by the x-ray diffracted signal. The data sets that emanate from this imaging technique are a hybrid of real space information (image spatial support) and reciprocal lattice space information (image contrast), and are intrinsically multidimensional (5D). By a judicious application of established unsupervised machine learning techniques and multivariate analysis to this multidimensional datamore » cube, we show how to extract features that can be ascribed physical interpretations in terms of common structural distortions, such as lattice tilts and dislocation arrays. Finally, we demonstrate this 'big data' approach to x-ray diffraction microscopy by identifying structural defects present in an epitaxial ferroelectric thin-film of lead zirconate titanate.« less

Computational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learning.

PubMed

Davatzikos, Christos

2016-10-01

The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges. Copyright © 2016. Published by Elsevier B.V.

Computational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learning

PubMed Central

Davatzikos, Christos

2017-01-01

The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges. PMID:27514582

MassImager: A software for interactive and in-depth analysis of mass spectrometry imaging data.

PubMed

He, Jiuming; Huang, Luojiao; Tian, Runtao; Li, Tiegang; Sun, Chenglong; Song, Xiaowei; Lv, Yiwei; Luo, Zhigang; Li, Xin; Abliz, Zeper

2018-07-26

Mass spectrometry imaging (MSI) has become a powerful tool to probe molecule events in biological tissue. However, it is a widely held viewpoint that one of the biggest challenges is an easy-to-use data processing software for discovering the underlying biological information from complicated and huge MSI dataset. Here, a user-friendly and full-featured MSI software including three subsystems, Solution, Visualization and Intelligence, named MassImager, is developed focusing on interactive visualization, in-situ biomarker discovery and artificial intelligent pathological diagnosis. Simplified data preprocessing and high-throughput MSI data exchange, serialization jointly guarantee the quick reconstruction of ion image and rapid analysis of dozens of gigabytes datasets. It also offers diverse self-defined operations for visual processing, including multiple ion visualization, multiple channel superposition, image normalization, visual resolution enhancement and image filter. Regions-of-interest analysis can be performed precisely through the interactive visualization between the ion images and mass spectra, also the overlaid optical image guide, to directly find out the region-specific biomarkers. Moreover, automatic pattern recognition can be achieved immediately upon the supervised or unsupervised multivariate statistical modeling. Clear discrimination between cancer tissue and adjacent tissue within a MSI dataset can be seen in the generated pattern image, which shows great potential in visually in-situ biomarker discovery and artificial intelligent pathological diagnosis of cancer. All the features are integrated together in MassImager to provide a deep MSI processing solution at the in-situ metabolomics level for biomarker discovery and future clinical pathological diagnosis. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Cardiovascular mortality prediction in veterans with arm exercise vs pharmacologic myocardial perfusion imaging.

PubMed

Martin, Wade H; Xian, Hong; Chandiramani, Pooja; Bainter, Emily; Klein, Andrew J P

2015-08-01

No data exist comparing outcome prediction from arm exercise vs pharmacologic myocardial perfusion imaging (MPI) stress test variables in patients unable to perform treadmill exercise. In this retrospective study, 2,173 consecutive lower extremity disabled veterans aged 65.4 ± 11.0years (mean ± SD) underwent either pharmacologic MPI (1730 patients) or arm exercise stress tests (443 patients) with MPI (n = 253) or electrocardiography alone (n = 190) between 1997 and 2002. Cox multivariate regression models and reclassification analysis by integrated discrimination improvement (IDI) were used to characterize stress test and MPI predictors of cardiovascular mortality at ≥10-year follow-up after inclusion of significant demographic, clinical, and other variables. Cardiovascular death occurred in 561 pharmacologic MPI and 102 arm exercise participants. Multivariate-adjusted cardiovascular mortality was predicted by arm exercise resting metabolic equivalents (hazard ratio [HR] 0.52, 95% CI 0.39-0.69, P < .001), 1-minute heart rate recovery (HR 0.61, 95% CI 0.44-0.86, P < .001), and pharmacologic and arm exercise delta (peak-rest) heart rate (both P < .001). Only an abnormal arm exercise MPI prognosticated cardiovascular death by multivariate Cox analysis (HR 1.98, 95% CI 1.04-3.77, P < .05). Arm exercise MPI defect number, type, and size provided IDI over covariates for prediction of cardiovascular mortality (IDI = 0.074-0.097). Only pharmacologic defect size prognosticated cardiovascular mortality (IDI = 0.022). Arm exercise capacity, heart rate recovery, and pharmacologic and arm exercise heart rate responses are robust predictors of cardiovascular mortality. Arm exercise MPI results are equivalent and possibly superior to pharmacologic MPI for cardiovascular mortality prediction in patients unable to perform treadmill exercise. Published by Elsevier Inc.

Oncologic relevance of magnetic resonance imaging-detected threatened mesorectal fascia for patients with mid or low rectal cancer: A longitudinal analysis before and after long-course, concurrent chemoradiotherapy.

PubMed

Son, Il Tae; Kim, Young Hoon; Lee, Kyoung Ho; Kang, Sung Il; Kim, Duck-Woo; Shin, Eun; Lee, Keun-Wook; Ahn, Soyeon; Kim, Jae-Sung; Kang, Sung-Bum

2017-07-01

The oncologic importance of threatened mesorectal fascia detected with magnetic resonance imaging is obscured by the heterogeneity of preoperative treatments. We evaluated the oncologic relevance of threatened mesorectal fascia detected with consecutive magnetic resonance imaging performed before and after long-course, concurrent chemoradiotherapy (LCRT) for mid or low rectal cancer. We evaluated 196 patients who underwent total mesorectal excision with LCRT. Threatened mesorectal fascia was defined as a shortest distance from tumor to mesorectal fascia of ≤ 1 mm on magnetic resonance imaging. Multivariate analyses for disease-free survival using magnetic resonance imaging-based parameters were conducted with a Cox proportional hazard model before and after LCRT, respectively. The pathologic positivity of the circumferential resection margin was greater for threatened mesorectal fascia than for clear mesorectal fascia (pre-LCRT, 14.8% vs 3.0%, P = .004; post-LCRT, 15.4% vs 4.5%, P = .025). At a median follow-up of 68 months, 3-year disease-free survival was worse for threatened mesorectal fascia than for clear mesorectal fascia (pre-LCRT, 77.0% vs 88.1%, P = .023; post-LCRT, 76.9% vs 86.6%, P = .029). On multivariate analyses, threatened mesorectal fascia on pre-LCRT magnetic resonance imaging was an independent factor for poor disease-free survival (hazard ratio = 2.153, 95% confidence interval, 1.07-4.32, P = .031), whereas threatened mesorectal fascia on post-LCRT magnetic resonance imaging was not (hazard ratio = 1.689, 95% confidence interval, 0.77-3.66, P = .189). This study confirms that magnetic resonance imaging-detected threatened mesorectal fascia predicts poor oncologic outcomes for mid or low rectal cancer and shows that the diagnostic performance of pre-LCRT magnetic resonance imaging is different from that of post-LCRT magnetic resonance imaging. Copyright © 2017 Elsevier Inc. All rights reserved.

Imaging genetics in attention-deficit/hyperactivity disorder and related neurodevelopmental domains: state of the art.

PubMed

Vilor-Tejedor, Natàlia; Cáceres, Alejandro; Pujol, Jesús; Sunyer, Jordi; González, Juan R

2017-12-01

Joint analysis of genetic and neuroimaging data, known as Imaging Genetics (IG), offers an opportunity to deepen our knowledge of the biological mechanisms of neurodevelopmental domains. There has been exponential growth in the literature on IG studies, which challenges the standardization of analysis methods in this field. In this review we give a complete up-to-date account of IG studies on attention deficit hyperactivity disorder (ADHD) and related neurodevelopmental domains, which serves as a reference catalog for researchers working on this neurological disorder. We searched MEDLINE/Pubmed and identified 37 articles on IG of ADHD that met our eligibility criteria. We carefully cataloged these articles according to imaging technique, genes and brain region, and summarized the main results and characteristics of each study. We found that IG studies on ADHD generally focus on dopaminergic genes and the structure of basal ganglia using structural Magnetic Resonance Imaging (MRI). We found little research involving multiple genetic factors and brain regions because of the scarce use of multivariate strategies in data analysis. IG of ADHD and related neurodevelopmental domains is still in its early stages, and a lack of replicated findings is one of the most pressing challenges in the field.

Detection of explosives on the surface of banknotes by Raman hyperspectral imaging and independent component analysis.

PubMed

Almeida, Mariana R; Correa, Deleon N; Zacca, Jorge J; Logrado, Lucio Paulo Lima; Poppi, Ronei J

2015-02-20

The aim of this study was to develop a methodology using Raman hyperspectral imaging and chemometric methods for identification of pre- and post-blast explosive residues on banknote surfaces. The explosives studied were of military, commercial and propellant uses. After the acquisition of the hyperspectral imaging, independent component analysis (ICA) was applied to extract the pure spectra and the distribution of the corresponding image constituents. The performance of the methodology was evaluated by the explained variance and the lack of fit of the models, by comparing the ICA recovered spectra with the reference spectra using correlation coefficients and by the presence of rotational ambiguity in the ICA solutions. The methodology was applied to forensic samples to solve an automated teller machine explosion case. Independent component analysis proved to be a suitable method of resolving curves, achieving equivalent performance with the multivariate curve resolution with alternating least squares (MCR-ALS) method. At low concentrations, MCR-ALS presents some limitations, as it did not provide the correct solution. The detection limit of the methodology presented in this study was 50 μg cm(-2). Copyright © 2014 Elsevier B.V. All rights reserved.

Successive Projections Algorithm-Multivariable Linear Regression Classifier for the Detection of Contaminants on Chicken Carcasses in Hyperspectral Images

NASA Astrophysics Data System (ADS)

Wu, W.; Chen, G. Y.; Kang, R.; Xia, J. C.; Huang, Y. P.; Chen, K. J.

2017-07-01

During slaughtering and further processing, chicken carcasses are inevitably contaminated by microbial pathogen contaminants. Due to food safety concerns, many countries implement a zero-tolerance policy that forbids the placement of visibly contaminated carcasses in ice-water chiller tanks during processing. Manual detection of contaminants is labor consuming and imprecise. Here, a successive projections algorithm (SPA)-multivariable linear regression (MLR) classifier based on an optimal performance threshold was developed for automatic detection of contaminants on chicken carcasses. Hyperspectral images were obtained using a hyperspectral imaging system. A regression model of the classifier was established by MLR based on twelve characteristic wavelengths (505, 537, 561, 562, 564, 575, 604, 627, 656, 665, 670, and 689 nm) selected by SPA , and the optimal threshold T = 1 was obtained from the receiver operating characteristic (ROC) analysis. The SPA-MLR classifier provided the best detection results when compared with the SPA-partial least squares (PLS) regression classifier and the SPA-least squares supported vector machine (LS-SVM) classifier. The true positive rate (TPR) of 100% and the false positive rate (FPR) of 0.392% indicate that the SPA-MLR classifier can utilize spatial and spectral information to effectively detect contaminants on chicken carcasses.

Categorical speech processing in Broca's area: an fMRI study using multivariate pattern-based analysis.

PubMed

Lee, Yune-Sang; Turkeltaub, Peter; Granger, Richard; Raizada, Rajeev D S

2012-03-14

Although much effort has been directed toward understanding the neural basis of speech processing, the neural processes involved in the categorical perception of speech have been relatively less studied, and many questions remain open. In this functional magnetic resonance imaging (fMRI) study, we probed the cortical regions mediating categorical speech perception using an advanced brain-mapping technique, whole-brain multivariate pattern-based analysis (MVPA). Normal healthy human subjects (native English speakers) were scanned while they listened to 10 consonant-vowel syllables along the /ba/-/da/ continuum. Outside of the scanner, individuals' own category boundaries were measured to divide the fMRI data into /ba/ and /da/ conditions per subject. The whole-brain MVPA revealed that Broca's area and the left pre-supplementary motor area evoked distinct neural activity patterns between the two perceptual categories (/ba/ vs /da/). Broca's area was also found when the same analysis was applied to another dataset (Raizada and Poldrack, 2007), which previously yielded the supramarginal gyrus using a univariate adaptation-fMRI paradigm. The consistent MVPA findings from two independent datasets strongly indicate that Broca's area participates in categorical speech perception, with a possible role of translating speech signals into articulatory codes. The difference in results between univariate and multivariate pattern-based analyses of the same data suggest that processes in different cortical areas along the dorsal speech perception stream are distributed on different spatial scales.

Pre-Adult MRI of Brain Cancer and Neurological Injury: Multivariate Analyses

PubMed Central

Levman, Jacob; Takahashi, Emi

2016-01-01

Brain cancer and neurological injuries, such as stroke, are life-threatening conditions for which further research is needed to overcome the many challenges associated with providing optimal patient care. Multivariate analysis (MVA) is a class of pattern recognition technique involving the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of neuroimaging challenges, including identifying variables associated with patient outcomes; understanding an injury’s etiology, development, and progression; creating diagnostic tests; assisting in treatment monitoring; and more. Compared to adults, imaging of the developing brain has attracted less attention from MVA researchers, however, remarkable MVA growth has occurred in recent years. This paper presents the results of a systematic review of the literature focusing on MVA technologies applied to brain injury and cancer in neurological fetal, neonatal, and pediatric magnetic resonance imaging (MRI). With a wide variety of MRI modalities providing physiologically meaningful biomarkers and new biomarker measurements constantly under development, MVA techniques hold enormous potential toward combining available measurements toward improving basic research and the creation of technologies that contribute to improving patient care. PMID:27446888

Novel active contour model based on multi-variate local Gaussian distribution for local segmentation of MR brain images

NASA Astrophysics Data System (ADS)

Zheng, Qiang; Li, Honglun; Fan, Baode; Wu, Shuanhu; Xu, Jindong

2017-12-01

Active contour model (ACM) has been one of the most widely utilized methods in magnetic resonance (MR) brain image segmentation because of its ability of capturing topology changes. However, most of the existing ACMs only consider single-slice information in MR brain image data, i.e., the information used in ACMs based segmentation method is extracted only from one slice of MR brain image, which cannot take full advantage of the adjacent slice images' information, and cannot satisfy the local segmentation of MR brain images. In this paper, a novel ACM is proposed to solve the problem discussed above, which is based on multi-variate local Gaussian distribution and combines the adjacent slice images' information in MR brain image data to satisfy segmentation. The segmentation is finally achieved through maximizing the likelihood estimation. Experiments demonstrate the advantages of the proposed ACM over the single-slice ACM in local segmentation of MR brain image series.

Multivariate Radiological-Based Models for the Prediction of Future Knee Pain: Data from the OAI

PubMed Central

Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Treviño, Victor; Tamez-Peña, José G.

2015-01-01

In this work, the potential of X-ray based multivariate prognostic models to predict the onset of chronic knee pain is presented. Using X-rays quantitative image assessments of joint-space-width (JSW) and paired semiquantitative central X-ray scores from the Osteoarthritis Initiative (OAI), a case-control study is presented. The pain assessments of the right knee at the baseline and the 60-month visits were used to screen for case/control subjects. Scores were analyzed at the time of pain incidence (T-0), the year prior incidence (T-1), and two years before pain incidence (T-2). Multivariate models were created by a cross validated elastic-net regularized generalized linear models feature selection tool. Univariate differences between cases and controls were reported by AUC, C-statistics, and ODDs ratios. Univariate analysis indicated that the medial osteophytes were significantly more prevalent in cases than controls: C-stat 0.62, 0.62, and 0.61, at T-0, T-1, and T-2, respectively. The multivariate JSW models significantly predicted pain: AUC = 0.695, 0.623, and 0.620, at T-0, T-1, and T-2, respectively. Semiquantitative multivariate models predicted paint with C-stat = 0.671, 0.648, and 0.645 at T-0, T-1, and T-2, respectively. Multivariate models derived from plain X-ray radiography assessments may be used to predict subjects that are at risk of developing knee pain. PMID:26504490

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis

PubMed Central

Galván-Tejada, Carlos E.; Zanella-Calzada, Laura A.; Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Gamboa-Rosales, Hamurabi; Garza-Veloz, Idalia; Martinez-Fierro, Margarita L.

2017-01-01

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions. PMID:28216571

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis.

PubMed

Galván-Tejada, Carlos E; Zanella-Calzada, Laura A; Galván-Tejada, Jorge I; Celaya-Padilla, José M; Gamboa-Rosales, Hamurabi; Garza-Veloz, Idalia; Martinez-Fierro, Margarita L

2017-02-14

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions.

A Novel Hyperspectral Microscopic Imaging System for Evaluating Fresh Degree of Pork.

PubMed

Xu, Yi; Chen, Quansheng; Liu, Yan; Sun, Xin; Huang, Qiping; Ouyang, Qin; Zhao, Jiewen

2018-04-01

This study proposed a rapid microscopic examination method for pork freshness evaluation by using the self-assembled hyperspectral microscopic imaging (HMI) system with the help of feature extraction algorithm and pattern recognition methods. Pork samples were stored for different days ranging from 0 to 5 days and the freshness of samples was divided into three levels which were determined by total volatile basic nitrogen (TVB-N) content. Meanwhile, hyperspectral microscopic images of samples were acquired by HMI system and processed by the following steps for the further analysis. Firstly, characteristic hyperspectral microscopic images were extracted by using principal component analysis (PCA) and then texture features were selected based on the gray level co-occurrence matrix (GLCM). Next, features data were reduced dimensionality by fisher discriminant analysis (FDA) for further building classification model. Finally, compared with linear discriminant analysis (LDA) model and support vector machine (SVM) model, good back propagation artificial neural network (BP-ANN) model obtained the best freshness classification with a 100 % accuracy rating based on the extracted data. The results confirm that the fabricated HMI system combined with multivariate algorithms has ability to evaluate the fresh degree of pork accurately in the microscopic level, which plays an important role in animal food quality control.

A Novel Hyperspectral Microscopic Imaging System for Evaluating Fresh Degree of Pork

PubMed Central

Xu, Yi; Chen, Quansheng; Liu, Yan; Sun, Xin; Huang, Qiping; Ouyang, Qin; Zhao, Jiewen

2018-01-01

Abstract This study proposed a rapid microscopic examination method for pork freshness evaluation by using the self-assembled hyperspectral microscopic imaging (HMI) system with the help of feature extraction algorithm and pattern recognition methods. Pork samples were stored for different days ranging from 0 to 5 days and the freshness of samples was divided into three levels which were determined by total volatile basic nitrogen (TVB-N) content. Meanwhile, hyperspectral microscopic images of samples were acquired by HMI system and processed by the following steps for the further analysis. Firstly, characteristic hyperspectral microscopic images were extracted by using principal component analysis (PCA) and then texture features were selected based on the gray level co-occurrence matrix (GLCM). Next, features data were reduced dimensionality by fisher discriminant analysis (FDA) for further building classification model. Finally, compared with linear discriminant analysis (LDA) model and support vector machine (SVM) model, good back propagation artificial neural network (BP-ANN) model obtained the best freshness classification with a 100 % accuracy rating based on the extracted data. The results confirm that the fabricated HMI system combined with multivariate algorithms has ability to evaluate the fresh degree of pork accurately in the microscopic level, which plays an important role in animal food quality control. PMID:29805285

Classification of river water pollution using Hyperion data

NASA Astrophysics Data System (ADS)

Kar, Soumyashree; Rathore, V. S.; Champati ray, P. K.; Sharma, Richa; Swain, S. K.

2016-06-01

A novel attempt is made to use hyperspectral remote sensing to identify the spatial variability of metal pollutants present in river water. It was also attempted to classify the hyperspectral image - Earth Observation-1 (EO-1) Hyperion data of an 8 km stretch of the river Yamuna, near Allahabad city in India depending on its chemical composition. For validating image analysis results, a total of 10 water samples were collected and chemically analyzed using Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES). Two different spectral libraries from field and image data were generated for the 10 sample locations. Advanced per-pixel supervised classifications such as Spectral Angle Mapper (SAM), SAM target finder using BandMax and Support Vector Machine (SVM) were carried out along with the unsupervised clustering procedure - Iterative Self-Organizing Data Analysis Technique (ISODATA). The results were compared and assessed with respect to ground data. Analytical Spectral Devices (ASD), Inc. spectroradiometer, FieldSpec 4 was used to generate the spectra of the water samples which were compiled into a spectral library and used for Spectral Absorption Depth (SAD) analysis. The spectral depth pattern of image and field spectral libraries was found to be highly correlated (correlation coefficient, R2 = 0.99) which validated the image analysis results with respect to the ground data. Further, we carried out a multivariate regression analysis to assess the varying concentrations of metal ions present in water based on the spectral depth of the corresponding absorption feature. Spectral Absorption Depth (SAD) analysis along with metal analysis of field data revealed the order in which the metals affected the river pollution, which was in conformity with the findings of Central Pollution Control Board (CPCB). Therefore, it is concluded that hyperspectral imaging provides opportunity that can be used for satellite based remote monitoring of water quality from space.

Fathers and Daughters: Using a Multivariate Analysis through Multiple Regression to Help Predict a Daughter's Self-Esteem as Experienced by College-Age Women in Rural Nebraska

ERIC Educational Resources Information Center

Beshaler, Mary E.

2010-01-01

Throughout her life, a woman makes decisions about behaviors, relationships, academic accomplishments, and achievements. What propels women to make these choices may be driven by an image of self. This feeling of self-worth or self-esteem is developed early in life with the help of her primary caregivers as found in her biological mother and…

Semi-automatic measuring of arteriovenous relation as a possible silent brain infarction risk index in hypertensive patients.

PubMed

Vázquez Dorrego, X M; Manresa Domínguez, J M; Heras Tebar, A; Forés, R; Girona Marcé, A; Alzamora Sas, M T; Delgado Martínez, P; Riba-Llena, I; Ugarte Anduaga, J; Beristain Iraola, A; Barandiaran Martirena, I; Ruiz Bilbao, S M; Torán Monserrat, P

2016-11-01

To evaluate the usefulness of a semiautomatic measuring system of arteriovenous relation (RAV) from retinographic images of hypertensive patients in assessing their cardiovascular risk and silent brain ischemia (ICS) detection. Semi-automatic measurement of arterial and venous width were performed with the aid of Imedos software and conventional fundus examination from the analysis of retinal images belonging to the 976 patients integrated in the cohort Investigating Silent Strokes in Hypertensives: a magnetic resonance imaging study (ISSYS), group of hypertensive patients. All patients have been subjected to a cranial magnetic resonance imaging (RMN) to assess the presence or absence of brain silent infarct. Retinal images of 768 patients were studied. Among the clinical findings observed, association with ICS was only detected in patients with microaneurysms (OR 2.50; 95% CI: 1.05-5.98) or altered RAV (<0.666) (OR: 4.22; 95% CI: 2.56-6.96). In multivariate logistic regression analysis adjusted by age and sex, only altered RAV continued demonstrating as a risk factor (OR: 3.70; 95% CI: 2.21-6.18). The results show that the semiautomatic analysis of the retinal vasculature from retinal images has the potential to be considered as an important vascular risk factor in hypertensive population. Copyright © 2016 Sociedad Española de Oftalmología. Publicado por Elsevier España, S.L.U. All rights reserved.

Feasibility of Image-Guided Transthoracic Core Needle Biopsy in the BATTLE Lung Trial

PubMed Central

Tam, Alda L.; Kim, Edward S.; Lee, J. Jack; Ensor, Joe E.; Hicks, Marshall E.; Tang, Ximing; Blumenschein, George R.; Alden, Christine M.; Erasmus, Jeremy J.; Tsao, Anne; Lippman, Scott M.; Hong, Waun K.; Wistuba, Ignacio I.; Gupta, Sanjay

2013-01-01

Purpose As therapy for non-small cell lung cancer (NSCLC) patients becomes more personalized, additional tissue in the form of core needle biopsies (CNBs) for biomarker analysis is increasingly required for determining appropriate treatment and for enrollment into clinical trials. We report our experience with small-caliber percutaneous transthoracic (PT) CNBs for the evaluation of multiple molecular biomarkers in BATTLE (Biomarker-integrated Approaches of Targeted Therapy for Lung Cancer Elimination), a personalized, targeted therapy NSCLC clinical trial. Methods The medical records of patients who underwent PTCNB for consideration of enrollment in BATTLE, were reviewed for diagnostic yield of 11 predetermined molecular markers, and procedural complications. Univariate and multivariate analyses of factors related to patient and lesion characteristics were performed to determine possible influences on diagnostic yield. Results One hundred and seventy PTCNBs were performed using 20-gauge biopsy needles in 151 NSCLC patients screened for the trial. 82.9% of the biopsy specimens were found to have adequate tumor tissue for analysis of the required biomarkers. On multivariate analysis, metastatic lesions were 5.4 times more likely to yield diagnostic tissue as compared to primary tumors (p = 0.0079). Pneumothorax and chest tube insertion rates were 15.3% and 9.4%, respectively. Conclusions Image-guided 20-gauge PTCNB is safe and provides adequate tissue for analysis of multiple biomarkers in the majority of patients being considered for enrollment into a personalized, targeted therapy NSCLC clinical trial. Metastatic lesions are more likely to yield diagnostic tissue as compared to primary tumors. PMID:23442309

Arterial compression of the retro-olivary sulcus of the medulla in essential hypertension: a multivariate analysis.

PubMed

Coffee, Robert E; Nicholas, Joyce S; Egan, Brent M; Rumboldt, Zoran; D'Agostino, Sabino; Patel, Sunil J

2005-11-01

Pulsatile arterial compression (AC) of the ventrolateral medulla (VLM) has been postulated to cause neurogenically mediated essential hypertension (EHTN). We aimed to establish whether the association between AC of specifically the retro-olivary sulcus (ROS) of the VLM and EHTN was significant, while controlling for other risks associated with EHTN. Case-control study. Posterior fossa magnetic resonance imaging scans of 131 subjects, including 58 subjects with EHTN and 73 normotensives, were reviewed to determine the presence of AC in the ROS. The history of other risk factors for EHTN was obtained by reviewing medical records. Multivariate logistic regression analysis of these data shows a significant association between AC in the ROS (right and/or left) and EHTN [odds ratio (OR) = 3.03, 95% confidence interval (CI) = 1.30, 7.06]. This analysis was done controlling for other known EHTN risk factors such as age, race, sex, diabetes, and obesity. A secondary analysis also controlling for these variables shows that AC of both the right and left ROS are independently associated with EHTN (right AC: OR = 5.04, 95% CI = 1.33, 19.17; left AC: OR = 3.39, 95% CI = 1.20, 9.60). In this retrospective study of subjects with EHTN and normotensive controls that had undergone magnetic resonance imaging of the posterior fossa, AC of the ROS on either side of the medulla is a significant independent risk factor in EHTN. Further studies are required to determine whether this is true for the general population of patients with neurogenically mediated EHTN.

Analysis of pulmonary pure ground-glass nodule in enhanced dual energy CT imaging for predicting invasive adenocarcinoma: comparing with conventional thin-section CT imaging

PubMed Central

Zhang, Ying; Tang, Jian; Xu, Jianrong

2017-01-01

Background To investigate the value of dual energy computed tomography (DECT) parameters (including iodine concentration and monochromatic CT numbers) for predicting pure ground-glass nodules (pGGNs) of invasive adenocarcinoma (IA). Methods A total of 55 resected pGGNs evaluated with both unenhanced thin-section CT (TSCT) and enhanced DECT scans were included. Correlations between histopathology [adenocarcinoma in situ (AIS), minimally IA (MIA), and IA] and CT scan characteristics were examined. CT scan and clinicodemographic data were investigated by univariate and multivariate analysis to identify features that helped distinguish IA from AIS or MIA. Results Both normalized iodine concentration (NIC) of IA and slope of spectral curve [slope(k)] were not significantly different between IA and AIS or MIA. Size, performance of pleural retraction and enhanced monochromatic CT attenuation values of 120–140 keV were significantly higher for IA. In multivariate regression analysis, size and enhanced monochromatic CT number of 140 keV were independent predictors for IA. Using the two parameters together, the diagnostic capacity of IA could be improved from 0.697 or 0.635 to 0.713. Conclusions DECT could help demonstrate blood supply and indicate invasion extent of pGGNs, and monochromatic CT number of higher energy (especially 140 keV) would be better for diagnosing IA than lower energies. Together with size of pGGNs, the diagnostic capacity of IA could be better. PMID:29312701

Texture analysis of pulmonary parenchymateous changes related to pulmonary thromboembolism in dogs - a novel approach using quantitative methods.

PubMed

Marschner, C B; Kokla, M; Amigo, J M; Rozanski, E A; Wiinberg, B; McEvoy, F J

2017-07-11

Diagnosis of pulmonary thromboembolism (PTE) in dogs relies on computed tomography pulmonary angiography (CTPA), but detailed interpretation of CTPA images is demanding for the radiologist and only large vessels may be evaluated. New approaches for better detection of smaller thrombi include dual energy computed tomography (DECT) as well as computer assisted diagnosis (CAD) techniques. The purpose of this study was to investigate the performance of quantitative texture analysis for detecting dogs with PTE using grey-level co-occurrence matrices (GLCM) and multivariate statistical classification analyses. CT images from healthy (n = 6) and diseased (n = 29) dogs with and without PTE confirmed on CTPA were segmented so that only tissue with CT numbers between -1024 and -250 Houndsfield Units (HU) was preserved. GLCM analysis and subsequent multivariate classification analyses were performed on texture parameters extracted from these images. Leave-one-dog-out cross validation and receiver operator characteristic (ROC) showed that the models generated from the texture analysis were able to predict healthy dogs with optimal levels of performance. Partial Least Square Discriminant Analysis (PLS-DA) obtained a sensitivity of 94% and a specificity of 96%, while Support Vector Machines (SVM) yielded a sensitivity of 99% and a specificity of 100%. The models, however, performed worse in classifying the type of disease in the diseased dog group: In diseased dogs with PTE sensitivities were 30% (PLS-DA) and 38% (SVM), and specificities were 80% (PLS-DA) and 89% (SVM). In diseased dogs without PTE the sensitivities of the models were 59% (PLS-DA) and 79% (SVM) and specificities were 79% (PLS-DA) and 82% (SVM). The results indicate that texture analysis of CTPA images using GLCM is an effective tool for distinguishing healthy from abnormal lung. Furthermore the texture of pulmonary parenchyma in dogs with PTE is altered, when compared to the texture of pulmonary parenchyma of healthy dogs. The models' poorer performance in classifying dogs within the diseased group, may be related to the low number of dogs compared to texture variables, a lack of balanced number of dogs within each group or a real lack of difference in the texture features among the diseased dogs.

Early Functional Connectome Integrity and 1-Year Recovery in Comatose Survivors of Cardiac Arrest.

PubMed

Sair, Haris I; Hannawi, Yousef; Li, Shanshan; Kornbluth, Joshua; Demertzi, Athena; Di Perri, Carol; Chabanne, Russell; Jean, Betty; Benali, Habib; Perlbarg, Vincent; Pekar, James; Luyt, Charles-Edouard; Galanaud, Damien; Velly, Lionel; Puybasset, Louis; Laureys, Steven; Caffo, Brian; Stevens, Robert D

2018-04-01

Purpose To assess whether early brain functional connectivity is associated with functional recovery 1 year after cardiac arrest (CA). Materials and Methods Enrolled in this prospective multicenter cohort were 46 patients who were comatose after CA. Principal outcome was cerebral performance category at 12 months, with favorable outcome (FO) defined as cerebral performance category 1 or 2. All participants underwent multiparametric structural and functional magnetic resonance (MR) imaging less than 4 weeks after CA. Within- and between-network connectivity was measured in dorsal attention network (DAN), default-mode network (DMN), salience network (SN), and executive control network (ECN) by using seed-based analysis of resting-state functional MR imaging data. Structural changes identified with fluid-attenuated inversion recovery and diffusion-weighted imaging sequences were analyzed by using validated morphologic scales. The association between connectivity measures, structural changes, and the principal outcome was explored with multivariable modeling. Results Patients underwent MR imaging a mean 12.6 days ± 5.6 (standard deviation) after CA. At 12 months, 11 patients had an FO. Patients with FO had higher within-DMN connectivity and greater anticorrelation between SN and DMN and between SN and ECN compared with patients with unfavorable outcome, an effect that was maintained after multivariable adjustment. Anticorrelation of SN-DMN predicted outcomes with higher accuracy than fluid-attenuated inversion recovery or diffusion-weighted imaging scores (area under the receiver operating characteristic curves, respectively, 0.88, 0.74, and 0.71). Conclusion MR imaging-based measures of cerebral functional network connectivity obtained in the acute phase of CA were independently associated with FO at 1 year, warranting validation as early markers of long-term recovery potential in patients with anoxic-ischemic encephalopathy. © RSNA, 2017.

Energetic Interrelationship between Spontaneous Low-Frequency Fluctuations in Regional Cerebral Blood Volume, Arterial Blood Pressure, Heart Rate, and Respiratory Rhythm

NASA Astrophysics Data System (ADS)

Katura, Takusige; Yagyu, Akihiko; Obata, Akiko; Yamazaki, Kyoko; Maki, Atsushi; Abe, Masanori; Tanaka, Naoki

2007-07-01

Strong spontaneous fluctuations around 0.1 and 0.3 Hz have been observed in blood-related brain-function measurements such as functional magnetic resonance imaging and optical topography (or functional near-infrared spectroscopy). These fluctuations seem to reflect the interaction between the cerebral circulation system and the systemic circulation system. We took an energetic viewpoint in our analysis of the interrelationships between fluctuations in cerebral blood volume (CBV), mean arterial blood pressure (MAP), heart rate (HR), and respiratory rhythm based on multivariate autoregressive modeling. This approach involves evaluating the contribution of each fluctuation or rhythm to specific ones by performing multivariate spectral analysis. The results we obtained show MAP and HR can account slightly for the fluctuation around 0.1 Hz in CBV, while the fluctuation around 0.3 Hz is derived mainly from the respiratory rhythm. During our presentation, we will report on the effects of posture on the interrelationship between the fluctuations and the respiratory rhythm.

Predicting Gilthead Sea Bream (Sparus aurata) Freshness by a Novel Combined Technique of 3D Imaging and SW-NIR Spectral Analysis.

PubMed

Ivorra, Eugenio; Verdu, Samuel; Sánchez, Antonio J; Grau, Raúl; Barat, José M

2016-10-19

A technique that combines the spatial resolution of a 3D structured-light (SL) imaging system with the spectral analysis of a hyperspectral short-wave near infrared system was developed for freshness predictions of gilthead sea bream on the first storage days (Days 0-6). This novel approach allows the hyperspectral analysis of very specific fish areas, which provides more information for freshness estimations. The SL system obtains a 3D reconstruction of fish, and an automatic method locates gilthead's pupils and irises. Once these regions are positioned, the hyperspectral camera acquires spectral information and a multivariate statistical study is done. The best region is the pupil with an R² of 0.92 and an RMSE of 0.651 for predictions. We conclude that the combination of 3D technology with the hyperspectral analysis offers plenty of potential and is a very promising technique to non destructively predict gilthead freshness.

Predicting Gilthead Sea Bream (Sparus aurata) Freshness by a Novel Combined Technique of 3D Imaging and SW-NIR Spectral Analysis

PubMed Central

Ivorra, Eugenio; Verdu, Samuel; Sánchez, Antonio J.; Grau, Raúl; Barat, José M.

2016-01-01

A technique that combines the spatial resolution of a 3D structured-light (SL) imaging system with the spectral analysis of a hyperspectral short-wave near infrared system was developed for freshness predictions of gilthead sea bream on the first storage days (Days 0–6). This novel approach allows the hyperspectral analysis of very specific fish areas, which provides more information for freshness estimations. The SL system obtains a 3D reconstruction of fish, and an automatic method locates gilthead’s pupils and irises. Once these regions are positioned, the hyperspectral camera acquires spectral information and a multivariate statistical study is done. The best region is the pupil with an R2 of 0.92 and an RMSE of 0.651 for predictions. We conclude that the combination of 3D technology with the hyperspectral analysis offers plenty of potential and is a very promising technique to non destructively predict gilthead freshness. PMID:27775556

Identification of phases, symmetries and defects through local crystallography

DOE PAGES

Belianinov, Alex; He, Qian; Kravchenko, Mikhail; ...

2015-07-20

Here we report that advances in electron and probe microscopies allow 10 pm or higher precision in measurements of atomic positions. This level of fidelity is sufficient to correlate the length (and hence energy) of bonds, as well as bond angles to functional properties of materials. Traditionally, this relied on mapping locally measured parameters to macroscopic variables, for example, average unit cell. This description effectively ignores the information contained in the microscopic degrees of freedom available in a high-resolution image. Here we introduce an approach for local analysis of material structure based on statistical analysis of individual atomic neighbourhoods. Clusteringmore » and multivariate algorithms such as principal component analysis explore the connectivity of lattice and bond structure, as well as identify minute structural distortions, thus allowing for chemical description and identification of phases. This analysis lays the framework for building image genomes and structure–property libraries, based on conjoining structural and spectral realms through local atomic behaviour.« less

Targeted and untargeted-metabolite profiling to track the compositional integrity of ginger during processing using digitally-enhanced HPTLC pattern recognition analysis.

PubMed

Ibrahim, Reham S; Fathy, Hoda

2018-03-30

Tracking the impact of commonly applied post-harvesting and industrial processing practices on the compositional integrity of ginger rhizome was implemented in this work. Untargeted metabolite profiling was performed using digitally-enhanced HPTLC method where the chromatographic fingerprints were extracted using ImageJ software then analysed with multivariate Principal Component Analysis (PCA) for pattern recognition. A targeted approach was applied using a new, validated, simple and fast HPTLC image analysis method for simultaneous quantification of the officially recognized markers 6-, 8-, 10-gingerol and 6-shogaol in conjunction with chemometric Hierarchical Clustering Analysis (HCA). The results of both targeted and untargeted metabolite profiling revealed that peeling, drying in addition to storage employed during processing have a great influence on ginger chemo-profile, the different forms of processed ginger shouldn't be used interchangeably. Moreover, it deemed necessary to consider the holistic metabolic profile for comprehensive evaluation of ginger during processing. Copyright © 2018. Published by Elsevier B.V.

Risk factors for and causes and treatment of recurrence of inferior vena cava type of Budd-Chiari syndrome after stenting in China: A retrospective analysis of a large cohort.

PubMed

Li, Wen-Dong; Yu, Hui-Ying; Qian, Ai-Min; Rong, Jian-Jie; Zhang, Ye-Qing; Li, Xiao-Qiang

2017-03-01

To explore the risk factors for recurrence of inferior vena cava (IVC)-type Budd-Chiari syndrome (BCS) after stenting and evaluate the feasibility and primary outcomes of endovascular therapies for recurrent BCS. A retrospective analysis of 219 patients was performed to identify risk factors for recurrence. The images of the recurrent patients during follow-up duration and interventional surgery were also reviewed to find the possible reasons of recurrence. The outcome of endovascular therapies for recurrent BCS was evaluated by Kaplan-Meier analysis. Among the 219 patients, 172 patients with primary IVC-type BCS underwent stenting and 28 patients experienced recurrence. Multivariate analysis identified age, Child-Pugh score, MELD and total bilirubin as independent recurrent indicators. Possible causes of recurrence include thrombosis in the stent, re-obstruction in or above the stent, and stent-related hepatic vein obstruction. Twenty-five patients with recurrent BCS underwent endovascular therapies with a few complications and achieved a high level of short- and mid-term patency. Age, total bilirubin and severity of liver function are the main risk factors for BCS recurrence. These risks might contribute to thrombosis or subsequent fibrous obstruction. Endovascular therapies are effective and safe management options that yield positive outcomes for recurrent BCS. • Risk factors for recurrent Budd-Chiari syndrome were identified by multivariate analysis. • Causes of recurrent Budd-Chiari syndrome were investigated by assessing radiological images. • There is a correlation between risk factors and causes of recurrence. • Endovascular therapies for recurrent Budd-Chiari syndrome are effective and safe.

Bayesian inference on risk differences: an application to multivariate meta-analysis of adverse events in clinical trials.

PubMed

Chen, Yong; Luo, Sheng; Chu, Haitao; Wei, Peng

2013-05-01

Multivariate meta-analysis is useful in combining evidence from independent studies which involve several comparisons among groups based on a single outcome. For binary outcomes, the commonly used statistical models for multivariate meta-analysis are multivariate generalized linear mixed effects models which assume risks, after some transformation, follow a multivariate normal distribution with possible correlations. In this article, we consider an alternative model for multivariate meta-analysis where the risks are modeled by the multivariate beta distribution proposed by Sarmanov (1966). This model have several attractive features compared to the conventional multivariate generalized linear mixed effects models, including simplicity of likelihood function, no need to specify a link function, and has a closed-form expression of distribution functions for study-specific risk differences. We investigate the finite sample performance of this model by simulation studies and illustrate its use with an application to multivariate meta-analysis of adverse events of tricyclic antidepressants treatment in clinical trials.

Intratumor partitioning and texture analysis of dynamic contrast-enhanced (DCE)-MRI identifies relevant tumor subregions to predict pathological response of breast cancer to neoadjuvant chemotherapy.

PubMed

Wu, Jia; Gong, Guanghua; Cui, Yi; Li, Ruijiang

2016-11-01

To predict pathological response of breast cancer to neoadjuvant chemotherapy (NAC) based on quantitative, multiregion analysis of dynamic contrast enhancement magnetic resonance imaging (DCE-MRI). In this Institutional Review Board-approved study, 35 patients diagnosed with stage II/III breast cancer were retrospectively investigated using 3T DCE-MR images acquired before and after the first cycle of NAC. First, principal component analysis (PCA) was used to reduce the dimensionality of the DCE-MRI data with high temporal resolution. We then partitioned the whole tumor into multiple subregions using k-means clustering based on the PCA-defined eigenmaps. Within each tumor subregion, we extracted four quantitative Haralick texture features based on the gray-level co-occurrence matrix (GLCM). The change in texture features in each tumor subregion between pre- and during-NAC was used to predict pathological complete response after NAC. Three tumor subregions were identified through clustering, each with distinct enhancement characteristics. In univariate analysis, all imaging predictors except one extracted from the tumor subregion associated with fast washout were statistically significant (P < 0.05) after correcting for multiple testing, with area under the receiver operating characteristic (ROC) curve (AUC) or AUCs between 0.75 and 0.80. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.79 (P = 0.002) in leave-one-out cross-validation. This improved upon conventional imaging predictors such as tumor volume (AUC = 0.53) and texture features based on whole-tumor analysis (AUC = 0.65). The heterogeneity of the tumor subregion associated with fast washout on DCE-MRI predicted pathological response to NAC in breast cancer. J. Magn. Reson. Imaging 2016;44:1107-1115. © 2016 International Society for Magnetic Resonance in Medicine.

Regression analysis for LED color detection of visual-MIMO system

NASA Astrophysics Data System (ADS)

Banik, Partha Pratim; Saha, Rappy; Kim, Ki-Doo

2018-04-01

Color detection from a light emitting diode (LED) array using a smartphone camera is very difficult in a visual multiple-input multiple-output (visual-MIMO) system. In this paper, we propose a method to determine the LED color using a smartphone camera by applying regression analysis. We employ a multivariate regression model to identify the LED color. After taking a picture of an LED array, we select the LED array region, and detect the LED using an image processing algorithm. We then apply the k-means clustering algorithm to determine the number of potential colors for feature extraction of each LED. Finally, we apply the multivariate regression model to predict the color of the transmitted LEDs. In this paper, we show our results for three types of environmental light condition: room environmental light, low environmental light (560 lux), and strong environmental light (2450 lux). We compare the results of our proposed algorithm from the analysis of training and test R-Square (%) values, percentage of closeness of transmitted and predicted colors, and we also mention about the number of distorted test data points from the analysis of distortion bar graph in CIE1931 color space.

Process perspective on image quality evaluation

NASA Astrophysics Data System (ADS)

Leisti, Tuomas; Halonen, Raisa; Kokkonen, Anna; Weckman, Hanna; Mettänen, Marja; Lensu, Lasse; Ritala, Risto; Oittinen, Pirkko; Nyman, Göte

2008-01-01

The psychological complexity of multivariate image quality evaluation makes it difficult to develop general image quality metrics. Quality evaluation includes several mental processes and ignoring these processes and the use of a few test images can lead to biased results. By using a qualitative/quantitative (Interpretation Based Quality, IBQ) methodology, we examined the process of pair-wise comparison in a setting, where the quality of the images printed by laser printer on different paper grades was evaluated. Test image consisted of a picture of a table covered with several objects. Three other images were also used, photographs of a woman, cityscape and countryside. In addition to the pair-wise comparisons, observers (N=10) were interviewed about the subjective quality attributes they used in making their quality decisions. An examination of the individual pair-wise comparisons revealed serious inconsistencies in observers' evaluations on the test image content, but not on other contexts. The qualitative analysis showed that this inconsistency was due to the observers' focus of attention. The lack of easily recognizable context in the test image may have contributed to this inconsistency. To obtain reliable knowledge of the effect of image context or attention on subjective image quality, a qualitative methodology is needed.

Effects of alopecia on body image and quality of life of Turkish cancer women with or without headscarf.

PubMed

Erol, Ozgul; Can, Gulbeyaz; Aydıner, Adnan

2012-10-01

The aim of this study was to find out the effects of chemotherapy-related alopecia on body image and quality of life of Turkish women who have cancer with or without headscarves and factors affecting them. This descriptive study was conducted with 204 women who received chemotherapy at the Istanbul University Institute of Oncology, Turkey. The Patient Description Form, Body Image Scale and Nightingale Symptom Assessment Scale were used in data collection. Statistical analyses were performed using descriptive statistics and non-parametric tests. Logistic regression analysis was done to predict the factors affecting body image and quality of life of the patients. No difference was found between women wearing headscarves and those who did not in respect of their body image. However, women who wore headscarves who had no alopecia felt less dissatisfied with their scars, and women not wearing headscarves who had no alopecia have been feeling less self-conscious, less dissatisfied with their appearance. There was difference in terms of quality of life: women wearing headscarves had worse physical, psychological and general well-being than others. Although there were many important factors, multivariate analysis showed that for body image, having alopecia and wearing headscarves; and for quality of life, having alopecia were the variables that had considerable effects.

Revealing representational content with pattern-information fMRI--an introductory guide.

PubMed

Mur, Marieke; Bandettini, Peter A; Kriegeskorte, Nikolaus

2009-03-01

Conventional statistical analysis methods for functional magnetic resonance imaging (fMRI) data are very successful at detecting brain regions that are activated as a whole during specific mental activities. The overall activation of a region is usually taken to indicate involvement of the region in the task. However, such activation analysis does not consider the multivoxel patterns of activity within a brain region. These patterns of activity, which are thought to reflect neuronal population codes, can be investigated by pattern-information analysis. In this framework, a region's multivariate pattern information is taken to indicate representational content. This tutorial introduction motivates pattern-information analysis, explains its underlying assumptions, introduces the most widespread methods in an intuitive way, and outlines the basic sequence of analysis steps.

Simultaneous imaging of fat crystallinity and crystal polymorphic types by Raman microspectroscopy.

PubMed

Motoyama, Michiyo; Ando, Masahiro; Sasaki, Keisuke; Nakajima, Ikuyo; Chikuni, Koichi; Aikawa, Katsuhiro; Hamaguchi, Hiro-O

2016-04-01

The crystalline states of fats, i.e., the crystallinity and crystal polymorphic types, strongly influence their physical properties in fat-based foods. Imaging of fat crystalline states has thus been a subject of abiding interest, but conventional techniques cannot image crystallinity and polymorphic types all at once. This article demonstrates a new technique using Raman microspectroscopy for simultaneously imaging the crystallinity and polymorphic types of fats. The crystallinity and β' crystal polymorph, which contribute to the hardness of fat-based food products, were quantitatively visualized in a model fat (porcine adipose tissue) by analyzing several key Raman bands. The emergence of the β crystal polymorph, which generally results in food product deterioration, was successfully imaged by analyzing the whole fingerprint regions of Raman spectra using multivariate curve resolution alternating least squares analysis. The results demonstrate that the crystalline states of fats can be nondestructively visualized and analyzed at the molecular level, in situ, without laborious sample pretreatments. Copyright © 2015 Elsevier Ltd. All rights reserved.

An Overview of data science uses in bioimage informatics.

PubMed

Chessel, Anatole

2017-02-15

This review aims at providing a practical overview of the use of statistical features and associated data science methods in bioimage informatics. To achieve a quantitative link between images and biological concepts, one typically replaces an object coming from an image (a segmented cell or intracellular object, a pattern of expression or localisation, even a whole image) by a vector of numbers. They range from carefully crafted biologically relevant measurements to features learnt through deep neural networks. This replacement allows for the use of practical algorithms for visualisation, comparison and inference, such as the ones from machine learning or multivariate statistics. While originating mainly, for biology, in high content screening, those methods are integral to the use of data science for the quantitative analysis of microscopy images to gain biological insight, and they are sure to gather more interest as the need to make sense of the increasing amount of acquired imaging data grows more pressing. Copyright © 2017 Elsevier Inc. All rights reserved.

Moving Beyond Univariate Post-Hoc Testing in Exercise Science: A Primer on Descriptive Discriminate Analysis.

PubMed

Barton, Mitch; Yeatts, Paul E; Henson, Robin K; Martin, Scott B

2016-12-01

There has been a recent call to improve data reporting in kinesiology journals, including the appropriate use of univariate and multivariate analysis techniques. For example, a multivariate analysis of variance (MANOVA) with univariate post hocs and a Bonferroni correction is frequently used to investigate group differences on multiple dependent variables. However, this univariate approach decreases power, increases the risk for Type 1 error, and contradicts the rationale for conducting multivariate tests in the first place. The purpose of this study was to provide a user-friendly primer on conducting descriptive discriminant analysis (DDA), which is a post-hoc strategy to MANOVA that takes into account the complex relationships among multiple dependent variables. A real-world example using the Statistical Package for the Social Sciences syntax and data from 1,095 middle school students on their body composition and body image are provided to explain and interpret the results from DDA. While univariate post hocs increased the risk for Type 1 error to 76%, the DDA identified which dependent variables contributed to group differences and which groups were different from each other. For example, students in the very lean and Healthy Fitness Zone categories for body mass index experienced less pressure to lose weight, more satisfaction with their body, and higher physical self-concept than the Needs Improvement Zone groups. However, perceived pressure to gain weight did not contribute to group differences because it was a suppressor variable. Researchers are encouraged to use DDA when investigating group differences on multiple correlated dependent variables to determine which variables contributed to group differences.

Intrinsic Connectivity Provides the Baseline Framework for Variability in Motor Performance: A Multivariate Fusion Analysis of Low- and High-Frequency Resting-State Oscillations and Antisaccade Performance.

PubMed

Jamadar, Sharna D; Egan, Gary F; Calhoun, Vince D; Johnson, Beth; Fielding, Joanne

2016-07-01

Intrinsic brain activity provides the functional framework for the brain's full repertoire of behavioral responses; that is, a common mechanism underlies intrinsic and extrinsic neural activity, with extrinsic activity building upon the underlying baseline intrinsic activity. The generation of a motor movement in response to sensory stimulation is one of the most fundamental functions of the central nervous system. Since saccadic eye movements are among our most stereotyped motor responses, we hypothesized that individual variability in the ability to inhibit a prepotent saccade and make a voluntary antisaccade would be related to individual variability in intrinsic connectivity. Twenty-three individuals completed the antisaccade task and resting-state functional magnetic resonance imaging (fMRI). A multivariate analysis of covariance identified relationships between fMRI oscillations (0.01-0.2 Hz) of resting-state networks determined using high-dimensional independent component analysis and antisaccade performance (latency, error rate). Significant multivariate relationships between antisaccade latency and directional error rate were obtained in independent components across the entire brain. Some of the relationships were obtained in components that overlapped substantially with the task; however, many were obtained in components that showed little overlap with the task. The current results demonstrate that even in the absence of a task, spectral power in regions showing little overlap with task activity predicts an individual's performance on a saccade task.

Multivariate analysis in thoracic research.

PubMed

Mengual-Macenlle, Noemí; Marcos, Pedro J; Golpe, Rafael; González-Rivas, Diego

2015-03-01

Multivariate analysis is based in observation and analysis of more than one statistical outcome variable at a time. In design and analysis, the technique is used to perform trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest. The development of multivariate methods emerged to analyze large databases and increasingly complex data. Since the best way to represent the knowledge of reality is the modeling, we should use multivariate statistical methods. Multivariate methods are designed to simultaneously analyze data sets, i.e., the analysis of different variables for each person or object studied. Keep in mind at all times that all variables must be treated accurately reflect the reality of the problem addressed. There are different types of multivariate analysis and each one should be employed according to the type of variables to analyze: dependent, interdependence and structural methods. In conclusion, multivariate methods are ideal for the analysis of large data sets and to find the cause and effect relationships between variables; there is a wide range of analysis types that we can use.

Investigation of cell wall composition related to stem lodging resistance in wheat (Triticum aestivum L.) by FTIR spectroscopy.

PubMed

Wang, Jian; Zhu, Jinmao; Huang, RuZhu; Yang, YuSheng

2012-07-01

We explored the rapid qualitative analysis of wheat cultivars with good lodging resistances by Fourier transform infrared resonance (FTIR) spectroscopy and multivariate statistical analysis. FTIR imaging showing that wheat stem cell walls were mainly composed of cellulose, pectin, protein, and lignin. Principal components analysis (PCA) was used to eliminate multicollinearity among multiple peak absorptions. PCA revealed the developmental internodes of wheat stems could be distributed from low to high along the load of the second principal component, which was consistent with the corresponding bands of cellulose in the FTIR spectra of the cell walls. Furthermore, four distinct stem populations could also be identified by spectral features related to their corresponding mechanical properties via PCA and cluster analysis. Histochemical staining of four types of wheat stems with various abilities to resist lodging revealed that cellulose contributed more than lignin to the ability to resist lodging. These results strongly suggested that the main cell wall component responsible for these differences was cellulose. Therefore, the combination of multivariate analysis and FTIR could rapidly screen wheat cultivars with good lodging resistance. Furthermore, the application of these methods to a much wider range of cultivars of unknown mechanical properties promises to be of interest.

Ripening-dependent metabolic changes in the volatiles of pineapple (Ananas comosus (L.) Merr.) fruit: II. Multivariate statistical profiling of pineapple aroma compounds based on comprehensive two-dimensional gas chromatography-mass spectrometry.

PubMed

Steingass, Christof Björn; Jutzi, Manfred; Müller, Jenny; Carle, Reinhold; Schmarr, Hans-Georg

2015-03-01

Ripening-dependent changes of pineapple volatiles were studied in a nontargeted profiling analysis. Volatiles were isolated via headspace solid phase microextraction and analyzed by comprehensive 2D gas chromatography and mass spectrometry (HS-SPME-GC×GC-qMS). Profile patterns presented in the contour plots were evaluated applying image processing techniques and subsequent multivariate statistical data analysis. Statistical methods comprised unsupervised hierarchical cluster analysis (HCA) and principal component analysis (PCA) to classify the samples. Supervised partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) regression were applied to discriminate different ripening stages and describe the development of volatiles during postharvest storage, respectively. Hereby, substantial chemical markers allowing for class separation were revealed. The workflow permitted the rapid distinction between premature green-ripe pineapples and postharvest-ripened sea-freighted fruits. Volatile profiles of fully ripe air-freighted pineapples were similar to those of green-ripe fruits postharvest ripened for 6 days after simulated sea freight export, after PCA with only two principal components. However, PCA considering also the third principal component allowed differentiation between air-freighted fruits and the four progressing postharvest maturity stages of sea-freighted pineapples.

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

Wu, J; Gong, G; Cui, Y

Purpose: To predict early pathological response of breast cancer to neoadjuvant chemotherapy (NAC) based on quantitative, multi-region analysis of dynamic contrast enhancement magnetic resonance imaging (DCE-MRI). Methods: In this institution review board-approved study, 35 patients diagnosed with stage II/III breast cancer were retrospectively investigated using DCE-MR images acquired before and after the first cycle of NAC. First, principal component analysis (PCA) was used to reduce the dimensionality of the DCE-MRI data with a high-temporal resolution. We then partitioned the whole tumor into multiple subregions using k-means clustering based on the PCA-defined eigenmaps. Within each tumor subregion, we extracted four quantitativemore » Haralick texture features based on the gray-level co-occurrence matrix (GLCM). The change in texture features in each tumor subregion between pre- and during-NAC was used to predict pathological complete response after NAC. Results: Three tumor subregions were identified through clustering, each with distinct enhancement characteristics. In univariate analysis, all imaging predictors except one extracted from the tumor subregion associated with fast wash-out were statistically significant (p< 0.05) after correcting for multiple testing, with area under the ROC curve or AUCs between 0.75 and 0.80. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.79 (p = 0.002) in leave-one-out cross validation. This improved upon conventional imaging predictors such as tumor volume (AUC=0.53) and texture features based on whole-tumor analysis (AUC=0.65). Conclusion: The heterogeneity of the tumor subregion associated with fast wash-out on DCE-MRI predicted early pathological response to neoadjuvant chemotherapy in breast cancer.« less

Relationship between diverse patient body size- and image acquisition-related factors, and quantitative and qualitative image quality in coronary computed tomography angiography: a multicenter observational study.

PubMed

Utsunomiya, Daisuke; Tanaka, Ryoichi; Yoshioka, Kunihiro; Awai, Kazuo; Mochizuki, Teruhito; Matsunaga, Naofumi; Ichikawa, Tomoaki; Kanematsu, Masayuki; Kim, Tonsok; Yamashita, Yasuyuki

2016-08-01

We investigated the effects of patient- and image acquisition-related factors on the image quality in coronary CT angiography (CCTA). We enrolled 1197 patients (728 men; 65 ± 12 years). All underwent CCTA under the routine scan protocol in 23 participating hospitals. The subjective image quality (3-point Likert scale: excellent, good, and poor) and the attenuation of the left and right coronary artery (LCA, RCA) were recorded; the effects of patient and image acquisition-related factors on vascular attenuation were then compared. The mean LCA attenuation was 515.2 ± 65.8 (excellent), 401.4 ± 63.4 (good), and 319.5 ± 47.6 HU (poor). The corresponding RCA attenuation was 496.6 ± 67.6, 390.5 ± 58.5, and 308.5 ± 50.7 HU, respectively. Univariate analysis revealed significant associations between sufficient coronary attenuation (> 400 HU) and the age, gender, body surface area (BSA), number of detectors, contrast synchronization, scan mode, and the fractional contrast dose. Multivariate analysis revealed that the bolus tracking method, prospective electrocardiogram gating, and fractional contrast dose were significantly associated with sufficient coronary enhancement. BSA and fractional contrast dose are the most important patient- and image acquisition-related factors for sufficient coronary attenuation in CCTA.

[Body image disorder in 100 Tunisian female breast cancer patients].

PubMed

Faten, Ellouze; Nader, Marrakchi; Raies, Hend; Sana, Masmoudi; Amel, Mezlini; Fadhel, M'rad Mohamed

2018-04-01

This study aimed at tracking the prevalence of body image disorder in a population of Tunisian women followed for breast cancer and the factors associated with it. The cross-sectional study was conducted at Salah-Azaiez Institute in Tunis, over a period of four months. One hundred outpatients followed for confirmed breast cancer were recruited. The questionnaire targeted the women's sexuality and their couple relationships, along with their socio-demographic, clinical, and therapeutic characteristics. The scales used were BIS, HADS, and FSFI. The prevalence of body image disorder according to BIS was 45% with an average of 11.5±11.2 among the interrogated patients, 24.7% of which reported an alteration in their couple relationships and 47% in their sexual relations. In univariate analysis, body image disorder was associated with family support, change in couple relationship, depression and anxiety. Body image disorder and sexual dysfunction were interrelated: each of them fostered the prevalence of the other. Multivariate analysis showed that occupational activity was an independent predictor and the absence of anxiety an independent protective factor. Body image disorder was an independent predictive factor of depression and anxiety. The quality of couple relation and sexuality, along with the impact of the patient's surrounding are decisive for the protection or alteration of her body image. Copyright © 2018 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.

Correlative and multivariate analysis of increased radon concentration in underground laboratory.

PubMed

Maletić, Dimitrije M; Udovičić, Vladimir I; Banjanac, Radomir M; Joković, Dejan R; Dragić, Aleksandar L; Veselinović, Nikola B; Filipović, Jelena

2014-11-01

The results of analysis using correlative and multivariate methods, as developed for data analysis in high-energy physics and implemented in the Toolkit for Multivariate Analysis software package, of the relations of the variation of increased radon concentration with climate variables in shallow underground laboratory is presented. Multivariate regression analysis identified a number of multivariate methods which can give a good evaluation of increased radon concentrations based on climate variables. The use of the multivariate regression methods will enable the investigation of the relations of specific climate variable with increased radon concentrations by analysis of regression methods resulting in 'mapped' underlying functional behaviour of radon concentrations depending on a wide spectrum of climate variables. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Multivariate Methods for Meta-Analysis of Genetic Association Studies.

PubMed

Dimou, Niki L; Pantavou, Katerina G; Braliou, Georgia G; Bagos, Pantelis G

2018-01-01

Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

Identifying Neural Patterns of Functional Dyspepsia Using Multivariate Pattern Analysis: A Resting-State fMRI Study

PubMed Central

Liu, Peng; Qin, Wei; Wang, Jingjing; Zeng, Fang; Zhou, Guangyu; Wen, Haixia; von Deneen, Karen M.; Liang, Fanrong; Gong, Qiyong; Tian, Jie

2013-01-01

Background Previous imaging studies on functional dyspepsia (FD) have focused on abnormal brain functions during special tasks, while few studies concentrated on the resting-state abnormalities of FD patients, which might be potentially valuable to provide us with direct information about the neural basis of FD. The main purpose of the current study was thereby to characterize the distinct patterns of resting-state function between FD patients and healthy controls (HCs). Methodology/Principal Findings Thirty FD patients and thirty HCs were enrolled and experienced 5-mintue resting-state scanning. Based on the support vector machine (SVM), we applied multivariate pattern analysis (MVPA) to investigate the differences of resting-state function mapped by regional homogeneity (ReHo). A classifier was designed by using the principal component analysis and the linear SVM. Permutation test was then employed to identify the significant contribution to the final discrimination. The results displayed that the mean classifier accuracy was 86.67%, and highly discriminative brain regions mainly included the prefrontal cortex (PFC), orbitofrontal cortex (OFC), supplementary motor area (SMA), temporal pole (TP), insula, anterior/middle cingulate cortex (ACC/MCC), thalamus, hippocampus (HIPP)/parahippocamus (ParaHIPP) and cerebellum. Correlation analysis revealed significant correlations between ReHo values in certain regions of interest (ROI) and the FD symptom severity and/or duration, including the positive correlations between the dmPFC, pACC and the symptom severity; whereas, the positive correlations between the MCC, OFC, insula, TP and FD duration. Conclusions These findings indicated that significantly distinct patterns existed between FD patients and HCs during the resting-state, which could expand our understanding of the neural basis of FD. Meanwhile, our results possibly showed potential feasibility of functional magnetic resonance imaging diagnostic assay for FD. PMID:23874543

Differentiation of orbital lymphoma and idiopathic orbital inflammatory pseudotumor: combined diagnostic value of conventional MRI and histogram analysis of ADC maps.

PubMed

Ren, Jiliang; Yuan, Ying; Wu, Yingwei; Tao, Xiaofeng

2018-05-02

The overlap of morphological feature and mean ADC value restricted clinical application of MRI in the differential diagnosis of orbital lymphoma and idiopathic orbital inflammatory pseudotumor (IOIP). In this paper, we aimed to retrospectively evaluate the combined diagnostic value of conventional magnetic resonance imaging (MRI) and whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps in the differentiation of the two lesions. In total, 18 patients with orbital lymphoma and 22 patients with IOIP were included, who underwent both conventional MRI and diffusion weighted imaging before treatment. Conventional MRI features and histogram parameters derived from ADC maps, including mean ADC (ADC mean ), median ADC (ADC median ), skewness, kurtosis, 10th, 25th, 75th and 90th percentiles of ADC (ADC 10 , ADC 25 , ADC 75 , ADC 90 ) were evaluated and compared between orbital lymphoma and IOIP. Multivariate logistic regression analysis was used to identify the most valuable variables for discriminating. Differential model was built upon the selected variables and receiver operating characteristic (ROC) analysis was also performed to determine the differential ability of the model. Multivariate logistic regression showed ADC 10 (P = 0.023) and involvement of orbit preseptal space (P = 0.029) were the most promising indexes in the discrimination of orbital lymphoma and IOIP. The logistic model defined by ADC 10 and involvement of orbit preseptal space was built, which achieved an AUC of 0.939, with sensitivity of 77.30% and specificity of 94.40%. Conventional MRI feature of involvement of orbit preseptal space and ADC histogram parameter of ADC 10 are valuable in differential diagnosis of orbital lymphoma and IOIP.

Multivariate statistical analysis software technologies for astrophysical research involving large data bases

NASA Technical Reports Server (NTRS)

Djorgovski, S. George

1994-01-01

We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complete database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful, and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications, and has produced real, published results.

Predictive spectroscopy and chemical imaging based on novel optical systems

NASA Astrophysics Data System (ADS)

Nelson, Matthew Paul

1998-10-01

This thesis describes two futuristic optical systems designed to surpass contemporary spectroscopic methods for predictive spectroscopy and chemical imaging. These systems are advantageous to current techniques in a number of ways including lower cost, enhanced portability, shorter analysis time, and improved S/N. First, a novel optical approach to predicting chemical and physical properties based on principal component analysis (PCA) is proposed and evaluated. A regression vector produced by PCA is designed into the structure of a set of paired optical filters. Light passing through the paired filters produces an analog detector signal directly proportional to the chemical/physical property for which the regression vector was designed. Second, a novel optical system is described which takes a single-shot approach to chemical imaging with high spectroscopic resolution using a dimension-reduction fiber-optic array. Images are focused onto a two- dimensional matrix of optical fibers which are drawn into a linear distal array with specific ordering. The distal end is imaged with a spectrograph equipped with an ICCD camera for spectral analysis. Software is used to extract the spatial/spectral information contained in the ICCD images and deconvolute them into wave length-specific reconstructed images or position-specific spectra which span a multi-wavelength space. This thesis includes a description of the fabrication of two dimension-reduction arrays as well as an evaluation of the system for spatial and spectral resolution, throughput, image brightness, resolving power, depth of focus, and channel cross-talk. PCA is performed on the images by treating rows of the ICCD images as spectra and plotting the scores of each PC as a function of reconstruction position. In addition, iterative target transformation factor analysis (ITTFA) is performed on the spectroscopic images to generate ``true'' chemical maps of samples. Univariate zero-order images, univariate first-order spectroscopic images, bivariate first-order spectroscopic images, and multivariate first-order spectroscopic images of the temporal development of laser-induced plumes are presented and interpreted. Reconstructed chemical images generated using bivariate and trivariate wavelength techniques, bimodal and trimodal PCA methods, and bimodal and trimodal ITTFA approaches are also included.

MR image features predicting hemorrhagic transformation in acute cerebral infarction: a multimodal study.

PubMed

Liu, Chunming; Dong, Zhengchao; Xu, Liang; Khursheed, Aiman; Dong, Longchun; Liu, Zhenxing; Yang, Jun; Liu, Jun

2015-11-01

The aims of this study were to observe magnetic resonance imaging (MRI) features and the frequency of hemorrhagic transformation (HT) in patients with acute cerebral infarction and to identify the risk factors of HT. We first performed multimodal MRI (anatomical, diffusion weighted, and susceptibility weighted) scans on 87 patients with acute cerebral infarction within 24 hours after symptom onset and documented the image findings. We then performed follow-up examinations 3 days to 2 weeks after the onset or whenever the conditions of the patients worsened within 3 days. We utilized univariate statistics to identify the correlations between HT and image features and used multivariate logistical regression to correct for confounding factors to determine relevant independent image features of HT. HT was observed in 17 out of total 87 patients (19.5 %). The infarct size (p = 0.021), cerebral microbleeds (CMBs) (p = 0.004), relative apparent diffusion (rADC) (p = 0.023), and venous anomalies (p = 0.000) were significantly related with HT in the univariate statistics. Multivariate analysis demonstrated that CMBs (odd ratio (OR) = 0.082; 95 % confidence interval (CI) = 0.011-0.597; p = 0.014), rADC (OR = 0.000; 95 % CI = 0.000-0.692; p = 0.041), and venous anomalies (OR = 0.066; 95 % CI = 0.011-0.403; p = 0.003) were independent risk factors for HT. The frequency of HT is 19.5 % in this study. CMBs, rADC, and venous anomalies are independent risk factors for HT of acute cerebral infarction.

Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer.

PubMed

Ali, H Raza; Dariush, Aliakbar; Provenzano, Elena; Bardwell, Helen; Abraham, Jean E; Iddawela, Mahesh; Vallier, Anne-Laure; Hiller, Louise; Dunn, Janet A; Bowden, Sarah J; Hickish, Tamas; McAdam, Karen; Houston, Stephen; Irwin, Mike J; Pharoah, Paul D P; Brenton, James D; Walton, Nicholas A; Earl, Helena M; Caldas, Carlos

2016-02-16

There is a need to improve prediction of response to chemotherapy in breast cancer in order to improve clinical management and this may be achieved by harnessing computational metrics of tissue pathology. We investigated the association between quantitative image metrics derived from computational analysis of digital pathology slides and response to chemotherapy in women with breast cancer who received neoadjuvant chemotherapy. We digitised tissue sections of both diagnostic and surgical samples of breast tumours from 768 patients enrolled in the Neo-tAnGo randomized controlled trial. We subjected digital images to systematic analysis optimised for detection of single cells. Machine-learning methods were used to classify cells as cancer, stromal or lymphocyte and we computed estimates of absolute numbers, relative fractions and cell densities using these data. Pathological complete response (pCR), a histological indicator of chemotherapy response, was the primary endpoint. Fifteen image metrics were tested for their association with pCR using univariate and multivariate logistic regression. Median lymphocyte density proved most strongly associated with pCR on univariate analysis (OR 4.46, 95 % CI 2.34-8.50, p < 0.0001; observations = 614) and on multivariate analysis (OR 2.42, 95 % CI 1.08-5.40, p = 0.03; observations = 406) after adjustment for clinical factors. Further exploratory analyses revealed that in approximately one quarter of cases there was an increase in lymphocyte density in the tumour removed at surgery compared to diagnostic biopsies. A reduction in lymphocyte density at surgery was strongly associated with pCR (OR 0.28, 95 % CI 0.17-0.47, p < 0.0001; observations = 553). A data-driven analysis of computational pathology reveals lymphocyte density as an independent predictor of pCR. Paradoxically an increase in lymphocyte density, following exposure to chemotherapy, is associated with a lack of pCR. Computational pathology can provide objective, quantitative and reproducible tissue metrics and represents a viable means of outcome prediction in breast cancer. ClinicalTrials.gov NCT00070278 ; 03/10/2003.

Long-term follow-up after near-infrared spectroscopy coronary imaging: Insights from the lipid cORe plaque association with CLinical events (ORACLE-NIRS) registry.

PubMed

Danek, Barbara Anna; Karatasakis, Aris; Karacsonyi, Judit; Alame, Aya; Resendes, Erica; Kalsaria, Pratik; Nguyen-Trong, Phuong-Khanh J; Rangan, Bavana V; Roesle, Michele; Abdullah, Shuaib; Banerjee, Subhash; Brilakis, Emmanouil S

Coronary lipid core plaque may be associated with the incidence of subsequent cardiovascular events. We analyzed outcomes of 239 patients who underwent near-infrared spectroscopy (NIRS) coronary imaging between 2009-2011. Multivariable Cox regression was used to identify variables independently associated with the incidence of major adverse cardiovascular events (MACE; cardiac mortality, acute coronary syndromes (ACS), stroke, and unplanned revascularization) during follow-up. Mean patient age was 64±9years, 99% were men, and 50% were diabetic, presenting with stable coronary artery disease (61%) or an acute coronary syndrome (ACS, 39%). Target vessel pre-stenting median lipid core burden index (LCBI) was 88 [interquartile range, IQR 50-130]. Median LCBI in non-target vessels was 57 [IQR 26-94]. Median follow-up was 5.3years. The 5-year MACE rate was 37.5% (cardiac mortality was 15.0%). On multivariable analysis the following variables were associated with MACE: diabetes mellitus, prior percutaneous coronary intervention performed at index angiography, and non-target vessel LCBI. Non-target vessel LCBI of 77 was determined using receiver-operating characteristic curve analysis to be a threshold for prediction of MACE in our cohort. The adjusted hazard ratio (HR) for non-target vessel LCBI ≥77 was 14.05 (95% confidence interval (CI) 2.47-133.51, p=0.002). The 5-year cumulative incidence of events in the above-threshold group was 58.0% vs. 13.1% in the below-threshold group. During long-term follow-up of patients who underwent NIRS imaging, high LCBI in a non-PCI target vessel was associated with increased incidence of MACE. Published by Elsevier Inc.

Multivariate pattern analysis of obsessive-compulsive disorder using structural neuroanatomy.

PubMed

Hu, Xinyu; Liu, Qi; Li, Bin; Tang, Wanjie; Sun, Huaiqiang; Li, Fei; Yang, Yanchun; Gong, Qiyong; Huang, Xiaoqi

2016-02-01

Magnetic resonance imaging (MRI) studies have revealed brain structural abnormalities in obsessive-compulsive disorder (OCD) patients, involving both gray matter (GM) and white matter (WM). However, the results of previous publications were based on average differences between groups, which limited their usages in clinical practice. Therefore, the aim of this study was to examine whether the application of multivariate pattern analysis (MVPA) to high-dimensional structural images would allow accurate discrimination between OCD patients and healthy control subjects (HCS). High-resolution T1-weighted images were acquired from 33 OCD patients and 33 demographically matched HCS in a 3.0 T scanner. Differences in GM and WM volume between OCD and HCS were examined using two types of well-established MVPA techniques: support vector machine (SVM) and Gaussian process classifier (GPC). We also drew a receiver operating characteristic (ROC) curve to evaluate the performance of each classifier. The classification accuracies for both classifiers using GM and WM anatomy were all above 75%. The highest classification accuracy (81.82%, P<0.001) was achieved with the SVM classifier using WM information. Regional brain anomalies with high discriminative power were based on three distributed networks including the fronto-striatal circuit, the temporo-parieto-occipital junction and the cerebellum. Our study illustrated that both GM and WM anatomical features may be useful in differentiating OCD patients from HCS. WM volume using the SVM approach showed the highest accuracy in our population for revealing group differences, which suggested its potential diagnostic role in detecting highly enriched OCD patients at the level of the individual. Copyright © 2015 Elsevier B.V. and ECNP. All rights reserved.

Neuro-Magnetic Resonance Imaging in Hand, Foot, and Mouth Disease: Finding in 412 Patients and Prognostic Features.

PubMed

Lian, Zhou-Yang; Li, He-Hong; Zhang, Bin; Dong, Yu-Hao; Deng, Wu-Xu; Liu, Jing; Luo, Xiao-Ning; Huang, Biao; Liang, Chang-Hong; Zhang, Shui-Xing

The aims of this study were to describe the neuroimaging findings in hand, foot, and mouth disease and determine those who may provide prognosis. Magnetic resonance imaging scans in 412 severe hand, foot, and mouth disease between 2009 and 2014 were retrospectively evaluated. The patients who had the neurological signs were followed for 6 months to 1 year. According to the good or poor prognosis, 2 groups were categorized. The incidence of lesions in different sites between the 2 groups was compared, and multivariate analysis was used to look for risk factors. The major sites of involvement for all patients with percentages were the medulla oblongata (16.1%), spinal anterior nerve roots (12.4%), thoracic segments (11.1%), brain or spinal meninges (8.3%), and so on. There were 347 patients (84.2%) with good prognosis and 65 (15.8%) with poor prognosis in the follow-up. There was a significantly higher rate of lesions involving the cerebral white substance, thalamus, medulla oblongata, pons, midbrain, and spinal cord in the group with poor prognosis. Multivariate analysis showed 2 independent risk factors associated with poor prognosis: lesions located in the medulla oblongata (P < 0.015) and spinal cord (P < 0.001) on magnetic resonance imaging; the latter was the most significant prognostic factor (odds ratio, 29.11; P < 0.001). We found that the distribution patterns for all patients mainly involved the medulla oblongata, spinal anterior nerve roots, thoracic segments, and brain or spinal meninges. Our findings suggested that patients with lesions located in the medulla oblongata and spinal cord may be closely monitored for early intervention and meticulous management. For children with the symptom of nervous system, they are strongly recommended for magnetic resonance examination.

Estimation and Psychometric Analysis of Component Profile Scores via Multivariate Generalizability Theory

ERIC Educational Resources Information Center

Grochowalski, Joseph H.

2015-01-01

Component Universe Score Profile analysis (CUSP) is introduced in this paper as a psychometric alternative to multivariate profile analysis. The theoretical foundations of CUSP analysis are reviewed, which include multivariate generalizability theory and constrained principal components analysis. Because CUSP is a combination of generalizability…

LGE Provides Incremental Prognostic Information Over Serum Biomarkers in AL Cardiac Amyloidosis.

PubMed

Boynton, Samuel J; Geske, Jeffrey B; Dispenzieri, Angela; Syed, Imran S; Hanson, Theodore J; Grogan, Martha; Araoz, Philip A

2016-06-01

This study sought to determine the prognostic value of cardiac magnetic resonance (CMR) late gadolinium enhancement (LGE) in amyloid light chain (AL) cardiac amyloidosis. Cardiac involvement is the major determinant of mortality in AL amyloidosis. CMR LGE is a marker of amyloid infiltration of the myocardium. The purpose of this study was to evaluate retrospectively the prognostic value of CMR LGE for determining all-cause mortality in AL amyloidosis and to compare the prognostic power with the biomarker stage. Seventy-six patients with histologically proven AL amyloidosis underwent CMR LGE imaging. LGE was categorized as global, focal patchy, or none. Global LGE was considered present if it was visualized on LGE images or if the myocardium nulled before the blood pool on a cine multiple inversion time (TI) sequence. CMR morphologic and functional evaluation, echocardiographic diastolic evaluation, and cardiac biomarker staging were also performed. Subjects' charts were reviewed for all-cause mortality. Cox proportional hazards analysis was used to evaluate survival in univariate and multivariate analysis. There were 40 deaths, and the median study follow-up period was 34.4 months. Global LGE was associated with all-cause mortality in univariate analysis (hazard ratio = 2.93; p < 0.001). In multivariate modeling with biomarker stage, global LGE remained prognostic (hazard ratio = 2.43; p = 0.01). Diffuse LGE provides incremental prognosis over cardiac biomarker stage in patients with AL cardiac amyloidosis. Copyright © 2016 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Differentiation of infiltrative cholangiocarcinoma from benign common bile duct stricture using three-dimensional dynamic contrast-enhanced MRI with MRCP.

PubMed

Yu, X-R; Huang, W-Y; Zhang, B-Y; Li, H-Q; Geng, D-Y

2014-06-01

To retrospectively evaluate the criteria for discriminating infiltrative cholangiocarcinoma from benign common bile duct (CBD) stricture using three-dimensional dynamic contrast-enhanced (3D-DCE) magnetic resonance imaging (MRI) combined with magnetic resonance cholangiopancreatography (MRCP) imaging and to determine the predictors for cholangiocarcinoma versus benign CBD stricture. 3D-DCE MRI and MRCP images in 28 patients with infiltrative cholangiocarcinoma and 23 patients with benign causes of CBD stricture were reviewed retrospectively. The final diagnosis was based on surgical or biopsy records. Two radiologists analysed the MRI images for asymmetry, including the wall thickness, length, and enhancement pattern of the narrowed CBD segment, and upstream CBD dilatation. MRI findings that could be used as predictors were identified by univariate analysis and multivariable stepwise logistic regression analysis. Malignant strictures were significantly thicker (4.4 ± 1.2 mm) and longer (16.7 ± 7.7 mm) than the benign strictures (p < 0.05), and upstream CBD dilatation was larger in the infiltrative cholangiocarcinoma cases (20.7 ± 5.7 mm) than in the benign cases (16.5 ± 5.2 mm; p = 0.018). During both the portal venous and equilibrium phases, hyperenhancement was more frequently observed in malignant cases than in benign cases (p < 0.001). The results of the multivariable stepwise logistic regression analysis showed that both hyperenhancement of the involved CBD during the equilibrium phase and the ductal thickness were significant predictors for malignant strictures. When two diagnostic predictive values were used in combination, almost all patients with malignant strictures (n = 26, 92.9%) and benign strictures (n = 21, 91.3%) were correctly identified; the overall accuracy was 92.2% with correct classifications in 47 of the 51 patients. Infiltrative cholangiocarcinoma and benign CBD strictures could be effectively differentiated using DCE-MRI and MRCP based on hyperenhancement during the equilibrium phase and bile wall thickness of the involved segment. Copyright © 2014 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Predictors of Nondiagnostic Ultrasound for Appendicitis.

PubMed

Keller, Christine; Wang, Nancy E; Imler, Daniel L; Vasanawala, Shreyas S; Bruzoni, Matias; Quinn, James V

2017-03-01

Ionizing radiation and cost make ultrasound (US), when available, the first imaging study for the diagnosis of suspected pediatric appendicitis. US is less sensitive and specific than computed tomography (CT) or magnetic resonance imaging (MRI) scans, which are often performed after nondiagnostic US. We sought to determine predictors of nondiagnostic US in order to guide efficient ordering of imaging studies. A prospective cohort study of consecutive patients 4 to 30 years of age with suspected appendicitis took place at an emergency department with access to 24/7 US, MRI, and CT capabilities. Patients with US as their initial study were identified. Clinical (i.e., duration of illness, highest fever, and right lower quadrant pain) and demographic (i.e., age and sex) variables were collected. Body mass index (BMI) was calculated based on Centers for Disease Control and Prevention criteria; BMI >85th percentile was categorized as overweight. Patients were followed until day 7. Univariate and stepwise multivariate logistic regression analysis was performed. Over 3 months, 106 patients had US first for suspected appendicitis; 52 (49%) had nondiagnostic US results. Eighteen patients had appendicitis, and there were no missed cases after discharge. On univariate analysis, male sex, a yearly increase in age, and overweight BMI were associated with nondiagnostic US (p < 0.05). In the multivariate model, only BMI (odds ratio 4.9 [95% CI 2.0-12.2]) and age (odds ratio 1.1 [95% CI 1.02-1.20]) were predictors. Sixty-eight percent of nondiagnostic US results occurred in overweight patients. Overweight and older patients are more likely to have a nondiagnostic US or appendicitis, and it may be more efficient to consider alternatives to US first for these patients. Also, this information about the accuracy of US to diagnose suspected appendicitis may be useful to clinicians who wish to engage in shared decision-making with the parents or guardians of children regarding imaging options for children with acute abdominal pain. Copyright © 2016 Elsevier Inc. All rights reserved.

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

Cullen, David A; Koestner, Roland; Kukreja, Ratan

Improved conditions for imaging and spectroscopic mapping of thin perfluorosulfonic acid (PFSA) ionomer layers in fuel cell electrodes by scanning transmission electron microscopy (STEM) have been investigated. These conditions are first identified on model systems of Nafion ionomer-coated nanostructured thin films and nanoporous Si. The optimized conditions are then applied in a quantitative study of the ionomer through-layer loading for two typical electrode catalyst coatings using electron energy loss and energy dispersive X-ray spectroscopy in the transmission electron microscope. The e-beam induced damage to the perfluorosulfonic acid (PFSA) ionomer is quantified by following the fluorine mass loss with electron exposuremore » and is then mitigated by a few orders of magnitude using cryogenic specimen cooling and a higher incident electron voltage. Multivariate statistical analysis is also applied to the analysis of spectrum images for data denoising and unbiased separation of independent components related to the catalyst, ionomer, and support.« less

Label-free cell-cycle analysis by high-throughput quantitative phase time-stretch imaging flow cytometry

NASA Astrophysics Data System (ADS)

Mok, Aaron T. Y.; Lee, Kelvin C. M.; Wong, Kenneth K. Y.; Tsia, Kevin K.

2018-02-01

Biophysical properties of cells could complement and correlate biochemical markers to characterize a multitude of cellular states. Changes in cell size, dry mass and subcellular morphology, for instance, are relevant to cell-cycle progression which is prevalently evaluated by DNA-targeted fluorescence measurements. Quantitative-phase microscopy (QPM) is among the effective biophysical phenotyping tools that can quantify cell sizes and sub-cellular dry mass density distribution of single cells at high spatial resolution. However, limited camera frame rate and thus imaging throughput makes QPM incompatible with high-throughput flow cytometry - a gold standard in multiparametric cell-based assay. Here we present a high-throughput approach for label-free analysis of cell cycle based on quantitative-phase time-stretch imaging flow cytometry at a throughput of > 10,000 cells/s. Our time-stretch QPM system enables sub-cellular resolution even at high speed, allowing us to extract a multitude (at least 24) of single-cell biophysical phenotypes (from both amplitude and phase images). Those phenotypes can be combined to track cell-cycle progression based on a t-distributed stochastic neighbor embedding (t-SNE) algorithm. Using multivariate analysis of variance (MANOVA) discriminant analysis, cell-cycle phases can also be predicted label-free with high accuracy at >90% in G1 and G2 phase, and >80% in S phase. We anticipate that high throughput label-free cell cycle characterization could open new approaches for large-scale single-cell analysis, bringing new mechanistic insights into complex biological processes including diseases pathogenesis.

Towards exaggerated emphysema stereotypes

NASA Astrophysics Data System (ADS)

Chen, C.; Sørensen, L.; Lauze, F.; Igel, C.; Loog, M.; Feragen, A.; de Bruijne, M.; Nielsen, M.

2012-03-01

Classification is widely used in the context of medical image analysis and in order to illustrate the mechanism of a classifier, we introduce the notion of an exaggerated image stereotype based on training data and trained classifier. The stereotype of some image class of interest should emphasize/exaggerate the characteristic patterns in an image class and visualize the information the employed classifier relies on. This is useful for gaining insight into the classification and serves for comparison with the biological models of disease. In this work, we build exaggerated image stereotypes by optimizing an objective function which consists of a discriminative term based on the classification accuracy, and a generative term based on the class distributions. A gradient descent method based on iterated conditional modes (ICM) is employed for optimization. We use this idea with Fisher's linear discriminant rule and assume a multivariate normal distribution for samples within a class. The proposed framework is applied to computed tomography (CT) images of lung tissue with emphysema. The synthesized stereotypes illustrate the exaggerated patterns of lung tissue with emphysema, which is underpinned by three different quantitative evaluation methods.

Cognitive versus Software-Assisted Registration: Development of a New Nomogram Predicting Prostate Cancer at MRI-Targeted Biopsies.

PubMed

Kaufmann, Sascha; Russo, Giorgio I; Thaiss, Wolfgang; Notohamiprodjo, Mike; Bamberg, Fabian; Bedke, Jens; Morgia, Giuseppe; Nikolaou, Konstantin; Stenzl, Arnulf; Kruck, Stephan

2018-04-03

Multiparametric magnetic resonance imaging (mpMRI) is gaining acceptance to guide targeted biopsy (TB) in prostate cancer (PC) diagnosis. We aimed to compare the detection rate of software-assisted fusion TB (SA-TB) versus cognitive fusion TB (COG-TB) for PC and to evaluate potential clinical features in detecting PC and clinically significant PC (csPC) at TB. This was a retrospective cohort study of patients with rising and/or persistently elevated prostate-specific antigen (PSA) undergoing mpMRI followed by either transperineal SA-TB or transrectal COG-TB. The analysis showed a matched-paired analysis between SA-TB versus COG-TB without differences in clinical or radiological characteristics. Differences among detection of PC/csPC among groups were analyzed. A multivariable logistic regression model predicting PC at TB was fitted. The model was evaluated using the receiver operating characteristic-derived area under the curve, goodness of fit test, and decision-curve analyses. One hundred ninety-one and 87 patients underwent SA-TB or COG-TB, respectively. The multivariate logistic analysis showed that SA-TB was associated with overall PC (odds ratio [OR], 5.70; P < .01) and PC at TB (OR, 3.00; P < .01) but not with overall csPC (P = .40) and csPC at TB (P = .40). A nomogram predicting PC at TB was constructed using the Prostate Imaging Reporting and Data System version 2.0, age, PSA density and biopsy technique, showing improved clinical risk prediction against a threshold probability of 10% with a c-index of 0.83. In patients with suspected PC, software-assisted biopsy detects most cancers and outperforms the cognitive approach in targeting magnetic resonance imaging-visible lesions. Furthermore, we introduced a prebiopsy nomogram for the probability of PC in TB. Copyright © 2018 Elsevier Inc. All rights reserved.

Routine perioperative ketorolac administration is not associated with hemorrhage in pediatric neurosurgery patients.

PubMed

Richardson, Marlin Dustin; Palmeri, Nicholas O; Williams, Sarah A; Torok, Michelle R; O'Neill, Brent R; Handler, Michael H; Hankinson, Todd C

2016-01-01

OBJECT NSAIDs are effective perioperative analgesics. Many surgeons are reluctant to use NSAIDs perioperatively because of a theoretical increase in the risk for bleeding events. The authors assessed the effect of routine perioperative ketorolac use on intracranial hemorrhage in children undergoing a wide range of neurosurgical procedures. METHODS A retrospective single-institution analysis of 1451 neurosurgical cases was performed. Data included demographics, type of surgery, and perioperative ketorolac use. Outcomes included bleeding events requiring return to the operating room, bleeding seen on postoperative imaging, and the development of renal failure or gastrointestinal tract injury. Variables associated with both the exposure and outcomes (p < 0.20) were evaluated as potential confounders for bleeding on postoperative imaging, and multivariable logistic regression was performed. Bivariable analysis was performed for bleeding events. Odds ratios and 95% CIs were estimated. RESULTS Of the 1451 patients, 955 received ketorolac. Multivariate regression analysis demonstrated no significant association between clinically significant bleeding events (OR 0.69; 95% CI 0.15-3.1) or radiographic hemorrhage (OR 0.81; 95% CI 0.43-1.51) and the perioperative administration of ketorolac. Treatment with a medication that creates a known bleeding risk (OR 3.11; 95% CI 1.01-9.57), surgical procedure (OR 2.35; 95% CI 1.11-4.94), and craniotomy/craniectomy (OR 2.43; 95% CI 1.19-4.94) were associated with a significantly elevated risk for radiographically identified hemorrhage. CONCLUSIONS Short-term ketorolac therapy does not appear to be associated with a statistically significant increase in the risk of bleeding documented on postoperative imaging in pediatric neurosurgical patients and may be considered as part of a perioperative analgesic regimen. Although no association was found between ketorolac and clinically significant bleeding events, a larger study needs to be conducted to control for confounding factors, because of the rarity of these events.

The increased use of computed tomography angiography and magnetic resonance angiography as the sole imaging modalities prior to infrainguinal bypass has had no effect on outcomes.

PubMed

Shue, Bing; Damle, Rachelle N; Flahive, Julie; Kalish, Jeffrey A; Stone, David H; Patel, Virendra I; Schanzer, Andres; Baril, Donald T

2015-08-01

Angiography remains the gold standard imaging modality before infrainguinal bypass. Computed tomography angiography (CTA) and magnetic resonance angiography (MRA) have emerged as noninvasive alternatives for preoperative imaging. We sought to examine contemporary trends in the utilization of CTA and MRA as isolated imaging modalities before infrainguinal bypass and to compare outcomes following infrainguinal bypass in patients who underwent CTA or MRA versus those who underwent conventional arteriography. Patients undergoing infrainguinal bypass within the Vascular Study Group of New England were identified (2003-2012). Patients were stratified by preoperative imaging modality: CTA/MRA alone or conventional angiography. Trends in utilization of these modalities were examined and demographics of these groups were compared. Primary end points included primary patency, secondary patency, and major adverse limb events (MALE) at 1 year as determined by Kaplan-Meier analysis. Multivariable Cox proportional hazards models were constructed to evaluate the effect of imaging modality on primary patency, secondary patency, and MALE after adjusting for confounders. In 3123 infrainguinal bypasses, CTA/MRA alone was used in 462 cases (15%) and angiography was used in 2661 cases (85%). Use of CTA/MRA alone increased over time, with 52 (11%) bypasses performed between 2003 and 2005, 189 (41%) bypasses performed between 2006 and 2009, and 221 (48%) bypasses performed between 2010 and 2012 (P < 0.001). Patients with CTA/MRA alone, compared with patients with angiography, more frequently underwent bypass for claudication (33% vs. 26%, P = 0.001) or acute limb ischemia (13% vs. 5%, P < 0.0001), more frequently had prosthetic conduits (39% vs. 30%, P = 0.001), and less frequently had tibial/pedal targets (32% vs. 40%, P = 0.002). After adjusting for these and other confounders, multivariable analysis demonstrated that the use of CTA/MRA alone was not associated with a significant difference in 1 year primary patency (hazard ratio [HR] 0.95, 95% confidence interval [CI] 0.78-1.16), secondary patency (HR 1.30, 95% CI 0.99-1.72), or MALE (HR 1.08, 95% CI 0.89-1.32). CTA and MRA are being increasingly used as the sole preoperative imaging modality before infrainguinal bypass. This shift in practice patterns appears to have no measurable effect on outcomes at 1 year. Copyright © 2015 Elsevier Inc. All rights reserved.

Infrared imaging spectroscopy and chemometric tools for in situ analysis of an imiquimod pharmaceutical preparation presented as cream

NASA Astrophysics Data System (ADS)

Carneiro, Renato Lajarim; Poppi, Ronei Jesus

2014-01-01

In the present work the homogeneity of a pharmaceutical formulation presented as a cream was studied using infrared imaging spectroscopy and chemometric methodologies such as principal component analysis (PCA) and multivariate curve resolution with alternating least squares (MCR-ALS). A cream formulation, presented as an emulsion, was prepared using imiquimod as the active pharmaceutical ingredient (API) and the excipients: water, vaseline, an emulsifier and a carboxylic acid in order to dissolve the API. After exposure at 45 °C during 3 months to perform accelerated stability test, the presence of some crystals was observed, indicating homogeneity problems in the formulation. PCA exploratory analysis showed that the crystal composition was different from the composition of the emulsion, since the score maps presented crystal structures in the emulsion. MCR-ALS estimated the spectra of the crystals and the emulsion. The crystals presented amine and C-H bands, suggesting that the precipitate was a salt formed by carboxylic acid and imiquimod. These results indicate the potential of infrared imaging spectroscopy in conjunction with chemometric methodologies as an analytical tool to ensure the quality of cream formulations in the pharmaceutical industry.

Diagnostic concordance between mobile interfaces and conventional workstations for emergency imaging assessment.

PubMed

Venson, José Eduardo; Bevilacqua, Fernando; Berni, Jean; Onuki, Fabio; Maciel, Anderson

2018-05-01

Mobile devices and software are now available with sufficient computing power, speed and complexity to allow for real-time interpretation of radiology exams. In this paper, we perform a multivariable user study that investigates concordance of image-based diagnoses provided using mobile devices on the one hand and conventional workstations on the other hand. We performed a between-subjects task-analysis using CT, MRI and radiography datasets. Moreover, we investigated the adequacy of the screen size, image quality, usability and the availability of the tools necessary for the analysis. Radiologists, members of several teams, participated in the experiment under real work conditions. A total of 64 studies with 93 main diagnoses were analyzed. Our results showed that 56 cases were classified with complete concordance (87.69%), 5 cases with almost complete concordance (7.69%) and 1 case (1.56%) with partial concordance. Only 2 studies presented discordance between the reports (3.07%). The main reason to explain the cause of those disagreements was the lack of multiplanar reconstruction tool in the mobile viewer. Screen size and image quality had no direct impact on the mobile diagnosis process. We concluded that for images from emergency modalities, a mobile interface provides accurate interpretation and swift response, which could benefit patients' healthcare. Copyright © 2018 Elsevier B.V. All rights reserved.

Prediction of survival with multi-scale radiomic analysis in glioblastoma patients.

PubMed

Chaddad, Ahmad; Sabri, Siham; Niazi, Tamim; Abdulkarim, Bassam

2018-06-19

We propose a multiscale texture features based on Laplacian-of Gaussian (LoG) filter to predict progression free (PFS) and overall survival (OS) in patients newly diagnosed with glioblastoma (GBM). Experiments use the extracted features derived from 40 patients of GBM with T1-weighted imaging (T1-WI) and Fluid-attenuated inversion recovery (FLAIR) images that were segmented manually into areas of active tumor, necrosis, and edema. Multiscale texture features were extracted locally from each of these areas of interest using a LoG filter and the relation between features to OS and PFS was investigated using univariate (i.e., Spearman's rank correlation coefficient, log-rank test and Kaplan-Meier estimator) and multivariate analyses (i.e., Random Forest classifier). Three and seven features were statistically correlated with PFS and OS, respectively, with absolute correlation values between 0.32 and 0.36 and p < 0.05. Three features derived from active tumor regions only were associated with OS (p < 0.05) with hazard ratios (HR) of 2.9, 3, and 3.24, respectively. Combined features showed an AUC value of 85.37 and 85.54% for predicting the PFS and OS of GBM patients, respectively, using the random forest (RF) classifier. We presented a multiscale texture features to characterize the GBM regions and predict he PFS and OS. The efficiency achievable suggests that this technique can be developed into a GBM MR analysis system suitable for clinical use after a thorough validation involving more patients. Graphical abstract Scheme of the proposed model for characterizing the heterogeneity of GBM regions and predicting the overall survival and progression free survival of GBM patients. (1) Acquisition of pretreatment MRI images; (2) Affine registration of T1-WI image with its corresponding FLAIR images, and GBM subtype (phenotypes) labelling; (3) Extraction of nine texture features from the three texture scales fine, medium, and coarse derived from each of GBM regions; (4) Comparing heterogeneity between GBM regions by ANOVA test; Survival analysis using Univariate (Spearman rank correlation between features and survival (i.e., PFS and OS) based on each of the GBM regions, Kaplan-Meier estimator and log-rank test to predict the PFS and OS of patient groups that grouped based on median of feature), and multivariate (random forest model) for predicting the PFS and OS of patients groups that grouped based on median of PFS and OS.

Multivariate analysis applied to the study of spatial distributions found in drug-eluting stent coatings by confocal Raman microscopy.

PubMed

Balss, Karin M; Long, Frederick H; Veselov, Vladimir; Orana, Argjenta; Akerman-Revis, Eugena; Papandreou, George; Maryanoff, Cynthia A

2008-07-01

Multivariate data analysis was applied to confocal Raman measurements on stents coated with the polymers and drug used in the CYPHER Sirolimus-eluting Coronary Stents. Partial least-squares (PLS) regression was used to establish three independent calibration curves for the coating constituents: sirolimus, poly(n-butyl methacrylate) [PBMA], and poly(ethylene-co-vinyl acetate) [PEVA]. The PLS calibrations were based on average spectra generated from each spatial location profiled. The PLS models were tested on six unknown stent samples to assess accuracy and precision. The wt % difference between PLS predictions and laboratory assay values for sirolimus was less than 1 wt % for the composite of the six unknowns, while the polymer models were estimated to be less than 0.5 wt % difference for the combined samples. The linearity and specificity of the three PLS models were also demonstrated with the three PLS models. In contrast to earlier univariate models, the PLS models achieved mass balance with better accuracy. This analysis was extended to evaluate the spatial distribution of the three constituents. Quantitative bitmap images of drug-eluting stent coatings are presented for the first time to assess the local distribution of components.

Body Image of Women Submitted to Breast Cancer Treatment

PubMed

Guedes, Thais Sousa Rodrigues; Dantas de Oliveira, Nayara Priscila; Holanda, Ayrton Martins; Reis, Mariane Albuquerque; Silva, Clécia Patrocínio da; Rocha e Silva, Bárbara Layse; Cancela, Marianna de Camargo; de Souza, Dyego Leandro Bezerra

2018-06-25

Background: The study of body image includes the perception of women regarding the physical appearance of their own body. The objective of the present study was to verify the prevalence of body image dissatisfaction and its associated factors in women submitted to breast cancer treatment. Methods: A cross-sectional study carried out with 103 female residents of the municipality of Natal (Northeast Brazil), diagnosed with breast cancer who had undergone cancer treatment for at least 12 months prior to the study, and remained under clinical monitoring. The variable body image was measured through the validated Body Image Scale (BIS). Socioeconomic variables and clinical history were also collected through an individual interview with each participant. The Pearson’s chi-squared test (Fisher’s Exact) was utilized for bivariate analysis, calculating the prevalence ratio with 95% confidence interval. Poisson regression with robust variance was utilized for multivariate analysis. The statistical significance considered was 0.05. Results: The prevalence of body image dissatisfaction was 74.8% CI (65%-82%). Statistically significant associations were observed between body image and multi-professional follow-up (p=0.009) and return to employment after treatment (p=0.022). Conclusion: It was concluded that women who reported employment after cancer treatment presented more alterations in self-perception concerning their appearance. Patients who did not receive multi-professional follow-up reported negative body image, evidencing the need for strategies that increase and improve healthcare, aiming to meet the demands of this population. Creative Commons Attribution License

Multivariate meta-analysis: potential and promise.

PubMed

Jackson, Dan; Riley, Richard; White, Ian R

2011-09-10

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day 'Multivariate meta-analysis' event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd.

Hyperspectral imaging and multivariate analysis in the dried blood spots investigations

NASA Astrophysics Data System (ADS)

Majda, Alicja; Wietecha-Posłuszny, Renata; Mendys, Agata; Wójtowicz, Anna; Łydżba-Kopczyńska, Barbara

2018-04-01

The aim of this study was to apply a new methodology using the combination of the hyperspectral imaging and the dry blood spot (DBS) collecting. Application of the hyperspectral imaging is fast and non-destructive. DBS method offers the advantage also on the micro-invasive blood collecting and low volume of required sample. During experimental step, the reflected light was recorded by two hyperspectral systems. The collection of 776 spectral bands in the VIS-NIR range (400-1000 nm) and 256 spectral bands in the SWIR range (970-2500 nm) was applied. Pixel has the size of 8 × 8 and 30 × 30 µm for VIS-NIR and SWIR camera, respectively. The obtained data in the form of hyperspectral cubes were treated with chemometric methods, i.e., minimum noise fraction and principal component analysis. It has been shown that the application of these methods on this type of data, by analyzing the scatter plots, allows a rapid analysis of the homogeneity of DBS, and the selection of representative areas for further analysis. It also gives the possibility of tracking the dynamics of changes occurring in biological traces applied on the surface. For the analyzed 28 blood samples, described method allowed to distinguish those blood stains because of time of apply.

Hot Spot and Whole-Tumor Enumeration of CD8+ Tumor-Infiltrating Lymphocytes Utilizing Digital Image Analysis Is Prognostic in Triple-Negative Breast Cancer.

PubMed

McIntire, Patrick J; Irshaid, Lina; Liu, Yifang; Chen, Zhengming; Menken, Faith; Nowak, Eugene; Shin, Sandra J; Ginter, Paula S

2018-05-07

CD8 + tumor-infiltrating lymphocytes (TILs) have emerged as a prognostic indicator in triple-negative breast cancer (TNBC). There is debate surrounding the prognostic value of hot spots for CD8 + TIL enumeration. We compared hot spot versus whole-tumor CD8 + TIL enumeration in prognosticating TNBC using immunohistochemistry on whole tissue sections and quantification by digital image analysis (Halo imaging analysis software; Indica Labs, Corrales, NM). A wide range of clinically relevant hot spot sizes was evaluated. CD8 + TIL enumeration was independently statistically significant for all hot spot sizes and whole-tumor annotations for disease-free survival by multivariate analysis. A 10× objective (2.2 mm diameter) hot spot was found to correlate significantly with overall survival (P = .04), while the remaining hot spots and whole-tumor CD8 + TIL enumeration did not (P > .05). Statistical significance was not demonstrated when comparing between hot spots and whole-tumor annotations, as the groups had overlapping confidence intervals. CD8 + TIL hot spot enumeration is equivalent to whole-tumor enumeration for prognostication in TNBC and may serve as a good alternative methodology in future studies and clinical practice. Copyright © 2018 Elsevier Inc. All rights reserved.

Use of Neuroanatomical Pattern Classification to Identify Subjects in At-Risk Mental States of Psychosis and Predict Disease Transition

PubMed Central

Koutsouleris, Nikolaos; Meisenzahl, Eva M.; Davatzikos, Christos; Bottlender, Ronald; Frodl, Thomas; Scheuerecker, Johanna; Schmitt, Gisela; Zetzsche, Thomas; Decker, Petra; Reiser, Maximilian; Möller, Hans-Jürgen; Gaser, Christian

2014-01-01

Context Identification of individuals at high risk of developing psychosis has relied on prodromal symptomatology. Recently, machine learning algorithms have been successfully used for magnetic resonance imaging–based diagnostic classification of neuropsychiatric patient populations. Objective To determine whether multivariate neuroanatomical pattern classification facilitates identification of individuals in different at-risk mental states (ARMS) of psychosis and enables the prediction of disease transition at the individual level. Design Multivariate neuroanatomical pattern classification was performed on the structural magnetic resonance imaging data of individuals in early or late ARMS vs healthy controls (HCs). The predictive power of the method was then evaluated by categorizing the baseline imaging data of individuals with transition to psychosis vs those without transition vs HCs after 4 years of clinical follow-up. Classification generalizability was estimated by cross-validation and by categorizing an independent cohort of 45 new HCs. Setting Departments of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany. Participants The first classification analysis included 20 early and 25 late at-risk individuals and 25 matched HCs. The second analysis consisted of 15 individuals with transition, 18 without transition, and 17 matched HCs. Main Outcome Measures Specificity, sensitivity, and accuracy of classification. Results The 3-group, cross-validated classification accuracies of the first analysis were 86% (HCs vs the rest), 91% (early at-risk individuals vs the rest), and 86% (late at-risk individuals vs the rest). The accuracies in the second analysis were 90% (HCs vs the rest), 88% (individuals with transition vs the rest), and 86% (individuals without transition vs the rest). Independent HCs were correctly classified in 96% (first analysis) and 93% (second analysis) of cases. Conclusions Different ARMSs and their clinical outcomes may be reliably identified on an individual basis by assessing patterns of whole-brain neuroanatomical abnormalities. These patterns may serve as valuable biomarkers for the clinician to guide early detection in the prodromal phase of psychosis. PMID:19581561

Risk Stratification Among Men With Prostate Imaging Reporting and Data System Version 2 Category 3 Transition Zone Lesions: Is Biopsy Always Necessary?

PubMed Central

Felker, Ely R.; Raman, Steven S.; Margolis, Daniel J.; Lu, David S. K.; Shaheen, Nicholas; Natarajan, Shyam; Sharma, Devi; Huang, Jiaoti; Dorey, Fred; Marks, Leonard S.

2017-01-01

OBJECTIVE The objective of our study was to determine the clinical and MRI characteristics of clinically significant prostate cancer (PCA) (Gleason score ≥ 3 + 4) in men with Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) category 3 transition zone (TZ) lesions. MATERIALS AND METHODS From 2014 to 2016, 865 men underwent prostate MRI and MRI/ultrasound (US) fusion biopsy (FB). A subset of 90 FB-naïve men with 96 PI-RADSv2 category 3 TZ lesions was identified. Patients were imaged at 3 T using a body coil. Images were assigned a PI-RADSv2 category by an experienced radiologist. Using clinical data and imaging features, we performed univariate and multivariate analyses to identify predictors of clinically significant PCA. RESULTS The mean patient age was 66 years, and the mean prostate-specific antigen density (PSAD) was 0.13 ng/mL2. PCA was detected in 34 of 96 (35%) lesions, 14 of which (15%) harbored clinically significant PCA. In univariate analysis, DWI score, prostate volume, and PSAD were significant predictors (p < 0.05) of clinically significant PCA with a suggested significance for apparent diffusion coefficient (ADC) and prostate-specific antigen value (p < 0.10). On multivariate analysis, PSAD and lesion ADC were the most important covariates. The combination of both PSAD of 0.15 ng/mL2 or greater and an ADC value of less than 1000 mm2/s yielded an AUC of 0.91 for clinically significant PCA (p < 0.001). If FB had been restricted to these criteria, only 10 of 90 men would have undergone biopsy, resulting in diagnosis of clinically significant PCA in 60% with eight men (9%) misdiagnosed (false-negative). CONCLUSION The yield of FB in men with PI-RADSv2 category 3 TZ lesions for clinically significant PCA is 15% but significantly improves to 60% (AUC > 0.9) among men with PSAD of 0.15 ng/mL2 or greater and lesion ADC value of less than 1000 mm2/s. PMID:28858541

Doppler-shifted fluorescence imaging of velocity fields in supersonic reacting flows

NASA Technical Reports Server (NTRS)

Allen, M. G.; Davis, S. J.; Kessler, W. J.; Sonnenfroh, D. M.

1992-01-01

The application of Doppler-shifted fluorescence imaging of velocity fields in supersonic reacting flows is analyzed. Focussing on fluorescence of the OH molecule in typical H2-air Scramjet flows, the effects of uncharacterized variations in temperature, pressure, and collisional partner composition across the measurement plane are examined. Detailed measurements of the (1,0) band OH lineshape variations in H2-air combustions are used, along with single-pulse and time-averaged measurements of an excimer-pumped dye laser, to predict the performance of a model velocimeter with typical Scramjet flow properties. The analysis demonstrates the need for modification and control of the laser bandshape in order to permit accurate velocity measurements in the presence of multivariant flow properties.

Determinants of image quality of rotational angiography for on-line assessment of frame geometry after transcatheter aortic valve implantation.

PubMed

Rodríguez-Olivares, Ramón; El Faquir, Nahid; Rahhab, Zouhair; Maugenest, Anne-Marie; Van Mieghem, Nicolas M; Schultz, Carl; Lauritsch, Guenter; de Jaegere, Peter P T

2016-07-01

To study the determinants of image quality of rotational angiography using dedicated research prototype software for motion compensation without rapid ventricular pacing after the implantation of four commercially available catheter-based valves. Prospective observational study including 179 consecutive patients who underwent transcatheter aortic valve implantation (TAVI) with either the Medtronic CoreValve (MCS), Edward-SAPIEN Valve (ESV), Boston Sadra Lotus (BSL) or Saint-Jude Portico Valve (SJP) in whom rotational angiography (R-angio) with motion compensation 3D image reconstruction was performed. Image quality was evaluated from grade 1 (excellent image quality) to grade 5 (strongly degraded). Distinction was made between good (grades 1, 2) and poor image quality (grades 3-5). Clinical (gender, body mass index, Agatston score, heart rate and rhythm, artifacts), procedural (valve type) and technical variables (isocentricity) were related with the image quality assessment. Image quality was good in 128 (72 %) and poor in 51 (28 %) patients. By univariable analysis only valve type (BSL) and the presence of an artefact negatively affected image quality. By multivariate analysis (in which BMI was forced into the model) BSL valve (Odds 3.5, 95 % CI [1.3-9.6], p = 0.02), presence of an artifact (Odds 2.5, 95 % CI [1.2-5.4], p = 0.02) and BMI (Odds 1.1, 95 % CI [1.0-1.2], p = 0.04) were independent predictors of poor image quality. Rotational angiography with motion compensation 3D image reconstruction using a dedicated research prototype software offers good image quality for the evaluation of frame geometry after TAVI in the majority of patients. Valve type, presence of artifacts and higher BMI negatively affect image quality.

A Review of Mid-Infrared and Near-Infrared Imaging: Principles, Concepts and Applications in Plant Tissue Analysis.

PubMed

Türker-Kaya, Sevgi; Huck, Christian W

2017-01-20

Plant cells, tissues and organs are composed of various biomolecules arranged as structurally diverse units, which represent heterogeneity at microscopic levels. Molecular knowledge about those constituents with their localization in such complexity is very crucial for both basic and applied plant sciences. In this context, infrared imaging techniques have advantages over conventional methods to investigate heterogeneous plant structures in providing quantitative and qualitative analyses with spatial distribution of the components. Thus, particularly, with the use of proper analytical approaches and sampling methods, these technologies offer significant information for the studies on plant classification, physiology, ecology, genetics, pathology and other related disciplines. This review aims to present a general perspective about near-infrared and mid-infrared imaging/microspectroscopy in plant research. It is addressed to compare potentialities of these methodologies with their advantages and limitations. With regard to the organization of the document, the first section will introduce the respective underlying principles followed by instrumentation, sampling techniques, sample preparations, measurement, and an overview of spectral pre-processing and multivariate analysis. The last section will review selected applications in the literature.

Ultrasound Assessment of Human Meniscus.

PubMed

Viren, Tuomas; Honkanen, Juuso T; Danso, Elvis K; Rieppo, Lassi; Korhonen, Rami K; Töyräs, Juha

2017-09-01

The aim of the present study was to evaluate the applicability of ultrasound imaging to quantitative assessment of human meniscus in vitro. Meniscus samples (n = 26) were harvested from 13 knee joints of non-arthritic human cadavers. Subsequently, three locations (anterior, center and posterior) from each meniscus were imaged with two ultrasound transducers (frequencies 9 and 40 MHz), and quantitative ultrasound parameters were determined. Furthermore, partial-least-squares regression analysis was applied for ultrasound signal to determine the relations between ultrasound scattering and meniscus integrity. Significant correlations between measured and predicted meniscus compositions and mechanical properties were obtained (R 2  = 0.38-0.69, p < 0.05). The relationship between conventional ultrasound parameters and integrity of the meniscus was weaker. To conclude, ultrasound imaging exhibited a potential for evaluation of meniscus integrity. Higher ultrasound frequency combined with multivariate analysis of ultrasound backscattering was found to be the most sensitive for evaluation of meniscus integrity. Copyright © 2017 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Multivariate analysis of correlation between electrophysiological and hemodynamic responses during cognitive processing

PubMed Central

Kujala, Jan; Sudre, Gustavo; Vartiainen, Johanna; Liljeström, Mia; Mitchell, Tom; Salmelin, Riitta

2014-01-01

Animal and human studies have frequently shown that in primary sensory and motor regions the BOLD signal correlates positively with high-frequency and negatively with low-frequency neuronal activity. However, recent evidence suggests that this relationship may also vary across cortical areas. Detailed knowledge of the possible spectral diversity between electrophysiological and hemodynamic responses across the human cortex would be essential for neural-level interpretation of fMRI data and for informative multimodal combination of electromagnetic and hemodynamic imaging data, especially in cognitive tasks. We applied multivariate partial least squares correlation analysis to MEG–fMRI data recorded in a reading paradigm to determine the correlation patterns between the data types, at once, across the cortex. Our results revealed heterogeneous patterns of high-frequency correlation between MEG and fMRI responses, with marked dissociation between lower and higher order cortical regions. The low-frequency range showed substantial variance, with negative and positive correlations manifesting at different frequencies across cortical regions. These findings demonstrate the complexity of the neurophysiological counterparts of hemodynamic fluctuations in cognitive processing. PMID:24518260

Prognostic impact of intestinal wall thickening in hospitalized patients with heart failure.

PubMed

Ikeda, Yuki; Ishii, Shunsuke; Fujita, Teppei; Iida, Yuichiro; Kaida, Toyoji; Nabeta, Takeru; Maekawa, Emi; Yanagisawa, Tomoyoshi; Koitabashi, Toshimi; Takeuchi, Ichiro; Inomata, Takayuki; Ako, Junya

2017-03-01

Intestine-cardiovascular relationship has been increasingly recognized as a key factor in patients with heart disease. We aimed to identify the relationships among intestinal wall edema, cardiac function, and adverse clinical events in hospitalized heart failure (HF) patients. Abdominal computed tomographic images of 168 hospitalized HF patients were retrospectively investigated for identification of average colon wall thickness (CWT) from the ascending to sigmoid colon. Relationships between average CWT and echocardiographic parameters, blood sampling data, and primary outcomes including readmission for deteriorated HF and all-cause mortality were evaluated. Among the echocardiographic parameters, lower left ventricular diastolic function was correlated with higher average CWT. In multivariate analysis, higher logarithmic C-reactive protein level, lower estimated glomerular filtration rate, lower peripheral blood lymphocyte count, higher E/E' ratio, and extremely higher/lower defecation frequency were independently correlated with higher average CWT. Multivariate Cox-hazard analysis demonstrated that higher average CWT was independently related to higher incidence of primary outcomes. In hospitalized HF patients, increased CWT was associated with lower cardiac performance, and predicted poorer long-term clinical outcomes. Copyright © 2016. Published by Elsevier B.V.

A statistical approach for segregating cognitive task stages from multivariate fMRI BOLD time series.

PubMed

Demanuele, Charmaine; Bähner, Florian; Plichta, Michael M; Kirsch, Peter; Tost, Heike; Meyer-Lindenberg, Andreas; Durstewitz, Daniel

2015-01-01

Multivariate pattern analysis can reveal new information from neuroimaging data to illuminate human cognition and its disturbances. Here, we develop a methodological approach, based on multivariate statistical/machine learning and time series analysis, to discern cognitive processing stages from functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) time series. We apply this method to data recorded from a group of healthy adults whilst performing a virtual reality version of the delayed win-shift radial arm maze (RAM) task. This task has been frequently used to study working memory and decision making in rodents. Using linear classifiers and multivariate test statistics in conjunction with time series bootstraps, we show that different cognitive stages of the task, as defined by the experimenter, namely, the encoding/retrieval, choice, reward and delay stages, can be statistically discriminated from the BOLD time series in brain areas relevant for decision making and working memory. Discrimination of these task stages was significantly reduced during poor behavioral performance in dorsolateral prefrontal cortex (DLPFC), but not in the primary visual cortex (V1). Experimenter-defined dissection of time series into class labels based on task structure was confirmed by an unsupervised, bottom-up approach based on Hidden Markov Models. Furthermore, we show that different groupings of recorded time points into cognitive event classes can be used to test hypotheses about the specific cognitive role of a given brain region during task execution. We found that whilst the DLPFC strongly differentiated between task stages associated with different memory loads, but not between different visual-spatial aspects, the reverse was true for V1. Our methodology illustrates how different aspects of cognitive information processing during one and the same task can be separated and attributed to specific brain regions based on information contained in multivariate patterns of voxel activity.

Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation

PubMed Central

Nilssen, Ingunn; Eide, Ingvar; de Oliveira Figueiredo, Marcia Abreu; de Souza Tâmega, Frederico Tapajós; Nattkemper, Tim W.

2016-01-01

This paper presents a machine learning based approach for analyses of photos collected from laboratory experiments conducted to assess the potential impact of water-based drill cuttings on deep-water rhodolith-forming calcareous algae. This pilot study uses imaging technology to quantify and monitor the stress levels of the calcareous algae Mesophyllum engelhartii (Foslie) Adey caused by various degrees of light exposure, flow intensity and amount of sediment. A machine learning based algorithm was applied to assess the temporal variation of the calcareous algae size (∼ mass) and color automatically. Measured size and color were correlated to the photosynthetic efficiency (maximum quantum yield of charge separation in photosystem II, ΦPSIImax) and degree of sediment coverage using multivariate regression. The multivariate regression showed correlations between time and calcareous algae sizes, as well as correlations between fluorescence and calcareous algae colors. PMID:27285611

Multivariate Models for Normal and Binary Responses in Intervention Studies

ERIC Educational Resources Information Center

Pituch, Keenan A.; Whittaker, Tiffany A.; Chang, Wanchen

2016-01-01

Use of multivariate analysis (e.g., multivariate analysis of variance) is common when normally distributed outcomes are collected in intervention research. However, when mixed responses--a set of normal and binary outcomes--are collected, standard multivariate analyses are no longer suitable. While mixed responses are often obtained in…

Factors associated with mixed dementia vs Alzheimer disease in elderly Mexican adults.

PubMed

Moreno Cervantes, C; Mimenza Alvarado, A; Aguilar Navarro, S; Alvarado Ávila, P; Gutiérrez Gutiérrez, L; Juárez Arellano, S; Ávila Funes, J A

2017-06-01

Mixed dementia (DMix) refers to dementia resulting from Alzheimer disease in addition to cerebrovascular disease. The study objectives were to determine the clinical and imaging factors associated with Dmix and compare them to those associated with Alzheimer disease. Cross-sectional study including 225 subjects aged 65 years and over from a memory clinic in a tertiary hospital in Mexico City. All patients underwent clinical, neuropsychological, and brain imaging studies. We included patients diagnosed with DMix or Alzheimer disease (AD). A multivariate analysis was used to determine factors associated with DMix. We studied 137 subjects diagnosed with Dmix. Compared to patients with AD, Dmix patients were older and more likely to present diabetes, hypertension, dyslipidaemia, and history of cerebrovascular disease (P<.05). The multivariate analysis showed that hypertension (OR 1.92, CI 1.62-28.82; P=.009), white matter disease (OR 3.61, CI 8.55-159.80; P<.001), and lacunar infarcts (OR 3.35, CI 1.97-412.34; P=.014) were associated with Dmix, whereas a history of successfully treated depression showed an inverse association (OR 0.11, CI 0.02-0-47; P=.004) CONCLUSIONS: DMix may be more frequent than AD. Risk factors such as advanced age and other potentially modifiable factors were associated with this type of dementia. Clinicians should understand and be able to define Dmix. Copyright © 2016 Sociedad Española de Neurología. Publicado por Elsevier España, S.L.U. All rights reserved.

Increased Prognostic Value of Query Amyloid Late Enhancement Score in Light-Chain Cardiac Amyloidosis.

PubMed

Wan, Ke; Sun, Jiayu; Han, Yuchi; Liu, Hong; Yang, Dan; Li, Weihao; Wang, Jie; Cheng, Wei; Zhang, Qing; Zeng, Zhi; Chen, Yucheng

2018-02-23

Late gadolinium enhancement (LGE) pattern is a powerful imaging biomarker for prognosis of cardiac amyloidosis. It is unknown if the query amyloid late enhancement (QALE) score in light-chain (AL) amyloidosis could provide increased prognostic value compared with LGE pattern.Methods and Results:Seventy-eight consecutive patients with AL amyloidosis underwent contrast-enhanced cardiovascular magnetic resonance imaging. Patients with cardiac involvement were grouped by LGE pattern and analyzed using QALE score. Receiver operating characteristic curve was used to identify the optimal cut-off for QALE score in predicting all-cause mortality. Survival of these patients was analyzed with the Kaplan-Meier method and multivariate Cox regression. During a median follow-up of 34 months, 53 of 78 patients died. The optimal cut-off for QALE score to predict mortality at 12-month follow-up was 9.0. On multivariate Cox analysis, QALE score ≥9 (HR, 5.997; 95% CI: 2.665-13.497; P<0.001) and log N-terminal pro-brain natriuretic peptide (HR, 1.525; 95% CI: 1.112-2.092; P=0.009) were the only 2 independent predictors of all-cause mortality. On Kaplan-Meier analysis, patients with subendocardial LGE can be further risk stratified using QALE score ≥9. The QALE scoring system provides powerful independent prognostic value in AL cardiac amyloidosis. QALE score ≥9 has added value to differentiate prognosis in AL amyloidosis patients with a subendocardial LGE pattern.

CT imaging of ovarian yolk sac tumor with emphasis on differential diagnosis

PubMed Central

Li, Yang-Kang; Zheng, Yu; Lin, Jian-Bang; Xu, Gui-Xiao; Cai, Ai-Qun; Zhou, Xiu-Guo; Zhang, Guo-Jun

2015-01-01

Ovarian yolk sac tumors (YSTs) are rare neoplasms. No radiological study has been done to compare the imaging findings between this type of tumor and other ovarian tumors. Here we analyzed the CT findings of 11 pathologically proven ovarian YSTs and compared their imaging findings with 18 other types of ovarian tumors in the same age range. Patient age, tumor size, tumor shape, ascites and metastasis of two groups did not differ significantly (P > 0.05). A mixed solid-cystic nature, intratumoral hemorrhage, marked enhancement and dilated intratumoral vessel of two groups differed significantly (P < 0.05). The area under the ROC curve of four significant CT features was 0.679, 0.707, 0.705, and 1.000, respectively. Multivariate logistic regression analysis identified two independent signs of YST: intratumoral hemorrhage and marked enhancement. Our results show that certain suggestive CT signs that may be valuable for improving the accuracy of imaging diagnosis of YST and may be helpful in distinguishing YST from other ovarian tumors. PMID:26074455

Role of PI-RADS Version 2 for Prediction of Upgrading in Biopsy-Proven Prostate Cancer With Gleason Score 6.

PubMed

Song, Wan; Bang, Seok Hwan; Jeon, Hwang Gyun; Jeong, Byong Chang; Seo, Seong Il; Jeon, Seong Soo; Choi, Han Yong; Kim, Chan Kyo; Lee, Hyun Moo

2018-02-23

The objective of this study was to investigate the effect of Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) on prediction of postoperative Gleason score (GS) upgrading for patients with biopsy GS 6 prostate cancer. We retrospectively reviewed 443 patients who underwent magnetic resonance imaging (MRI) and radical prostatectomy for biopsy-proven GS 6 prostate cancer between January 2011 and December 2013. Preoperative clinical variables and pathologic GS were examined, and all MRI findings were assessed with PI-RADSv2. Receiver operating characteristic curves were used to compare predictive accuracies of multivariate logistic regression models with or without PI-RADSv2. Of the total 443 patients, 297 (67.0%) experienced GS upgrading postoperatively. PI-RADSv2 scores 1 to 3 and 4 to 5 were identified in 157 (25.4%) and 286 (64.6%) patients, respectively, and the rate of GS upgrading was 54.1% and 74.1%, respectively (P < .001). In multivariate analysis, prostate-specific antigen density > 0.16 ng/mL 2 , number of positive cores ≥ 2, maximum percentage of cancer per core > 20, and PI-RADSv2 score 4 to 5 were independent predictors influencing GS upgrading (each P < .05). When predictive accuracies of multivariate models with or without PI-RADSv2 were compared, the model including PI-RADSv2 was shown to have significantly higher accuracy (area under the curve, 0.729 vs. 0.703; P = .041). Use of PI-RADSv2 is an independent predictor of postoperative GS upgrading and increases the predictive accuracy of GS upgrading. PI-RADSv2 might be used as a preoperative imaging tool to determine risk classification and to help counsel patients with regard to treatment decision and prognosis of disease. Copyright © 2018 Elsevier Inc. All rights reserved.

Resolving human object recognition in space and time

PubMed Central

Cichy, Radoslaw Martin; Pantazis, Dimitrios; Oliva, Aude

2014-01-01

A comprehensive picture of object processing in the human brain requires combining both spatial and temporal information about brain activity. Here, we acquired human magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) responses to 92 object images. Multivariate pattern classification applied to MEG revealed the time course of object processing: whereas individual images were discriminated by visual representations early, ordinate and superordinate category levels emerged relatively later. Using representational similarity analysis, we combine human fMRI and MEG to show content-specific correspondence between early MEG responses and primary visual cortex (V1), and later MEG responses and inferior temporal (IT) cortex. We identified transient and persistent neural activities during object processing, with sources in V1 and IT., Finally, human MEG signals were correlated to single-unit responses in monkey IT. Together, our findings provide an integrated space- and time-resolved view of human object categorization during the first few hundred milliseconds of vision. PMID:24464044

Relationship between body image and breast self-examination intentions and behavior among female university students in Malaysia.

PubMed

Samah, Asnarulkhadi Abu; Ahmadian, Maryam

2014-01-01

This study aimed to examine the relationship between body image satisfaction and breast self-screening behavior and intentions. The sample for this cross-sectional study consisted of 842 female university students who were recruited from a number of public and private universities. Data were obtained between the months of November and December, 2013, using multistage random cluster sampling. Main research variables were breast cancer screening behavior and intentions, demographic factors, and the total scores on each of the Multidimensional Body-Self Relations Questionnaire (MBSRQ-Appearance Scales) subscales. Results of multivariate analysis showed that having higher satisfaction and more positive evaluation of appearance were related to having performed breast self-examination more frequently in the last year and intending to perform breast self-examination more frequently in the next year. Longitudinal research can potentially provide detailed information about overall body image satisfaction and breast cancer screening behavior among various communities.

Different MR features for differentiation of intrahepatic mass-forming cholangiocarcinoma from hepatocellular carcinoma according to tumor size.

PubMed

Ni, Ting; Shang, Xiao-Sha; Wang, Wen-Tao; Hu, Xin-Xing; Zeng, Meng-Su; Rao, Sheng-Xiang

2018-06-05

To identify reliable magnetic resonance (MR) features for distinguishing mass-forming type of intrahepatic cholangiocarcinoma (IMCC) from hepatocellular carcinoma (HCC) based on tumor size. This retrospective study included 395 patients with pathologically confirmed IMCCs (n = 180) and HCCs (n = 215) who underwent pre-operative contrast-enhanced MRI including diffusion-weighted imaging (DWI). MR features were evaluated and clinical data were also recorded. All the characteristics were compared in small (≤3 cm) and large tumor (>3 cm) groups by univariate analysis and subsequently calculated by multivariable logistic regression analysis. Multivariable analysis revealed that rim arterial phase hyperenhancement [odds ratios (ORs) = 13.16], biliary dilation (OR = 23.42) and CA19-9 (OR = 21.45) were significant predictors of large IMCCs (n = 138), and washout appearance (OR = 0.036), enhancing capsule appearance (OR = 0.039), fat in mass (OR = 0.057), chronic liver disease (OR = 0.088) and alpha fetoprotein (OR = 0.019) were more frequently found in large HCCs (n = 143). For small IMCCs (n = 42) and HCCs (n = 72), rim arterial phase hyperenhancement (OR = 9.68), target appearance at DWI (OR = 12.51), alpha fetoprotein (OR = 0.12) and sex (OR = 0.20) were independent predictors in multivariate analysis. Valuable MR features and clinical factors varied for differential diagnosis of IMCCs and HCCs according to tumor size. Advances in knowledge: MR features for differential diagnosis of large IMCC and HCC (>3 cm) are in keeping with that recommended by LI-RADS. However, for small IMCCs and HCCs (≤3 cm), only rim enhancement on arterial phase and target appearance at DWI are reliable predictors.

Large-scale image-based profiling of single-cell phenotypes in arrayed CRISPR-Cas9 gene perturbation screens.

PubMed

de Groot, Reinoud; Lüthi, Joel; Lindsay, Helen; Holtackers, René; Pelkmans, Lucas

2018-01-23

High-content imaging using automated microscopy and computer vision allows multivariate profiling of single-cell phenotypes. Here, we present methods for the application of the CISPR-Cas9 system in large-scale, image-based, gene perturbation experiments. We show that CRISPR-Cas9-mediated gene perturbation can be achieved in human tissue culture cells in a timeframe that is compatible with image-based phenotyping. We developed a pipeline to construct a large-scale arrayed library of 2,281 sequence-verified CRISPR-Cas9 targeting plasmids and profiled this library for genes affecting cellular morphology and the subcellular localization of components of the nuclear pore complex (NPC). We conceived a machine-learning method that harnesses genetic heterogeneity to score gene perturbations and identify phenotypically perturbed cells for in-depth characterization of gene perturbation effects. This approach enables genome-scale image-based multivariate gene perturbation profiling using CRISPR-Cas9. © 2018 The Authors. Published under the terms of the CC BY 4.0 license.

Hounsfield unit of screw trajectory as a predictor of pedicle screw loosening after single level lumber interbody fusion.

PubMed

Sakai, Yusuke; Takenaka, Shota; Matsuo, Yohei; Fujiwara, Hiroyasu; Honda, Hirotsugu; Makino, Takahiro; Kaito, Takashi

2018-06-01

This study aims to clarify the clinical potential of Hounsfield unit (HU), measured on computed tomography (CT) images, as a predictor of pedicle screw (PS) loosening, compared to bone mineral density (BMD). A total of 206 screws in 52 patients (21 men and 31 women; mean age 68.2 years) were analyzed retrospectively. The screws were classified into two groups depending on their screw loosening status on 3-month follow-up CT (loosening screw group vs. non-loosening screw group). Preoperative HU of the trajectory was evaluated by superimposing preoperative and postoperative CT images using three-dimensional image analysis software. Age, sex, body mass index, screw size, BMD of lumbar, and HU of screw trajectory were analyzed in association with screw loosening. Multivariate logistic regression analysis was performed, and the thresholds for PS loosening risk factors were evaluated using a continuous numerical variable and receiver operating characteristic (ROC) curve analyses. The area under the curve (AUC) was used to determine the diagnostic performance, and values > 0.75 were considered to represent good performance. The loosening screw group contained 24 screws (12%). Multivariate analysis revealed that the significant independent risk factors were not BMD but male sex [P = 0.028; odds ratio (OR) 2.852, 95% confidence interval (CI) 1.120-7.258] and HU of screw trajectory (P = 0.006; OR 0.989, 95% CI 0.980-0.997). ROC curve analysis demonstrated that the AUC for HU of screw trajectory for women was 0.880 (95% CI 0.798-0.961). The cutoff value was 153.5. AUC for men was 0.635 (95% CI 0.449-0.821), which was not considered to be a good performance. Low HU of screw trajectories was identified as a risk factor of PS loosening for women. For female patients with low HU, additional augmentation is recommended to prevent PS loosening. Copyright © 2018 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.

Rapid discrimination of different Apiaceae species based on HPTLC fingerprints and targeted flavonoids determination using multivariate image analysis.

PubMed

Shawky, Eman; Abou El Kheir, Rasha M

2018-02-11

Species of Apiaceae are used in folk medicine as spices and in officinal medicinal preparations of drugs. They are an excellent source of phenolics exhibiting antioxidant activity, which are of great benefit to human health. Discrimination among Apiaceae medicinal herbs remains an intricate challenge due to their morphological similarity. In this study, a combined "untargeted" and "targeted" approach to investigate different Apiaceae plants species was proposed by using the merging of high-performance thin layer chromatography (HPTLC)-image analysis and pattern recognition methods which were used for fingerprinting and classification of 42 different Apiaceae samples collected from Egypt. Software for image processing was applied for fingerprinting and data acquisition. HPTLC fingerprint assisted by principal component analysis (PCA) and hierarchical cluster analysis (HCA)-heat maps resulted in a reliable untargeted approach for discrimination and classification of different samples. The "targeted" approach was performed by developing and validating an HPTLC method allowing the quantification of eight flavonoids. The combination of quantitative data with PCA and HCA-heat-maps allowed the different samples to be discriminated from each other. The use of chemometrics tools for evaluation of fingerprints reduced expense and analysis time. The proposed method can be adopted for routine discrimination and evaluation of the phytochemical variability in different Apiaceae species extracts. Copyright © 2018 John Wiley & Sons, Ltd.

Fourier Transform Infrared Imaging analysis of dental pulp inflammatory diseases.

PubMed

Giorgini, E; Sabbatini, S; Conti, C; Rubini, C; Rocchetti, R; Fioroni, M; Memè, L; Orilisi, G

2017-05-01

Fourier Transform Infrared microspectroscopy let characterize the macromolecular composition and distribution of tissues and cells, by studying the interaction between infrared radiation and matter. Therefore, we hypothesize to exploit this analytical tool in the analysis of inflamed pulps, to detect the different biochemical features related to various degrees of inflammation. IR maps of 13 irreversible and 12 hyperplastic pulpitis, together with 10 normal pulps, were acquired, compared with histological findings and submitted to multivariate (HCA, PCA, SIMCA) and statistical (one-way ANOVA) analysis. The fit of convoluted bands let calculate meaningful band area ratios (means ± s.d., P < 0.05). The infrared imaging analysis pin-pointed higher amounts of water and lower quantities of type I collagen in all inflamed pulps. Specific vibrational markers were defined for irreversible pulpitis (Lipids/Total Biomass, PhII/Total Biomass, CH 2 /CH 3 , and Ty/AII) and hyperplastic ones (OH/Total Biomass, Collagen/Total Biomass, and CH 3 Collagen/Total Biomass). The study confirmed that FTIR microspectroscopy let discriminate tissues' biological features. The infrared imaging analysis evidenced, in inflamed pulps, alterations in tissues' structure and composition. Changes in lipid metabolism, increasing amounts of tyrosine, and the occurrence of phosphorylative processes were highlighted in irreversible pulpitis, while high amounts of water and low quantities of type I collagen were detected in hyperplastic samples. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Deconstructing multivariate decoding for the study of brain function.

PubMed

Hebart, Martin N; Baker, Chris I

2017-08-04

Multivariate decoding methods were developed originally as tools to enable accurate predictions in real-world applications. The realization that these methods can also be employed to study brain function has led to their widespread adoption in the neurosciences. However, prior to the rise of multivariate decoding, the study of brain function was firmly embedded in a statistical philosophy grounded on univariate methods of data analysis. In this way, multivariate decoding for brain interpretation grew out of two established frameworks: multivariate decoding for predictions in real-world applications, and classical univariate analysis based on the study and interpretation of brain activation. We argue that this led to two confusions, one reflecting a mixture of multivariate decoding for prediction or interpretation, and the other a mixture of the conceptual and statistical philosophies underlying multivariate decoding and classical univariate analysis. Here we attempt to systematically disambiguate multivariate decoding for the study of brain function from the frameworks it grew out of. After elaborating these confusions and their consequences, we describe six, often unappreciated, differences between classical univariate analysis and multivariate decoding. We then focus on how the common interpretation of what is signal and noise changes in multivariate decoding. Finally, we use four examples to illustrate where these confusions may impact the interpretation of neuroimaging data. We conclude with a discussion of potential strategies to help resolve these confusions in interpreting multivariate decoding results, including the potential departure from multivariate decoding methods for the study of brain function. Copyright © 2017. Published by Elsevier Inc.

Multivariate meta-analysis: Potential and promise

PubMed Central

Jackson, Dan; Riley, Richard; White, Ian R

2011-01-01

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day ‘Multivariate meta-analysis’ event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd. PMID:21268052

Heterogeneity of Glucose Metabolism in Esophageal Cancer Measured by Fractal Analysis of Fluorodeoxyglucose Positron Emission Tomography Image: Correlation between Metabolic Heterogeneity and Survival.

PubMed

Tochigi, Toru; Shuto, Kiyohiko; Kono, Tsuguaki; Ohira, Gaku; Tohma, Takayuki; Gunji, Hisashi; Hayano, Koichi; Narushima, Kazuo; Fujishiro, Takeshi; Hanaoka, Toshiharu; Akutsu, Yasunori; Okazumi, Shinichi; Matsubara, Hisahiro

2017-01-01

Intratumoral heterogeneity is a well-recognized characteristic feature of cancer. The purpose of this study is to assess the heterogeneity of the intratumoral glucose metabolism using fractal analysis, and evaluate its prognostic value in patients with esophageal squamous cell carcinoma (ESCC). 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) studies of 79 patients who received curative surgery were evaluated. FDG-PET images were analyzed using fractal analysis software, where differential box-counting method was employed to calculate the fractal dimension (FD) of the tumor lesion. Maximum standardized uptake value (SUVmax) and FD were compared with overall survival (OS). The median SUVmax and FD of ESCCs in this cohort were 13.8 and 1.95, respectively. In univariate analysis performed using Cox's proportional hazard model, T stage and FD showed significant associations with OS (p = 0.04, p < 0.0001, respectively), while SUVmax did not (p = 0.1). In Kaplan-Meier analysis, the low FD tumor (<1.95) showed a significant association with favorable OS (p < 0.0001). In wthe multivariate analysis among TNM staging, serum tumor markers, FD, and SUVmax, the FD was identified as the only independent prognostic factor for OS (p = 0.0006; hazards ratio 0.251, 95% CI 0.104-0.562). Metabolic heterogeneity measured by fractal analysis can be a novel imaging biomarker for survival in patients with ESCC. © 2016 S. Karger AG, Basel.

GENETIC INFLUENCE OF APOE4 GENOTYPE ON HIPPOCAMPAL MORPHOMETRY - AN N=725 SURFACE-BASED ADNI STUDY

PubMed Central

Shi, Jie; Leporé, Natasha; Gutman, Boris A.; Thompson, Paul M.; Baxter, Leslie C.; Caselli, Richard L.; Wang, Yalin

2014-01-01

The apolipoprotein E (APOE) e4 allele is the most prevalent genetic risk factor for Alzheimer’s disease (AD). Hippocampal volumes are generally smaller in AD patients carrying the e4 allele compared to e4 non-carriers. Here we examined the effect of APOE e4 on hippocampal morphometry in a large imaging database – the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We automatically segmented and constructed hippocampal surfaces from the baseline MR images of 725 subjects with known APOE genotype information including 167 with AD, 354 with mild cognitive impairment (MCI), and 204 normal controls. High-order correspondences between hippocampal surfaces were enforced across subjects with a novel inverse consistent surface fluid registration method. Multivariate statistics consisting of multivariate tensor-based morphometry (mTBM) and radial distance were computed for surface deformation analysis. Using Hotelling’s T2 test, we found significant morphological deformation in APOE e4 carriers relative to non-carriers in the entire cohort as well as in the non-demented (pooled MCI and control) subjects, affecting the left hippocampus more than the right, and this effect was more pronounced in e4 homozygotes than heterozygotes. Our findings are consistent with previous studies that showed e4 carriers exhibit accelerated hippocampal atrophy; we extend these findings to a novel measure of hippocampal morphometry. Hippocampal morphometry has significant potential as an imaging biomarker of early stage AD. PMID:24453132

Identifying the association between contrast enhancement pattern, surgical resection, and prognosis in anaplastic glioma patients.

PubMed

Wang, Yinyan; Wang, Kai; Wang, Jiangfei; Li, Shaowu; Ma, Jun; Dai, Jianping; Jiang, Tao

2016-04-01

Contrast enhancement observable on magnetic resonance (MR) images reflects the destructive features of malignant gliomas. This study aimed to investigate the relationship between radiologic patterns of tumor enhancement, extent of resection, and prognosis in patients with anaplastic gliomas (AGs). Clinical data from 268 patients with histologically confirmed AGs were retrospectively analyzed. Contrast enhancement patterns were classified based on preoperative T1-contrast MR images. Univariate and multivariate analyses were performed to evaluate the prognostic value of MR enhancement patterns on progression-free survival (PFS) and overall survival (OS). The pattern of tumor contrast enhancement was associated with the extent of surgical resection in AGs. A gross total resection was more likely to be achieved for AGs with focal enhancement than those with diffuse (p = 0.001) or ring-like (p = 0.024) enhancement. Additionally, patients with focal-enhanced AGs had a significantly longer PFS and OS than those with diffuse (log-rank, p = 0.025 and p = 0.031, respectively) or ring-like (log-rank, p = 0.008 and p = 0.011, respectively) enhanced AGs. Furthermore, multivariate analysis identified the pattern of tumor enhancement as a significant predictor of PFS (p = 0.016, hazard ratio [HR] = 1.485) and OS (p = 0.030, HR = 1.446). Our results suggested that the contrast enhancement pattern on preoperative MR images was associated with the extent of resection and predictive of survival outcomes in AG patients.

Metabolomic Analysis of Oxidative and Glycolytic Skeletal Muscles by Matrix-Assisted Laser Desorption/IonizationMass Spectrometric Imaging (MALDI MSI)

NASA Astrophysics Data System (ADS)

Tsai, Yu-Hsuan; Garrett, Timothy J.; Carter, Christy S.; Yost, Richard A.

2015-06-01

Skeletal muscles are composed of heterogeneous muscle fibers that have different physiological, morphological, biochemical, and histological characteristics. In this work, skeletal muscles extensor digitorum longus, soleus, and whole gastrocnemius were analyzed by matrix-assisted laser desorption/ionization mass spectrometry to characterize small molecule metabolites of oxidative and glycolytic muscle fiber types as well as to visualize biomarker localization. Multivariate data analysis such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed to extract significant features. Different metabolic fingerprints were observed from oxidative and glycolytic fibers. Higher abundances of biomolecules such as antioxidant anserine as well as acylcarnitines were observed in the glycolytic fibers, whereas taurine and some nucleotides were found to be localized in the oxidative fibers.

Multivariate Longitudinal Analysis with Bivariate Correlation Test

PubMed Central

Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

2016-01-01

In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model’s parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated. PMID:27537692

Multivariate Longitudinal Analysis with Bivariate Correlation Test.

PubMed

Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

2016-01-01

In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model's parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated.

Characterizing pigments with hyperspectral imaging variable false-color composites

NASA Astrophysics Data System (ADS)

Hayem-Ghez, Anita; Ravaud, Elisabeth; Boust, Clotilde; Bastian, Gilles; Menu, Michel; Brodie-Linder, Nancy

2015-11-01

Hyperspectral imaging has been used for pigment characterization on paintings for the last 10 years. It is a noninvasive technique, which mixes the power of spectrophotometry and that of imaging technologies. We have access to a visible and near-infrared hyperspectral camera, ranging from 400 to 1000 nm in 80-160 spectral bands. In order to treat the large amount of data that this imaging technique generates, one can use statistical tools such as principal component analysis (PCA). To conduct the characterization of pigments, researchers mostly use PCA, convex geometry algorithms and the comparison of resulting clusters to database spectra with a specific tolerance (like the Spectral Angle Mapper tool on the dedicated software ENVI). Our approach originates from false-color photography and aims at providing a simple tool to identify pigments thanks to imaging spectroscopy. It can be considered as a quick first analysis to see the principal pigments of a painting, before using a more complete multivariate statistical tool. We study pigment spectra, for each kind of hue (blue, green, red and yellow) to identify the wavelength maximizing spectral differences. The case of red pigments is most interesting because our methodology can discriminate the red pigments very well—even red lakes, which are always difficult to identify. As for the yellow and blue categories, it represents a good progress of IRFC photography for pigment discrimination. We apply our methodology to study the pigments on a painting by Eustache Le Sueur, a French painter of the seventeenth century. We compare the results to other noninvasive analysis like X-ray fluorescence and optical microscopy. Finally, we draw conclusions about the advantages and limits of the variable false-color image method using hyperspectral imaging.

Multivariate analysis of factors predicting prostate dose in intensity-modulated radiotherapy

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

Tomita, Tsuneyuki; Nakamura, Mitsuhiro, E-mail: m_nkmr@kuhp.kyoto-u.ac.jp; Hirose, Yoshinori

We conducted a multivariate analysis to determine relationships between prostate radiation dose and the state of surrounding organs, including organ volumes and the internal angle of the levator ani muscle (LAM), based on cone-beam computed tomography (CBCT) images after bone matching. We analyzed 270 CBCT data sets from 30 consecutive patients receiving intensity-modulated radiation therapy for prostate cancer. With patients in the supine position on a couch with the HipFix system, data for center of mass (COM) displacement of the prostate and the state of individual organs were acquired and compared between planning CT and CBCT scans. Dose distributions weremore » then recalculated based on CBCT images. The relative effects of factors on the variance in COM, dose covering 95% of the prostate volume (D{sub 95%}), and percentage of prostate volume covered by the 100% isodose line (V{sub 100%}) were evaluated by a backward stepwise multiple regression analysis. COM displacement in the anterior-posterior direction (COM{sub AP}) correlated significantly with the rectum volume (δVr) and the internal LAM angle (δθ; R = 0.63). Weak correlations were seen for COM in the left-right (R = 0.18) and superior-inferior directions (R = 0.31). Strong correlations between COM{sub AP} and prostate D{sub 95%} and V{sub 100%} were observed (R ≥ 0.69). Additionally, the change ratios in δVr and δθ remained as predictors of prostate D{sub 95%} and V{sub 100%}. This study shows statistically that maintaining the same rectum volume and LAM state for both the planning CT simulation and treatment is important to ensure the correct prostate dose in the supine position with bone matching.« less

Uterine fibroids: Influence of "T2-Rim sign" on immediate therapeutic responses to magnetic resonance imaging-guided high-intensity focused ultrasound ablation.

PubMed

Yeo, Sin Yuin; Kim, Young-Sun; Lim, Hyo Keun; Rhim, Hyunchul; Jung, Sin-Ho; Hwang, Na Young

2017-12-01

To investigate the influence of a high-signal-intensity peripheral rim on T2-weighted MR images (i.e., T2-rim sign) on the immediate therapeutic responses of MR-guided high intensity focused ultrasound (MR-HIFU) ablation of uterine fibroids. This retrospective study was approved by the institutional review board, and patient informed consent was obtained for MR-HIFU ablation. In total, 196 fibroids (diameter 6.2±2.6cm) in 123 women (age 43.4±5.0 years) who underwent MR-HIFU ablation from January 2013 to April 2016 were included. The effects of a T2-rim sign on the immediate therapeutic responses (non-perfused volume [NPV] ratio, ablation efficiency [NPV/treatment cell volume], ablation quality [grade 1-5, poor to excellent]) were investigated with univariable and multivariable analyses using generalized estimating equation (GEE) analysis. In multivariable analysis, T2 signal intensity ratio of fibroids-to-skeletal muscle, relative peak enhancement of fibroids, and subcutaneous fat thickness were also considered. The presence of a T2-rim sign significantly lowered the NPV ratio (54.0±28.0% vs. 83.7±17.7%), ablation efficiency (0.6±0.5 vs. 1.3±0.6), ablation quality (3.1±1.2 vs. 4.2±0.8), (P<0.0001). GEE analysis showed that the presence of a T2-rim sign was independently significant for ablation efficiency and ablation quality (P<0.05). Uterine fibroids with a T2-rim sign showed significantly poorer immediate therapeutic responses to MR-HIFU ablation. Copyright © 2017 Elsevier B.V. All rights reserved.

A Java-based fMRI processing pipeline evaluation system for assessment of univariate general linear model and multivariate canonical variate analysis-based pipelines.

PubMed

Zhang, Jing; Liang, Lichen; Anderson, Jon R; Gatewood, Lael; Rottenberg, David A; Strother, Stephen C

2008-01-01

As functional magnetic resonance imaging (fMRI) becomes widely used, the demands for evaluation of fMRI processing pipelines and validation of fMRI analysis results is increasing rapidly. The current NPAIRS package, an IDL-based fMRI processing pipeline evaluation framework, lacks system interoperability and the ability to evaluate general linear model (GLM)-based pipelines using prediction metrics. Thus, it can not fully evaluate fMRI analytical software modules such as FSL.FEAT and NPAIRS.GLM. In order to overcome these limitations, a Java-based fMRI processing pipeline evaluation system was developed. It integrated YALE (a machine learning environment) into Fiswidgets (a fMRI software environment) to obtain system interoperability and applied an algorithm to measure GLM prediction accuracy. The results demonstrated that the system can evaluate fMRI processing pipelines with univariate GLM and multivariate canonical variates analysis (CVA)-based models on real fMRI data based on prediction accuracy (classification accuracy) and statistical parametric image (SPI) reproducibility. In addition, a preliminary study was performed where four fMRI processing pipelines with GLM and CVA modules such as FSL.FEAT and NPAIRS.CVA were evaluated with the system. The results indicated that (1) the system can compare different fMRI processing pipelines with heterogeneous models (NPAIRS.GLM, NPAIRS.CVA and FSL.FEAT) and rank their performance by automatic performance scoring, and (2) the rank of pipeline performance is highly dependent on the preprocessing operations. These results suggest that the system will be of value for the comparison, validation, standardization and optimization of functional neuroimaging software packages and fMRI processing pipelines.

Predictors of rapid spontaneous resolution of acute subdural hematoma.

PubMed

Fujimoto, Kenji; Otsuka, Tadahiro; Yoshizato, Kimio; Kuratsu, Jun-ichi

2014-03-01

Acute subdural hematoma (ASDH) usually requires emergency surgical decompression, but rare cases exhibit rapid spontaneous resolution. The aim of this retrospective study was to identify factors predictive of spontaneous ASDH resolution. A total of 366 consecutive patients with ASDH treated between January 2006 and September 2012 were identified in our hospital database. Patients with ASDH clot thickness >10mm in the frontoparietotemporal region and showing a midline shift >10mm on the initial computed tomography (CT) scan were divided into two groups according to subsequent spontaneous resolution. Univariate and multivariate logistic regression analyses were used to identify factors predictive of rapid spontaneous ASDH resolution. Fifty-six ASDH patients met study criteria and 18 demonstrated rapid spontaneous resolution (32%). Majority of these patients were not operated because of poor prognosis/condition and in accordance to family wishes. Univariate analysis revealed significant differences in use of antiplatelet agents before head injury and in the incidence of a low-density band between the hematoma and inner wall of the skull bone on the initial CT. Use of antiplatelet agents before head injury (OR 19.6, 95% CI 1.5-260.1, p=0.02) and the low-density band on CT images (OR 40.3, 95% CI 3.1-520.2, p=0.005) were identified as independent predictive factors by multivariate analysis. Our analysis suggested that use of antiplatelet agents before head injury and a low-density band between the hematoma and inner skull bone on CT images (indicative of cerebrospinal fluid infusion into the subdural space) increase the probability of rapid spontaneous resolution. Copyright © 2013 Elsevier B.V. All rights reserved.

Corticospinal excitability as a predictor of functional gains at the affected upper limb following robotic training in chronic stroke survivors.

PubMed

Milot, Marie-Hélène; Spencer, Steven J; Chan, Vicky; Allington, James P; Klein, Julius; Chou, Cathy; Pearson-Fuhrhop, Kristin; Bobrow, James E; Reinkensmeyer, David J; Cramer, Steven C

2014-01-01

Robotic training can help improve function of a paretic limb following a stroke, but individuals respond differently to the training. A predictor of functional gains might improve the ability to select those individuals more likely to benefit from robot-based therapy. Studies evaluating predictors of functional improvement after a robotic training are scarce. One study has found that white matter tract integrity predicts functional gains following a robotic training of the hand and wrist. Objective. To determine the predictive ability of behavioral and brain measures in order to improve selection of individuals for robotic training. Twenty subjects with chronic stroke participated in an 8-week course of robotic exoskeletal training for the arm. Before training, a clinical evaluation, functional magnetic resonance imaging (fMRI), diffusion tensor imaging, and transcranial magnetic stimulation (TMS) were each measured as predictors. Final functional gain was defined as change in the Box and Block Test (BBT). Measures significant in bivariate analysis were fed into a multivariate linear regression model. Training was associated with an average gain of 6 ± 5 blocks on the BBT (P < .0001). Bivariate analysis revealed that lower baseline motor-evoked potential (MEP) amplitude on TMS, and lower laterality M1 index on fMRI each significantly correlated with greater BBT change. In the multivariate linear regression analysis, baseline MEP magnitude was the only measure that remained significant. Subjects with lower baseline MEP magnitude benefited the most from robotic training of the affected arm. These subjects might have reserve remaining for the training to boost corticospinal excitability, translating into functional gains. © The Author(s) 2014.

Prognostic value of Ki-67 index in adult medulloblastoma after accounting for molecular subgroup: a retrospective clinical and molecular analysis.

PubMed

Zhao, Fu; Zhang, Jing; Li, Peng; Zhou, Qiangyi; Zhang, Shun; Zhao, Chi; Wang, Bo; Yang, Zhijun; Li, Chunde; Liu, Pinan

2018-04-23

Medulloblastoma (MB) is a rare primary brain tumor in adults. We previously evaluated that combining both clinical and molecular classification could improve current risk stratification for adult MB. In this study, we aimed to identify the prognostic value of Ki-67 index in adult MB. Ki-67 index of 51 primary adult MBs was reassessed using a computer-based image analysis (Image-Pro Plus). All patients were followed up ranging from 12 months up to 15 years. Gene expression profiling and immunochemistry were used to establish the molecular subgroups in adult MB. Combined risk stratification models were designed based on clinical characteristics, molecular classification and Ki-67 index, and identified by multivariable Cox proportional hazards analysis. In our cohort, the mean Ki-67 value was 30.0 ± 11.3% (range 6.56-63.55%). The average Ki-67 value was significantly higher in LC/AMB than in CMB and DNMB (P = .001). Among three molecular subgroups, Group 4-tumors had the highest average Ki-67 value compared with WNT- and SHH-tumors (P = .004). Patients with Ki-67 index large than 30% displayed poorer overall survival (OS) and progression free survival (PFS) than those with Ki-67 less than 30% (OS: P = .001; PFS: P = .006). Ki-67 index (i.e. > 30%, < 30%) was identified as an independent significant prognostic factor (OS: P = .017; PFS: P = .024) by using multivariate Cox proportional hazards model. In conclusion, Ki-67 index can be considered as a valuable independent prognostic biomarker for adult patients with MB.

Application of the angle measure technique as image texture analysis method for the identification of uranium ore concentrate samples: New perspective in nuclear forensics.

PubMed

Fongaro, Lorenzo; Ho, Doris Mer Lin; Kvaal, Knut; Mayer, Klaus; Rondinella, Vincenzo V

2016-05-15

The identification of interdicted nuclear or radioactive materials requires the application of dedicated techniques. In this work, a new approach for characterizing powder of uranium ore concentrates (UOCs) is presented. It is based on image texture analysis and multivariate data modelling. 26 different UOCs samples were evaluated applying the Angle Measure Technique (AMT) algorithm to extract textural features on samples images acquired at 250× and 1000× magnification by Scanning Electron Microscope (SEM). At both magnifications, this method proved effective to classify the different types of UOC powder based on the surface characteristics that depend on particle size, homogeneity, and graininess and are related to the composition and processes used in the production facilities. Using the outcome data from the application of the AMT algorithm, the total explained variance was higher than 90% with Principal Component Analysis (PCA), while partial least square discriminant analysis (PLS-DA) applied only on the 14 black colour UOCs powder samples, allowed their classification only on the basis of their surface texture features (sensitivity>0.6; specificity>0.6). This preliminary study shows that this method was able to distinguish samples with similar composition, but obtained from different facilities. The mean angle spectral data obtained by the image texture analysis using the AMT algorithm can be considered as a specific fingerprint or signature of UOCs and could be used for nuclear forensic investigation. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Risk Factors for Chronic Subdural Hematoma Recurrence Identified Using Quantitative Computed Tomography Analysis of Hematoma Volume and Density.

PubMed

Stavrinou, Pantelis; Katsigiannis, Sotirios; Lee, Jong Hun; Hamisch, Christina; Krischek, Boris; Mpotsaris, Anastasios; Timmer, Marco; Goldbrunner, Roland

2017-03-01

Chronic subdural hematoma (CSDH), a common condition in elderly patients, presents a therapeutic challenge with recurrence rates of 33%. We aimed to identify specific prognostic factors for recurrence using quantitative analysis of hematoma volume and density. We retrospectively reviewed radiographic and clinical data of 227 CSDHs in 195 consecutive patients who underwent evacuation of the hematoma through a single burr hole, 2 burr holes, or a mini-craniotomy. To examine the relationship between hematoma recurrence and various clinical, radiologic, and surgical factors, we used quantitative image-based analysis to measure the hematoma and trapped air volumes and the hematoma densities. Recurrence of CSDH occurred in 35 patients (17.9%). Multivariate logistic regression analysis revealed that the percentage of hematoma drained and postoperative CSDH density were independent risk factors for recurrence. All 3 evacuation methods were equally effective in draining the hematoma (71.7% vs. 73.7% vs. 71.9%) without observable differences in postoperative air volume captured in the subdural space. Quantitative image analysis provided evidence that percentage of hematoma drained and postoperative CSDH density are independent prognostic factors for subdural hematoma recurrence. Copyright © 2016 Elsevier Inc. All rights reserved.

Upsampling to 400-ms Resolution for Assessing Effective Connectivity in Functional Magnetic Resonance Imaging Data with Granger Causality

PubMed Central

Kerr, Deborah L.; Nitschke, Jack B.

2013-01-01

Abstract Granger causality analysis of functional magnetic resonance imaging (fMRI) blood-oxygen-level-dependent signal data allows one to infer the direction and magnitude of influence that brain regions exert on one another. We employed a method for upsampling the time resolution of fMRI data that does not require additional interpolation beyond the interpolation that is regularly used for slice-timing correction. The mathematics for this new method are provided, and simulations demonstrate its viability. Using fMRI, 17 snake phobics and 19 healthy controls viewed snake, disgust, and neutral fish video clips preceded by anticipatory cues. Multivariate Granger causality models at the native 2-sec resolution and at the upsampled 400-ms resolution assessed directional associations of fMRI data among 13 anatomical regions of interest identified in prior research on anxiety and emotion. Superior sensitivity was observed for the 400-ms model, both for connectivity within each group and for group differences in connectivity. Context-dependent analyses for the 400-ms multivariate Granger causality model revealed the specific trial types showing group differences in connectivity. This is the first demonstration of effective connectivity of fMRI data using a method for achieving 400-ms resolution without sacrificing accuracy available at 2-sec resolution. PMID:23134194

Multivariate Regression Analysis and Slaughter Livestock,

DTIC Science & Technology

AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

Relevance of magnetic resonance imaging for early detection and diagnosis of Alzheimer disease.

PubMed

Teipel, Stefan J; Grothe, Michel; Lista, Simone; Toschi, Nicola; Garaci, Francesco G; Hampel, Harald

2013-05-01

Hippocampus volumetry currently is the best-established imaging biomarker for AD. However, the effect of multicenter acquisition on measurements of hippocampus volume needs to be explicitly considered when it is applied in large clinical trials, for example by using mixed-effects models to take the clustering of data within centers into account. The marker needs further validation in respect of the underlying neurobiological substrate and potential confounds such as vascular disease, inflammation, hydrocephalus, and alcoholism, and with regard to clinical outcomes such as cognition but also to demographic and socioeconomic outcomes such as mortality and institutionalization. The use of hippocampus volumetry for risk stratification of predementia study samples will further increase with the availability of automated measurement approaches. An important step in this respect will be the development of a standard hippocampus tracing protocol that harmonizes the large range of presently available manual protocols. In the near future, regionally differentiated automated methods will become available together with an appropriate statistical model, such as multivariate analysis of deformation fields, or techniques such as cortical-thickness measurements that yield a meaningful metrics for the detection of treatment effects. More advanced imaging protocols, including DTI, DSI, and functional MRI, are presently being used in monocenter and first multicenter studies. In the future these techniques will be relevant for the risk stratification in phase IIa type studies (small proof-of-concept trials). By contrast, the application of the broader established structural imaging biomarkers, such as hippocampus volume, for risk stratification and as surrogate end point is already today part of many clinical trial protocols. However, clinical care will also be affected by these new technologies. Radiologic expert centers already offer “dementia screening” for well-off middle-aged people who undergo an MRI scan with subsequent automated, typically VBM-based analysis, and determination of z-score deviation from a matched control cohort. Next-generation scanner software will likely include radiologic expert systems for automated segmentation, deformation-based morphometry, and multivariate analysis of anatomic MRI scans for the detection of a typical AD pattern. As these developments will start to change medical practice, first for selected subject groups that can afford this type of screening but later eventually also for other cohorts, clinicians must become aware of the potentials and limitations of these technologies. It is decidedly unclear to date how a middle-aged cognitively intact subject with a seemingly AD-positive MRI scan should be clinically advised. There is no evidence for individual risk prediction and even less for specific treatments. Thus, the development of preclinical diagnostic imaging poses not only technical but also ethical problems that must be critically discussed on the basis of profound knowledge. From a neurobiological point of view, the main determinants of cognitive impairment in AD are the density of synapses and neurons in distributed cortical and subcortical networks. MRI-based measures of regional gray matter volume and associated multivariate analysis techniques of regional interactions of gray matter densities provide insight into the onset and temporal dynamics of cortical atrophy as a close proxy for regional neuronal loss and a basis of functional impairment in specific neuronal networks. From the clinical point of view, clinicians must bear in mind that patients do not suffer from hippocampus atrophy or disconnection but from memory impairment, and that dementia screening in asymptomatic subjects should not be used outside of clinical studies. Copyright © 2013 Elsevier Inc. All rights reserved.

Quantitative Ultrasound Using Texture Analysis of Myofascial Pain Syndrome in the Trapezius.

PubMed

Kumbhare, Dinesh A; Ahmed, Sara; Behr, Michael G; Noseworthy, Michael D

2018-01-01

Objective-The objective of this study is to assess the discriminative ability of textural analyses to assist in the differentiation of the myofascial trigger point (MTrP) region from normal regions of skeletal muscle. Also, to measure the ability to reliably differentiate between three clinically relevant groups: healthy asymptomatic, latent MTrPs, and active MTrP. Methods-18 and 19 patients were identified with having active and latent MTrPs in the trapezius muscle, respectively. We included 24 healthy volunteers. Images were obtained by research personnel, who were blinded with respect to the clinical status of the study participant. Histograms provided first-order parameters associated with image grayscale. Haralick, Galloway, and histogram-related features were used in texture analysis. Blob analysis was conducted on the regions of interest (ROIs). Principal component analysis (PCA) was performed followed by multivariate analysis of variance (MANOVA) to determine the statistical significance of the features. Results-92 texture features were analyzed for factorability using Bartlett's test of sphericity, which was significant. The Kaiser-Meyer-Olkin measure of sampling adequacy was 0.94. PCA demonstrated rotated eigenvalues of the first eight components (each comprised of multiple texture features) explained 94.92% of the cumulative variance in the ultrasound image characteristics. The 24 features identified by PCA were included in the MANOVA as dependent variables, and the presence of a latent or active MTrP or healthy muscle were independent variables. Conclusion-Texture analysis techniques can discriminate between the three clinically relevant groups.

Assessing carotid atherosclerosis by fiber-optic multispectral photoacoustic tomography

NASA Astrophysics Data System (ADS)

Hui, Jie; Li, Rui; Wang, Pu; Phillips, Evan; Bruning, Rebecca; Liao, Chien-Sheng; Sturek, Michael; Goergen, Craig J.; Cheng, Ji-Xin

2015-03-01

Atherosclerotic plaque at the carotid bifurcation is the underlying cause of the majority of ischemic strokes. Noninvasive imaging and quantification of the compositional changes preceding gross anatomic changes within the arterial wall is essential for diagnosis of disease. Current imaging modalities such as duplex ultrasound, computed tomography, positron emission tomography are limited by the lack of compositional contrast and the detection of flow-limiting lesions. Although high-resolution magnetic resonance imaging has been developed to characterize atherosclerotic plaque composition, its accessibility for wide clinical use is limited. Here, we demonstrate a fiber-based multispectral photoacoustic tomography system for excitation of lipids and external acoustic detection of the generated ultrasound. Using sequential ultrasound imaging of ex vivo preparations we achieved ~2 cm imaging depth and chemical selectivity for assessment of human arterial plaques. A multivariate curve resolution alternating least squares analysis method was applied to resolve the major chemical components, including intravascular lipid, intramuscular fat, and blood. These results show the promise of detecting carotid plaque in vivo through esophageal fiber-optic excitation of lipids and external acoustic detection of the generated ultrasound. This imaging system has great potential for serving as a point-ofcare device for early diagnosis of carotid artery disease in the clinic.

Self-Organizing-Map Program for Analyzing Multivariate Data

NASA Technical Reports Server (NTRS)

Li, P. Peggy; Jacob, Joseph C.; Block, Gary L.; Braverman, Amy J.

2005-01-01

SOM_VIS is a computer program for analysis and display of multidimensional sets of Earth-image data typified by the data acquired by the Multi-angle Imaging Spectro-Radiometer [MISR (a spaceborne instrument)]. In SOM_VIS, an enhanced self-organizing-map (SOM) algorithm is first used to project a multidimensional set of data into a nonuniform three-dimensional lattice structure. The lattice structure is mapped to a color space to obtain a color map for an image. The Voronoi cell-refinement algorithm is used to map the SOM lattice structure to various levels of color resolution. The final result is a false-color image in which similar colors represent similar characteristics across all its data dimensions. SOM_VIS provides a control panel for selection of a subset of suitably preprocessed MISR radiance data, and a control panel for choosing parameters to run SOM training. SOM_VIS also includes a component for displaying the false-color SOM image, a color map for the trained SOM lattice, a plot showing an original input vector in 36 dimensions of a selected pixel from the SOM image, the SOM vector that represents the input vector, and the Euclidean distance between the two vectors.

Investigation of the composition of anabolic tablets using near infrared spectroscopy and Raman chemical imaging.

PubMed

Rebiere, Hervé; Ghyselinck, Céline; Lempereur, Laurent; Brenier, Charlotte

2016-01-01

The use of performance enhancing drugs is a widespread phenomenon in professional and leisure sports. A spectroscopic study was carried out on anabolic tablets labelled as 5 mg methandienone tablets provided by police departments. The analytical approach was based on a two-step methodology: a fast analysis of tablets using near infrared (NIR) spectroscopy to assess sample homogeneity based on their global composition, followed by Raman chemical imaging of one sample per NIR profile to obtain information on sample formulation. NIR spectroscopy assisted by a principal components analysis (PCA) enabled fast discrimination of different profiles based on the excipient formulation. Raman hyperspectral imaging and multivariate curve resolution - alternating least square (MCR-ALS) provided chemical images of the distribution of the active substance and excipients within tablets and facilitated identification of the active compounds. The combination of NIR spectroscopy and Raman chemical imaging highlighted dose-to-dose variations and succeeded in the discrimination of four different formulations out of eight similar samples of anabolic tablets. Some samples contained either methandienone or methyltestosterone whereas one sample did not contain an active substance. Other ingredients were sucrose, lactose, starch or talc. Both techniques were fast and non-destructive and therefore can be carried out as exploratory methods prior to destructive screening methods. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Hyperspectral stimulated emission depletion microscopy and methods of use thereof

DOEpatents

Timlin, Jerilyn A; Aaron, Jesse S

2014-04-01

A hyperspectral stimulated emission depletion ("STED") microscope system for high-resolution imaging of samples labeled with multiple fluorophores (e.g., two to ten fluorophores). The hyperspectral STED microscope includes a light source, optical systems configured for generating an excitation light beam and a depletion light beam, optical systems configured for focusing the excitation and depletion light beams on a sample, and systems for collecting and processing data generated by interaction of the excitation and depletion light beams with the sample. Hyperspectral STED data may be analyzed using multivariate curve resolution analysis techniques to deconvolute emission from the multiple fluorophores. The hyperspectral STED microscope described herein can be used for multi-color, subdiffraction imaging of samples (e.g., materials and biological materials) and for analyzing a tissue by Forster Resonance Energy Transfer ("FRET").

Lithologic discrimination and alteration mapping from AVIRIS Data, Socorro, New Mexico

NASA Technical Reports Server (NTRS)

Beratan, K. K.; Delillo, N.; Jacobson, A.; Blom, R.; Chapin, C. E.

1993-01-01

Geologic maps are, by their very nature, interpretive documents. In contrasts, images prepared from AVIRIS data can be used as uninterpreted, and thus unbiased, geologic maps. We are having significant success applying AVIRIS data in this non-quantitative manner to geologic problems. Much of our success has come from the power of the Linked Windows Interactive Data System. LinkWinds is a visual data analysis and exploration system under development at JPL which is designed to rapidly and interactively investigate large multivariate data sets. In this paper, we present information on the analysis technique, and preliminary results from research on potassium metasomatism, a distinctive and structurally significant type of alteration associated with crustal extension.

Use of collateral information to improve LANDSAT classification accuracies

NASA Technical Reports Server (NTRS)

Strahler, A. H. (Principal Investigator)

1981-01-01

Methods to improve LANDSAT classification accuracies were investigated including: (1) the use of prior probabilities in maximum likelihood classification as a methodology to integrate discrete collateral data with continuously measured image density variables; (2) the use of the logit classifier as an alternative to multivariate normal classification that permits mixing both continuous and categorical variables in a single model and fits empirical distributions of observations more closely than the multivariate normal density function; and (3) the use of collateral data in a geographic information system as exercised to model a desired output information layer as a function of input layers of raster format collateral and image data base layers.

Infrared spectral imaging as a novel approach for histopathological recognition in colon cancer diagnosis

NASA Astrophysics Data System (ADS)

Nallala, Jayakrupakar; Gobinet, Cyril; Diebold, Marie-Danièle; Untereiner, Valérie; Bouché, Olivier; Manfait, Michel; Sockalingum, Ganesh Dhruvananda; Piot, Olivier

2012-11-01

Innovative diagnostic methods are the need of the hour that could complement conventional histopathology for cancer diagnosis. In this perspective, we propose a new concept based on spectral histopathology, using IR spectral micro-imaging, directly applied to paraffinized colon tissue array stabilized in an agarose matrix without any chemical pre-treatment. In order to correct spectral interferences from paraffin and agarose, a mathematical procedure is implemented. The corrected spectral images are then processed by a multivariate clustering method to automatically recover, on the basis of their intrinsic molecular composition, the main histological classes of the normal and the tumoral colon tissue. The spectral signatures from different histological classes of the colonic tissues are analyzed using statistical methods (Kruskal-Wallis test and principal component analysis) to identify the most discriminant IR features. These features allow characterizing some of the biomolecular alterations associated with malignancy. Thus, via a single analysis, in a label-free and nondestructive manner, main changes associated with nucleotide, carbohydrates, and collagen features can be identified simultaneously between the compared normal and the cancerous tissues. The present study demonstrates the potential of IR spectral imaging as a complementary modern tool, to conventional histopathology, for an objective cancer diagnosis directly from paraffin-embedded tissue arrays.

Genetic Particle Swarm Optimization-Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection.

PubMed

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-07-30

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm.

The Pathways for Intelligible Speech: Multivariate and Univariate Perspectives

PubMed Central

Evans, S.; Kyong, J.S.; Rosen, S.; Golestani, N.; Warren, J.E.; McGettigan, C.; Mourão-Miranda, J.; Wise, R.J.S.; Scott, S.K.

2014-01-01

An anterior pathway, concerned with extracting meaning from sound, has been identified in nonhuman primates. An analogous pathway has been suggested in humans, but controversy exists concerning the degree of lateralization and the precise location where responses to intelligible speech emerge. We have demonstrated that the left anterior superior temporal sulcus (STS) responds preferentially to intelligible speech (Scott SK, Blank CC, Rosen S, Wise RJS. 2000. Identification of a pathway for intelligible speech in the left temporal lobe. Brain. 123:2400–2406.). A functional magnetic resonance imaging study in Cerebral Cortex used equivalent stimuli and univariate and multivariate analyses to argue for the greater importance of bilateral posterior when compared with the left anterior STS in responding to intelligible speech (Okada K, Rong F, Venezia J, Matchin W, Hsieh IH, Saberi K, Serences JT,Hickok G. 2010. Hierarchical organization of human auditory cortex: evidence from acoustic invariance in the response to intelligible speech. 20: 2486–2495.). Here, we also replicate our original study, demonstrating that the left anterior STS exhibits the strongest univariate response and, in decoding using the bilateral temporal cortex, contains the most informative voxels showing an increased response to intelligible speech. In contrast, in classifications using local “searchlights” and a whole brain analysis, we find greater classification accuracy in posterior rather than anterior temporal regions. Thus, we show that the precise nature of the multivariate analysis used will emphasize different response profiles associated with complex sound to speech processing. PMID:23585519

Recognizing different tissues in human fetal femur cartilage by label-free Raman microspectroscopy

NASA Astrophysics Data System (ADS)

Kunstar, Aliz; Leijten, Jeroen; van Leuveren, Stefan; Hilderink, Janneke; Otto, Cees; van Blitterswijk, Clemens A.; Karperien, Marcel; van Apeldoorn, Aart A.

2012-11-01

Traditionally, the composition of bone and cartilage is determined by standard histological methods. We used Raman microscopy, which provides a molecular "fingerprint" of the investigated sample, to detect differences between the zones in human fetal femur cartilage without the need for additional staining or labeling. Raman area scans were made from the (pre)articular cartilage, resting, proliferative, and hypertrophic zones of growth plate and endochondral bone within human fetal femora. Multivariate data analysis was performed on Raman spectral datasets to construct cluster images with corresponding cluster averages. Cluster analysis resulted in detection of individual chondrocyte spectra that could be separated from cartilage extracellular matrix (ECM) spectra and was verified by comparing cluster images with intensity-based Raman images for the deoxyribonucleic acid/ribonucleic acid (DNA/RNA) band. Specific dendrograms were created using Ward's clustering method, and principal component analysis (PCA) was performed with the separated and averaged Raman spectra of cells and ECM of all measured zones. Overall (dis)similarities between measured zones were effectively visualized on the dendrograms and main spectral differences were revealed by PCA allowing for label-free detection of individual cartilaginous zones and for label-free evaluation of proper cartilaginous matrix formation for future tissue engineering and clinical purposes.

ADC texture—An imaging biomarker for high-grade glioma?

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

Brynolfsson, Patrik; Hauksson, Jón; Karlsson, Mikael

2014-10-15

Purpose: Survival for high-grade gliomas is poor, at least partly explained by intratumoral heterogeneity contributing to treatment resistance. Radiological evaluation of treatment response is in most cases limited to assessment of tumor size months after the initiation of therapy. Diffusion-weighted magnetic resonance imaging (MRI) and its estimate of the apparent diffusion coefficient (ADC) has been widely investigated, as it reflects tumor cellularity and proliferation. The aim of this study was to investigate texture analysis of ADC images in conjunction with multivariate image analysis as a means for identification of pretreatment imaging biomarkers. Methods: Twenty-three consecutive high-grade glioma patients were treatedmore » with radiotherapy (2 Gy/60 Gy) with concomitant and adjuvant temozolomide. ADC maps and T1-weighted anatomical images with and without contrast enhancement were collected prior to treatment, and (residual) tumor contrast enhancement was delineated. A gray-level co-occurrence matrix analysis was performed on the ADC maps in a cuboid encapsulating the tumor in coronal, sagittal, and transversal planes, giving a total of 60 textural descriptors for each tumor. In addition, similar examinations and analyses were performed at day 1, week 2, and week 6 into treatment. Principal component analysis (PCA) was applied to reduce dimensionality of the data, and the five largest components (scores) were used in subsequent analyses. MRI assessment three months after completion of radiochemotherapy was used for classifying tumor progression or regression. Results: The score scatter plots revealed that the first, third, and fifth components of the pretreatment examinations exhibited a pattern that strongly correlated to survival. Two groups could be identified: one with a median survival after diagnosis of 1099 days and one with 345 days, p = 0.0001. Conclusions: By combining PCA and texture analysis, ADC texture characteristics were identified, which seems to hold pretreatment prognostic information, independent of known prognostic factors such as age, stage, and surgical procedure. These findings encourage further studies with a larger patient cohort.« less

Cytologic separation of branchial cleft cyst from metastatic cystic squamous cell carcinoma: A multivariate analysis of nineteen cytomorphologic features.

PubMed

Layfield, Lester J; Esebua, Magda; Schmidt, Robert L

2016-07-01

The separation of branchial cleft cysts from metastatic cystic squamous cell carcinomas in adults can be clinically and cytologically challenging. Diagnostic accuracy for separation is reported to be as low as 75% prompting some authors to recommend frozen section evaluation of suspected branchial cleft cysts before resection. We evaluated 19 cytologic features to determine which were useful in this distinction. Thirty-three cases (21 squamous carcinoma and 12 branchial cysts) of histologically confirmed cystic lesions of the lateral neck were graded for the presence or absence of 19 cytologic features by two cytopathologists. The cytologic features were analyzed for agreement between observers and underwent multivariate analysis for correlation with the diagnosis of carcinoma. Interobserver agreement was greatest for increased nuclear/cytoplasmic (N/C) ratio, pyknotic nuclei, and irregular nuclear membranes. Recursive partitioning analysis showed increased N/C ratio, small clusters of cells, and irregular nuclear membranes were the best discriminators. The distinction of branchial cleft cysts from cystic squamous cell carcinoma is cytologically difficult. Both digital image analysis and p16 testing have been suggested as aids in this separation, but analysis of cytologic features remains the main method for diagnosis. In an analysis of 19 cytologic features, we found that high nuclear cytoplasmic ratio, irregular nuclear membranes, and small cell clusters were most helpful in their distinction. Diagn. Cytopathol. 2016;44:561-567. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Using computer assisted image analysis to determine the optimal Ki67 threshold for predicting outcome of invasive breast cancer

PubMed Central

Tay, Timothy Kwang Yong; Thike, Aye Aye; Pathmanathan, Nirmala; Jara-Lazaro, Ana Richelia; Iqbal, Jabed; Sng, Adeline Shi Hui; Ye, Heng Seow; Lim, Jeffrey Chun Tatt; Koh, Valerie Cui Yun; Tan, Jane Sie Yong; Yeong, Joe Poh Sheng; Chow, Zi Long; Li, Hui Hua; Cheng, Chee Leong; Tan, Puay Hoon

2018-01-01

Background Ki67 positivity in invasive breast cancers has an inverse correlation with survival outcomes and serves as an immunohistochemical surrogate for molecular subtyping of breast cancer, particularly ER positive breast cancer. The optimal threshold of Ki67 in both settings, however, remains elusive. We use computer assisted image analysis (CAIA) to determine the optimal threshold for Ki67 in predicting survival outcomes and differentiating luminal B from luminal A breast cancers. Methods Quantitative scoring of Ki67 on tissue microarray (TMA) sections of 440 invasive breast cancers was performed using Aperio ePathology ImmunoHistochemistry Nuclear Image Analysis algorithm, with TMA slides digitally scanned via Aperio ScanScope XT System. Results On multivariate analysis, tumours with Ki67 ≥14% had an increased likelihood of recurrence (HR 1.941, p=0.021) and shorter overall survival (HR 2.201, p=0.016). Similar findings were observed in the subset of 343 ER positive breast cancers (HR 2.409, p=0.012 and HR 2.787, p=0.012 respectively). The value of Ki67 associated with ER+HER2-PR<20% tumours (Luminal B subtype) was found to be <17%. Conclusion Using CAIA, we found optimal thresholds for Ki67 that predict a poorer prognosis and an association with the Luminal B subtype of breast cancer. Further investigation and validation of these thresholds are recommended. PMID:29545924

Multivariate calibration in Laser-Induced Breakdown Spectroscopy quantitative analysis: The dangers of a 'black box' approach and how to avoid them

NASA Astrophysics Data System (ADS)

Safi, A.; Campanella, B.; Grifoni, E.; Legnaioli, S.; Lorenzetti, G.; Pagnotta, S.; Poggialini, F.; Ripoll-Seguer, L.; Hidalgo, M.; Palleschi, V.

2018-06-01

The introduction of multivariate calibration curve approach in Laser-Induced Breakdown Spectroscopy (LIBS) quantitative analysis has led to a general improvement of the LIBS analytical performances, since a multivariate approach allows to exploit the redundancy of elemental information that are typically present in a LIBS spectrum. Software packages implementing multivariate methods are available in the most diffused commercial and open source analytical programs; in most of the cases, the multivariate algorithms are robust against noise and operate in unsupervised mode. The reverse of the coin of the availability and ease of use of such packages is the (perceived) difficulty in assessing the reliability of the results obtained which often leads to the consideration of the multivariate algorithms as 'black boxes' whose inner mechanism is supposed to remain hidden to the user. In this paper, we will discuss the dangers of a 'black box' approach in LIBS multivariate analysis, and will discuss how to overcome them using the chemical-physical knowledge that is at the base of any LIBS quantitative analysis.

Linear regression analysis and its application to multivariate chromatographic calibration for the quantitative analysis of two-component mixtures.

PubMed

Dinç, Erdal; Ozdemir, Abdil

2005-01-01

Multivariate chromatographic calibration technique was developed for the quantitative analysis of binary mixtures enalapril maleate (EA) and hydrochlorothiazide (HCT) in tablets in the presence of losartan potassium (LST). The mathematical algorithm of multivariate chromatographic calibration technique is based on the use of the linear regression equations constructed using relationship between concentration and peak area at the five-wavelength set. The algorithm of this mathematical calibration model having a simple mathematical content was briefly described. This approach is a powerful mathematical tool for an optimum chromatographic multivariate calibration and elimination of fluctuations coming from instrumental and experimental conditions. This multivariate chromatographic calibration contains reduction of multivariate linear regression functions to univariate data set. The validation of model was carried out by analyzing various synthetic binary mixtures and using the standard addition technique. Developed calibration technique was applied to the analysis of the real pharmaceutical tablets containing EA and HCT. The obtained results were compared with those obtained by classical HPLC method. It was observed that the proposed multivariate chromatographic calibration gives better results than classical HPLC.

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

Apte, A; Veeraraghavan, H; Oh, J

Purpose: To present an open source and free platform to facilitate radiomics research — The “Radiomics toolbox” in CERR. Method: There is scarcity of open source tools that support end-to-end modeling of image features to predict patient outcomes. The “Radiomics toolbox” strives to fill the need for such a software platform. The platform supports (1) import of various kinds of image modalities like CT, PET, MR, SPECT, US. (2) Contouring tools to delineate structures of interest. (3) Extraction and storage of image based features like 1st order statistics, gray-scale co-occurrence and zonesize matrix based texture features and shape features andmore » (4) Statistical Analysis. Statistical analysis of the extracted features is supported with basic functionality that includes univariate correlations, Kaplan-Meir curves and advanced functionality that includes feature reduction and multivariate modeling. The graphical user interface and the data management are performed with Matlab for the ease of development and readability of code and features for wide audience. Open-source software developed with other programming languages is integrated to enhance various components of this toolbox. For example: Java-based DCM4CHE for import of DICOM, R for statistical analysis. Results: The Radiomics toolbox will be distributed as an open source, GNU copyrighted software. The toolbox was prototyped for modeling Oropharyngeal PET dataset at MSKCC. The analysis will be presented in a separate paper. Conclusion: The Radiomics Toolbox provides an extensible platform for extracting and modeling image features. To emphasize new uses of CERR for radiomics and image-based research, we have changed the name from the “Computational Environment for Radiotherapy Research” to the “Computational Environment for Radiological Research”.« less

Improved classification and visualization of healthy and pathological hard dental tissues by modeling specular reflections in NIR hyperspectral images

NASA Astrophysics Data System (ADS)

Usenik, Peter; Bürmen, Miran; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan

2012-03-01

Despite major improvements in dental healthcare and technology, dental caries remains one of the most prevalent chronic diseases of modern society. The initial stages of dental caries are characterized by demineralization of enamel crystals, commonly known as white spots, which are difficult to diagnose. Near-infrared (NIR) hyperspectral imaging is a new promising technique for early detection of demineralization which can classify healthy and pathological dental tissues. However, due to non-ideal illumination of the tooth surface the hyperspectral images can exhibit specular reflections, in particular around the edges and the ridges of the teeth. These reflections significantly affect the performance of automated classification and visualization methods. Cross polarized imaging setup can effectively remove the specular reflections, however is due to the complexity and other imaging setup limitations not always possible. In this paper, we propose an alternative approach based on modeling the specular reflections of hard dental tissues, which significantly improves the classification accuracy in the presence of specular reflections. The method was evaluated on five extracted human teeth with corresponding gold standard for 6 different healthy and pathological hard dental tissues including enamel, dentin, calculus, dentin caries, enamel caries and demineralized regions. Principal component analysis (PCA) was used for multivariate local modeling of healthy and pathological dental tissues. The classification was performed by employing multiple discriminant analysis. Based on the obtained results we believe the proposed method can be considered as an effective alternative to the complex cross polarized imaging setups.

Fourier Transform Infrared (FT-IR) and Laser Ablation Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS) Imaging of Cerebral Ischemia: Combined Analysis of Rat Brain Thin Cuts Toward Improved Tissue Classification.

PubMed

Balbekova, Anna; Lohninger, Hans; van Tilborg, Geralda A F; Dijkhuizen, Rick M; Bonta, Maximilian; Limbeck, Andreas; Lendl, Bernhard; Al-Saad, Khalid A; Ali, Mohamed; Celikic, Minja; Ofner, Johannes

2018-02-01

Microspectroscopic techniques are widely used to complement histological studies. Due to recent developments in the field of chemical imaging, combined chemical analysis has become attractive. This technique facilitates a deepened analysis compared to single techniques or side-by-side analysis. In this study, rat brains harvested one week after induction of photothrombotic stroke were investigated. Adjacent thin cuts from rats' brains were imaged using Fourier transform infrared (FT-IR) microspectroscopy and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The LA-ICP-MS data were normalized using an internal standard (a thin gold layer). The acquired hyperspectral data cubes were fused and subjected to multivariate analysis. Brain regions affected by stroke as well as unaffected gray and white matter were identified and classified using a model based on either partial least squares discriminant analysis (PLS-DA) or random decision forest (RDF) algorithms. The RDF algorithm demonstrated the best results for classification. Improved classification was observed in the case of fused data in comparison to individual data sets (either FT-IR or LA-ICP-MS). Variable importance analysis demonstrated that both molecular and elemental content contribute to the improved RDF classification. Univariate spectral analysis identified biochemical properties of the assigned tissue types. Classification of multisensor hyperspectral data sets using an RDF algorithm allows access to a novel and in-depth understanding of biochemical processes and solid chemical allocation of different brain regions.

Early functional MRI activation predicts motor outcome after ischemic stroke: a longitudinal, multimodal study.

PubMed

Du, Juan; Yang, Fang; Zhang, Zhiqiang; Hu, Jingze; Xu, Qiang; Hu, Jianping; Zeng, Fanyong; Lu, Guangming; Liu, Xinfeng

2018-05-15

An accurate prediction of long term outcome after stroke is urgently required to provide early individualized neurorehabilitation. This study aimed to examine the added value of early neuroimaging measures and identify the best approaches for predicting motor outcome after stroke. This prospective study involved 34 first-ever ischemic stroke patients (time since stroke: 1-14 days) with upper limb impairment. All patients underwent baseline multimodal assessments that included clinical (age, motor impairment), neurophysiological (motor-evoked potentials, MEP) and neuroimaging (diffusion tensor imaging and motor task-based fMRI) measures, and also underwent reassessment 3 months after stroke. Bivariate analysis and multivariate linear regression models were used to predict the motor scores (Fugl-Meyer assessment, FMA) at 3 months post-stroke. With bivariate analysis, better motor outcome significantly correlated with (1) less initial motor impairment and disability, (2) less corticospinal tract injury, (3) the initial presence of MEPs, (4) stronger baseline motor fMRI activations. In multivariate analysis, incorporating neuroimaging data improved the predictive accuracy relative to only clinical and neurophysiological assessments. Baseline fMRI activation in SMA was an independent predictor of motor outcome after stroke. A multimodal model incorporating fMRI and clinical measures best predicted the motor outcome following stroke. fMRI measures obtained early after stroke provided independent prediction of long-term motor outcome.

Applying image quality in cell phone cameras: lens distortion

NASA Astrophysics Data System (ADS)

Baxter, Donald; Goma, Sergio R.; Aleksic, Milivoje

2009-01-01

This paper describes the framework used in one of the pilot studies run under the I3A CPIQ initiative to quantify overall image quality in cell-phone cameras. The framework is based on a multivariate formalism which tries to predict overall image quality from individual image quality attributes and was validated in a CPIQ pilot program. The pilot study focuses on image quality distortions introduced in the optical path of a cell-phone camera, which may or may not be corrected in the image processing path. The assumption is that the captured image used is JPEG compressed and the cellphone camera is set to 'auto' mode. As the used framework requires that the individual attributes to be relatively perceptually orthogonal, in the pilot study, the attributes used are lens geometric distortion (LGD) and lateral chromatic aberrations (LCA). The goal of this paper is to present the framework of this pilot project starting with the definition of the individual attributes, up to their quantification in JNDs of quality, a requirement of the multivariate formalism, therefore both objective and subjective evaluations were used. A major distinction in the objective part from the 'DSC imaging world' is that the LCA/LGD distortions found in cell-phone cameras, rarely exhibit radial behavior, therefore a radial mapping/modeling cannot be used in this case.

Hyperspectral infrared nanoimaging of organic samples based on Fourier transform infrared nanospectroscopy

PubMed Central

Amenabar, Iban; Poly, Simon; Goikoetxea, Monika; Nuansing, Wiwat; Lasch, Peter; Hillenbrand, Rainer

2017-01-01

Infrared nanospectroscopy enables novel possibilities for chemical and structural analysis of nanocomposites, biomaterials or optoelectronic devices. Here we introduce hyperspectral infrared nanoimaging based on Fourier transform infrared nanospectroscopy with a tunable bandwidth-limited laser continuum. We describe the technical implementations and present hyperspectral infrared near-field images of about 5,000 pixel, each one covering the spectral range from 1,000 to 1,900 cm−1. To verify the technique and to demonstrate its application potential, we imaged a three-component polymer blend and a melanin granule in a human hair cross-section, and demonstrate that multivariate data analysis can be applied for extracting spatially resolved chemical information. Particularly, we demonstrate that distribution and chemical interaction between the polymer components can be mapped with a spatial resolution of about 30 nm. We foresee wide application potential of hyperspectral infrared nanoimaging for valuable chemical materials characterization and quality control in various fields ranging from materials sciences to biomedicine. PMID:28198384

Hyperspectral infrared nanoimaging of organic samples based on Fourier transform infrared nanospectroscopy

NASA Astrophysics Data System (ADS)

Amenabar, Iban; Poly, Simon; Goikoetxea, Monika; Nuansing, Wiwat; Lasch, Peter; Hillenbrand, Rainer

2017-02-01

Infrared nanospectroscopy enables novel possibilities for chemical and structural analysis of nanocomposites, biomaterials or optoelectronic devices. Here we introduce hyperspectral infrared nanoimaging based on Fourier transform infrared nanospectroscopy with a tunable bandwidth-limited laser continuum. We describe the technical implementations and present hyperspectral infrared near-field images of about 5,000 pixel, each one covering the spectral range from 1,000 to 1,900 cm-1. To verify the technique and to demonstrate its application potential, we imaged a three-component polymer blend and a melanin granule in a human hair cross-section, and demonstrate that multivariate data analysis can be applied for extracting spatially resolved chemical information. Particularly, we demonstrate that distribution and chemical interaction between the polymer components can be mapped with a spatial resolution of about 30 nm. We foresee wide application potential of hyperspectral infrared nanoimaging for valuable chemical materials characterization and quality control in various fields ranging from materials sciences to biomedicine.

Quality control of the paracetamol drug by chemometrics and imaging spectroscopy in the near infrared region

NASA Astrophysics Data System (ADS)

Baptistao, Mariana; Rocha, Werickson Fortunato de Carvalho; Poppi, Ronei Jesus

2011-09-01

In this work, it was used imaging spectroscopy and chemometric tools for the development and analysis of paracetamol and excipients in pharmaceutical formulations. It was also built concentration maps to study the distribution of the drug in the tablets surface. Multivariate models based on PLS regression were developed for paracetamol and excipients concentrations prediction. For the construction of the models it was used 31 samples in the tablet form containing the active principle in a concentration range of 30.0-90.0% (w/w) and errors below to 5% were obtained for validation samples. Finally, the study of the distribution in the drug was performed through the distribution maps of concentration of active principle and excipients. The analysis of maps showed the complementarity between the active principle and excipients in the tablets. The region with a high concentration of a constituent must have, necessarily, absence or low concentration of the other one. Thus, an alternative method for the paracetamol drug quality monitoring is presented.

Multivariate Cluster Analysis.

ERIC Educational Resources Information Center

McRae, Douglas J.

Procedures for grouping students into homogeneous subsets have long interested educational researchers. The research reported in this paper is an investigation of a set of objective grouping procedures based on multivariate analysis considerations. Four multivariate functions that might serve as criteria for adequate grouping are given and…

The antagonistic effect between STAT1 and Survivin and its clinical significance in gastric cancer.

PubMed

Deng, Hao; Zhen, Hongyan; Fu, Zhengqi; Huang, Xuan; Zhou, Hongyan; Liu, Lijiang

2012-01-01

In previous studies, we observed that STAT1 and Survivin correlated negatively with gastric cancer tissues, and that the functions of the IFN-γ-STAT1 pathway and Survivin in gastric cancer are the same as those reported for other types of cancer. In this study, the SGC7901 gastric cancer cell line and 83 gastric cancer specimens were used to confirm the relationship between STAT1 and Survivin, as well as the clinical significance of this relationship in gastric cancer. IFN-γ and STAT1 and Survivin antisense oligonucleotides (ASONs) were used to knock down the expression in SGC7901 cells. The protein expression of STAT1 and Survivin was tested by immunocytochemical and image analysis methods. A gastric cancer tissue microarray was prepared and tested by immunohistochemical methods. Data were analyzed by the Spearman's rank correlation analysis, the χ(2) test and Cox's multivariate regression analysis. Upon knockdown of IFN-γ, STAT1 and Survivin expression by ASON in the SGC7901 cell line, an antagonistic effect was observed between STAT1 and Survivin. In gastric cancer tissues, STAT1 showed a negative correlation with depth of invasion (p<0.05) in gastric cancer tissues exhibiting a negative Survivin protein expression. Furthermore, in tissues exhibiting a negative STAT1 protein expression, Survivin correlated negatively with N stage (p<0.05). Pathological and molecular markers were used to conduct Cox's multivariate regression analysis, and depth of invasion and N stage were found to be prognostic factors (p<0.05). On the other hand, in tissues exhibiting a negative Survivin protein expression, Cox's multivariate regression analysis revealed that the differentiation type and STAT1 protein expression were prognostic factors (p<0.05). There is an antagonistic effect between STAT1 and Survivin in gastric cancer, and this antagonistic effect is of clinical significance in gastric cancer.

Sexual dimorphism of the mandible in a contemporary Chinese Han population.

PubMed

Dong, Hongmei; Deng, Mohong; Wang, WenPeng; Zhang, Ji; Mu, Jiao; Zhu, Guanghui

2015-10-01

A present limitation of forensic anthropology practice in China is the lack of population-specific criteria on contemporary human skeletons. In this study, a sample of 203 maxillofacial Cone beam computed tomography (CBCT) images, including 96 male and 107 female cases (20-65 years old), was analyzed to explore mandible sexual dimorphism in a population of contemporary adult Han Chinese to investigate the potential use of the mandible as sex indicator. A three-dimensional image from mandible CBCT scans was reconstructed using the SimPlant Pro 11.40 software. Nine linear and two angular parameters were measured. Discriminant function analysis (DFA) and logistic regression analysis (LRA) were used to develop the mathematics models for sex determination. All of the linear measurements studied and one angular measurement were found to be sexually dimorphic, with the maximum mandibular length and bi-condylar breadth being the most dimorphic by univariate DFA and LRA respectively. The cross-validated sex allocation accuracies on multivariate were ranged from 84.2% (direct DFA), 83.5% (direct LRA), 83.3% (stepwise DFA) to 80.5% (stepwise LRA). In general, multivariate DFA yielded a higher accuracy and LRA obtained a lower sex bias, and therefore both DFA and LRA had their own advantages for sex determination by the mandible in this sample. These results suggest that the mandible expresses sexual dimorphism in the contemporary adult Han Chinese population, indicating an excellent sexual discriminatory ability. Cone beam computed tomography scanning can be used as alternative source for contemporary osteometric techniques. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Diagnostic Significance of Cortical Superficial Siderosis for Alzheimer Disease in Patients with Cognitive Impairment.

PubMed

Inoue, Y; Nakajima, M; Uetani, H; Hirai, T; Ueda, M; Kitajima, M; Utsunomiya, D; Watanabe, M; Hashimoto, M; Ikeda, M; Yamashita, Y; Ando, Y

2016-02-01

Because the diagnostic significance of cortical superficial siderosis for Alzheimer disease and the association between cortical superficial siderosis and the topographic distribution of cerebral microbleeds have been unclear, we investigated the association between cortical superficial siderosis and clinicoradiologic characteristics of patients with cognitive impairment. We studied 347 patients (217 women, 130 men; mean age, 74 ± 9 years) who visited our memory clinic and underwent MR imaging (3T SWI). We analyzed the association between cortical superficial siderosis and the topographic distribution of cerebral microbleeds plus clinical characteristics including types of dementia. We used multivariate logistic regression analysis to determine the diagnostic significance of cortical superficial siderosis for Alzheimer disease. Twelve patients (3.5%) manifested cortical superficial siderosis. They were older (P = .026) and had strictly lobar cerebral microbleeds significantly more often than did patients without cortical superficial siderosis (50.0% versus 19.4%, P = .02); the occurrence of strictly deep and mixed cerebral microbleeds, however, did not differ in the 2 groups. Alzheimer disease was diagnosed in 162 (46.7%) patients. Of these, 8 patients (4.9%) had cortical superficial siderosis. In the multivariate logistic regression analysis for the diagnosis of Alzheimer disease, lacunar infarcts were negatively and independently associated with Alzheimer disease (P = .007). Although cortical superficial siderosis was associated with a strictly lobar cerebral microbleed location, it was not independently associated with Alzheimer disease in a memory clinic setting. Additional studies are required to investigate the temporal changes of these cerebral amyloid angiopathy-related MR imaging findings. © 2016 by American Journal of Neuroradiology.

Signal intensity of the pancreas on magnetic resonance imaging: Prediction of postoperative pancreatic fistula after a distal pancreatectomy using a triple-row stapler.

PubMed

Arai, Takuma; Kobayashi, Akira; Yokoyama, Takahide; Ohya, Ayumi; Fujinaga, Yasunari; Shimizu, Akira; Motoyama, Hiroaki; Furusawa, Norihiko; Sakai, Hiroshi; Uehara, Takeshi; Kadoya, Masumi; Miyagawa, Shin-Ichi

2015-01-01

The aim of this study was to evaluate the impact of the pancreatic signal intensity (SI) on magnetic resonance imaging (MRI) findings for predicting the development of pancreatic fistula (PF) after a distal pancreatectomy (DP) involving a triple-row stapler closure. A multivariate logistic regression analysis was used to identify risk factors for clinical PF, as defined by the International Study Group on Pancreatic Fistula grade B or C. The pancreas-to-muscle SI ratio was evaluated using fat-suppressed T1-weighted MRI. Of the 41 enrolled patients, 8 (19.5%) developed clinical PF. The pancreatic thickness (≥15 mm) and SI ratio (≥1.3) were identified as independent predictors of clinical PF in a multivariate analysis. Clinical PF was observed in one patient with a thick pancreas and a low SI ratio (14.3%), whereas it was observed in 60% of the patients with a thick pancreas and a high SI ratio. The area under the receiver operating characteristic curve for a predictive model consisting of the two factors was 0.87 (95% confidence interval, 0.75 to 0.99), the level of which tended to be greater than that for pancreatic thickness alone (0.81, p = 0.09). The SI ratio as evaluated using MRI might be useful for predicting clinical PF in patients with the pancreatic thickness ≥15 mm after DP involving a stapler closure. Copyright © 2015 IAP and EPC. Published by Elsevier B.V. All rights reserved.

Early Postoperative Magnetic Resonance Imaging in Detecting Radicular Pain After Lumbar Decompression Surgery: Retrospective Study of the Relationship Between Dural Sac Cross-sectional Area and Postoperative Radicular Pain.

PubMed

Futatsugi, Toshimasa; Takahashi, Jun; Oba, Hiroki; Ikegami, Shota; Mogami, Yuji; Shibata, Syunichi; Ohji, Yoshihito; Tanikawa, Hirotaka; Kato, Hiroyuki

2017-07-01

A retrospective analysis. To evaluate the association between early postoperative dural sac cross-sectional area (DCSA) and radicular pain. The correlation between postoperative magnetic resonance imaging (MRI) findings and postoperative neurological symptoms after lumbar decompression surgery is controversial. This study included 115 patients who underwent lumbar decompression surgery followed by MRI within 7 days postoperatively. There were 46 patients with early postoperative radicular pain, regardless of whether the pain was mild or similar to that before surgery. The intervertebral level with the smallest DCSA was identified on MRI and compared preoperatively and postoperatively. Risk factors for postoperative radicular pain were determined using univariate and multivariate analyses. Subanalysis according to absence/presence of a residual suction drain also was performed. Multivariate regression analysis showed that smaller postoperative DCSA was significantly associated with early postoperative radicular pain (per -10 mm; odds ratio, 1.26). The best cutoff value for radicular pain was early postoperative DCSA of 67.7 mm. Even with a cutoff value of <70 mm, sensitivity and specificity are 74.3% and 75.0%, respectively. Early postoperative DCSA was significantly larger before suction drain removal than after (119.7±10.1 vs. 93.9±5.4 mm). Smaller DCSA in the early postoperative period was associated with radicular pain after lumbar decompression surgery. The best cutoff value for postoperative radicular pain was 67.7 mm. Absence of a suction drain at the time of early postoperative MRI was related to smaller DCSA.

Computed Tomographic Evaluation of Posttreatment Soft-Tissue Changes by Using a Lymphedema Scoring System in Patients with Oral Cancer.

PubMed

Akashi, Masaya; Teraoka, Shun; Kakei, Yasumasa; Kusumoto, Junya; Hasegawa, Takumi; Minamikawa, Tsutomu; Hashikawa, Kazunobu; Komori, Takahide

2018-04-01

This study aimed to evaluate posttreatment soft-tissue changes in patients with oral cancer with computed tomography (CT). To accomplish that purpose, a scoring system was established, referring to the criteria of lower leg lymphedema (LE). One hundred and six necks in 95 patients who underwent oral oncologic surgery with neck dissection (ND) were analyzed retrospectively using routine follow-up CT images. A two-point scoring system to evaluate soft-tissue changes (so-called "LE score") was established as follows: Necks with a "honeycombing" appearance were assigned 1 point. Necks with "taller than wide" fat lobules were assigned 1 point. Necks with neither appearance were assigned 0 points. Comparisons between patients with LE score ≥1 and LE score = 0 at 6 months postoperatively were performed using the Fisher exact test for discrete variables and the Mann-Whitney U test for continuous variables. Univariate predictors associated with posttreatment changes (i.e., LE score ≥1 at 6 months postoperatively) were entered into a multivariate logistic regression analysis. Values of p < 0.05 were considered to indicate statistical significance. The occurrence of the posttreatment soft-tissue changes was 32%. Multivariate logistic regression analysis showed that postoperative radiation therapy (RT) and bilateral ND were potential risk factors of posttreatment soft-tissue changes on CT images. Sequential evaluation of "honeycombing" and the "taller than wide" appearances on routine follow-up CT revealed the persistence of posttreatment soft-tissue changes in patients who underwent oral cancer treatment, and those potential risk factors were postoperative RT and bilateral ND.

Practice and Learning: Spatiotemporal Differences in Thalamo-Cortical-Cerebellar Networks Engagement across Learning Phases in Schizophrenia.

PubMed

Korostil, Michele; Remington, Gary; McIntosh, Anthony Randal

2016-01-01

Understanding how practice mediates the transition of brain-behavior networks between early and later stages of learning is constrained by the common approach to analysis of fMRI data. Prior imaging studies have mostly relied on a single scan, and parametric, task-related analyses. Our experiment incorporates a multisession fMRI lexicon-learning experiment with multivariate, whole-brain analysis to further knowledge of the distributed networks supporting practice-related learning in schizophrenia (SZ). Participants with SZ were compared with healthy control (HC) participants as they learned a novel lexicon during two fMRI scans over a several day period. All participants were trained to equal task proficiency prior to scanning. Behavioral-Partial Least Squares, a multivariate analytic approach, was used to analyze the imaging data. Permutation testing was used to determine statistical significance and bootstrap resampling to determine the reliability of the findings. With practice, HC participants transitioned to a brain-accuracy network incorporating dorsostriatal regions in late-learning stages. The SZ participants did not transition to this pattern despite comparable behavioral results. Instead, successful learners with SZ were differentiated primarily on the basis of greater engagement of perceptual and perceptual-integration brain regions. There is a different spatiotemporal unfolding of brain-learning relationships in SZ. In SZ, given the same amount of practice, the movement from networks suggestive of effortful learning toward subcortically driven procedural one differs from HC participants. Learning performance in SZ is driven by varying levels of engagement in perceptual regions, which suggests perception itself is impaired and may impact downstream, "higher level" cognition.

Confocal Raman imaging and chemometrics applied to solve forensic document examination involving crossed lines and obliteration cases by a depth profiling study.

PubMed

Borba, Flávia de Souza Lins; Jawhari, Tariq; Saldanha Honorato, Ricardo; de Juan, Anna

2017-03-27

This article describes a non-destructive analytical method developed to solve forensic document examination problems involving crossed lines and obliteration. Different strategies combining confocal Raman imaging and multivariate curve resolution-alternating least squares (MCR-ALS) are presented. Multilayer images were acquired at subsequent depth layers into the samples. It is the first time that MCR-ALS is applied to multilayer images for forensic purposes. In this context, this method provides a single set of pure spectral ink signatures and related distribution maps for all layers examined from the sole information in the raw measurement. Four cases were investigated, namely, two concerning crossed lines with different degrees of ink similarity and two related to obliteration, where previous or no knowledge about the identity of the obliterated ink was available. In the crossing line scenario, MCR-ALS analysis revealed the ink nature and the chronological order in which strokes were drawn. For obliteration cases, results making active use of information about the identity of the obliterated ink in the chemometric analysis were of similar quality as those where the identity of the obliterated ink was unknown. In all obliteration scenarios, the identity of inks and the obliterated text were satisfactorily recovered. The analytical methodology proposed is of general use for analytical forensic document examination problems, and considers different degrees of complexity and prior available information. Besides, the strategies of data analysis proposed can be applicable to any other kind of problem in which multilayer Raman images from multicomponent systems have to be interpreted.

Estimation of T2* Relaxation Time of Breast Cancer: Correlation with Clinical, Imaging and Pathological Features

PubMed Central

Seo, Mirinae; Jahng, Geon-Ho; Sohn, Yu-Mee; Rhee, Sun Jung; Oh, Jang-Hoon; Won, Kyu-Yeoun

2017-01-01

Objective The purpose of this study was to estimate the T2* relaxation time in breast cancer, and to evaluate the association between the T2* value with clinical-imaging-pathological features of breast cancer. Materials and Methods Between January 2011 and July 2013, 107 consecutive women with 107 breast cancers underwent multi-echo T2*-weighted imaging on a 3T clinical magnetic resonance imaging system. The Student's t test and one-way analysis of variance were used to compare the T2* values of cancer for different groups, based on the clinical-imaging-pathological features. In addition, multiple linear regression analysis was performed to find independent predictive factors associated with the T2* values. Results Of the 107 breast cancers, 92 were invasive and 15 were ductal carcinoma in situ (DCIS). The mean T2* value of invasive cancers was significantly longer than that of DCIS (p = 0.029). Signal intensity on T2-weighted imaging (T2WI) and histologic grade of invasive breast cancers showed significant correlation with T2* relaxation time in univariate and multivariate analysis. Breast cancer groups with higher signal intensity on T2WI showed longer T2* relaxation time (p = 0.005). Cancer groups with higher histologic grade showed longer T2* relaxation time (p = 0.017). Conclusion The T2* value is significantly longer in invasive cancer than in DCIS. In invasive cancers, T2* relaxation time is significantly longer in higher histologic grades and high signal intensity on T2WI. Based on these preliminary data, quantitative T2* mapping has the potential to be useful in the characterization of breast cancer. PMID:28096732

Analysis of energy dispersive x-ray diffraction profiles for material identification, imaging and system control

NASA Astrophysics Data System (ADS)

Cook, Emily Jane

2008-12-01

This thesis presents the analysis of low angle X-ray scatter measurements taken with an energy dispersive system for substance identification, imaging and system control. Diffraction measurements were made on illicit drugs, which have pseudo- crystalline structures and thus produce diffraction patterns comprising a se ries of sharp peaks. Though the diffraction profiles of each drug are visually characteristic, automated detection systems require a substance identification algorithm, and multivariate analysis was selected as suitable. The software was trained with measured diffraction data from 60 samples covering 7 illicit drugs and 5 common cutting agents, collected with a range of statistical qual ities and used to predict the content of 7 unknown samples. In all cases the constituents were identified correctly and the contents predicted to within 15%. Soft tissues exhibit broad peaks in their diffraction patterns. Diffraction data were collected from formalin fixed breast tissue samples and used to gen erate images. Maximum contrast between healthy and suspicious regions was achieved using momentum transfer windows 1.04-1.10 and 1.84-1.90 nm_1. The resulting images had an average contrast of 24.6% and 38.9% compared to the corresponding transmission X-ray images (18.3%). The data was used to simulate the feedback for an adaptive imaging system and the ratio of the aforementioned momentum transfer regions found to be an excellent pa rameter. Investigation into the effects of formalin fixation on human breast tissue and animal tissue equivalents indicated that fixation in standard 10% buffered formalin does not alter the diffraction profiles of tissue in the mo mentum transfer regions examined, though 100% unbuffered formalin affects the profile of porcine muscle tissue (a substitute for glandular and tumourous tissue), though fat is unaffected.

Using cystoscopy to segment bladder tumors with a multivariate approach in different color spaces.

PubMed

Freitas, Nuno R; Vieira, Pedro M; Lima, Estevao; Lima, Carlos S

2017-07-01

Nowadays the diagnosis of bladder lesions relies upon cystoscopy examination and depends on the interpreter's experience. State of the art of bladder tumor identification are based on 3D reconstruction, using CT images (Virtual Cystoscopy) or images where the structures are exalted with the use of pigmentation, but none uses white light cystoscopy images. An initial attempt to automatically identify tumoral tissue was already developed by the authors and this paper will develop this idea. Traditional cystoscopy images processing has a huge potential to improve early tumor detection and allows a more effective treatment. In this paper is described a multivariate approach to do segmentation of bladder cystoscopy images, that will be used to automatically detect and improve physician diagnose. Each region can be assumed as a normal distribution with specific parameters, leading to the assumption that the distribution of intensities is a Gaussian Mixture Model (GMM). Region of high grade and low grade tumors, usually appears with higher intensity than normal regions. This paper proposes a Maximum a Posteriori (MAP) approach based on pixel intensities read simultaneously in different color channels from RGB, HSV and CIELab color spaces. The Expectation-Maximization (EM) algorithm is used to estimate the best multivariate GMM parameters. Experimental results show that the proposed method does bladder tumor segmentation into two classes in a more efficient way in RGB even in cases where the tumor shape is not well defined. Results also show that the elimination of component L from CIELab color space does not allow definition of the tumor shape.

Radiation Dose–Dependent Hippocampal Atrophy Detected With Longitudinal Volumetric Magnetic Resonance Imaging

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

Seibert, Tyler M.; Karunamuni, Roshan; Bartsch, Hauke

Purpose: After radiation therapy (RT) to the brain, patients often experience memory impairment, which may be partially mediated by damage to the hippocampus. Hippocampal sparing in RT planning is the subject of recent and ongoing clinical trials. Calculating appropriate hippocampal dose constraints would be improved by efficient in vivo measurements of hippocampal damage. In this study we sought to determine whether brain RT was associated with dose-dependent hippocampal atrophy. Methods and Materials: Hippocampal volume was measured with magnetic resonance imaging (MRI) in 52 patients who underwent fractionated, partial brain RT for primary brain tumors. Study patients had high-resolution, 3-dimensional volumetric MRI beforemore » and 1 year after RT. Images were processed using software with clearance from the US Food and Drug Administration and Conformité Européene marking for automated measurement of hippocampal volume. Automated results were inspected visually for accuracy. Tumor and surgical changes were censored. Mean hippocampal dose was tested for correlation with hippocampal atrophy 1 year after RT. Average hippocampal volume change was also calculated for hippocampi receiving high (>40 Gy) or low (<10 Gy) mean RT dose. A multivariate analysis was conducted with linear mixed-effects modeling to evaluate other potential predictors of hippocampal volume change, including patient (random effect), age, hemisphere, sex, seizure history, and baseline volume. Statistical significance was evaluated at α = 0.05. Results: Mean hippocampal dose was significantly correlated with hippocampal volume loss (r=−0.24, P=.03). Mean hippocampal volume was significantly reduced 1 year after high-dose RT (mean −6%, P=.009) but not after low-dose RT. In multivariate analysis, both RT dose and patient age were significant predictors of hippocampal atrophy (P<.01). Conclusions: The hippocampus demonstrates radiation dose–dependent atrophy after treatment for brain tumors. Quantitative MRI is a noninvasive imaging technique capable of measuring radiation effects on intracranial structures. This technique could be investigated as a potential biomarker for development of reliable dose constraints for improved cognitive outcomes.« less

[Relationship between multi-slice spiral CT angiography imaging features and in-hospital death of patients with aortic dissection].

PubMed

Xiao, Z Y; Wang, H J; Yao, C L; Gu, G R; Xue, Y; Yin, J; Chen, J; Zhang, C; Tong, C Y; Song, Z J

2017-03-24

Objective: To explore the imaging manifestations of multi-slice spiral CT angiography (CTA) and relationship with in-hospital death in patients with aortic dissection (AD). Methods: The clinical data of 429 patients with AD who underwent CTA in Zhongshan Hospital of Fudan University between January 2009 and January 2016 were retrospectively analyzed. AD patients were divided into 2 groups, including operation group who underwent surgery or interventional therapy (370 cases) and non-operation group who underwent medical conservative treatment(59 cases). The multi-slice spiral CTA imaging features of AD were analyzed, and multivariate logistic regression analysis was used to investigate the relationship between imaging manifestations and in-hospital death in AD patients. Results: There were 12 cases (3.24%) of in-hospital death in operation group, and 28 cases (47.46%) of in-hospital death in non-operation group( P <0.001). AD involved different vascular branches. Multi-slice spiral CTA can clearly show the dissection of true and false lumen, and intimal tear was detected in 363 (84.62%) cases, outer wall calcification was revealed in 63 (14.69%) cases, and thrombus formation was present in 227 (52.91%) cases. The multivariate logistic regression analysis showed that the number of branch vessels involved ( OR =1.374, 95% CI 1.081-1.745, P =0.009) and tearing false lumen range( OR =2.059, 95% CI 1.252-3.385, P =0.004) were independent risk factors of in-hospital death in AD patients, and the number of branch vessels involved ( OR =1.600, 95% CI 1.062-2.411, P =0.025) was independent risk factor of in-hospital death in the operation group, while the tearing false lumen range ( OR =2.315, 95% CI 1.019-5.262, P =0.045) was independent risk factor of in-hospital death of non-operation group. Conclusions: Multi-slice spiral CTA can clearly show the entire AD, true and false lumen, intimal tear, wall calcification and thrombosis of AD patients. The number of branch vessels involved and tearing false lumen range are the independent risk factors of in-hospital death in AD patients.

Automated classification and visualization of healthy and pathological dental tissues based on near-infrared hyper-spectral imaging

NASA Astrophysics Data System (ADS)

Usenik, Peter; Bürmen, Miran; Vrtovec, Tomaž; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan

2011-03-01

Despite major improvements in dental healthcare and technology, dental caries remains one of the most prevalent chronic diseases of modern society. The initial stages of dental caries are characterized by demineralization of enamel crystals, commonly known as white spots which are difficult to diagnose. If detected early enough, such demineralization can be arrested and reversed by non-surgical means through well established dental treatments (fluoride therapy, anti-bacterial therapy, low intensity laser irradiation). Near-infrared (NIR) hyper-spectral imaging is a new promising technique for early detection of demineralization based on distinct spectral features of healthy and pathological dental tissues. In this study, we apply NIR hyper-spectral imaging to classify and visualize healthy and pathological dental tissues including enamel, dentin, calculus, dentin caries, enamel caries and demineralized areas. For this purpose, a standardized teeth database was constructed consisting of 12 extracted human teeth with different degrees of natural dental lesions imaged by NIR hyper-spectral system, X-ray and digital color camera. The color and X-ray images of teeth were presented to a clinical expert for localization and classification of the dental tissues, thereby obtaining the gold standard. Principal component analysis was used for multivariate local modeling of healthy and pathological dental tissues. Finally, the dental tissues were classified by employing multiple discriminant analysis. High agreement was observed between the resulting classification and the gold standard with the classification sensitivity and specificity exceeding 85 % and 97 %, respectively. This study demonstrates that NIR hyper-spectral imaging has considerable diagnostic potential for imaging hard dental tissues.

Application of copulas to improve covariance estimation for partial least squares.

PubMed

D'Angelo, Gina M; Weissfeld, Lisa A

2013-02-20

Dimension reduction techniques, such as partial least squares, are useful for computing summary measures and examining relationships in complex settings. Partial least squares requires an estimate of the covariance matrix as a first step in the analysis, making this estimate critical to the results. In addition, the covariance matrix also forms the basis for other techniques in multivariate analysis, such as principal component analysis and independent component analysis. This paper has been motivated by an example from an imaging study in Alzheimer's disease where there is complete separation between Alzheimer's and control subjects for one of the imaging modalities. This separation occurs in one block of variables and does not occur with the second block of variables resulting in inaccurate estimates of the covariance. We propose the use of a copula to obtain estimates of the covariance in this setting, where one set of variables comes from a mixture distribution. Simulation studies show that the proposed estimator is an improvement over the standard estimators of covariance. We illustrate the methods from the motivating example from a study in the area of Alzheimer's disease. Copyright © 2012 John Wiley & Sons, Ltd.

Comparative forensic soil analysis of New Jersey state parks using a combination of simple techniques with multivariate statistics.

PubMed

Bonetti, Jennifer; Quarino, Lawrence

2014-05-01

This study has shown that the combination of simple techniques with the use of multivariate statistics offers the potential for the comparative analysis of soil samples. Five samples were obtained from each of twelve state parks across New Jersey in both the summer and fall seasons. Each sample was examined using particle-size distribution, pH analysis in both water and 1 M CaCl2 , and a loss on ignition technique. Data from each of the techniques were combined, and principal component analysis (PCA) and canonical discriminant analysis (CDA) were used for multivariate data transformation. Samples from different locations could be visually differentiated from one another using these multivariate plots. Hold-one-out cross-validation analysis showed error rates as low as 3.33%. Ten blind study samples were analyzed resulting in no misclassifications using Mahalanobis distance calculations and visual examinations of multivariate plots. Seasonal variation was minimal between corresponding samples, suggesting potential success in forensic applications. © 2014 American Academy of Forensic Sciences.

Neuroanatomical morphometric characterization of sex differences in youth using statistical learning.

PubMed

Sepehrband, Farshid; Lynch, Kirsten M; Cabeen, Ryan P; Gonzalez-Zacarias, Clio; Zhao, Lu; D'Arcy, Mike; Kesselman, Carl; Herting, Megan M; Dinov, Ivo D; Toga, Arthur W; Clark, Kristi A

2018-05-15

Exploring neuroanatomical sex differences using a multivariate statistical learning approach can yield insights that cannot be derived with univariate analysis. While gross differences in total brain volume are well-established, uncovering the more subtle, regional sex-related differences in neuroanatomy requires a multivariate approach that can accurately model spatial complexity as well as the interactions between neuroanatomical features. Here, we developed a multivariate statistical learning model using a support vector machine (SVM) classifier to predict sex from MRI-derived regional neuroanatomical features from a single-site study of 967 healthy youth from the Philadelphia Neurodevelopmental Cohort (PNC). Then, we validated the multivariate model on an independent dataset of 682 healthy youth from the multi-site Pediatric Imaging, Neurocognition and Genetics (PING) cohort study. The trained model exhibited an 83% cross-validated prediction accuracy, and correctly predicted the sex of 77% of the subjects from the independent multi-site dataset. Results showed that cortical thickness of the middle occipital lobes and the angular gyri are major predictors of sex. Results also demonstrated the inferential benefits of going beyond classical regression approaches to capture the interactions among brain features in order to better characterize sex differences in male and female youths. We also identified specific cortical morphological measures and parcellation techniques, such as cortical thickness as derived from the Destrieux atlas, that are better able to discriminate between males and females in comparison to other brain atlases (Desikan-Killiany, Brodmann and subcortical atlases). Copyright © 2018 Elsevier Inc. All rights reserved.

Multicenter study of quantitative computed tomography analysis using a computer-aided three-dimensional system in patients with idiopathic pulmonary fibrosis.

PubMed

Iwasawa, Tae; Kanauchi, Tetsu; Hoshi, Toshiko; Ogura, Takashi; Baba, Tomohisa; Gotoh, Toshiyuki; Oba, Mari S

2016-01-01

To evaluate the feasibility of automated quantitative analysis with a three-dimensional (3D) computer-aided system (i.e., Gaussian histogram normalized correlation, GHNC) of computed tomography (CT) images from different scanners. Each institution's review board approved the research protocol. Informed patient consent was not required. The participants in this multicenter prospective study were 80 patients (65 men, 15 women) with idiopathic pulmonary fibrosis. Their mean age was 70.6 years. Computed tomography (CT) images were obtained by four different scanners set at different exposures. We measured the extent of fibrosis using GHNC, and used Pearson's correlation analysis, Bland-Altman plots, and kappa analysis to directly compare the GHNC results with manual scoring by radiologists. Multiple linear regression analysis was performed to determine the association between the CT data and forced vital capacity (FVC). For each scanner, the extent of fibrosis as determined by GHNC was significantly correlated with the radiologists' score. In multivariate analysis, the extent of fibrosis as determined by GHNC was significantly correlated with FVC (p < 0.001). There was no significant difference between the results obtained using different CT scanners. Gaussian histogram normalized correlation was feasible, irrespective of the type of CT scanner used.

Quantifying the impact of between-study heterogeneity in multivariate meta-analyses

PubMed Central

Jackson, Dan; White, Ian R; Riley, Richard D

2012-01-01

Measures that quantify the impact of heterogeneity in univariate meta-analysis, including the very popular I2 statistic, are now well established. Multivariate meta-analysis, where studies provide multiple outcomes that are pooled in a single analysis, is also becoming more commonly used. The question of how to quantify heterogeneity in the multivariate setting is therefore raised. It is the univariate R2 statistic, the ratio of the variance of the estimated treatment effect under the random and fixed effects models, that generalises most naturally, so this statistic provides our basis. This statistic is then used to derive a multivariate analogue of I2, which we call . We also provide a multivariate H2 statistic, the ratio of a generalisation of Cochran's heterogeneity statistic and its associated degrees of freedom, with an accompanying generalisation of the usual I2 statistic, . Our proposed heterogeneity statistics can be used alongside all the usual estimates and inferential procedures used in multivariate meta-analysis. We apply our methods to some real datasets and show how our statistics are equally appropriate in the context of multivariate meta-regression, where study level covariate effects are included in the model. Our heterogeneity statistics may be used when applying any procedure for fitting the multivariate random effects model. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22763950

Functional connectomics from a "big data" perspective.

PubMed

Xia, Mingrui; He, Yong

2017-10-15

In the last decade, explosive growth regarding functional connectome studies has been observed. Accumulating knowledge has significantly contributed to our understanding of the brain's functional network architectures in health and disease. With the development of innovative neuroimaging techniques, the establishment of large brain datasets and the increasing accumulation of published findings, functional connectomic research has begun to move into the era of "big data", which generates unprecedented opportunities for discovery in brain science and simultaneously encounters various challenging issues, such as data acquisition, management and analyses. Big data on the functional connectome exhibits several critical features: high spatial and/or temporal precision, large sample sizes, long-term recording of brain activity, multidimensional biological variables (e.g., imaging, genetic, demographic, cognitive and clinic) and/or vast quantities of existing findings. We review studies regarding functional connectomics from a big data perspective, with a focus on recent methodological advances in state-of-the-art image acquisition (e.g., multiband imaging), analysis approaches and statistical strategies (e.g., graph theoretical analysis, dynamic network analysis, independent component analysis, multivariate pattern analysis and machine learning), as well as reliability and reproducibility validations. We highlight the novel findings in the application of functional connectomic big data to the exploration of the biological mechanisms of cognitive functions, normal development and aging and of neurological and psychiatric disorders. We advocate the urgent need to expand efforts directed at the methodological challenges and discuss the direction of applications in this field. Copyright © 2017 Elsevier Inc. All rights reserved.

Combined image and genomic analysis of high-grade serous ovarian cancer reveals PTEN loss as a common driver event and prognostic classifier.

PubMed

Martins, Filipe C; Santiago, Ines de; Trinh, Anne; Xian, Jian; Guo, Anne; Sayal, Karen; Jimenez-Linan, Mercedes; Deen, Suha; Driver, Kristy; Mack, Marie; Aslop, Jennifer; Pharoah, Paul D; Markowetz, Florian; Brenton, James D

2014-12-17

TP53 and BRCA1/2 mutations are the main drivers in high-grade serous ovarian carcinoma (HGSOC). We hypothesise that combining tissue phenotypes from image analysis of tumour sections with genomic profiles could reveal other significant driver events. Automatic estimates of stromal content combined with genomic analysis of TCGA HGSOC tumours show that stroma strongly biases estimates of PTEN expression. Tumour-specific PTEN expression was tested in two independent cohorts using tissue microarrays containing 521 cases of HGSOC. PTEN loss or downregulation occurred in 77% of the first cohort by immunofluorescence and 52% of the validation group by immunohistochemistry, and is associated with worse survival in a multivariate Cox-regression model adjusted for study site, age, stage and grade. Reanalysis of TCGA data shows that hemizygous loss of PTEN is common (36%) and expression of PTEN and expression of androgen receptor are positively associated. Low androgen receptor expression was associated with reduced survival in data from TCGA and immunohistochemical analysis of the first cohort. PTEN loss is a common event in HGSOC and defines a subgroup with significantly worse prognosis, suggesting the rational use of drugs to target PI3K and androgen receptor pathways for HGSOC. This work shows that integrative approaches combining tissue phenotypes from images with genomic analysis can resolve confounding effects of tissue heterogeneity and should be used to identify new drivers in other cancers.

Multivariate Statistical Analysis Software Technologies for Astrophysical Research Involving Large Data Bases

NASA Technical Reports Server (NTRS)

Djorgovski, S. G.

1994-01-01

We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complex database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects of the SKICAT system, and of some of the scientific results achieved to date. We also developed a user-friendly package for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications and has produced real, published results.

Analyzing Multiple Outcomes in Clinical Research Using Multivariate Multilevel Models

PubMed Central

Baldwin, Scott A.; Imel, Zac E.; Braithwaite, Scott R.; Atkins, David C.

2014-01-01

Objective Multilevel models have become a standard data analysis approach in intervention research. Although the vast majority of intervention studies involve multiple outcome measures, few studies use multivariate analysis methods. The authors discuss multivariate extensions to the multilevel model that can be used by psychotherapy researchers. Method and Results Using simulated longitudinal treatment data, the authors show how multivariate models extend common univariate growth models and how the multivariate model can be used to examine multivariate hypotheses involving fixed effects (e.g., does the size of the treatment effect differ across outcomes?) and random effects (e.g., is change in one outcome related to change in the other?). An online supplemental appendix provides annotated computer code and simulated example data for implementing a multivariate model. Conclusions Multivariate multilevel models are flexible, powerful models that can enhance clinical research. PMID:24491071

Distributed effects of methylphenidate on the network structure of the resting brain: a connectomic pattern classification analysis.

PubMed

Sripada, Chandra Sekhar; Kessler, Daniel; Welsh, Robert; Angstadt, Michael; Liberzon, Israel; Phan, K Luan; Scott, Clayton

2013-11-01

Methylphenidate is a psychostimulant medication that produces improvements in functions associated with multiple neurocognitive systems. To investigate the potentially distributed effects of methylphenidate on the brain's intrinsic network architecture, we coupled resting state imaging with multivariate pattern classification. In a within-subject, double-blind, placebo-controlled, randomized, counterbalanced, cross-over design, 32 healthy human volunteers received either methylphenidate or placebo prior to two fMRI resting state scans separated by approximately one week. Resting state connectomes were generated by placing regions of interest at regular intervals throughout the brain, and these connectomes were submitted for support vector machine analysis. We found that methylphenidate produces a distributed, reliably detected, multivariate neural signature. Methylphenidate effects were evident across multiple resting state networks, especially visual, somatomotor, and default networks. Methylphenidate reduced coupling within visual and somatomotor networks. In addition, default network exhibited decoupling with several task positive networks, consistent with methylphenidate modulation of the competitive relationship between these networks. These results suggest that connectivity changes within and between large-scale networks are potentially involved in the mechanisms by which methylphenidate improves attention functioning. Copyright © 2013 Elsevier Inc. All rights reserved.

Cell Death, Inflammation, Tumor Burden, and Proliferation Blood Biomarkers Predict Lung Cancer Radiotherapy Response and Correlate With Tumor Volume and Proliferation Imaging.

PubMed

Salem, Ahmed; Mistry, Hitesh; Backen, Alison; Hodgson, Clare; Koh, Pek; Dean, Emma; Priest, Lynsey; Haslett, Kate; Trigonis, Ioannis; Jackson, Alan; Asselin, Marie-Claude; Dive, Caroline; Renehan, Andrew; Faivre-Finn, Corinne; Blackhall, Fiona

2018-05-01

There is an unmet need to develop noninvasive biomarkers to stratify patients in drug-radiotherapy trials. In this pilot study we investigated lung cancer radiotherapy response and toxicity blood biomarkers and correlated findings with tumor volume and proliferation imaging. Blood samples were collected before and during (day 21) radiotherapy. Twenty-six cell-death, hypoxia, angiogenesis, inflammation, proliferation, invasion, and tumor-burden biomarkers were evaluated. Clinical and laboratory data were collected. Univariate analysis was performed on small-cell and non-small-cell lung cancer (NSCLC) whereas multivariate analysis focused on NSCLC. Blood samples from 78 patients were analyzed. Sixty-one (78.2%) harbored NSCLC, 48 (61.5%) received sequential chemoradiotherapy. Of tested baseline biomarkers, undetectable interleukin (IL)-1b (hazard ratio [HR], 4.02; 95% confidence interval [CI], 2.04-7.93; P < .001) was the only significant survival covariate. Of routinely collected laboratory tests, high baseline neutrophil count was a significant survival covariate (HR, 1.07; 95% CI, 1.02-1.11; P = .017). Baseline IL-1b and neutrophil count were prognostic for survival in a multivariate model. The addition of day-21 cytokeratin-19 antigen modestly improved this model's survival prediction (concordance probability, 0.75-0.78). Chemotherapy (P < .001) and baseline keratinocyte growth factor (P = .019) predicted acute esophagitis, but only chemotherapy remained significant after Bonferroni correction. Baseline angioprotein-1 and hepatocyte growth factor showed a direct correlation with tumor volume whereas changes in vascular cell adhesion molecule 1 showed significant correlations with 18F-fluorothymidine (FLT) positron emission tomography (PET). Select biomarkers are prognostic after radiotherapy in this lung cancer series. The correlation between circulating biomarkers and 18F-FLT PET is shown, to our knowledge for the first time, highlighting their potential role as imaging surrogates. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Impact of Consolidation Radiation Therapy in Stage III-IV Diffuse Large B-cell Lymphoma With Negative Post-Chemotherapy Radiologic Imaging

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

Dorth, Jennifer A., E-mail: jennifer.dorth@duke.edu; Prosnitz, Leonard R.; Broadwater, Gloria

2012-11-01

Purpose: While consolidation radiation therapy (i.e., RT administered after chemotherapy) is routine treatment for patients with early-stage diffuse large B-cell lymphoma (DLBCL), the role of consolidation RT in stage III-IV DLBCL is controversial. Methods and Materials: Cases of patients with stage III-IV DLBCL treated from 1991 to 2009 at Duke University, who achieved a complete response to chemotherapy were reviewed. Clinical outcomes were calculated using the Kaplan-Meier method and were compared between patients who did and did not receive RT, using the log-rank test. A multivariate analysis was performed using Cox proportional hazards model. Results: Seventy-nine patients were identified. Chemotherapymore » (median, 6 cycles) consisted of anti-CD20 antibody rituximab combined with cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP; 65%); cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP; 22%); or other (13%). Post-chemotherapy imaging consisted of positron emission tomography (PET)/computed tomography (CT) (73%); gallium with CT (14%); or CT only (13%). Consolidation RT (median, 25 Gy) was given to involved sites of disease in 38 (48%) patients. Receipt of consolidation RT was associated with improved in-field control (92% vs. 69%, respectively, p = 0.028) and event-free survival (85% vs. 65%, respectively, p = 0.014) but no difference in overall survival (85% vs. 78%, respectively, p = 0.15) when compared to patients who did not receive consolidation RT. On multivariate analysis, no RT was predictive of increased risk of in-field failure (hazard ratio [HR], 8.01, p = 0.014) and worse event-free survival (HR, 4.3, p = 0.014). Conclusions: Patients with stage III-IV DLBCL who achieve negative post-chemotherapy imaging have improved in-field control and event-free survival with low-dose consolidation RT.« less

Predictors of recurrence in pheochromocytoma.

PubMed

Press, Danielle; Akyuz, Muhammet; Dural, Cem; Aliyev, Shamil; Monteiro, Rosebel; Mino, Jeff; Mitchell, Jamie; Hamrahian, Amir; Siperstein, Allan; Berber, Eren

2014-12-01

The recurrence rate of pheochromocytoma after adrenalectomy is 6.5-16.5%. This study aims to identify predictors of recurrence and optimal biochemical testing and imaging for detecting the recurrence of pheochromocytoma. In this retrospective study we reviewed all patients who underwent adrenalectomy for pheochromocytoma during a 14-year period at a single institution. One hundred thirty-five patients had adrenalectomy for pheochromocytoma. Eight patients (6%) developed recurrent disease. The median time from initial operation to diagnosis of recurrence was 35 months. On multivariate analysis, tumor size >5 cm was an independent predictor of recurrence. One patient with recurrence died, 4 had stable disease, 2 had progression of disease, and 1 was cured. Recurrence was diagnosed by increases in plasma and/or urinary metanephrines and positive imaging in 6 patients (75%), and by positive imaging and normal biochemical levels in 2 patients (25%). Patients with large tumors (>5 cm) should be followed vigilantly for recurrence. Because 25% of patients with recurrence had normal biochemical levels, we recommend routine imaging and testing of plasma or urinary metanephrines for prompt diagnosis of recurrence. Copyright © 2014 Elsevier Inc. All rights reserved.

Integration of co-localized glandular morphometry and protein biomarker expression in immunofluorescent images for prostate cancer prognosis

NASA Astrophysics Data System (ADS)

Scott, Richard; Khan, Faisal M.; Zeineh, Jack; Donovan, Michael; Fernandez, Gerardo

2015-03-01

Immunofluorescent (IF) image analysis of tissue pathology has proven to be extremely valuable and robust in developing prognostic assessments of disease, particularly in prostate cancer. There have been significant advances in the literature in quantitative biomarker expression as well as characterization of glandular architectures in discrete gland rings. However, while biomarker and glandular morphometric features have been combined as separate predictors in multivariate models, there is a lack of integrative features for biomarkers co-localized within specific morphological sub-types; for example the evaluation of androgen receptor (AR) expression within Gleason 3 glands only. In this work we propose a novel framework employing multiple techniques to generate integrated metrics of morphology and biomarker expression. We demonstrate the utility of the approaches in predicting clinical disease progression in images from 326 prostate biopsies and 373 prostatectomies. Our proposed integrative approaches yield significant improvements over existing IF image feature metrics. This work presents some of the first algorithms for generating innovative characteristics in tissue diagnostics that integrate co-localized morphometry and protein biomarker expression.

118: THE ASSOCIATION BETWEEN BODY IMAGE AND HYPOACTIVE SEXUAL DESIRE DISORDER AMONG REPRODUCTIVE AGE WOMEN: AN IRANIAN SETTING

PubMed Central

Hamzehgardeshi, Zeinab; Malary, Mina; Moosazadeh, Mahmood; Khani, Soghra; Pourasghar, Mehdi

2017-01-01

Background and aims Hypoactive Sexual Desire Disorder (HSDD) is the most prevalent sexual disorder among women that may pop up due to worrying about Body Image (BI). Body image is a complicated concept encompassing the biological, psychological and social factors. The current study aims to analyze the relationship between BI and HSDD among Iranian women in their reproductive age. Methods this research is cross-sectional (descriptive –analytical) performed on 1000 woman in their reproductive age (15–49 yrs). The samples have been selected by systematic random sampling method. The data collection tool includes demographics and Sexual Interest and Desire Inventory-Female (SIDI-F) plus the Female Sexual Distress Scale-Revised (FSDS-R) for measuring HSDD completed as self-report by the samples. To analyze the data, univariate and multivariate regression test have been applied. Results The mean age of the study community has been 32.09±7.33. After adjusting the effect of the confounder variables by logistic regression multivariate analysis, the odd ratio for HSDD in the individuals not satisfied or slightly satisfied with their BI has been OR: 4.2 (95% CI: 1.98–9.05) and OR: 3.9 (95% CI: 2.29–6.65), respectively, times more than those highly contended with their BI. Conclusion this study depicted that dissatisfaction with BI is a determinant factor of HSDD. Thus taking this factor into account when acquiring sexual history can be useful order to provide optimal sexual counseling.

External root resorption of the second molar associated with mesially and horizontally impacted mandibular third molar: evidence from cone beam computed tomography.

PubMed

Wang, Dongmiao; He, Xiaotong; Wang, Yanling; Li, Zhongwu; Zhu, Yumin; Sun, Chao; Ye, Jinhai; Jiang, Hongbing; Cheng, Jie

2017-05-01

The aim of the present study was to assess the incidence and risk factors of ERR in second molars with mesially and horizontally impacted mandibular third molars using cone beam computed tomography (CBCT) images from patients in a Chinese tertiary referral hospital. A total number of 216 patients with 362 mesially and horizontally impacted mandibular third molars who were treated at our institution from 2014 to 2015 was retrospectively included. The ERR in second molars was identified on CBCT multiplanar images. The associations between incidence of ERR and multiple clinical parameters were statistically analyzed by Chi-square test. Moreover, the risk factors for ERR in second molars were further assessed by multivariate regression analysis. The overall incidence of ERR in second molars was 20.17 % (73/362) as detected on CBCT images. The presence of ERR significantly associated with patients age and impaction depth of mandibular third molars. However, no significant relationship was found between ERR severity and impaction depth or ERR location. Multivariate regression analyses further revealed age over 35 years and impaction depth as important risk factors affecting the ERR incidence caused by mesial and horizontal impaction of mandibular third molar. ERR in second molar resulted from mesially and horizontally impacted mandibular third molar is not very rare and can be reliably identified via CBCT scan. Given the possibility of ERR associated with third molar impaction, the prophylactic removal of these impacted teeth could be considered especially for those patients with over 35 years and mesially and horizontally impacted teeth.

Prognostic factors of non-functioning pancreatic neuroendocrine tumor revisited: The value of WHO 2010 classification.

PubMed

Bu, Jiyoung; Youn, Sangmin; Kwon, Wooil; Jang, Kee Taek; Han, Sanghyup; Han, Sunjong; You, Younghun; Heo, Jin Seok; Choi, Seong Ho; Choi, Dong Wook

2018-02-01

Various factors have been reported as prognostic factors of non-functional pancreatic neuroendocrine tumors (NF-pNETs). There remains some controversy as to the factors which might actually serve to successfully prognosticate future manifestation and diagnosis of NF-pNETs. As well, consensus regarding management strategy has never been achieved. The aim of this study is to further investigate potential prognostic factors using a large single-center cohort to help determine the management strategy of NF-pNETs. During the time period 1995 through 2013, 166 patients with NF-pNETs who underwent surgery in Samsung Medical Center were entered in a prospective database, and those factors thought to represent predictors of prognosis were tested in uni- and multivariate models. The median follow-up time was 46.5 months; there was a maximum follow-up period of 217 months. The five-year overall survival and disease-free survival rates were 88.5% and 77.0%, respectively. The 2010 WHO classification was found to be the only prognostic factor which affects overall survival and disease-free survival in multivariate analysis. Also, pathologic tumor size and preoperative image tumor size correlated strongly with the WHO grades ( p <0.001, and p <0.001). Our study demonstrates that 2010 WHO classification represents a valuable prognostic factor of NF-pNETs and tumor size on preoperative image correlated with WHO grade. In view of the foregoing, the preoperative image size is thought to represent a reasonable reference with regard to determination and development of treatment strategy of NF-pNETs.

A Comparative Performance Analysis of Multispectral and RGB Imaging on HER2 Status Evaluation for the Prediction of Breast Cancer Prognosis.

PubMed

Liu, Wenlou; Wang, Linwei; Liu, Jiuyang; Yuan, Jingping; Chen, Jiamei; Wu, Han; Xiang, Qingming; Yang, Guifang; Li, Yan

2016-12-01

Despite the extensive application of multispectral imaging (MSI) in biomedical multidisciplinary researches, there is a paucity of data available regarding the implication of MSI in tumor prognosis prediction. We compared the behaviors of multispectral (MS) and conventional red-green-blue (RGB) images on assessment of human epidermal growth factor receptor 2 (HER2) immunohistochemistry to explore their impact on outcome in patients with invasive breast cancer (BC). Tissue microarrays containing 240 BC patients were introduced to compare the performance of MS and RGB imaging methods on the quantitative assessment of HER2 status and the prognostic value of 5-year disease-free survival (5-DFS). Both the total and average signal optical density values of HER2 MS and RGB images were analyzed, and all patients were divided into two groups based on the different 5-DFS. The quantification of HER2 MS images was negatively correlated with 5-DFS in lymph node-negative and -positive patients (P<.05), but RGB images were not in lymph node-positive patients (P=.101). Multivariate analysis indicated that the hazard ratio (HR) of HER2 MS was higher than that of HER2 RGB (HR=2.454; 95% confidence interval [CI], 1.636-3.681 vs HR=2.060; 95% CI, 1.361-3.119). Additionally, area under curve (AUC) by receiver operating characteristic analysis for HER2 MS was greater than that for HER2 RGB (AUC=0.649; 95% CI, 0.577-0.722 vs AUC=0.596; 95% CI, 0.522-0.670) in predicting the risk for recurrence. More importantly, the quantification of HER2 MS images has higher prediction accuracy than that of HER2 RGB images (69.6% vs 65.0%) on 5-DFS. Our study suggested that better information on BC prognosis could be obtained from the quantification of HER2 MS images and MS images might perform better in predicting BC prognosis than conventional RGB images. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Adverse radiation effect after stereotactic radiosurgery for brain metastases: incidence, time course, and risk factors.

PubMed

Sneed, Penny K; Mendez, Joe; Vemer-van den Hoek, Johanna G M; Seymour, Zachary A; Ma, Lijun; Molinaro, Annette M; Fogh, Shannon E; Nakamura, Jean L; McDermott, Michael W

2015-08-01

The authors sought to determine the incidence, time course, and risk factors for overall adverse radiation effect (ARE) and symptomatic ARE after stereotactic radiosurgery (SRS) for brain metastases. All cases of brain metastases treated from 1998 through 2009 with Gamma Knife SRS at UCSF were considered. Cases with less than 3 months of follow-up imaging, a gap of more than 8 months in imaging during the 1st year, or inadequate imaging availability were excluded. Brain scans and pathology reports were reviewed to ensure consistent scoring of dates of ARE, treatment failure, or both; in case of uncertainty, the cause of lesion worsening was scored as indeterminate. Cumulative incidence of ARE and failure were estimated with the Kaplan-Meier method with censoring at last imaging. Univariate and multivariate Cox proportional hazards analyses were performed. Among 435 patients and 2200 brain metastases evaluable, the median patient survival time was 17.4 months and the median lesion imaging follow-up was 9.9 months. Calculated on the basis of 2200 evaluable lesions, the rates of treatment failure, ARE, concurrent failure and ARE, and lesion worsening with indeterminate cause were 9.2%, 5.4%, 1.4%, and 4.1%, respectively. Among 118 cases of ARE, approximately 60% were symptomatic and 85% occurred 3-18 months after SRS (median 7.2 months). For 99 ARE cases managed without surgery or bevacizumab, the probabilities of improvement observed on imaging were 40%, 57%, and 76% at 6, 12, and 18 months after onset of ARE. The most important risk factors for ARE included prior SRS to the same lesion (with 20% 1-year risk of symptomatic ARE vs 3%, 4%, and 8% for no prior treatment, prior whole brain radiotherapy [WBRT], or concurrent WBRT) and any of these volume parameters: target, prescription isodose, 12-Gy, or 10-Gy volume. Excluding lesions treated with repeat SRS, the 1-year probabilities of ARE were < 1%, 1%, 3%, 10%, and 14% for maximum diameter 0.3-0.6 cm, 0.7-1.0 cm, 1.1-1.5 cm, 1.6-2.0 cm, and 2.1-5.1 cm, respectively. The 1-year probabilities of symptomatic ARE leveled off at 13%-14% for brain metastases maximum diameter > 2.1 cm, target volume > 1.2 cm(3), prescription isodose volume > 1.8 cm(3), 12-Gy volume > 3.3 cm(3), and 10-Gy volume > 4.3 cm(3), excluding lesions treated with repeat SRS. On both univariate and multivariate analysis, capecitabine, but not other systemic therapy within 1 month of SRS, appeared to increase ARE risk. For the multivariate analysis considering only metastases with target volume > 1.0 cm(3), risk factors for ARE included prior SRS, kidney primary tumor, connective tissue disorder, and capecitabine. Although incidence of ARE after SRS was low overall, risk increased rapidly with size and volume, leveling off at a 1-year cumulative incidence of 13%-14%. This study describes the time course of ARE and provides risk estimates by various lesion characteristics and treatment parameters to aid in decision-making and patient counseling.

Accumulated Delivered Dose Response of Stereotactic Body Radiation Therapy for Liver Metastases

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

Swaminath, Anand; Massey, Christine; Brierley, James D.

2015-11-01

Purpose: To determine whether the accumulated dose using image guided radiation therapy is a stronger predictor of clinical outcomes than the planned dose in stereotactic body radiation therapy (SBRT) for liver metastases. Methods and Materials: From 2003 to 2009, 81 patients with 142 metastases were treated in institutional review board–approved SBRT studies (5-10 fractions). Patients were treated during free breathing (with or without abdominal compression) or with controlled exhale breath-holding. SBRT was planned on a static exhale computed tomography (CT) scan, and the minimum planning target volume dose to 0.5 cm{sup 3} (minPTV) was recorded. The accumulated minimum dose to themore » 0.5 cm{sup 3} gross tumor volume (accGTV) was calculated after performing dose accumulation from exported image guided radiation therapy data sets registered to the planning CT using rigid (2-dimensional MV/kV orthogonal) or deformable (3-dimensional/4-dimensional cone beam CT) image registration. Univariate and multivariate Cox regression models assessed the factors influencing the time to local progression (TTLP). Hazard ratios for accGTV and minPTV were compared using model goodness-of-fit and bootstrapping. Results: Overall, the accGTV dose exceeded the minPTV dose in 98% of the lesions. For 5 to 6 fractions, accGTV doses of >45 Gy were associated with 1-year local control of 86%. On univariate analysis, the cancer subtype (breast), smaller tumor volume, and increased dose were significant predictors for improved TTLP. The dose and volume were uncorrelated; the accGTV dose and minPTV dose were correlated and were tested separately on multivariate models. Breast cancer subtype, accGTV dose (P<.001), and minPTV dose (P=.02) retained significance in the multivariate models. The univariate hazard ratio for TTLP for 5-Gy increases in accGTV versus minPTV was 0.67 versus 0.74 (all patients; 95% confidence interval of difference 0.03-0.14). Goodness-of-fit testing confirmed the accGTV dose as a stronger dose–response predictor than the minPTV dose. Conclusions: The accGTV dose is a better predictor of TTLP than the minPTV dose for liver metastasis SBRT. The use of modern image guided radiation therapy in future analyses of dose–response outcomes should increase the concordance between the planned and delivered doses.« less

Radiographical findings in patients with liver cirrhosis and hepatic encephalopathy.

PubMed

Elwir, Saleh; Hal, Hassan; Veith, Joshua; Schreibman, Ian; Kadry, Zakiyah; Riley, Thomas

2016-08-01

Hepatic encephalopathy is a common complication encountered in patients with liver cirrhosis. Hepatic encephalopathy is not reflected in the current liver transplant allocation system. Correlation was sought between hepatic encephalopathy with findings detected on radiographic imaging studies and the patient's clinical profile. A retrospective analysis was conducted of patients with cirrhosis, who presented for liver transplant evaluation in 2009 and 2010. Patients with hepatocellular carcinoma, ejection fraction less than 60% and who had a TIPS (transjugular intrahepatic portosystemic shunting) procedure or who did not complete the evaluation were excluded. Statistical analysis was performed and variables found to be significant on univariate analysis (P < 0.05) were analysed by a multivariate logistic regression model. A total of 117 patients met the inclusion criteria and were divided into a hepatic encephalopathy group (n = 58) and a control group (n = 59). Univariate analysis found that a smaller portal vein diameter, smaller liver antero-posterior diameter, liver nodularity and use of diuretics or centrally acting medications showed significant correlation with hepatic encephalopathy. This association was confirmed for smaller portal vein, use of diuretics and centrally acting medications in the multivariate analysis. A decrease in portal vein diameter was associated with increased risk of encephalopathy. Identifying patients with smaller portal vein diameter may warrant screening for encephalopathy by more advanced psychometric testing, and more aggressive control of constipation and other factors that may precipitate encephalopathy. © The Author(s) 2015. Published by Oxford University Press and the Digestive Science Publishing Co. Limited.

Unresectable Hepatocellular Carcinoma: MR Imaging after Intraarterial Therapy. Part I. Identification and Validation of Volumetric Functional Response Criteria

PubMed Central

Bonekamp, Susanne; Li, Zhen; Geschwind, Jean-François H.; Halappa, Vivek Gowdra; Corona-Villalobos, Celia Pamela; Reyes, Diane; Pawlik, Timothy M.; Bonekamp, David; Eng, John

2013-01-01

Purpose: To identify and validate the optimal thresholds for volumetric functional MR imaging response criteria to predict overall survival after intraarterial treatment (IAT) in patients with unresectable hepatocellular carcinoma (HCC). Materials and Methods: Institutional review board approval and waiver of informed consent were obtained. A total of 143 patients who had undergone MR imaging before and 3–4 weeks after the first cycle of IAT were included. MR imaging analysis of one representative HCC index lesion was performed with proprietary software after initial treatment. Subjects were randomly divided into training (n = 114 [79.7%]) and validation (n = 29 [20.3%]) data sets. Uni- and multivariate Cox models were used to determine the best cutoffs, as well as survival differences, between response groups in the validation data set. Results: Optimal cutoffs in the training data set were 23% increase in apparent diffusion coefficient (ADC) and 65% decrease in volumetric enhancement in the portal venous phase (VE). Subsequently, 25% increase in ADC and 65% decrease in VE were used to stratify patients in the validation data set. Comparison of ADC responders (n = 12 [58.6%]) with nonresponders (n = 17 [34.5%]) showed significant differences in survival (25th percentile survival, 11.2 vs 4.9 months, respectively; P = .008), as did VE responders (n = 9 [31.0%]) compared with nonresponders (n = 20 [69.0%]; 25th percentile survival, 11.5 vs 5.1 months, respectively; P = .01). Stratification of patients with a combination of the criteria resulted in significant differences in survival between patients with lesions that fulfilled both criteria (n = 6 [20.7%]; too few cases to determine 25th percentile), one criterion (n = 9 [31.0%]; 25th percentile survival, 6.0 months), and neither criterion (n = 14 [48.3%]; 25th percentile survival, 5.1 months; P = .01). The association between the two criteria and overall survival remained significant in a multivariate analysis that included age, sex, Barcelona Clinic for Liver Cancer stage, and number of follow-up treatments. Conclusion: After IAT for unresectable HCC, patients can be stratified into significantly different survival categories based on responder versus nonresponder status according to MR imaging ADC and VE cutoffs. © RSNA, 2013 PMID:23616631

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.

Analysis techniques for multivariate root loci. [a tool in linear control systems

NASA Technical Reports Server (NTRS)

Thompson, P. M.; Stein, G.; Laub, A. J.

1980-01-01

Analysis and techniques are developed for the multivariable root locus and the multivariable optimal root locus. The generalized eigenvalue problem is used to compute angles and sensitivities for both types of loci, and an algorithm is presented that determines the asymptotic properties of the optimal root locus.

Methods for presentation and display of multivariate data

NASA Technical Reports Server (NTRS)

Myers, R. H.

1981-01-01

Methods for the presentation and display of multivariate data are discussed with emphasis placed on the multivariate analysis of variance problems and the Hotelling T(2) solution in the two-sample case. The methods utilize the concepts of stepwise discrimination analysis and the computation of partial correlation coefficients.

A Primer on Multivariate Analysis of Variance (MANOVA) for Behavioral Scientists

ERIC Educational Resources Information Center

Warne, Russell T.

2014-01-01

Reviews of statistical procedures (e.g., Bangert & Baumberger, 2005; Kieffer, Reese, & Thompson, 2001; Warne, Lazo, Ramos, & Ritter, 2012) show that one of the most common multivariate statistical methods in psychological research is multivariate analysis of variance (MANOVA). However, MANOVA and its associated procedures are often not…

Identification of trace additives in polymer materials by attenuated total reflection Fourier transform infrared mapping coupled with multivariate curve resolution

NASA Astrophysics Data System (ADS)

Li, Qian; Tang, Yongjiao; Yan, Zhiwei; Zhang, Pudun

2017-06-01

Although multivariate curve resolution (MCR) has been applied to the analysis of Fourier transform infrared (FTIR) imaging, it is still problematic to determine the number of components. The reported methods at present tend to cause the components of low concentration missed. In this paper a new idea was proposed to resolve this problem. First, MCR calculation was repeated by increasing the number of components sequentially, then each retrieved pure spectrum of as-resulted MCR component was directly compared with a real-world pixel spectrum of the local high concentration in the corresponding MCR map. One component was affirmed only if the characteristic bands of the MCR component had been included in its pixel spectrum. This idea was applied to attenuated total reflection (ATR)/FTIR mapping for identifying the trace additives in blind polymer materials and satisfactory results were acquired. The successful demonstration of this novel approach opens up new possibilities for analyzing additives in polymer materials.

Comparison of digital imaging screening and indirect ophthalmoscopy for retinopathy of prematurity.

PubMed

Ezz El Din, Zahraa Mohamed; El Sada, Mohamed Ahmed; Ali, Aliaa Adel; Al Husseiny, Khalid; Yousef, Aly Abdel Rahman

2015-01-01

The aims of this study were to determine the incidence and severity of retinopathy of prematurity (ROP) using digital imaging screening, confirm findings by indirect opthalmoscopy, and document risk factors of ROP in the neonatal intensive care unit (NICU) of a large tertiary hospital in a developing country. This prospective cohort study included infants with gestational age (GA) ≤ 32 wk, birth weight (BW) ≤ 1,500 g, or older and heavier neonates who were critically ill. Two hundred twenty two eyes (111 infants) were screened with digital imaging (Ret-Cam) and indirect ophthalmoscopy until retinal vascularization was complete or the disease regressed. Perinatal risk factors for ROP were analyzed. The overall incidence of ROP was 18.9 %. The incidence of ROP requiring treatment was 5.4 % (12/222) of the total eyes screened. Lower GA and blood transfusion were independent risk factors associated with ROP by multivariate analysis (p = 0.001, OR = 0.562, 95 % CI = 0.395-0.802, and p = 0.027, OR = 6.11, 95 % CI = 1.22-30.44, respectively). Digital imaging facilitated timely screening and detection of ROP, and enabled transfer of images, allowing early intervention for patients who required treatment.

Feature-based fusion of medical imaging data.

PubMed

Calhoun, Vince D; Adali, Tülay

2009-09-01

The acquisition of multiple brain imaging types for a given study is a very common practice. There have been a number of approaches proposed for combining or fusing multitask or multimodal information. These can be roughly divided into those that attempt to study convergence of multimodal imaging, for example, how function and structure are related in the same region of the brain, and those that attempt to study the complementary nature of modalities, for example, utilizing temporal EEG information and spatial functional magnetic resonance imaging information. Within each of these categories, one can attempt data integration (the use of one imaging modality to improve the results of another) or true data fusion (in which multiple modalities are utilized to inform one another). We review both approaches and present a recent computational approach that first preprocesses the data to compute features of interest. The features are then analyzed in a multivariate manner using independent component analysis. We describe the approach in detail and provide examples of how it has been used for different fusion tasks. We also propose a method for selecting which combination of modalities provides the greatest value in discriminating groups. Finally, we summarize and describe future research topics.

Integrated analysis of dynamic FET PET/CT parameters, histology, and methylation profiling of 44 gliomas.

PubMed

Röhrich, Manuel; Huang, Kristin; Schrimpf, Daniel; Albert, Nathalie L; Hielscher, Thomas; von Deimling, Andreas; Schüller, Ulrich; Dimitrakopoulou-Strauss, Antonia; Haberkorn, Uwe

2018-05-07

Dynamic 18 F-FET PET/CT is a powerful tool for the diagnosis of gliomas. 18 F-FET PET time-activity curves (TAC) allow differentiation between histological low-grade gliomas (LGG) and high-grade gliomas (HGG). Molecular methods such as epigenetic profiling are of rising importance for glioma grading and subclassification. Here, we analysed dynamic 18 F-FET PET data, and the histological and epigenetic features of 44 gliomas. Dynamic 18 F-FET PET was performed in 44 patients with newly diagnosed, untreated glioma: 10 WHO grade II glioma, 13 WHO grade III glioma and 21 glioblastoma (GBM). All patients underwent stereotactic biopsy or tumour resection after 18 F-FET PET imaging. As well as histological analysis of tissue samples, DNA was subjected to epigenetic analysis using the Illumina 850 K methylation array. TACs, standardized uptake values corrected for background uptake in healthy tissue (SUVmax/BG), time to peak (TTP) and kinetic modelling parameters were correlated with histological diagnoses and with epigenetic signatures. Multivariate analyses were performed to evaluate the diagnostic accuracy of 18 F-FET PET in relation to the tumour groups identified by histological and methylation-based analysis. Epigenetic profiling led to substantial tumour reclassification, with six grade II/III gliomas reclassified as GBM. Overlap of HGG-typical TACs and LGG-typical TACs was dramatically reduced when tumours were clustered on the basis of their methylation profile. SUVmax/BG values of GBM were higher than those of LGGs following both histological diagnosis and methylation-based diagnosis. The differences in TTP between GBMs and grade II/III gliomas were greater following methylation-based diagnosis than following histological diagnosis. Kinetic modeling showed that relative K1 and fractal dimension (FD) values significantly differed in histology- and methylation-based GBM and grade II/III glioma between those diagnosed histologically and those diagnosed by methylation analysis. Multivariate analysis revealed slightly greater diagnostic accuracy with methylation-based diagnosis. IDH-mutant gliomas and GBM subgroups tended to differ in their 18 F-FET PET kinetics. The status of dynamic 18 F-FET PET as a biologically and clinically relevant imaging modality is confirmed in the context of molecular glioma diagnosis.

Multivariate and repeated measures (MRM): A new toolbox for dependent and multimodal group-level neuroimaging data

PubMed Central

McFarquhar, Martyn; McKie, Shane; Emsley, Richard; Suckling, John; Elliott, Rebecca; Williams, Stephen

2016-01-01

Repeated measurements and multimodal data are common in neuroimaging research. Despite this, conventional approaches to group level analysis ignore these repeated measurements in favour of multiple between-subject models using contrasts of interest. This approach has a number of drawbacks as certain designs and comparisons of interest are either not possible or complex to implement. Unfortunately, even when attempting to analyse group level data within a repeated-measures framework, the methods implemented in popular software packages make potentially unrealistic assumptions about the covariance structure across the brain. In this paper, we describe how this issue can be addressed in a simple and efficient manner using the multivariate form of the familiar general linear model (GLM), as implemented in a new MATLAB toolbox. This multivariate framework is discussed, paying particular attention to methods of inference by permutation. Comparisons with existing approaches and software packages for dependent group-level neuroimaging data are made. We also demonstrate how this method is easily adapted for dependency at the group level when multiple modalities of imaging are collected from the same individuals. Follow-up of these multimodal models using linear discriminant functions (LDA) is also discussed, with applications to future studies wishing to integrate multiple scanning techniques into investigating populations of interest. PMID:26921716

Multivariate Analysis and Machine Learning in Cerebral Palsy Research

PubMed Central

Zhang, Jing

2017-01-01

Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP. PMID:29312134

Multivariate Analysis and Machine Learning in Cerebral Palsy Research.

PubMed

Zhang, Jing

2017-01-01

Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.

Quality by design case study: an integrated multivariate approach to drug product and process development.

PubMed

Huang, Jun; Kaul, Goldi; Cai, Chunsheng; Chatlapalli, Ramarao; Hernandez-Abad, Pedro; Ghosh, Krishnendu; Nagi, Arwinder

2009-12-01

To facilitate an in-depth process understanding, and offer opportunities for developing control strategies to ensure product quality, a combination of experimental design, optimization and multivariate techniques was integrated into the process development of a drug product. A process DOE was used to evaluate effects of the design factors on manufacturability and final product CQAs, and establish design space to ensure desired CQAs. Two types of analyses were performed to extract maximal information, DOE effect & response surface analysis and multivariate analysis (PCA and PLS). The DOE effect analysis was used to evaluate the interactions and effects of three design factors (water amount, wet massing time and lubrication time), on response variables (blend flow, compressibility and tablet dissolution). The design space was established by the combined use of DOE, optimization and multivariate analysis to ensure desired CQAs. Multivariate analysis of all variables from the DOE batches was conducted to study relationships between the variables and to evaluate the impact of material attributes/process parameters on manufacturability and final product CQAs. The integrated multivariate approach exemplifies application of QbD principles and tools to drug product and process development.

Advances in fMRI Real-Time Neurofeedback.

PubMed

Watanabe, Takeo; Sasaki, Yuka; Shibata, Kazuhisa; Kawato, Mitsuo

2017-12-01

Functional magnetic resonance imaging (fMRI) neurofeedback is a type of biofeedback in which real-time online fMRI signals are used to self-regulate brain function. Since its advent in 2003 significant progress has been made in fMRI neurofeedback techniques. Specifically, the use of implicit protocols, external rewards, multivariate analysis, and connectivity analysis has allowed neuroscientists to explore a possible causal involvement of modified brain activity in modified behavior. These techniques have also been integrated into groundbreaking new neurofeedback technologies, specifically decoded neurofeedback (DecNef) and functional connectivity-based neurofeedback (FCNef). By modulating neural activity and behavior, DecNef and FCNef have substantially advanced both basic and clinical research. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Visual Search with Image Modification in Age-Related Macular Degeneration

PubMed Central

Wiecek, Emily; Jackson, Mary Lou; Dakin, Steven C.; Bex, Peter

2012-01-01

Purpose. AMD results in loss of central vision and a dependence on low-resolution peripheral vision. While many image enhancement techniques have been proposed, there is a lack of quantitative comparison of the effectiveness of enhancement. We developed a natural visual search task that uses patients' eye movements as a quantitative and functional measure of the efficacy of image modification. Methods. Eye movements of 17 patients (mean age = 77 years) with AMD were recorded while they searched for target objects in natural images. Eight different image modification methods were implemented and included manipulations of local image or edge contrast, color, and crowding. In a subsequent task, patients ranked their preference of the image modifications. Results. Within individual participants, there was no significant difference in search duration or accuracy across eight different image manipulations. When data were collapsed across all image modifications, a multivariate model identified six significant predictors for normalized search duration including scotoma size and acuity, as well as interactions among scotoma size, age, acuity, and contrast (P < 0.05). Additionally, an analysis of image statistics showed no correlation with search performance across all image modifications. Rank ordering of enhancement methods based on participants' preference revealed a trend that participants preferred the least modified images (P < 0.05). Conclusions. There was no quantitative effect of image modification on search performance. A better understanding of low- and high-level components of visual search in natural scenes is necessary to improve future attempts at image enhancement for low vision patients. Different search tasks may require alternative image modifications to improve patient functioning and performance. PMID:22930725

Gas-induced susceptibility artefacts on diffusion-weighted MRI of the rectum at 1.5 T - Effect of applying a micro-enema to improve image quality.

PubMed

van Griethuysen, Joost J M; Bus, Elyse M; Hauptmann, Michael; Lahaye, Max J; Maas, Monique; Ter Beek, Leon C; Beets, Geerard L; Bakers, Frans C H; Beets-Tan, Regina G H; Lambregts, Doenja M J

2018-02-01

Assess whether application of a micro-enema can reduce gas-induced susceptibility artefacts in Single-shot Echo Planar Imaging (EPI) Diffusion-weighted imaging of the rectum at 1.5 T. Retrospective analysis of n = 50 rectal cancer patients who each underwent multiple DWI-MRIs (1.5 T) from 2012 to 2016 as part of routine follow-up during a watch-and-wait approach after chemoradiotherapy. From March 2014 DWI-MRIs were routinely acquired after application of a preparatory micro-enema (Microlax ® ; 5 ml; self-administered shortly before acquisition); before March 2014 no bowel preparation was given. In total, 335 scans were scored by an experienced reader for the presence/severity of gas-artefacts (on b1000 DWI), ranging from 0 (no artefact) to 5 (severe artefact). A score ≥3 (moderate-severe) was considered a clinically relevant artefact. A random sample of 100 scans was re-assessed by a second independent reader to study inter-observer effects. Scores were compared between the scans performed without and with a preparatory micro-enema using univariable and multivariable logistic regression taking into account potential confounding factors (age/gender, acquisition parameters, MRI-hardware, rectoscopy prior to MRI). Clinically relevant gas-artefacts were seen in 24.3% (no micro-enema) vs. 3.7% (micro-enema), odds ratios were 0.118 in univariable and 0.230 in multivariable regression (P = 0.0005 and 0.0291). Mean severity score (±SD) was 1.19 ± 1.71 (no-enema) vs 0.32 ± 0.77 (micro-enema), odds ratios were 0.321 (P < 0.0001) and 0.489 (P = 0.0461) in uni- and multivariable regression, respectively. Inter-observer agreement was excellent (κ0.85). Use of a preparatory micro-enema shortly before rectal EPI-DWI examinations performed at 1.5 T MRI significantly reduces both the incidence and severity of gas-induced artefacts, compared to examinations performed without bowel preparation. Copyright © 2017 Elsevier B.V. All rights reserved.

Estimating an Effect Size in One-Way Multivariate Analysis of Variance (MANOVA)

ERIC Educational Resources Information Center

Steyn, H. S., Jr.; Ellis, S. M.

2009-01-01

When two or more univariate population means are compared, the proportion of variation in the dependent variable accounted for by population group membership is eta-squared. This effect size can be generalized by using multivariate measures of association, based on the multivariate analysis of variance (MANOVA) statistics, to establish whether…

Dangers in Using Analysis of Covariance Procedures.

ERIC Educational Resources Information Center

Campbell, Kathleen T.

Problems associated with the use of analysis of covariance (ANCOVA) as a statistical control technique are explained. Three problems relate to the use of "OVA" methods (analysis of variance, analysis of covariance, multivariate analysis of variance, and multivariate analysis of covariance) in general. These are: (1) the wasting of information when…

Fluorescence guided evaluation of photodynamic therapy as acne treatment

NASA Astrophysics Data System (ADS)

Ericson, Marica B.; Horfelt, Camilla; Cheng, Elaine; Larsson, Frida; Larko, Olle; Wennberg, Ann-Marie

2005-08-01

Photodynamic therapy (PDT) is an attractive alternative treatment for patients with acne because of its efficiency and few side effects. Propionibacterium acnes (P.acnes) are bacteria present in the skin, which produce endogenous porphyrins that act as photosensitisers. In addition, application of aminolaevulinic acid or its methyl ester (mALA) results in increased accumulation of porphyrins in the pilosebaceous units. This makes it possible to treat acne with PDT. This initial study investigates the possibility of fluorescence imaging as assessment tool in adjunct to PDT of patients with acne. Twenty-four patients with acne on the cheeks have been treated with PDT with and without mALA. Fluorescence images have been obtained before and after treatment. The clinical acne score was assessed as base line before PDT, and at every follow up visit. Additionally the amount of P.acnes was determined. The clinical evaluation showed a general improvement of acne, even though no difference between treatment with and without mALA was observed. By performing texture analysis and multivariate data analsysis on the fluorescence images, the extracted texture features were found to correlate with the corresponding clinical assessment (67%) and amount of P.acnes (72%). The analysis showed that features describing the highly fluorescent pores could be related to the clinical assessment. This result suggests that fluorescence imaging can be used as an objective assessment of acne, but further improvement of the technique is possible, for example by including colour images.

Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data

PubMed Central

Havlicek, Martin; Jan, Jiri; Brazdil, Milan; Calhoun, Vince D.

2015-01-01

Increasing interest in understanding dynamic interactions of brain neural networks leads to formulation of sophisticated connectivity analysis methods. Recent studies have applied Granger causality based on standard multivariate autoregressive (MAR) modeling to assess the brain connectivity. Nevertheless, one important flaw of this commonly proposed method is that it requires the analyzed time series to be stationary, whereas such assumption is mostly violated due to the weakly nonstationary nature of functional magnetic resonance imaging (fMRI) time series. Therefore, we propose an approach to dynamic Granger causality in the frequency domain for evaluating functional network connectivity in fMRI data. The effectiveness and robustness of the dynamic approach was significantly improved by combining a forward and backward Kalman filter that improved estimates compared to the standard time-invariant MAR modeling. In our method, the functional networks were first detected by independent component analysis (ICA), a computational method for separating a multivariate signal into maximally independent components. Then the measure of Granger causality was evaluated using generalized partial directed coherence that is suitable for bivariate as well as multivariate data. Moreover, this metric provides identification of causal relation in frequency domain, which allows one to distinguish the frequency components related to the experimental paradigm. The procedure of evaluating Granger causality via dynamic MAR was demonstrated on simulated time series as well as on two sets of group fMRI data collected during an auditory sensorimotor (SM) or auditory oddball discrimination (AOD) tasks. Finally, a comparison with the results obtained from a standard time-invariant MAR model was provided. PMID:20561919

Multivariate analyses applied to fetal, neonatal and pediatric MRI of neurodevelopmental disorders

PubMed Central

Levman, Jacob; Takahashi, Emi

2015-01-01

Multivariate analysis (MVA) is a class of statistical and pattern recognition methods that involve the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of medical neuroimaging-related challenges including identifying variables associated with a measure of clinical importance (i.e. patient outcome), creating diagnostic tests, assisting in characterizing developmental disorders, understanding disease etiology, development and progression, assisting in treatment monitoring and much more. Compared to adults, imaging of developing immature brains has attracted less attention from MVA researchers. However, remarkable MVA research growth has occurred in recent years. This paper presents the results of a systematic review of the literature focusing on MVA technologies applied to neurodevelopmental disorders in fetal, neonatal and pediatric magnetic resonance imaging (MRI) of the brain. The goal of this manuscript is to provide a concise review of the state of the scientific literature on studies employing brain MRI and MVA in a pre-adult population. Neurological developmental disorders addressed in the MVA research contained in this review include autism spectrum disorder, attention deficit hyperactivity disorder, epilepsy, schizophrenia and more. While the results of this review demonstrate considerable interest from the scientific community in applications of MVA technologies in pediatric/neonatal/fetal brain MRI, the field is still young and considerable research growth remains ahead of us. PMID:26640765

Multivariate analyses applied to fetal, neonatal and pediatric MRI of neurodevelopmental disorders.

PubMed

Levman, Jacob; Takahashi, Emi

2015-01-01

Multivariate analysis (MVA) is a class of statistical and pattern recognition methods that involve the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of medical neuroimaging-related challenges including identifying variables associated with a measure of clinical importance (i.e. patient outcome), creating diagnostic tests, assisting in characterizing developmental disorders, understanding disease etiology, development and progression, assisting in treatment monitoring and much more. Compared to adults, imaging of developing immature brains has attracted less attention from MVA researchers. However, remarkable MVA research growth has occurred in recent years. This paper presents the results of a systematic review of the literature focusing on MVA technologies applied to neurodevelopmental disorders in fetal, neonatal and pediatric magnetic resonance imaging (MRI) of the brain. The goal of this manuscript is to provide a concise review of the state of the scientific literature on studies employing brain MRI and MVA in a pre-adult population. Neurological developmental disorders addressed in the MVA research contained in this review include autism spectrum disorder, attention deficit hyperactivity disorder, epilepsy, schizophrenia and more. While the results of this review demonstrate considerable interest from the scientific community in applications of MVA technologies in pediatric/neonatal/fetal brain MRI, the field is still young and considerable research growth remains ahead of us.

Highly sensitive time-resolved thermography and multivariate image analysis of the cerebral cortex for intrasurgical diagnostics

NASA Astrophysics Data System (ADS)

Hollmach, Julia; Hoffmann, Nico; Schnabel, Christian; Küchler, Saskia; Sobottka, Stephan; Kirsch, Matthias; Schackert, Gabriele; Koch, Edmund; Steiner, Gerald

2013-03-01

Time-resolved thermography is a novel method to assess thermal variations and heterogeneities in tissue and blood. The recent generation of thermal cameras provides a sensitivity of less than mK. This high sensitivity in conjunction with non-invasive, label-free and radiation-free monitoring makes thermography a promising tool for intrasurgical diagnostics. In brain surgery, time-resolved thermography can be employed to distinguish between normal and anomalous tissue. In this study, we investigated and discussed the potential of time-resolved thermography in neurosurgery for the intraoperative detection and demarcation of tumor borders. Algorithms for segmentation, reduction of movement artifacts and image fusion were developed. The preprocessed image stacks were subjected to discrete wavelet transform to examine individual frequency components. K-means clustering was used for image evaluation to reveal similarities within the image sequence. The image evaluation shows significant differences for both types of tissue. Tumor and normal tissues have different time characteristics in heat production and transfer. Furthermore, tumor could be highlighted. These results demonstrate that time-resolved thermography is able to support the detection of tumors in a contactless manner without any side effects for the tissue. The intraoperative usage of time-resolved thermography improves the accuracy of tumor resections to prevent irreversible brain damage during surgery.

MIDAS: Regionally linear multivariate discriminative statistical mapping.

PubMed

Varol, Erdem; Sotiras, Aristeidis; Davatzikos, Christos

2018-07-01

Statistical parametric maps formed via voxel-wise mass-univariate tests, such as the general linear model, are commonly used to test hypotheses about regionally specific effects in neuroimaging cross-sectional studies where each subject is represented by a single image. Despite being informative, these techniques remain limited as they ignore multivariate relationships in the data. Most importantly, the commonly employed local Gaussian smoothing, which is important for accounting for registration errors and making the data follow Gaussian distributions, is usually chosen in an ad hoc fashion. Thus, it is often suboptimal for the task of detecting group differences and correlations with non-imaging variables. Information mapping techniques, such as searchlight, which use pattern classifiers to exploit multivariate information and obtain more powerful statistical maps, have become increasingly popular in recent years. However, existing methods may lead to important interpretation errors in practice (i.e., misidentifying a cluster as informative, or failing to detect truly informative voxels), while often being computationally expensive. To address these issues, we introduce a novel efficient multivariate statistical framework for cross-sectional studies, termed MIDAS, seeking highly sensitive and specific voxel-wise brain maps, while leveraging the power of regional discriminant analysis. In MIDAS, locally linear discriminative learning is applied to estimate the pattern that best discriminates between two groups, or predicts a variable of interest. This pattern is equivalent to local filtering by an optimal kernel whose coefficients are the weights of the linear discriminant. By composing information from all neighborhoods that contain a given voxel, MIDAS produces a statistic that collectively reflects the contribution of the voxel to the regional classifiers as well as the discriminative power of the classifiers. Critically, MIDAS efficiently assesses the statistical significance of the derived statistic by analytically approximating its null distribution without the need for computationally expensive permutation tests. The proposed framework was extensively validated using simulated atrophy in structural magnetic resonance imaging (MRI) and further tested using data from a task-based functional MRI study as well as a structural MRI study of cognitive performance. The performance of the proposed framework was evaluated against standard voxel-wise general linear models and other information mapping methods. The experimental results showed that MIDAS achieves relatively higher sensitivity and specificity in detecting group differences. Together, our results demonstrate the potential of the proposed approach to efficiently map effects of interest in both structural and functional data. Copyright © 2018. Published by Elsevier Inc.

A methodology for investigating interdependencies between measured throughfall, meteorological variables and canopy structure on a small catchment.

NASA Astrophysics Data System (ADS)

Maurer, Thomas; Gustavos Trujillo Siliézar, Carlos; Oeser, Anne; Pohle, Ina; Hinz, Christoph

2016-04-01

In evolving initial landscapes, vegetation development depends on a variety of feedback effects. One of the less understood feedback loops is the interaction between throughfall and plant canopy development. The amount of throughfall is governed by the characteristics of the vegetation canopy, whereas vegetation pattern evolution may in turn depend on the spatio-temporal distribution of throughfall. Meteorological factors that may influence throughfall, while at the same time interacting with the canopy, are e.g. wind speed, wind direction and rainfall intensity. Our objective is to investigate how throughfall, vegetation canopy and meteorological variables interact in an exemplary eco-hydrological system in its initial development phase, in which the canopy is very heterogeneous and rapidly changing. For that purpose, we developed a methodological approach combining field methods, raster image analysis and multivariate statistics. The research area for this study is the Hühnerwasser ('Chicken Creek') catchment in Lower Lusatia, Brandenburg, Germany, where after eight years of succession, the spatial distribution of plant species is highly heterogeneous, leading to increasingly differentiated throughfall patterns. The constructed 6-ha catchment offers ideal conditions for our study due to the rapidly changing vegetation structure and the availability of complementary monitoring data. Throughfall data were obtained by 50 tipping bucket rain gauges arranged in two transects and connected via a wireless sensor network that cover the predominant vegetation types on the catchment (locust copses, dense sallow thorn bushes and reeds, base herbaceous and medium-rise small-reed vegetation, and open areas covered by moss and lichens). The spatial configuration of the vegetation canopy for each measurement site was described via digital image analysis of hemispheric photographs of the canopy using the ArcGIS Spatial Analyst, GapLight and ImageJ software. Meteorological data from two on-site weather stations (wind direction, wind speed, air temperature, air humidity, insolation, soil temperature, precipitation) were provided by the 'Research Platform Chicken Creek' (https://www.tu-cottbus.de/projekte/en/oekosysteme/startseite.html). Data were combined and multivariate statistical analysis (PCA, cluster analysis, regression trees) were conducted using the R-software to i) obtain statistical indices describing the relevant characteristics of the data and ii) to identify the determining factors for throughfall intensity. The methodology is currently tested and results will be presented. Preliminary evaluation of the image analysis approach showed only marginal, systematic deviation of results for the different software tools applied, which makes the developed workflow a viable tool for canopy characterization. Results from this study will have a broad spectrum of possible applications, for instance the development / calibration of rainfall interception models, the incorporation into eco-hydrological models, or to test the fault tolerance of wireless rainfall sensor networks.

Global spectral graph wavelet signature for surface analysis of carpal bones

NASA Astrophysics Data System (ADS)

Masoumi, Majid; Rezaei, Mahsa; Ben Hamza, A.

2018-02-01

Quantitative shape comparison is a fundamental problem in computer vision, geometry processing and medical imaging. In this paper, we present a spectral graph wavelet approach for shape analysis of carpal bones of the human wrist. We employ spectral graph wavelets to represent the cortical surface of a carpal bone via the spectral geometric analysis of the Laplace-Beltrami operator in the discrete domain. We propose global spectral graph wavelet (GSGW) descriptor that is isometric invariant, efficient to compute, and combines the advantages of both low-pass and band-pass filters. We perform experiments on shapes of the carpal bones of ten women and ten men from a publicly-available database of wrist bones. Using one-way multivariate analysis of variance (MANOVA) and permutation testing, we show through extensive experiments that the proposed GSGW framework gives a much better performance compared to the global point signature embedding approach for comparing shapes of the carpal bones across populations.

Global spectral graph wavelet signature for surface analysis of carpal bones.

PubMed

Masoumi, Majid; Rezaei, Mahsa; Ben Hamza, A

2018-02-05

Quantitative shape comparison is a fundamental problem in computer vision, geometry processing and medical imaging. In this paper, we present a spectral graph wavelet approach for shape analysis of carpal bones of the human wrist. We employ spectral graph wavelets to represent the cortical surface of a carpal bone via the spectral geometric analysis of the Laplace-Beltrami operator in the discrete domain. We propose global spectral graph wavelet (GSGW) descriptor that is isometric invariant, efficient to compute, and combines the advantages of both low-pass and band-pass filters. We perform experiments on shapes of the carpal bones of ten women and ten men from a publicly-available database of wrist bones. Using one-way multivariate analysis of variance (MANOVA) and permutation testing, we show through extensive experiments that the proposed GSGW framework gives a much better performance compared to the global point signature embedding approach for comparing shapes of the carpal bones across populations.

M-DAS: System for multispectral data analysis. [in Saginaw Bay, Michigan

NASA Technical Reports Server (NTRS)

Johnson, R. H.

1975-01-01

M-DAS is a ground data processing system designed for analysis of multispectral data. M-DAS operates on multispectral data from LANDSAT, S-192, M2S and other sources in CCT form. Interactive training by operator-investigators using a variable cursor on a color display was used to derive optimum processing coefficients and data on cluster separability. An advanced multivariate normal-maximum likelihood processing algorithm was used to produce output in various formats: color-coded film images, geometrically corrected map overlays, moving displays of scene sections, coverage tabulations and categorized CCTs. The analysis procedure for M-DAS involves three phases: (1) screening and training, (2) analysis of training data to compute performance predictions and processing coefficients, and (3) processing of multichannel input data into categorized results. Typical M-DAS applications involve iteration between each of these phases. A series of photographs of the M-DAS display are used to illustrate M-DAS operation.

Genetic Particle Swarm Optimization–Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection

PubMed Central

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-01-01

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm. PMID:27483285

A correlative and quantitative imaging approach enabling characterization of primary cell-cell communication: Case of human CD4+ T cell-macrophage immunological synapses.

PubMed

Kasprowicz, Richard; Rand, Emma; O'Toole, Peter J; Signoret, Nathalie

2018-05-22

Cell-to-cell communication engages signaling and spatiotemporal reorganization events driven by highly context-dependent and dynamic intercellular interactions, which are difficult to capture within heterogeneous primary cell cultures. Here, we present a straightforward correlative imaging approach utilizing commonly available instrumentation to sample large numbers of cell-cell interaction events, allowing qualitative and quantitative characterization of rare functioning cell-conjugates based on calcium signals. We applied this approach to examine a previously uncharacterized immunological synapse, investigating autologous human blood CD4 + T cells and monocyte-derived macrophages (MDMs) forming functional conjugates in vitro. Populations of signaling conjugates were visualized, tracked and analyzed by combining live imaging, calcium recording and multivariate statistical analysis. Correlative immunofluorescence was added to quantify endogenous molecular recruitments at the cell-cell junction. By analyzing a large number of rare conjugates, we were able to define calcium signatures associated with different states of CD4 + T cell-MDM interactions. Quantitative image analysis of immunostained conjugates detected the propensity of endogenous T cell surface markers and intracellular organelles to polarize towards cell-cell junctions with high and sustained calcium signaling profiles, hence defining immunological synapses. Overall, we developed a broadly applicable approach enabling detailed single cell- and population-based investigations of rare cell-cell communication events with primary cells.

Multivariate Analysis of Genotype-Phenotype Association.

PubMed

Mitteroecker, Philipp; Cheverud, James M; Pavlicev, Mihaela

2016-04-01

With the advent of modern imaging and measurement technology, complex phenotypes are increasingly represented by large numbers of measurements, which may not bear biological meaning one by one. For such multivariate phenotypes, studying the pairwise associations between all measurements and all alleles is highly inefficient and prevents insight into the genetic pattern underlying the observed phenotypes. We present a new method for identifying patterns of allelic variation (genetic latent variables) that are maximally associated-in terms of effect size-with patterns of phenotypic variation (phenotypic latent variables). This multivariate genotype-phenotype mapping (MGP) separates phenotypic features under strong genetic control from less genetically determined features and thus permits an analysis of the multivariate structure of genotype-phenotype association, including its dimensionality and the clustering of genetic and phenotypic variables within this association. Different variants of MGP maximize different measures of genotype-phenotype association: genetic effect, genetic variance, or heritability. In an application to a mouse sample, scored for 353 SNPs and 11 phenotypic traits, the first dimension of genetic and phenotypic latent variables accounted for >70% of genetic variation present in all 11 measurements; 43% of variation in this phenotypic pattern was explained by the corresponding genetic latent variable. The first three dimensions together sufficed to account for almost 90% of genetic variation in the measurements and for all the interpretable genotype-phenotype association. Each dimension can be tested as a whole against the hypothesis of no association, thereby reducing the number of statistical tests from 7766 to 3-the maximal number of meaningful independent tests. Important alleles can be selected based on their effect size (additive or nonadditive effect on the phenotypic latent variable). This low dimensionality of the genotype-phenotype map has important consequences for gene identification and may shed light on the evolvability of organisms. Copyright © 2016 by the Genetics Society of America.

Emergency department imaging: are weather and calendar factors associated with imaging volume?

PubMed

Burns, K; Chernyak, V; Scheinfeld, M H

2016-12-01

To identify weather and calendar factors that would enable prediction of daily emergency department (ED) imaging volume to aid appropriate scheduling of imaging resources for efficient ED function. Daily ED triage and imaging volumes for radiography, computed tomography (CT), and ultrasound were obtained from hospital databases for the period between January 2011 and December 2013 at a large tertiary urban hospital with a Level II trauma centre. These data were tabulated alongside daily weather conditions (temperature, wind and precipitation), day of week, season, and holidays. Multivariate analysis was performed. Pearson correlations were used to measure the association between number of imaging studies performed and ED triage volume. For every additional 50 triaged patients, the odds of having high (imaging volume ≥90th percentile) radiography, CT, and ultrasound volume increased by 4.3 times (p<0.001), 1.5 times (p=0.02), and 1.4 times (p=0.02), respectively. Tuesday was an independent predictor of high radiography volume (odds ratio=2.8) and Monday was an independent predictor of high CT volume (odds ratio=3.0). Weekday status was an independent factor increasing the odds of a high US volume compared to Saturday (odds ratios ranging from 5.6-9.8). Weather factors and other calendar variables were not independent predictors of high imaging volume. Using Pearson correlations, ED triage volume correlated with number of radiographs, CT, and ultrasound examinations with r=0.73, 0.37, and 0.41, respectively (p<0.0001). As ED triage volume was found to be the only factor associated with imaging volume for all techniques, analysis of predictors of ED triage volumes at a particular healthcare facility would be useful to determine imaging needs. Although calendar and weather factors were found to be minor or non-significant independent predictors of ED imaging utilisation, these may be important in influencing the actual number of ED triages. Copyright © 2016 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Automated processing of label-free Raman microscope images of macrophage cells with standardized regression for high-throughput analysis.

PubMed

Milewski, Robert J; Kumagai, Yutaro; Fujita, Katsumasa; Standley, Daron M; Smith, Nicholas I

2010-11-19

Macrophages represent the front lines of our immune system; they recognize and engulf pathogens or foreign particles thus initiating the immune response. Imaging macrophages presents unique challenges, as most optical techniques require labeling or staining of the cellular compartments in order to resolve organelles, and such stains or labels have the potential to perturb the cell, particularly in cases where incomplete information exists regarding the precise cellular reaction under observation. Label-free imaging techniques such as Raman microscopy are thus valuable tools for studying the transformations that occur in immune cells upon activation, both on the molecular and organelle levels. Due to extremely low signal levels, however, Raman microscopy requires sophisticated image processing techniques for noise reduction and signal extraction. To date, efficient, automated algorithms for resolving sub-cellular features in noisy, multi-dimensional image sets have not been explored extensively. We show that hybrid z-score normalization and standard regression (Z-LSR) can highlight the spectral differences within the cell and provide image contrast dependent on spectral content. In contrast to typical Raman imaging processing methods using multivariate analysis, such as single value decomposition (SVD), our implementation of the Z-LSR method can operate nearly in real-time. In spite of its computational simplicity, Z-LSR can automatically remove background and bias in the signal, improve the resolution of spatially distributed spectral differences and enable sub-cellular features to be resolved in Raman microscopy images of mouse macrophage cells. Significantly, the Z-LSR processed images automatically exhibited subcellular architectures whereas SVD, in general, requires human assistance in selecting the components of interest. The computational efficiency of Z-LSR enables automated resolution of sub-cellular features in large Raman microscopy data sets without compromise in image quality or information loss in associated spectra. These results motivate further use of label free microscopy techniques in real-time imaging of live immune cells.

Bone marrow fat content is correlated with hepatic fat content in paediatric non-alcoholic fatty liver disease.

PubMed

Yu, N Y; Wolfson, T; Middleton, M S; Hamilton, G; Gamst, A; Angeles, J E; Schwimmer, J B; Sirlin, C B

2017-05-01

To investigate the relationship between bone marrow fat content and hepatic fat content in children with known or suspected non-alcoholic fatty liver disease (NAFLD). This was an institutional review board-approved, Health Insurance Portability and Accountability Act (HIPAA)-compliant, cross-sectional, prospective analysis of data collected between October 2010 to March 2013 in 125 children with known or suspected NAFLD. Written informed consent was obtained for same-day research magnetic resonance imaging (MRI) of the lumbar spine, liver, and abdominal adiposity. Lumbar spine bone marrow proton density fat fraction (PDFF) and hepatic PDFF were estimated using complex-based MRI (C-MRI) techniques and magnitude-based MRI (M-MRI), respectively. Visceral adipose tissue (VAT) and subcutaneous adipose tissue (SCAT) were quantified using high-resolution MRI. All images were acquired by two MRI technologists. Hepatic M-MRI images were analysed by an image analyst; all other images were analysed by a single investigator. The relationship between lumbar spine bone marrow PDFF and hepatic PDFF was assessed with and without adjusting for the presence of covariates using correlation and regression analysis. Lumbar spine bone marrow PDFF was positively associated with hepatic PDFF in children with known or suspected NAFLD prior to adjusting for covariates (r=0.33, p=0.0002). Lumbar spine bone marrow PDFF was positively associated with hepatic PDFF in children with known or suspected NAFLD (r=0.24, p=0.0079) after adjusting for age, sex, body mass index z-score, VAT, and SCAT in a multivariable regression analysis. Bone marrow fat content is positively associated with hepatic fat content in children with known or suspected NAFLD. Further research is needed to confirm these results and understand their clinical and biological implications. Copyright © 2016 The Royal College of Radiologists. All rights reserved.

Prostate cancer: role of pretreatment multiparametric 3-T MRI in predicting biochemical recurrence after radical prostatectomy.

PubMed

Park, Jung Jae; Kim, Chan Kyo; Park, Sung Yoon; Park, Byung Kwan; Lee, Hyun Moo; Cho, Seong Whi

2014-05-01

The purpose of this study is to retrospectively investigate whether pretreatment multiparametric MRI findings can predict biochemical recurrence in patients who underwent radical prostatectomy (RP) for localized prostate cancer. In this study, 282 patients with biopsy-proven prostate cancer who received RP underwent pretreatment MRI using a phased-array coil at 3 T, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI (DCE-MRI). MRI variables included apparent tumor presence on combined imaging sequences, extracapsular extension, and tumor size on DWI or DCE-MRI. Clinical variables included baseline prostate-specific antigen (PSA) level, clinical stage, and Gleason score at biopsy. The relationship between clinical and imaging variables and biochemical recurrence was evaluated using Cox regression analysis. After a median follow-up of 26 months, biochemical recurrence developed in 61 patients (22%). Univariate analysis revealed that all the imaging and clinical variables were significantly associated with biochemical recurrence (p < 0.01). On multivariate analysis, however, baseline PSA level (p = 0.002), Gleason score at biopsy (p = 0.024), and apparent tumor presence on combined T2WI, DWI, and DCE-MRI (p = 0.047) were the only significant independent predictors of biochemical recurrence. Of the independent predictors, apparent tumor presence on combined T2WI, DWI, and DCE-MRI showed the highest hazard ratio (2.38) compared with baseline PSA level (hazard ratio, 1.05) and Gleason score at biopsy (hazard ratio, 1.34). The apparent tumor presence on combined T2WI, DWI, and DCE-MRI of pretreatment MRI is an independent predictor of biochemical recurrence after RP. This finding may be used to construct a predictive model for biochemical recurrence after surgery.

Quantifying Abdominal Adipose Tissue and Thigh Muscle Volume and Hepatic Proton Density Fat Fraction: Repeatability and Accuracy of an MR Imaging-based, Semiautomated Analysis Method.

PubMed

Middleton, Michael S; Haufe, William; Hooker, Jonathan; Borga, Magnus; Dahlqvist Leinhard, Olof; Romu, Thobias; Tunón, Patrik; Hamilton, Gavin; Wolfson, Tanya; Gamst, Anthony; Loomba, Rohit; Sirlin, Claude B

2017-05-01

Purpose To determine the repeatability and accuracy of a commercially available magnetic resonance (MR) imaging-based, semiautomated method to quantify abdominal adipose tissue and thigh muscle volume and hepatic proton density fat fraction (PDFF). Materials and Methods This prospective study was institutional review board- approved and HIPAA compliant. All subjects provided written informed consent. Inclusion criteria were age of 18 years or older and willingness to participate. The exclusion criterion was contraindication to MR imaging. Three-dimensional T1-weighted dual-echo body-coil images were acquired three times. Source images were reconstructed to generate water and calibrated fat images. Abdominal adipose tissue and thigh muscle were segmented, and their volumes were estimated by using a semiautomated method and, as a reference standard, a manual method. Hepatic PDFF was estimated by using a confounder-corrected chemical shift-encoded MR imaging method with hybrid complex-magnitude reconstruction and, as a reference standard, MR spectroscopy. Tissue volume and hepatic PDFF intra- and interexamination repeatability were assessed by using intraclass correlation and coefficient of variation analysis. Tissue volume and hepatic PDFF accuracy were assessed by means of linear regression with the respective reference standards. Results Adipose and thigh muscle tissue volumes of 20 subjects (18 women; age range, 25-76 years; body mass index range, 19.3-43.9 kg/m 2 ) were estimated by using the semiautomated method. Intra- and interexamination intraclass correlation coefficients were 0.996-0.998 and coefficients of variation were 1.5%-3.6%. For hepatic MR imaging PDFF, intra- and interexamination intraclass correlation coefficients were greater than or equal to 0.994 and coefficients of variation were less than or equal to 7.3%. In the regression analyses of manual versus semiautomated volume and spectroscopy versus MR imaging, PDFF slopes and intercepts were close to the identity line, and correlations of determination at multivariate analysis (R 2 ) ranged from 0.744 to 0.994. Conclusion This MR imaging-based, semiautomated method provides high repeatability and accuracy for estimating abdominal adipose tissue and thigh muscle volumes and hepatic PDFF. © RSNA, 2017.

PCA based clustering for brain tumor segmentation of T1w MRI images.

PubMed

Kaya, Irem Ersöz; Pehlivanlı, Ayça Çakmak; Sekizkardeş, Emine Gezmez; Ibrikci, Turgay

2017-03-01

Medical images are huge collections of information that are difficult to store and process consuming extensive computing time. Therefore, the reduction techniques are commonly used as a data pre-processing step to make the image data less complex so that a high-dimensional data can be identified by an appropriate low-dimensional representation. PCA is one of the most popular multivariate methods for data reduction. This paper is focused on T1-weighted MRI images clustering for brain tumor segmentation with dimension reduction by different common Principle Component Analysis (PCA) algorithms. Our primary aim is to present a comparison between different variations of PCA algorithms on MRIs for two cluster methods. Five most common PCA algorithms; namely the conventional PCA, Probabilistic Principal Component Analysis (PPCA), Expectation Maximization Based Principal Component Analysis (EM-PCA), Generalize Hebbian Algorithm (GHA), and Adaptive Principal Component Extraction (APEX) were applied to reduce dimensionality in advance of two clustering algorithms, K-Means and Fuzzy C-Means. In the study, the T1-weighted MRI images of the human brain with brain tumor were used for clustering. In addition to the original size of 512 lines and 512 pixels per line, three more different sizes, 256 × 256, 128 × 128 and 64 × 64, were included in the study to examine their effect on the methods. The obtained results were compared in terms of both the reconstruction errors and the Euclidean distance errors among the clustered images containing the same number of principle components. According to the findings, the PPCA obtained the best results among all others. Furthermore, the EM-PCA and the PPCA assisted K-Means algorithm to accomplish the best clustering performance in the majority as well as achieving significant results with both clustering algorithms for all size of T1w MRI images. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

The Role of Visual and Semantic Properties in the Emergence of Category-Specific Patterns of Neural Response in the Human Brain.

PubMed

Coggan, David D; Baker, Daniel H; Andrews, Timothy J

2016-01-01

Brain-imaging studies have found distinct spatial and temporal patterns of response to different object categories across the brain. However, the extent to which these categorical patterns of response reflect higher-level semantic or lower-level visual properties of the stimulus remains unclear. To address this question, we measured patterns of EEG response to intact and scrambled images in the human brain. Our rationale for using scrambled images is that they have many of the visual properties found in intact images, but do not convey any semantic information. Images from different object categories (bottle, face, house) were briefly presented (400 ms) in an event-related design. A multivariate pattern analysis revealed categorical patterns of response to intact images emerged ∼80-100 ms after stimulus onset and were still evident when the stimulus was no longer present (∼800 ms). Next, we measured the patterns of response to scrambled images. Categorical patterns of response to scrambled images also emerged ∼80-100 ms after stimulus onset. However, in contrast to the intact images, distinct patterns of response to scrambled images were mostly evident while the stimulus was present (∼400 ms). Moreover, scrambled images were able to account only for all the variance in the intact images at early stages of processing. This direct manipulation of visual and semantic content provides new insights into the temporal dynamics of object perception and the extent to which different stages of processing are dependent on lower-level or higher-level properties of the image.

Fully Polarimetric Passive W-band Millimeter Wave Imager for Wide Area Search

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

Tedeschi, Jonathan R.; Bernacki, Bruce E.; Sheen, David M.

2013-09-27

We describe the design and phenomenology imaging results of a fully polarimetric W-band millimeter wave (MMW) radiometer developed by Pacific Northwest National Laboratory for wide-area search. Operating from 92 - 94 GHz, the W-band radiometer employs a Dicke switching heterodyne design isolating the horizontal and vertical mm-wave components with 40 dB of polarization isolation. Design results are presented for both infinite conjugate off-axis parabolic and finite conjugate off-axis elliptical fore-optics using optical ray tracing and diffraction calculations. The received linear polarizations are down-converted to a microwave frequency band and recombined in a phase-shifting network to produce all six orthogonal polarizationmore » states of light simultaneously, which are used to calculate the Stokes parameters for display and analysis. The resulting system performance produces a heterodyne receiver noise equivalent delta temperature (NEDT) of less than 150m Kelvin. The radiometer provides novel imaging capability by producing all four of the Stokes parameters of light, which are used to create imagery based on the polarization states associated with unique scattering geometries and their interaction with the down welling MMW energy. The polarization states can be exploited in such a way that man-made objects can be located and highlighted in a cluttered scene using methods such as image comparison, color encoding of Stokes parameters, multivariate image analysis, and image fusion with visible and infrared imagery. We also present initial results using a differential imaging approach used to highlight polarization features and reduce common-mode noise. Persistent monitoring of a scene using the polarimetric passive mm-wave technique shows great promise for anomaly detection caused by human activity.« less

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

Kowalchik, Kristin V.; Vallow, Laura A., E-mail: vallow.laura@mayo.edu; McDonough, Michelle

Purpose: To study the utility of preoperative breast MRI for partial breast irradiation (PBI) patient selection, using multivariable analysis of significant risk factors to create a classification rule. Methods and Materials: Between 2002 and 2009, 712 women with newly diagnosed breast cancer underwent preoperative bilateral breast MRI at Mayo Clinic Florida. Of this cohort, 566 were retrospectively deemed eligible for PBI according to the National Surgical Adjuvant Breast and Bowel Project Protocol B-39 inclusion criteria using physical examination, mammogram, and/or ultrasound. Magnetic resonance images were then reviewed to determine their impact on patient eligibility. The patient and tumor characteristics weremore » evaluated to determine risk factors for altered PBI eligibility after MRI and to create a classification rule. Results: Of the 566 patients initially eligible for PBI, 141 (25%) were found ineligible because of pathologically proven MRI findings. Magnetic resonance imaging detected additional ipsilateral breast cancer in 118 (21%). Of these, 62 (11%) had more extensive disease than originally noted before MRI, and 64 (11%) had multicentric disease. Contralateral breast cancer was detected in 28 (5%). Four characteristics were found to be significantly associated with PBI ineligibility after MRI on multivariable analysis: premenopausal status (P=.021), detection by palpation (P<.001), first-degree relative with a history of breast cancer (P=.033), and lobular histology (P=.002). Risk factors were assigned a score of 0-2. The risk of altered PBI eligibility from MRI based on number of risk factors was 0:18%; 1:22%; 2:42%; 3:65%. Conclusions: Preoperative bilateral breast MRI altered the PBI recommendations for 25% of women. Women who may undergo PBI should be considered for breast MRI, especially those with lobular histology or with 2 or more of the following risk factors: premenopausal, detection by palpation, and first-degree relative with a history of breast cancer.« less

Sex differences in white matter abnormalities after mild traumatic brain injury: localization and correlation with outcome.

PubMed

Fakhran, Saeed; Yaeger, Karl; Collins, Michael; Alhilali, Lea

2014-09-01

To evaluate sex differences in diffusion-tensor imaging (DTI) white matter abnormalities after mild traumatic brain injury (mTBI) using tract-based spatial statistics (TBSS) and to compare associated clinical outcomes. The institutional review board approved this study, with waiver of informed consent. DTI in 69 patients with mTBI (47 male and 22 female patients) and 21 control subjects (10 male and 11 female subjects) with normal conventional magnetic resonance (MR) images were retrospectively reviewed. Fractional anisotropy (FA) maps were generated as a measure of white matter integrity. Patients with mTBI underwent serial neurocognitive testing with Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT). Correlation between sex, white matter FA values, ImPACT scores, and time to symptom resolution (TSR) were analyzed with multivariate analysis and TBSS. No significant difference in age was seen between males and females (control subjects, P = .3; patients with mTBI, P = .34). No significant difference was seen in initial ImPACT symptom scores (P = .33) between male and female patients with mTBI. Male patients with mTBI had significantly decreased FA values in the uncinate fasciculus (UF) bilaterally (mean FA, 0.425; 95% confidence interval: 0.375, 0.476) compared with female patients with mTBI and control subjects (P < .05), with a significantly longer TSR (P = .04). Multivariate analysis showed sex and UF FA values independently correlated with TSR longer than 3 months (adjusted odds ratios, 2.27 and 2.38; P = .04 and P < .001, respectively), but initial symptom severity did not (adjusted odds ratio, 1.15; P = .35). Relative sparing of the UF is seen in female compared with male patients after mTBI, with sex and UF FA values as stronger predictors of TSR than initial symptom severity.

Management of benign papilloma without atypia diagnosed at ultrasound-guided core needle biopsy: Scoring system for predicting malignancy.

PubMed

Ahn, Soo Kyung; Han, Wonshik; Moon, Hyeong-Gon; Kim, Min Kyoon; Noh, Dong-Young; Jung, Bong-Wha; Kim, Sung-Won; Ko, Eunyoung

2018-01-01

The management of benign intraductal papilloma diagnosed on core needle biopsy (CNB) remains unclear. This study was designed to evaluate factors predicting malignancy in patients diagnosed with benign papilloma without atypia at ultrasound-guided CNB and to develop a scoring system predicting malignancy based on clinical, radiological and pathological factors on further excisional biopsy. The study enrolled patients diagnosed with benign papillomas (including benign and atypical papillary lesions) at CNB. Multivariate analysis was used to identify relevant clinical, radiological and pathological factors that may predict malignancy. A total of 520 CNBs were diagnosed with benign or atypical papilloma. Of these, 452 were benign papilloma without atypia. Of the 250 lesions subsequently excised surgically from 234 women, 17 (6.8%) were diagnosed with malignancy. Multivariate analysis revealed that bloody nipple discharge, size on imaging ≥15 mm, BI-RADS≥4b, peripheral location and palpability were independent predictors of malignancy. A scoring system was developed based on logistic regression models and beta coefficients for each variable. The area under the ROC curve was 0.947 (95% CI: 0.913-0.981, p < 0.001) and a negative predictive value was 100%. In a validation set of 62 patients, an area under the ROC curve was 0.926 (95% CI: 0.857-0.995, p < 0.001). A scoring system predicting malignancy in patients diagnosed by CNB with benign papilloma without atypia was developed. This system was able to identify a subset of patients with lesions likely to be benign, indicating that imaging follow-up rather than surgical excision may be appropriate. Copyright © 2017 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.

CD133+CD54+CD44+ circulating tumor cells as a biomarker of treatment selection and liver metastasis in patients with colorectal cancer

PubMed Central

Wang, Cun; Huang, Qiaorong; Meng, Wentong; Yu, Yongyang; Yang, Lie; Peng, Zhihai; Hu, Jiankun; Li, Yuan; Mo, Xianming; Zhou, Zongguang

2016-01-01

Introduction Liver is the most common site of distant metastasis in colorectal cancer (CRC). Early diagnosis and appropriate treatment selection decides overall prognosis of patients. However, current diagnostic measures were basically imaging but not functional. Circulating tumor cells (CTCs) known as hold the key to understand the biology of metastatic mechanism provide a novel and auxiliary diagnostic strategy for CRC with liver metastasis (CRC-LM). Results The expression of CD133+ and CD133+CD54+CD44+ cellular subpopulations were higher in the peripheral blood of CRC-LM patients when compared with those without metastasis (P<0.001). Multivariate analysis proved the association between the expression of CD133+CD44+CD54+ cellular subpopulation and the existence of CRC-LM (P<0.001). The combination of abdominal CT/MRI, CEA and the CD133+CD44+CD54+ cellular subpopulation showed increased detection and discrimination rate for liver metastasis, with a sensitivity of 88.2% and a specificity of 92.4%. Meanwhile, it also show accurate predictive value for liver metastasis (OR=2.898, 95% C.I.1.374–6.110). Materials and Method Flow cytometry and multivariate analysis was performed to detect the expression of cancer initiating cells the correlation between cellular subpopulations and liver metastasis in patients with CRC. The receiver operating characteristic curves combined with the area under the curve were generated to compare the predictive ability of the cellular subpopulation for liver metastasis with current CT and MRI images. Conclusions The identification, expression and application of CTC subpopulations will provide an ideal cellular predictive marker for CRC liver metastasis and a potential marker for further investigation. PMID:27764803

Tumor response ratio predicts overall survival in breast cancer patients treated with neoadjuvant chemotherapy.

PubMed

Miller, Marian; Ottesen, Rebecca A; Niland, Joyce C; Kruper, Laura; Chen, Steven L; Vito, Courtney

2014-10-01

Neoadjuvant chemotherapy (NAC) is commonly used to treat locally advanced breast cancer. Pathologic complete response (pCR) predicts improved overall survival (OS); however, prognosis of patients with partial response remains unclear. We evaluated whether tumor response ratio (TRR) is a better predictor of OS than current staging methods. Using the National Comprehensive Cancer Network Breast Cancer Outcomes Database, we identified patients with stage I-III breast cancer who had NAC and pretreatment imaging at City of Hope (1997-2010). Patient demographics, tumor characteristics, and OS were analyzed. TRR was calculated as residual in-breast disease divided by size on pre-NAC imaging. Four TRR groups were stratified; TRR 0 (pCR), TRR > 0-0.4 (strong partial response, SPR), TRR > 0.4-1.0 (weak partial response, WPR), or TRR > 1.0 (tumor growth, TG). OS was estimated by the Kaplan-Meier method and tested by the log-rank test. Cox regression was performed to evaluate associations between OS and TRR in a multivariable analysis while controlling for potential confounders. There were 218 eligible patients identified; 59 (27 %) had pCR, 61 (28 %) SPR, 72 (33 %) WPR, and 26 (12 %) TG. Five-year OS decreased continuously with increasing TRR:pCR (90 %), SPR (79 %), WPR (66 %), and TG (60 %). TRR was the only measure that significantly predicted OS (p = 0.0035); pathologic stage (p = 0.23) and pre-NAC clinical tumor stage (cT) (p = 0.87) were not significant. TRR continued to be statistically significant by multivariable analysis (p = 0.016). TRR takes into account both pretreatment and residual disease and more accurately predicts OS than pathologic stage and pre-NAC cT. TRR may be useful to more accurately assess prognosis and OS in breast cancer patients undergoing NAC.

Satellite image collection optimization

NASA Astrophysics Data System (ADS)

Martin, William

2002-09-01

Imaging satellite systems represent a high capital cost. Optimizing the collection of images is critical for both satisfying customer orders and building a sustainable satellite operations business. We describe the functions of an operational, multivariable, time dynamic optimization system that maximizes the daily collection of satellite images. A graphical user interface allows the operator to quickly see the results of what if adjustments to an image collection plan. Used for both long range planning and daily collection scheduling of Space Imaging's IKONOS satellite, the satellite control and tasking (SCT) software allows collection commands to be altered up to 10 min before upload to the satellite.

Differences in Brain Glucose Metabolism During Preparation for 131I Ablation in Thyroid Cancer Patients: Thyroid Hormone Withdrawal Versus Recombinant Human Thyrotropin.

PubMed

Jeong, Hyeonseok S; Choi, Eun Kyoung; Song, In-Uk; Chung, Yong-An; Park, Jong-Sik; Oh, Jin Kyoung

2017-01-01

In preparation for 131 I ablation, temporary withdrawal of thyroid hormone is commonly used in patients with thyroid cancer after total thyroidectomy. The current study aimed to investigate brain glucose metabolism and its relationships with mood or cognitive function in these patients using 18 F-fluoro-2-deoxyglucose positron emission tomography ( 18 F-FDG-PET). A total of 40 consecutive adult patients with thyroid carcinoma who had undergone total thyroidectomy were recruited for this cross-sectional study. At the time of assessment, 20 patients were hypothyroid after two weeks of thyroid hormone withdrawal, while 20 received thyroid hormone replacement therapy and were euthyroid. All participants underwent brain 18 F-FDG-PET scans and completed mood questionnaires and cognitive tests. Multivariate spatial covariance analysis and univariate voxel-wise analysis were applied for the image data. The hypothyroid patients were more anxious and depressed than the euthyroid participants. The multivariate covariance analysis showed increases in glucose metabolism primarily in the bilateral insula and surrounding areas and concomitant decreases in the parieto-occipital regions in the hypothyroid group. The level of thyrotropin was positively associated with the individual expression of the covariance pattern. The decreased 18 F-FDG uptake in the right cuneus cluster from the univariate analysis was correlated with the increased thyrotropin level and greater depressive symptoms in the hypothyroid group. These results suggest that temporary hypothyroidism, even for a short period, may induce impairment in glucose metabolism and related affective symptoms.

The MIND PALACE: A Multi-Spectral Imaging and Spectroscopy Database for Planetary Science

NASA Astrophysics Data System (ADS)

Eshelman, E.; Doloboff, I.; Hara, E. K.; Uckert, K.; Sapers, H. M.; Abbey, W.; Beegle, L. W.; Bhartia, R.

2017-12-01

The Multi-Instrument Database (MIND) is the web-based home to a well-characterized set of analytical data collected by a suite of deep-UV fluorescence/Raman instruments built at the Jet Propulsion Laboratory (JPL). Samples derive from a growing body of planetary surface analogs, mineral and microbial standards, meteorites, spacecraft materials, and other astrobiologically relevant materials. In addition to deep-UV spectroscopy, datasets stored in MIND are obtained from a variety of analytical techniques obtained over multiple spatial and spectral scales including electron microscopy, optical microscopy, infrared spectroscopy, X-ray fluorescence, and direct fluorescence imaging. Multivariate statistical analysis techniques, primarily Principal Component Analysis (PCA), are used to guide interpretation of these large multi-analytical spectral datasets. Spatial co-referencing of integrated spectral/visual maps is performed using QGIS (geographic information system software). Georeferencing techniques transform individual instrument data maps into a layered co-registered data cube for analysis across spectral and spatial scales. The body of data in MIND is intended to serve as a permanent, reliable, and expanding database of deep-UV spectroscopy datasets generated by this unique suite of JPL-based instruments on samples of broad planetary science interest.

An Automated Method for High-Throughput Screening of Arabidopsis Rosette Growth in Multi-Well Plates and Its Validation in Stress Conditions.

PubMed

De Diego, Nuria; Fürst, Tomáš; Humplík, Jan F; Ugena, Lydia; Podlešáková, Kateřina; Spíchal, Lukáš

2017-01-01

High-throughput plant phenotyping platforms provide new possibilities for automated, fast scoring of several plant growth and development traits, followed over time using non-invasive sensors. Using Arabidops is as a model offers important advantages for high-throughput screening with the opportunity to extrapolate the results obtained to other crops of commercial interest. In this study we describe the development of a highly reproducible high-throughput Arabidopsis in vitro bioassay established using our OloPhen platform, suitable for analysis of rosette growth in multi-well plates. This method was successfully validated on example of multivariate analysis of Arabidopsis rosette growth in different salt concentrations and the interaction with varying nutritional composition of the growth medium. Several traits such as changes in the rosette area, relative growth rate, survival rate and homogeneity of the population are scored using fully automated RGB imaging and subsequent image analysis. The assay can be used for fast screening of the biological activity of chemical libraries, phenotypes of transgenic or recombinant inbred lines, or to search for potential quantitative trait loci. It is especially valuable for selecting genotypes or growth conditions that improve plant stress tolerance.

Chemical Discrimination of Cortex Phellodendri amurensis and Cortex Phellodendri chinensis by Multivariate Analysis Approach.

PubMed

Sun, Hui; Wang, Huiyu; Zhang, Aihua; Yan, Guangli; Han, Ying; Li, Yuan; Wu, Xiuhong; Meng, Xiangcai; Wang, Xijun

2016-01-01

As herbal medicines have an important position in health care systems worldwide, their current assessment, and quality control are a major bottleneck. Cortex Phellodendri chinensis (CPC) and Cortex Phellodendri amurensis (CPA) are widely used in China, however, how to identify species of CPA and CPC has become urgent. In this study, multivariate analysis approach was performed to the investigation of chemical discrimination of CPA and CPC. Principal component analysis showed that two herbs could be separated clearly. The chemical markers such as berberine, palmatine, phellodendrine, magnoflorine, obacunone, and obaculactone were identified through the orthogonal partial least squared discriminant analysis, and were identified tentatively by the accurate mass of quadruple-time-of-flight mass spectrometry. A total of 29 components can be used as the chemical markers for discrimination of CPA and CPC. Of them, phellodenrine is significantly higher in CPC than that of CPA, whereas obacunone and obaculactone are significantly higher in CPA than that of CPC. The present study proves that multivariate analysis approach based chemical analysis greatly contributes to the investigation of CPA and CPC, and showed that the identified chemical markers as a whole should be used to discriminate the two herbal medicines, and simultaneously the results also provided chemical information for their quality assessment. Multivariate analysis approach was performed to the investigate the herbal medicineThe chemical markers were identified through multivariate analysis approachA total of 29 components can be used as the chemical markers. UPLC-Q/TOF-MS-based multivariate analysis method for the herbal medicine samples Abbreviations used: CPC: Cortex Phellodendri chinensis, CPA: Cortex Phellodendri amurensis, PCA: Principal component analysis, OPLS-DA: Orthogonal partial least squares discriminant analysis, BPI: Base peaks ion intensity.

metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis.

PubMed

Cichonska, Anna; Rousu, Juho; Marttinen, Pekka; Kangas, Antti J; Soininen, Pasi; Lehtimäki, Terho; Raitakari, Olli T; Järvelin, Marjo-Riitta; Salomaa, Veikko; Ala-Korpela, Mika; Ripatti, Samuli; Pirinen, Matti

2016-07-01

A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness.Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies. Code is available at https://github.com/aalto-ics-kepaco anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis

PubMed Central

Cichonska, Anna; Rousu, Juho; Marttinen, Pekka; Kangas, Antti J.; Soininen, Pasi; Lehtimäki, Terho; Raitakari, Olli T.; Järvelin, Marjo-Riitta; Salomaa, Veikko; Ala-Korpela, Mika; Ripatti, Samuli; Pirinen, Matti

2016-01-01

Motivation: A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. Results: We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness. Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies. Availability and implementation: Code is available at https://github.com/aalto-ics-kepaco Contacts: anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27153689

18F-FET PET prior to recurrent high-grade glioma re-irradiation-additional prognostic value of dynamic time-to-peak analysis and early static summation images?

PubMed

Fleischmann, Daniel F; Unterrainer, Marcus; Bartenstein, Peter; Belka, Claus; Albert, Nathalie L; Niyazi, Maximilian

2017-04-01

Most high-grade gliomas (HGG) recur after initial multimodal therapy and re-irradiation (Re-RT) has been shown to be a valuable re-treatment option in selected patients. We evaluated the prognostic value of dynamic time-to-peak analysis and early static summation images in O-(2- 18 F-fluoroethyl)-l-tyrosine ( 18 F-FET) PET for patients treated with Re-RT ± concomitant bevacizumab. We retrospectively analyzed 72 patients suffering from recurrent HGG with 18 F-FET PET prior to Re-RT. PET analysis revealed the maximal tumor-to-background-ratio (TBR max ), the biological tumor volume, the number of PET-foci and pattern of time-activity-curves (TACs; increasing vs. decreasing). Furthermore, the novel PET parameters early TBR max (at 5-15 min post-injection) and minimal time-to-peak (TTP min ) were evaluated. Additional analysis was performed for gender, age, KPS, O6-methylguanine-DNA methyltransferase methylation status, isocitrate dehydrogenase 1 mutational status, WHO grade and concomitant bevacizumab therapy. The influence of PET and clinical parameters on post-recurrence survival (PRS) was investigated. Shorter TTP min was related to shorter PRS after Re-RT with 6 months for TTP min  < 12.5 min, 7 months for TTP min 12.5-25 min and 11 months for TTP min >25 min (p = 0.027). TTP min had a significant impact on PRS both on univariate (p = 0.027; continuous) and multivariate analysis (p = 0.011; continuous). Other factors significantly related to PRS on multivariate analysis were increasing vs. decreasing TACs (p = 0.008) and Karnofsky Performance Score (p = 0.015; <70 vs. ≥70). Early TBR max as well as the other conventional PET parameters were not significantly related to PRS on univariate analysis. Dynamic 18 F-FET PET with TTP min provides a high prognostic value for recurrent HGG prior to Re-RT, whereas early TBR max does not. Dynamic 18 F-FET PET using TTP min might help to personalize Re-RT treatment regimens in future through voxelwise TTP min analysis for dose painting purposes and PET-guided dose escalation.

Using Interactive Graphics to Teach Multivariate Data Analysis to Psychology Students

ERIC Educational Resources Information Center

Valero-Mora, Pedro M.; Ledesma, Ruben D.

2011-01-01

This paper discusses the use of interactive graphics to teach multivariate data analysis to Psychology students. Three techniques are explored through separate activities: parallel coordinates/boxplots; principal components/exploratory factor analysis; and cluster analysis. With interactive graphics, students may perform important parts of the…

Solfatara volcano subsurface imaging: two different approaches to process and interpret multi-variate data sets

NASA Astrophysics Data System (ADS)

Bernardinetti, Stefano; Bruno, Pier Paolo; Lavoué, François; Gresse, Marceau; Vandemeulebrouck, Jean; Revil, André

2017-04-01

The need to reduce model uncertainty and produce a more reliable geophysical imaging and interpretations is nowadays a fundamental task required to geophysics techniques applied in complex environments such as Solfatara Volcano. The use of independent geophysical methods allows to obtain many information on the subsurface due to the different sensitivities of the data towards parameters such as compressional and shearing wave velocities, bulk electrical conductivity, or density. The joint processing of these multiple physical properties can lead to a very detailed characterization of the subsurface and therefore enhance our imaging and our interpretation. In this work, we develop two different processing approaches based on reflection seismology and seismic P-wave tomography on one hand, and electrical data acquired over the same line, on the other hand. From these data, we obtain an image-guided electrical resistivity tomography and a post processing integration of tomographic results. The image-guided electrical resistivity tomography is obtained by regularizing the inversion of the electrical data with structural constraints extracted from a migrated seismic section using image processing tools. This approach enables to focus the reconstruction of electrical resistivity anomalies along the features visible in the seismic section, and acts as a guide for interpretation in terms of subsurface structures and processes. To integrate co-registrated P-wave velocity and electrical resistivity values, we apply a data mining tool, the k-means algorithm, to individuate relationships between the two set of variables. This algorithm permits to individuate different clusters with the objective to minimize the sum of squared Euclidean distances within each cluster and maximize it between clusters for the multivariate data set. We obtain a partitioning of the multivariate data set in a finite number of well-correlated clusters, representative of the optimum clustering of our geophysical variables (P-wave velocities and electrical resistivities). The result is an integrated tomography that shows a finite number of homogeneous geophysical facies, and therefore permits to highlight the main geological features of the subsurface.

Selective Thinning of the Perifoveal Inner Retina as an Early Sign of Hydroxychloroquine Retinal Toxicity

PubMed Central

Pasadhika, Sirichai; Fishman, Gerald A; Choi, Dongseok; Shahidi, Mahnaz

2013-01-01

Purpose To evaluate macular thickness profiles using spectral-domain optical coherence tomography (SDOCT) and image segmentation in patients with chronic exposure to hydroxychloroquine. Methods This study included 8 patients with chronic exposure to hydroxychloroquine (Group 1) and 8 controls (Group 2). Group 1 patients had no clinically-evident retinal toxicity. All subjects underwent SDOCT imaging of the macula. An image segmentation technique was used to measure thickness of 6 retinal layers at 200 µm intervals. A mixed-effects model was used for multivariate analysis. Results By measuring total retinal thickness either at the central macular (2800 µm in diameter), the perifoveal region 1200-µm-width ring surrounding the central macula), or the overall macular area (5200 µm in diameter), there were no significant differences in the thickness between Groups 1 and 2. On an image segmentation analysis, selective thinning of the inner plexiform + ganglion cell layers (p=0.021) was observed only in the perifoveal area of the patients in Group 1 compared to that of Group 2 by using the mixed-effects model analysis. Conclusions Our results suggest that chronic exposure to hydroxychloroquine is associated with thinning of the perifoveal inner retinal layers, especially in the ganglion cell and inner plexiform layers, even in the absence of functional or structural clinical changes involving the photoreceptor or retinal pigment epithelial cell layers. This may be a contributing factor as the reason most patients who have early detectable signs of drug toxicity present with paracentral or pericentral scotomas. PMID:20395978

Routine magnetic resonance imaging for idiopathic olfactory loss: a modeling-based economic evaluation.

PubMed

Rudmik, Luke; Smith, Kristine A; Soler, Zachary M; Schlosser, Rodney J; Smith, Timothy L

2014-10-01

Idiopathic olfactory loss is a common clinical scenario encountered by otolaryngologists. While trying to allocate limited health care resources appropriately, the decision to obtain a magnetic resonance imaging (MRI) scan to investigate for a rare intracranial abnormality can be difficult. To evaluate the cost-effectiveness of ordering routine MRI in patients with idiopathic olfactory loss. We performed a modeling-based economic evaluation with a time horizon of less than 1 year. Patients included in the analysis had idiopathic olfactory loss defined by no preceding viral illness or head trauma and negative findings of a physical examination and nasal endoscopy. Routine MRI vs no-imaging strategies. We developed a decision tree economic model from the societal perspective. Effectiveness, probability, and cost data were obtained from the published literature. Litigation rates and costs related to a missed diagnosis were obtained from the Physicians Insurers Association of America. A univariate threshold analysis and multivariate probabilistic sensitivity analysis were performed to quantify the degree of certainty in the economic conclusion of the reference case. The comparative groups included those who underwent routine MRI of the brain with contrast alone and those who underwent no brain imaging. The primary outcome was the cost per correct diagnosis of idiopathic olfactory loss. The mean (SD) cost for the MRI strategy totaled $2400.00 ($1717.54) and was effective 100% of the time, whereas the mean (SD) cost for the no-imaging strategy totaled $86.61 ($107.40) and was effective 98% of the time. The incremental cost-effectiveness ratio for the MRI strategy compared with the no-imaging strategy was $115 669.50, which is higher than most acceptable willingness-to-pay thresholds. The threshold analysis demonstrated that when the probability of having a treatable intracranial disease process reached 7.9%, the incremental cost-effectiveness ratio for MRI vs no imaging was $24 654.38. The probabilistic sensitivity analysis demonstrated that the no-imaging strategy was the cost-effective decision with 81% certainty at a willingness-to-pay threshold of $50 000. This economic evaluation suggests that the most cost-effective decision is to not obtain a routine MRI scan of the brain in patients with idiopathic olfactory loss. Outcomes from this study may be used to counsel patients and aid in the decision-making process.

Hyperspectral image reconstruction using RGB color for foodborne pathogen detection on agar plates

NASA Astrophysics Data System (ADS)

Yoon, Seung-Chul; Shin, Tae-Sung; Park, Bosoon; Lawrence, Kurt C.; Heitschmidt, Gerald W.

2014-03-01

This paper reports the latest development of a color vision technique for detecting colonies of foodborne pathogens grown on agar plates with a hyperspectral image classification model that was developed using full hyperspectral data. The hyperspectral classification model depended on reflectance spectra measured in the visible and near-infrared spectral range from 400 and 1,000 nm (473 narrow spectral bands). Multivariate regression methods were used to estimate and predict hyperspectral data from RGB color values. The six representative non-O157 Shiga-toxin producing Eschetichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) were grown on Rainbow agar plates. A line-scan pushbroom hyperspectral image sensor was used to scan 36 agar plates grown with pure STEC colonies at each plate. The 36 hyperspectral images of the agar plates were divided in half to create training and test sets. The mean Rsquared value for hyperspectral image estimation was about 0.98 in the spectral range between 400 and 700 nm for linear, quadratic and cubic polynomial regression models and the detection accuracy of the hyperspectral image classification model with the principal component analysis and k-nearest neighbors for the test set was up to 92% (99% with the original hyperspectral images). Thus, the results of the study suggested that color-based detection may be viable as a multispectral imaging solution without much loss of prediction accuracy compared to hyperspectral imaging.

Impact of Stress Cardiac Magnetic Resonance Imaging on Clinical Care

PubMed Central

McGraw, Sloane; Romano, Simone; Jue, Jennifer; Bauml, Michael A; Chung, Jaehoon; Farzaneh-Far, Afshin

2016-01-01

Given the rising costs of imaging, there is increasing pressure to provide evidence for direct additive impact on clinical care. Appropriate use criteria (AUC) were developed to optimize test-patient selection, and are increasingly used by payers to assess reimbursement. However, these criteria were created by expert consensus with limited systematic validation. The aims of this study were therefore to determine: 1) rates of active clinical change resulting from stress cardiovascular magnetic resonance (CMR) imaging; and 2) whether the AUC can predict these changes. We prospectively enrolled 350 consecutive outpatients referred for stress CMR. Categories of “active changes in clinical care” due to stress CMR were pre-defined. Appropriateness was classified according to the 2013 AUC. Multivariable logistic regression analysis was used to identify factors independently associated with active change. Overall, stress CMR led to an active change in clinical care in about 70% of patients. Rates of change in clinical care did not vary significantly across AUC categories (p=0.767). In a multivariable model adjusting for clinical variables and AUC, only ischemia (OR 6.896, 95% CI 2.637–18.032, p<0.001), known CAD (OR 0.300, 95% CI 0.161–0.559, p<0.001), and age (OR 0.977, 95% CI 0.954–1.000, p=0.050) independently predicted significant clinical change. In conclusion, stress CMR made a significant impact on clinical management, resulting in active change in clinical care in about 70% of patients. AUC categories were not an independent predictor of clinical change. Clinical change was independently associated with presence of ischemia, absence of known CAD, and younger age. PMID:27476576

Setting local rank constraints by orthogonal projections for image resolution analysis: application to the determination of a low dose pharmaceutical compound.

PubMed

Boiret, Mathieu; de Juan, Anna; Gorretta, Nathalie; Ginot, Yves-Michel; Roger, Jean-Michel

2015-09-10

Raman chemical imaging provides chemical and spatial information about pharmaceutical drug product. By using resolution methods on acquired spectra, the objective is to calculate pure spectra and distribution maps of image compounds. With multivariate curve resolution-alternating least squares, constraints are used to improve the performance of the resolution and to decrease the ambiguity linked to the final solution. Non negativity and spatial local rank constraints have been identified as the most powerful constraints to be used. In this work, an alternative method to set local rank constraints is proposed. The method is based on orthogonal projections pretreatment. For each drug product compound, raw Raman spectra are orthogonally projected to a basis including all the variability from the formulation compounds other than the product of interest. Presence or absence of the compound of interest is obtained by observing the correlations between the orthogonal projected spectra and a pure spectrum orthogonally projected to the same basis. By selecting an appropriate threshold, maps of presence/absence of compounds can be set up for all the product compounds. This method appears as a powerful approach to identify a low dose compound within a pharmaceutical drug product. The maps of presence/absence of compounds can be used as local rank constraints in resolution methods, such as multivariate curve resolution-alternating least squares process in order to improve the resolution of the system. The method proposed is particularly suited for pharmaceutical systems, where the identity of all compounds in the formulations is known and, therefore, the space of interferences can be well defined. Copyright © 2015 Elsevier B.V. All rights reserved.

Likelihood of Incomplete Kidney Tumor Ablation with Radio Frequency Energy: Degree of Enhancement Matters.

PubMed

Lay, Aaron H; Stewart, Jeremy; Canvasser, Noah E; Cadeddu, Jeffrey A; Gahan, Jeffrey C

2016-07-01

Larger size and clear cell histopathology are associated with worse outcomes for malignant renal tumors treated with radio frequency ablation. We hypothesize that greater tumor enhancement may be a risk factor for radio frequency ablation failure due to increased vascularity. A retrospective review of patients who underwent radio frequency ablation for renal tumors with contrast enhanced imaging available was performed. The change in Hounsfield units (HU) of the tumor from the noncontrast phase to the contrast enhanced arterial phase was calculated. Radio frequency ablation failure rates for biopsy confirmed malignant tumors were compared using the chi-squared test. Multivariate logistic analysis was performed to assess predictive variables for radio frequency ablation failure. Disease-free survival was calculated using Kaplan-Meier analysis. A total of 99 patients with biopsy confirmed malignant renal tumors and contrast enhanced imaging were identified. The incomplete ablation rate was significantly lower for tumors with enhancement less than 60 vs 60 HU or greater (0.0% vs 14.6%, p=0.005). On multivariate logistic regression analysis tumor enhancement 60 HU or greater (OR 1.14, p=0.008) remained a significant predictor of incomplete initial ablation. The 5-year disease-free survival for size less than 3 cm was 100% vs 69.2% for size 3 cm or greater (p <0.01), while 5-year disease-free survival for HU change less than 60 was 100% vs 92.4% for HU change 60 or greater (p=0.24). Biopsy confirmed malignant renal tumors, which exhibit a change in enhancement of 60 HU or greater, experience a higher rate of incomplete initial tumor ablation than tumors with enhancement less than 60 HU. Size 3 cm or greater portends worse 5-year disease-free survival after radio frequency ablation. The degree of enhancement should be considered when counseling patients before radio frequency ablation. Copyright © 2016 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Differentiating the grades of thymic epithelial tumor malignancy using textural features of intratumoral heterogeneity via (18)F-FDG PET/CT.

PubMed

Lee, Hyo Sang; Oh, Jungsu S; Park, Young Soo; Jang, Se Jin; Choi, Ik Soo; Ryu, Jin-Sook

2016-05-01

We aimed to explore the ability of textural heterogeneity indices determined by (18)F-FDG PET/CT for grading the malignancy of thymic epithelial tumors (TETs). We retrospectively enrolled 47 patients with pathologically proven TETs who underwent pre-treatment (18)F-FDG PET/CT. TETs were classified by pathological results into three subgroups with increasing grades of malignancy: low-risk thymoma (LRT; WHO classification A, AB and B1), high-risk thymoma (B2 and B3), and thymic carcinoma (TC). Using (18)F-FDG PET/CT, we obtained conventional imaging indices including SUVmax and 20 intratumoral heterogeneity indices: i.e., four local-scale indices derived from the neighborhood gray-tone difference matrix (NGTDM), eight regional-scale indices from the gray-level run-length matrix (GLRLM), and eight regional-scale indices from the gray-level size zone matrix (GLSZM). Area under the receiver operating characteristic curve (AUC) was used to demonstrate the abilities of the imaging indices for differentiating subgroups. Multivariable logistic regression analysis was performed to show the independent significance of the textural indices. Combined criteria using optimal cutoff values of the SUVmax and a best-performing heterogeneity index were applied to investigate whether they improved differentiation between the subgroups. Most of the GLRLM and GLSZM indices and the SUVmax showed good or fair discrimination (AUC >0.7) with best performance for some of the GLRLM indices and the SUVmax, whereas the NGTDM indices showed relatively inferior performance. The discriminative ability of some of the GLSZM indices was independent from that of SUVmax in multivariate analysis. Combined use of the SUVmax and a GLSZM index improved positive predictive values for LRT and TC. Texture analysis of (18)F-FDG PET/CT scans has the potential to differentiate between TET tumor grades; regional-scale indices from GLRLM and GLSZM perform better than local-scale indices from the NGTDM. The SUVmax and heterogeneity indices may have complementary value in differentiating TET subgroups.

A power analysis for multivariate tests of temporal trend in species composition.

PubMed

Irvine, Kathryn M; Dinger, Eric C; Sarr, Daniel

2011-10-01

Long-term monitoring programs emphasize power analysis as a tool to determine the sampling effort necessary to effectively document ecologically significant changes in ecosystems. Programs that monitor entire multispecies assemblages require a method for determining the power of multivariate statistical models to detect trend. We provide a method to simulate presence-absence species assemblage data that are consistent with increasing or decreasing directional change in species composition within multiple sites. This step is the foundation for using Monte Carlo methods to approximate the power of any multivariate method for detecting temporal trends. We focus on comparing the power of the Mantel test, permutational multivariate analysis of variance, and constrained analysis of principal coordinates. We find that the power of the various methods we investigate is sensitive to the number of species in the community, univariate species patterns, and the number of sites sampled over time. For increasing directional change scenarios, constrained analysis of principal coordinates was as or more powerful than permutational multivariate analysis of variance, the Mantel test was the least powerful. However, in our investigation of decreasing directional change, the Mantel test was typically as or more powerful than the other models.

Multivariate Granger causality: an estimation framework based on factorization of the spectral density matrix

PubMed Central

Wen, Xiaotong; Rangarajan, Govindan; Ding, Mingzhou

2013-01-01

Granger causality is increasingly being applied to multi-electrode neurophysiological and functional imaging data to characterize directional interactions between neurons and brain regions. For a multivariate dataset, one might be interested in different subsets of the recorded neurons or brain regions. According to the current estimation framework, for each subset, one conducts a separate autoregressive model fitting process, introducing the potential for unwanted variability and uncertainty. In this paper, we propose a multivariate framework for estimating Granger causality. It is based on spectral density matrix factorization and offers the advantage that the estimation of such a matrix needs to be done only once for the entire multivariate dataset. For any subset of recorded data, Granger causality can be calculated through factorizing the appropriate submatrix of the overall spectral density matrix. PMID:23858479

Lateralization of temporal lobe epilepsy by multimodal multinomial hippocampal response-driven models.

PubMed

Nazem-Zadeh, Mohammad-Reza; Elisevich, Kost V; Schwalb, Jason M; Bagher-Ebadian, Hassan; Mahmoudi, Fariborz; Soltanian-Zadeh, Hamid

2014-12-15

Multiple modalities are used in determining laterality in mesial temporal lobe epilepsy (mTLE). It is unclear how much different imaging modalities should be weighted in decision-making. The purpose of this study is to develop response-driven multimodal multinomial models for lateralization of epileptogenicity in mTLE patients based upon imaging features in order to maximize the accuracy of noninvasive studies. The volumes, means and standard deviations of FLAIR intensity and means of normalized ictal-interictal SPECT intensity of the left and right hippocampi were extracted from preoperative images of a retrospective cohort of 45 mTLE patients with Engel class I surgical outcomes, as well as images of a cohort of 20 control, nonepileptic subjects. Using multinomial logistic function regression, the parameters of various univariate and multivariate models were estimated. Based on the Bayesian model averaging (BMA) theorem, response models were developed as compositions of independent univariate models. A BMA model composed of posterior probabilities of univariate response models of hippocampal volumes, means and standard deviations of FLAIR intensity, and means of SPECT intensity with the estimated weighting coefficients of 0.28, 0.32, 0.09, and 0.31, respectively, as well as a multivariate response model incorporating all mentioned attributes, demonstrated complete reliability by achieving a probability of detection of one with no false alarms to establish proper laterality in all mTLE patients. The proposed multinomial multivariate response-driven model provides a reliable lateralization of mesial temporal epileptogenicity including those patients who require phase II assessment. Copyright © 2014 Elsevier B.V. All rights reserved.

Fourier Transform Infrared Spectroscopy (FTIR) and Multivariate Analysis for Identification of Different Vegetable Oils Used in Biodiesel Production

PubMed Central

Mueller, Daniela; Ferrão, Marco Flôres; Marder, Luciano; da Costa, Adilson Ben; de Cássia de Souza Schneider, Rosana

2013-01-01

The main objective of this study was to use infrared spectroscopy to identify vegetable oils used as raw material for biodiesel production and apply multivariate analysis to the data. Six different vegetable oil sources—canola, cotton, corn, palm, sunflower and soybeans—were used to produce biodiesel batches. The spectra were acquired by Fourier transform infrared spectroscopy using a universal attenuated total reflectance sensor (FTIR-UATR). For the multivariate analysis principal component analysis (PCA), hierarchical cluster analysis (HCA), interval principal component analysis (iPCA) and soft independent modeling of class analogy (SIMCA) were used. The results indicate that is possible to develop a methodology to identify vegetable oils used as raw material in the production of biodiesel by FTIR-UATR applying multivariate analysis. It was also observed that the iPCA found the best spectral range for separation of biodiesel batches using FTIR-UATR data, and with this result, the SIMCA method classified 100% of the soybean biodiesel samples. PMID:23539030

Multivariate meta-analysis for non-linear and other multi-parameter associations

PubMed Central

Gasparrini, A; Armstrong, B; Kenward, M G

2012-01-01

In this paper, we formalize the application of multivariate meta-analysis and meta-regression to synthesize estimates of multi-parameter associations obtained from different studies. This modelling approach extends the standard two-stage analysis used to combine results across different sub-groups or populations. The most straightforward application is for the meta-analysis of non-linear relationships, described for example by regression coefficients of splines or other functions, but the methodology easily generalizes to any setting where complex associations are described by multiple correlated parameters. The modelling framework of multivariate meta-analysis is implemented in the package mvmeta within the statistical environment R. As an illustrative example, we propose a two-stage analysis for investigating the non-linear exposure–response relationship between temperature and non-accidental mortality using time-series data from multiple cities. Multivariate meta-analysis represents a useful analytical tool for studying complex associations through a two-stage procedure. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22807043

The correlations of psychological status, quality of life, self-esteem, social support and body image disturbance in Chinese patients with Systemic Lupus Erythematosus.

PubMed

Zhao, Qian; Chen, Haoyang; Yan, Hongyan; He, Yan; Zhu, Li; Fu, WenTing; Shen, Biyu

2018-01-31

This study aimed (i) to complement existing research by focusing on body image disturbance issues in Chinese Systemic Lupus Erythematosus (SLE) patients; (ii) to investigate how Chinese patients make sense of disease diagnosis and perceived cultural influences within the context of their SLE. A total of 118 SLE patients underwent standardized laboratory examinations and completed several questionnaires. Independent sample t-test, Mann-Whitney U-test, Chi-square test, and multivariate analysis using backward stepwise logistic regression model were used to analyze these data. We found 18.3% SLE patients had BID, which were significantly higher than the control group (.8%). SLE patients are more concerned about their physical changes caused by disease. There were significant correlations among personal health insurance, complication of diabetes, appearance of new rash, depression, anxiety, self-esteem and BID in patients with SLE. Meanwhile, logistic regression analysis revealed that appearance of new rash and high anxiety were significantly associated with BID in SLE patients. In conclusion, it is beneficial to pay attention to the physical and mental health of patients with rheumatic disease from the perspective of body image, to understand their needs and to provide effective and effective service for them.

Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation.

PubMed

Asner, G P; Martin, R E; Knapp, D E; Tupayachi, R; Anderson, C B; Sinca, F; Vaughn, N R; Llactayo, W

2017-01-27

Functional biogeography may bridge a gap between field-based biodiversity information and satellite-based Earth system studies, thereby supporting conservation plans to protect more species and their contributions to ecosystem functioning. We used airborne laser-guided imaging spectroscopy with environmental modeling to derive large-scale, multivariate forest canopy functional trait maps of the Peruvian Andes-to-Amazon biodiversity hotspot. Seven mapped canopy traits revealed functional variation in a geospatial pattern explained by geology, topography, hydrology, and climate. Clustering of canopy traits yielded a map of forest beta functional diversity for land-use analysis. Up to 53% of each mapped, functionally distinct forest presents an opportunity for new conservation action. Mapping functional diversity advances our understanding of the biosphere to conserve more biodiversity in the face of land use and climate change. Copyright © 2017, American Association for the Advancement of Science.

Comorbid psychiatric diagnosis and psychological correlates of eating disorders in dance students.

PubMed

Liu, Chao-Yu; Tseng, Mei-Chih Meg; Chang, Chin-Hao; Fang, David; Lee, Ming-Been

2016-02-01

Although dancers are at risk for eating disorders (EDs), little is known about the features of EDs among the dance population. This study explores the prevalence of EDs, and their psychiatric comorbidities and correlates in dance students. In total, 442 female high-school dance students participated in a two-phase survey. All participants completed screening questionnaires as well as measures assessing teasing, self-esteem, perfectionism, body dissatisfaction, and personality. Of the participating students, 311 underwent the Structured Clinical Interview for DSM-IV-TR Axis I Disorders. Sixty-eight individuals (15.4%) had an ED by DSM-IV diagnosis. The prevalence of any co-occurring mood (47.1%) and anxiety disorders (30.9%) was high. Although low self-esteem, high neuroticism, and high psychological distress were associated with EDs in univariate analysis, only teasing for overweight and body image dissatisfaction were significantly associated with EDs by multivariate analysis. Prevention and intervention programs for dance students should include recognition and management of emotional disorders and strategies promoting positive body image and reducing the incidence of negative weight-related comments. Copyright © 2015. Published by Elsevier B.V.

Localized sinonasal mucosal melanoma: Outcomes and associations with stage, radiotherapy, and positron emission tomography response.

PubMed

Samstein, Robert M; Carvajal, Richard D; Postow, Michael A; Callahan, Margaret K; Shoushtari, Alexander N; Patel, Snehal G; Lee, Nancy Y; Barker, Christopher A

2016-09-01

Sinonasal mucosal melanoma is a rare neoplasm with a poor prognosis. Retrospective analysis was conducted on 78 patients with localized sinonasal mucosal melanoma treated at Memorial Sloan Kettering Cancer Center (MSKCC from 1998-2013). Demographic, tumor, imaging, and treatment factors were recorded and survival and disease-control outcomes were analyzed. Median overall survival (OS) and disease-specific survival (DSS) were 32 and 50 months, respectively. Median locoregional recurrence-free survival (LRFS) and distant recurrence-free survival (DRFS) were 43 and 12 months, respectively. Multivariate analysis demonstrated greater OS in nasal cavity tumors and earlier T classification. Radiotherapy (RT) was associated with significantly greater LRFS (5-years; 35% vs 59%; p = .01), but no difference in OS. Post-RT positron emission tomography (PET) response was associated with greater OS. Distant metastasis is the predominant mode of recurrence in sinonasal mucosal melanoma, but local recurrence remains common. RT is associated with improved local control, but no survival benefit. The prognostic value of post-RT PET imaging warrants further investigation. © 2016 Wiley Periodicals, Inc. Head Neck 38: 1310-1317, 2016. © 2016 Wiley Periodicals, Inc.

EEL spectroscopic tomography: towards a new dimension in nanomaterials analysis.

PubMed

Yedra, Lluís; Eljarrat, Alberto; Arenal, Raúl; Pellicer, Eva; Cabo, Moisés; López-Ortega, Alberto; Estrader, Marta; Sort, Jordi; Baró, Maria Dolors; Estradé, Sònia; Peiró, Francesca

2012-11-01

Electron tomography is a widely spread technique for recovering the three dimensional (3D) shape of nanostructured materials. Using a spectroscopic signal to achieve a reconstruction adds a fourth chemical dimension to the 3D structure. Up to date, energy filtering of the images in the transmission electron microscope (EFTEM) is the usual spectroscopic method even if most of the information in the spectrum is lost. Unlike EFTEM tomography, the use of electron energy-loss spectroscopy (EELS) spectrum images (SI) for tomographic reconstruction retains all chemical information, and the possibilities of this new approach still remain to be fully exploited. In this article we prove the feasibility of EEL spectroscopic tomography at low voltages (80 kV) and short acquisition times from data acquired using an aberration corrected instrument and data treatment by Multivariate Analysis (MVA), applied to Fe(x)Co((3-x))O(4)@Co(3)O(4) mesoporous materials. This approach provides a new scope into materials; the recovery of full EELS signal in 3D. Copyright © 2012 Elsevier B.V. All rights reserved.

The Potential of Multivariate Analysis in Assessing Students' Attitude to Curriculum Subjects

ERIC Educational Resources Information Center

Gaotlhobogwe, Michael; Laugharne, Janet; Durance, Isabelle

2011-01-01

Background: Understanding student attitudes to curriculum subjects is central to providing evidence-based options to policy makers in education. Purpose: We illustrate how quantitative approaches used in the social sciences and based on multivariate analysis (categorical Principal Components Analysis, Clustering Analysis and General Linear…

Two-sample tests and one-way MANOVA for multivariate biomarker data with nondetects.

PubMed

Thulin, M

2016-09-10

Testing whether the mean vector of a multivariate set of biomarkers differs between several populations is an increasingly common problem in medical research. Biomarker data is often left censored because some measurements fall below the laboratory's detection limit. We investigate how such censoring affects multivariate two-sample and one-way multivariate analysis of variance tests. Type I error rates, power and robustness to increasing censoring are studied, under both normality and non-normality. Parametric tests are found to perform better than non-parametric alternatives, indicating that the current recommendations for analysis of censored multivariate data may have to be revised. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

A non-iterative extension of the multivariate random effects meta-analysis.

PubMed

Makambi, Kepher H; Seung, Hyunuk

2015-01-01

Multivariate methods in meta-analysis are becoming popular and more accepted in biomedical research despite computational issues in some of the techniques. A number of approaches, both iterative and non-iterative, have been proposed including the multivariate DerSimonian and Laird method by Jackson et al. (2010), which is non-iterative. In this study, we propose an extension of the method by Hartung and Makambi (2002) and Makambi (2001) to multivariate situations. A comparison of the bias and mean square error from a simulation study indicates that, in some circumstances, the proposed approach perform better than the multivariate DerSimonian-Laird approach. An example is presented to demonstrate the application of the proposed approach.

Multivariate pattern analysis of fMRI data reveals deficits in distributed representations in schizophrenia

PubMed Central

Yoon, Jong H.; Tamir, Diana; Minzenberg, Michael J.; Ragland, J. Daniel; Ursu, Stefan; Carter, Cameron S.

2009-01-01

Background Multivariate pattern analysis is an alternative method of analyzing fMRI data, which is capable of decoding distributed neural representations. We applied this method to test the hypothesis of the impairment in distributed representations in schizophrenia. We also compared the results of this method with traditional GLM-based univariate analysis. Methods 19 schizophrenia and 15 control subjects viewed two runs of stimuli--exemplars of faces, scenes, objects, and scrambled images. To verify engagement with stimuli, subjects completed a 1-back matching task. A multi-voxel pattern classifier was trained to identify category-specific activity patterns on one run of fMRI data. Classification testing was conducted on the remaining run. Correlation of voxel-wise activity across runs evaluated variance over time in activity patterns. Results Patients performed the task less accurately. This group difference was reflected in the pattern analysis results with diminished classification accuracy in patients compared to controls, 59% and 72% respectively. In contrast, there was no group difference in GLM-based univariate measures. In both groups, classification accuracy was significantly correlated with behavioral measures. Both groups showed highly significant correlation between inter-run correlations and classification accuracy. Conclusions Distributed representations of visual objects are impaired in schizophrenia. This impairment is correlated with diminished task performance, suggesting that decreased integrity of cortical activity patterns is reflected in impaired behavior. Comparisons with univariate results suggest greater sensitivity of pattern analysis in detecting group differences in neural activity and reduced likelihood of non-specific factors driving these results. PMID:18822407

Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

PubMed Central

Krumin, Michael; Shoham, Shy

2010-01-01

Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden” Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method. PMID:20454705

A refined method for multivariate meta-analysis and meta-regression.

PubMed

Jackson, Daniel; Riley, Richard D

2014-02-20

Making inferences about the average treatment effect using the random effects model for meta-analysis is problematic in the common situation where there is a small number of studies. This is because estimates of the between-study variance are not precise enough to accurately apply the conventional methods for testing and deriving a confidence interval for the average effect. We have found that a refined method for univariate meta-analysis, which applies a scaling factor to the estimated effects' standard error, provides more accurate inference. We explain how to extend this method to the multivariate scenario and show that our proposal for refined multivariate meta-analysis and meta-regression can provide more accurate inferences than the more conventional approach. We explain how our proposed approach can be implemented using standard output from multivariate meta-analysis software packages and apply our methodology to two real examples. Copyright © 2013 John Wiley & Sons, Ltd.

Radiology reports for incidental thyroid nodules on CT and MRI: high variability across subspecialties.

PubMed

Grady, A T; Sosa, J A; Tanpitukpongse, T P; Choudhury, K R; Gupta, R T; Hoang, J K

2015-02-01

Variability in radiologists' reporting styles and recommendations for incidental thyroid nodules can lead to confusion among clinicians and may contribute to inconsistent patient care. Our aim was to describe reporting practices of radiologists for incidental thyroid nodules seen on CT and MR imaging and to determine factors that influence reporting styles. This is a retrospective study of patients with incidental thyroid nodules reported on CT and MR imaging between January and December 2011, identified by text search for "thyroid nodule" in all CT and MR imaging reports. The studies included CT and MR imaging scans of the neck, spine, and chest. Radiology reports were divided into those that mentioned the incidental thyroid nodules only in the "Findings" section versus those that reported the incidental thyroid nodules in the "Impression" section as well, because this latter reporting style gives more emphasis to the finding. Univariate and multivariate analyses were performed to identify radiologist, patient, and nodule characteristics that influenced reporting styles. Three hundred seventy-five patients met the criterion of having incidental thyroid nodules. One hundred thirty-eight (37%) patients had incidental thyroid nodules reported in the "Impression" section. On multivariate analysis, only radiologists' divisions and nodule size were associated with reporting in "Impression." Chest radiologists and neuroradiologists were more likely to report incidental thyroid nodules in the "Impression" section than their abdominal imaging colleagues, and larger incidental thyroid nodules were more likely to be reported in "Impression" (P ≤ .03). Seventy-three percent of patients with incidental thyroid nodules of ≥20 mm were reported in the "Impression" section, but higher variability in reporting was seen for incidental thyroid nodules measuring 10-14 mm and 15-19 mm, which were reported in "Impression" for 61% and 50% of patients, respectively. Reporting practices for incidental thyroid nodules detected on CT and MR imaging are predominantly influenced by nodule size and the radiologist's subspecialty. Reporting was highly variable for nodules measuring 10-19 mm; this finding can be partially attributed to different reporting styles among radiology subspecialty divisions. The variability demonstrated in this study further underscores the need to develop CT and MR imaging practice guidelines with the goal of standardizing reporting of incidental thyroid nodules and thereby potentially improving the consistency and quality of patient care. © 2015 by American Journal of Neuroradiology.

Layer-by-Layer Polyelectrolyte Encapsulation of Mycoplasma pneumoniae for Enhanced Raman Detection

PubMed Central

Rivera-Betancourt, Omar E.; Sheppard, Edward S.; Krause, Duncan C.; Dluhy, Richard A.

2014-01-01

Mycoplasma pneumoniae is a major cause of respiratory disease in humans and accounts for as much as 20% of all community-acquired pneumonia. Existing mycoplasma diagnosis is primarily limited by the poor success rate at culturing the bacteria from clinical samples. There is a critical need to develop a new platform for mycoplasma detection that has high sensitivity, specificity, and expediency. Here we report the layer-by-layer (LBL) encapsulation of M. pneumoniae cells with Ag nanoparticles in a matrix of the polyelectrolytes poly(allylamine hydrochloride) (PAH) and poly(styrene sulfonate) (PSS). We evaluated nanoparticle encapsulated mycoplasma cells as a platform for the differentiation of M. pneumoniae strains using surface enhanced Raman scattering (SERS) combined with multivariate statistical analysis. Three separate M. pneumoniae strains (M129, FH and II-3) were studied. Scanning electron microscopy and fluorescence imaging showed that the Ag nanoparticles were incorporated between the oppositely charged polyelectrolyte layers. SERS spectra showed that LBL encapsulation provides excellent spectral reproducibility. Multivariate statistical analysis of the Raman spectra differentiated the three M. pneumoniae strains with 97 – 100% specificity and sensitivity, and low (0.1 – 0.4) root mean square error. These results indicated that nanoparticle and polyelectrolyte encapsulation of M. pneumoniae is a potentially powerful platform for rapid and sensitive SERS-based bacterial identification. PMID:25017005

Low vascularity predicts favourable outcomes in leiomyoma patients treated with uterine artery embolization.

PubMed

Tang, Yixin; Chen, Chunlin; Duan, Hui; Ma, Ben; Liu, Ping

2016-10-01

To investigate the clinical factors predicting outcomes of leiomyoma treated with uterine artery embolization (UAE). A total of 183 uterine leiomyoma patients undergoing UAE were retrospectively analyzed. Patient age, characteristics of vascular supply in magnetic resonance imaging (MRI)/digital subtraction angiography (DSA), number, size and location of leiomyoma were recorded. Leiomyoma regrowth, new leiomyoma appearance and recurrence of any previously reported symptoms were carefully monitored over a mean follow-up of 30 months (median 32 months, range 12-80). Potential recurrence risk factors were analyzed by univariate and multivariate cox regression analysis. Twenty-three recurrences were recorded. The difference in the vascularity classification systems between MRI and DSA was not statistically significant (P = 0.059). High vascularity in MRI, high vascularity in DSA and multiple leiomyoma showed a significant risk of recurrence using univariate and multivariate analysis (P = 0.004, P < 0.001 and P = 0.023, respectively). The other factors were not significantly associated with leiomyoma recurrence (P > 0.05). Low vascularity and solitary leiomyoma indicated favourable outcomes in patients treated with UAE. • Low vascularity and solitary mass predicted favourable outcomes in UAE-treated patients. • MRI might provide information on vascularity in leiomyoma before UAE. • Variations in vascular supply, age, size, location were not associated with recurrence.

Exploring Raman spectroscopy for the evaluation of glaucomatous retinal changes

NASA Astrophysics Data System (ADS)

Wang, Qi; Grozdanic, Sinisa D.; Harper, Matthew M.; Hamouche, Nicolas; Kecova, Helga; Lazic, Tatjana; Yu, Chenxu

2011-10-01

Glaucoma is a chronic neurodegenerative disease characterized by apoptosis of retinal ganglion cells and subsequent loss of visual function. Early detection of glaucoma is critical for the prevention of permanent structural damage and irreversible vision loss. Raman spectroscopy is a technique that provides rapid biochemical characterization of tissues in a nondestructive and noninvasive fashion. In this study, we explored the potential of using Raman spectroscopy for detection of glaucomatous changes in vitro. Raman spectroscopic imaging was conducted on retinal tissues of dogs with hereditary glaucoma and healthy control dogs. The Raman spectra were subjected to multivariate discriminant analysis with a support vector machine algorithm, and a classification model was developed to differentiate disease tissues versus healthy tissues. Spectroscopic analysis of 105 retinal ganglion cells (RGCs) from glaucomatous dogs and 267 RGCs from healthy dogs revealed spectroscopic markers that differentiated glaucomatous specimens from healthy controls. Furthermore, the multivariate discriminant model differentiated healthy samples and glaucomatous samples with good accuracy [healthy 89.5% and glaucomatous 97.6% for the same breed (Basset Hounds); and healthy 85.0% and glaucomatous 85.5% for different breeds (Beagles versus Basset Hounds)]. Raman spectroscopic screening can be used for in vitro detection of glaucomatous changes in retinal tissue with a high specificity.

Exploring Raman spectroscopy for the evaluation of glaucomatous retinal changes.

PubMed

Wang, Qi; Grozdanic, Sinisa D; Harper, Matthew M; Hamouche, Nicolas; Kecova, Helga; Lazic, Tatjana; Yu, Chenxu

2011-10-01

Glaucoma is a chronic neurodegenerative disease characterized by apoptosis of retinal ganglion cells and subsequent loss of visual function. Early detection of glaucoma is critical for the prevention of permanent structural damage and irreversible vision loss. Raman spectroscopy is a technique that provides rapid biochemical characterization of tissues in a nondestructive and noninvasive fashion. In this study, we explored the potential of using Raman spectroscopy for detection of glaucomatous changes in vitro. Raman spectroscopic imaging was conducted on retinal tissues of dogs with hereditary glaucoma and healthy control dogs. The Raman spectra were subjected to multivariate discriminant analysis with a support vector machine algorithm, and a classification model was developed to differentiate disease tissues versus healthy tissues. Spectroscopic analysis of 105 retinal ganglion cells (RGCs) from glaucomatous dogs and 267 RGCs from healthy dogs revealed spectroscopic markers that differentiated glaucomatous specimens from healthy controls. Furthermore, the multivariate discriminant model differentiated healthy samples and glaucomatous samples with good accuracy [healthy 89.5% and glaucomatous 97.6% for the same breed (Basset Hounds); and healthy 85.0% and glaucomatous 85.5% for different breeds (Beagles versus Basset Hounds)]. Raman spectroscopic screening can be used for in vitro detection of glaucomatous changes in retinal tissue with a high specificity.

Partial Least Squares for Discrimination in fMRI Data

PubMed Central

Andersen, Anders H.; Rayens, William S.; Liu, Yushu; Smith, Charles D.

2011-01-01

Multivariate methods for discrimination were used in the comparison of brain activation patterns between groups of cognitively normal women who are at either high or low Alzheimer's disease risk based on family history and apolipoprotein-E4 status. Linear discriminant analysis (LDA) was preceded by dimension reduction using either principal component analysis (PCA), partial least squares (PLS), or a new oriented partial least squares (OrPLS) method. The aim was to identify a spatial pattern of functionally connected brain regions that was differentially expressed by the risk groups and yielded optimal classification accuracy. Multivariate dimension reduction is required prior to LDA when the data contains more feature variables than there are observations on individual subjects. Whereas PCA has been commonly used to identify covariance patterns in neuroimaging data, this approach only identifies gross variability and is not capable of distinguishing among-groups from within-groups variability. PLS and OrPLS provide a more focused dimension reduction by incorporating information on class structure and therefore lead to more parsimonious models for discrimination. Performance was evaluated in terms of the cross-validated misclassification rates. The results support the potential of using fMRI as an imaging biomarker or diagnostic tool to discriminate individuals with disease or high risk. PMID:22227352

Prognostic significance of MRI findings in patients with myxoid-round cell liposarcoma.

PubMed

Tateishi, Ukihide; Hasegawa, Tadashi; Beppu, Yasuo; Kawai, Akira; Satake, Mitsuo; Moriyama, Noriyuki

2004-03-01

The aims of this study were to determine the prognostic significance of MRI findings in patients with myxoid-round cell liposarcomas and to clarify which MRI features best indicate tumors with adverse clinical behavior. The initial MRI studies of 36 pathologically confirmed myxoid-round cell liposarcomas were retrospectively reviewed, and observations from this review were correlated with the histopathologic features. MR images were evaluated by two radiologists with agreement by consensus, and both univariate and multivariate analyses were conducted to evaluate survival with a median clinical follow-up of 33 months (range, 9-276 months). Statistically significant MRI findings that favored a diagnosis of intermediate- or high-grade tumor were large tumor size (> 10 cm), deeply situated tumor, tumor possessing irregular contours, absence of lobulation, absence of thin septa, presence of thick septa, absence of tumor capsule, high-intensity signal pattern, pronounced enhancement, and globular or nodular enhancement. Of these MRI findings, thin septa (p < 0.05), a tumor capsule (p < 0.01), and pronounced enhancement (p < 0.01) were associated significantly, according to univariate analysis, with overall survival. Multivariate analysis indicated that pronounced enhancement was associated significantly with overall survival (p < 0.05). Contrast-enhanced MRI findings can indicate a good or adverse prognosis in patients with myxoid-round cell liposarcomas.

Multivariate missing data in hydrology - Review and applications

NASA Astrophysics Data System (ADS)

Ben Aissia, Mohamed-Aymen; Chebana, Fateh; Ouarda, Taha B. M. J.

2017-12-01

Water resources planning and management require complete data sets of a number of hydrological variables, such as flood peaks and volumes. However, hydrologists are often faced with the problem of missing data (MD) in hydrological databases. Several methods are used to deal with the imputation of MD. During the last decade, multivariate approaches have gained popularity in the field of hydrology, especially in hydrological frequency analysis (HFA). However, treating the MD remains neglected in the multivariate HFA literature whereas the focus has been mainly on the modeling component. For a complete analysis and in order to optimize the use of data, MD should also be treated in the multivariate setting prior to modeling and inference. Imputation of MD in the multivariate hydrological framework can have direct implications on the quality of the estimation. Indeed, the dependence between the series represents important additional information that can be included in the imputation process. The objective of the present paper is to highlight the importance of treating MD in multivariate hydrological frequency analysis by reviewing and applying multivariate imputation methods and by comparing univariate and multivariate imputation methods. An application is carried out for multiple flood attributes on three sites in order to evaluate the performance of the different methods based on the leave-one-out procedure. The results indicate that, the performance of imputation methods can be improved by adopting the multivariate setting, compared to mean substitution and interpolation methods, especially when using the copula-based approach.

Measurement of drug and macromolecule diffusion across atherosclerotic rabbit aorta ex vivo by attenuated total reflection-Fourier transform infrared imaging

NASA Astrophysics Data System (ADS)

Palombo, Francesca; Danoux, Charlène B.; Weinberg, Peter D.; Kazarian, Sergei G.

2009-07-01

Diffusion of two model drugs-benzyl nicotinate and ibuprofen-and the plasma macromolecule albumin across atherosclerotic rabbit aorta was studied ex vivo by attenuated total reflection-Fourier transform infrared (ATR-FTIR) imaging. Solutions of these molecules were applied to the endothelial surface of histological sections of the aortic wall that were sandwiched between two impermeable surfaces. An array of spectra, each corresponding to a specific location in the section, was obtained at various times during solute diffusion into the wall and revealed the distribution of the solutes within the tissue. Benzyl nicotinate in Ringer's solution showed higher affinity for atherosclerotic plaque than for apparently healthy tissue. Transmural concentration profiles for albumin demonstrated its permeation across the section and were consistent with a relatively low distribution volume for the macromolecule in the middle of the wall. The ability of albumin to act as a drug carrier for ibuprofen, otherwise undetected within the tissue, was demonstrated by multivariate subtraction image analysis. In conclusion, ATR-FTIR imaging can be used to study transport processes in tissue samples with high spatial and temporal resolution and without the need to label the solutes under study.

Biomarkers for Musculoskeletal Pain Conditions: Use of Brain Imaging and Machine Learning.

PubMed

Boissoneault, Jeff; Sevel, Landrew; Letzen, Janelle; Robinson, Michael; Staud, Roland

2017-01-01

Chronic musculoskeletal pain condition often shows poor correlations between tissue abnormalities and clinical pain. Therefore, classification of pain conditions like chronic low back pain, osteoarthritis, and fibromyalgia depends mostly on self report and less on objective findings like X-ray or magnetic resonance imaging (MRI) changes. However, recent advances in structural and functional brain imaging have identified brain abnormalities in chronic pain conditions that can be used for illness classification. Because the analysis of complex and multivariate brain imaging data is challenging, machine learning techniques have been increasingly utilized for this purpose. The goal of machine learning is to train specific classifiers to best identify variables of interest on brain MRIs (i.e., biomarkers). This report describes classification techniques capable of separating MRI-based brain biomarkers of chronic pain patients from healthy controls with high accuracy (70-92%) using machine learning, as well as critical scientific, practical, and ethical considerations related to their potential clinical application. Although self-report remains the gold standard for pain assessment, machine learning may aid in the classification of chronic pain disorders like chronic back pain and fibromyalgia as well as provide mechanistic information regarding their neural correlates.

Quantitative chromatin pattern description in Feulgen-stained nuclei as a diagnostic tool to characterize the oligodendroglial and astroglial components in mixed oligo-astrocytomas.

PubMed

Decaestecker, C; Lopes, B S; Gordower, L; Camby, I; Cras, P; Martin, J J; Kiss, R; VandenBerg, S R; Salmon, I

1997-04-01

The oligoastrocytoma, as a mixed glioma, represents a nosologic dilemma with respect to precisely defining the oligodendroglial and astroglial phenotypes that constitute the neoplastic cell lineages of these tumors. In this study, cell image analysis with Feulgen-stained nuclei was used to distinguish between oligodendroglial and astrocytic phenotypes in oligodendrogliomas and astrocytomas and then applied to mixed oligoastrocytomas. Quantitative features with respect to chromatin pattern (30 variables) and DNA ploidy (8 variables) were evaluated on Feulgen-stained nuclei in a series of 71 gliomas using computer-assisted microscopy. These included 32 oligodendrogliomas (OLG group: 24 grade II and 8 grade III tumors according to the WHO classification), 32 astrocytomas (AST group: 13 grade II and 19 grade III tumors), and 7 oligoastrocytomas (OLGAST group). Initially, image analysis with multivariate statistical analyses (Discriminant Analysis) could identify each glial tumor group. Highly significant statistical differences were obtained distinguishing the morphonuclear features of oligodendrogliomas from those of astrocytomas, regardless of their histological grade. When compared with the 7 mixed oligoastrocytomas under study, 5 exhibited DNA ploidy and chromatin pattern characteristics similar to grade II oligodendrogliomas, I to grade III oligodendrogliomas, and I to grade II astrocytomas. Using multifactorial statistical analyses (Discriminant Analysis combined with Principal Component Analysis). It was possible to quantify the proportion of "typical" glial cell phenotypes that compose grade II and III oligodendrogliomas and grade II and III astrocytomas in each mixed glioma. Cytometric image analysis may be an important adjunct to routine histopathology for the reproducible identification of neoplasms containing a mixture of oligodendroglial and astrocytic phenotypes.

Development of Pattern Recognition Techniques for the Evaluation of Toxicant Impacts to Multispecies Systems

DTIC Science & Technology

1993-06-18

the exception. In the Standardized Aquatic Microcosm and the Mixed Flask Culture (MFC) microcosms, multivariate analysis and clustering methods...rule rather than the exception. In the Standardized Aquatic Microcosm and the Mixed Flask Culture (MFC) microcosms, multivariate analysis and...experiments using two microcosm protocols. We use nonmetric clustering, a multivariate pattern recognition technique developed by Matthews and Heame (1991

Arsenic distribution and valence state variation studied by fast hierarchical length-scale morphological, compositional, and speciation imaging at the Nanoscopium, Synchrotron Soleil

NASA Astrophysics Data System (ADS)

Somogyi, Andrea; Medjoubi, Kadda; Sancho-Tomas, Maria; Visscher, P. T.; Baranton, Gil; Philippot, Pascal

2017-09-01

The understanding of real complex geological, environmental and geo-biological processes depends increasingly on in-depth non-invasive study of chemical composition and morphology. In this paper we used scanning hard X-ray nanoprobe techniques in order to study the elemental composition, morphology and As speciation in complex highly heterogeneous geological samples. Multivariate statistical analytical techniques, such as principal component analysis and clustering were used for data interpretation. These measurements revealed the quantitative and valance state inhomogeneity of As and its relation to the total compositional and morphological variation of the sample at sub-μm scales.

Multivariate analysis for scanning tunneling spectroscopy data

NASA Astrophysics Data System (ADS)

Yamanishi, Junsuke; Iwase, Shigeru; Ishida, Nobuyuki; Fujita, Daisuke

2018-01-01

We applied principal component analysis (PCA) to two-dimensional tunneling spectroscopy (2DTS) data obtained on a Si(111)-(7 × 7) surface to explore the effectiveness of multivariate analysis for interpreting 2DTS data. We demonstrated that several components that originated mainly from specific atoms at the Si(111)-(7 × 7) surface can be extracted by PCA. Furthermore, we showed that hidden components in the tunneling spectra can be decomposed (peak separation), which is difficult to achieve with normal 2DTS analysis without the support of theoretical calculations. Our analysis showed that multivariate analysis can be an additional powerful way to analyze 2DTS data and extract hidden information from a large amount of spectroscopic data.

Pathological Gleason prediction through gland ring morphometry in immunofluorescent prostate cancer images

NASA Astrophysics Data System (ADS)

Scott, Richard; Khan, Faisal M.; Zeineh, Jack; Donovan, Michael; Fernandez, Gerardo

2016-03-01

The Gleason score is the most common architectural and morphological assessment of prostate cancer severity and prognosis. There have been numerous quantitative techniques developed to approximate and duplicate the Gleason scoring system. Most of these approaches have been developed in standard H and E brightfield microscopy. Immunofluorescence (IF) image analysis of tissue pathology has recently been proven to be extremely valuable and robust in developing prognostic assessments of disease, particularly in prostate cancer. There have been significant advances in the literature in quantitative biomarker expression as well as characterization of glandular architectures in discrete gland rings. In this work we leverage a new method of segmenting gland rings in IF images for predicting the pathological Gleason; both the clinical and the image specific grade, which may not necessarily be the same. We combine these measures with nuclear specific characteristics as assessed by the MST algorithm. Our individual features correlate well univariately with the Gleason grades, and in a multivariate setting have an accuracy of 85% in predicting the Gleason grade. Additionally, these features correlate strongly with clinical progression outcomes (CI of 0.89), significantly outperforming the clinical Gleason grades (CI of 0.78). This work presents the first assessment of morphological gland unit features from IF images for predicting the Gleason grade.

Multivariate Analysis of Schools and Educational Policy.

ERIC Educational Resources Information Center

Kiesling, Herbert J.

This report describes a multivariate analysis technique that approaches the problems of educational production function analysis by (1) using comparable measures of output across large experiments, (2) accounting systematically for differences in socioeconomic background, and (3) treating the school as a complete system in which different…

Multivariate detrending of fMRI signal drifts for real-time multiclass pattern classification.

PubMed

Lee, Dongha; Jang, Changwon; Park, Hae-Jeong

2015-03-01

Signal drift in functional magnetic resonance imaging (fMRI) is an unavoidable artifact that limits classification performance in multi-voxel pattern analysis of fMRI. As conventional methods to reduce signal drift, global demeaning or proportional scaling disregards regional variations of drift, whereas voxel-wise univariate detrending is too sensitive to noisy fluctuations. To overcome these drawbacks, we propose a multivariate real-time detrending method for multiclass classification that involves spatial demeaning at each scan and the recursive detrending of drifts in the classifier outputs driven by a multiclass linear support vector machine. Experiments using binary and multiclass data showed that the linear trend estimation of the classifier output drift for each class (a weighted sum of drifts in the class-specific voxels) was more robust against voxel-wise artifacts that lead to inconsistent spatial patterns and the effect of online processing than voxel-wise detrending. The classification performance of the proposed method was significantly better, especially for multiclass data, than that of voxel-wise linear detrending, global demeaning, and classifier output detrending without demeaning. We concluded that the multivariate approach using classifier output detrending of fMRI signals with spatial demeaning preserves spatial patterns, is less sensitive than conventional methods to sample size, and increases classification performance, which is a useful feature for real-time fMRI classification. Copyright © 2014 Elsevier Inc. All rights reserved.

Multivariate and repeated measures (MRM): A new toolbox for dependent and multimodal group-level neuroimaging data.

PubMed

McFarquhar, Martyn; McKie, Shane; Emsley, Richard; Suckling, John; Elliott, Rebecca; Williams, Stephen

2016-05-15

Repeated measurements and multimodal data are common in neuroimaging research. Despite this, conventional approaches to group level analysis ignore these repeated measurements in favour of multiple between-subject models using contrasts of interest. This approach has a number of drawbacks as certain designs and comparisons of interest are either not possible or complex to implement. Unfortunately, even when attempting to analyse group level data within a repeated-measures framework, the methods implemented in popular software packages make potentially unrealistic assumptions about the covariance structure across the brain. In this paper, we describe how this issue can be addressed in a simple and efficient manner using the multivariate form of the familiar general linear model (GLM), as implemented in a new MATLAB toolbox. This multivariate framework is discussed, paying particular attention to methods of inference by permutation. Comparisons with existing approaches and software packages for dependent group-level neuroimaging data are made. We also demonstrate how this method is easily adapted for dependency at the group level when multiple modalities of imaging are collected from the same individuals. Follow-up of these multimodal models using linear discriminant functions (LDA) is also discussed, with applications to future studies wishing to integrate multiple scanning techniques into investigating populations of interest. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Insights to Galaxy Evolution Utilizing a Multivariate Comparison of Circumgalactic OVI and MgII

NASA Astrophysics Data System (ADS)

Lewis, James; Churchill, Christopher; Nielsen, Nikole; Kacprzak, Glenn; Muzahid, Sowgat; Charlton, Jane

2018-01-01

We present a promising multivariate method to categorize inter-related astronomical data in meaningful ways. We use data from the MAGIICAT and "Multiphase Galaxy Halos" surveys and limit our sample to those galaxies which are imaged with the Hubble Space Telescope and for which the Circumgalactic Medium (CGM) is measured using high-resolution quasar spectra (HIRES/COS). Utilizing the method to categorize data about the CGM and its host galaxy yields distinct categories of CGM-galaxy pairs that imply a common fate for the outflows of MgII and OVI in redder galaxies. The analysis reveals a lack of circumgalactic OVI in lower mass, bluer (younger) galaxies, and that as the blue galaxies gain mass and age along the green valley strong OVI appears in the CGM predominately along the minor axes. But as the galaxies continue to gain mass and age into the red sequence strong OVI gas is found primarily along the major axes. Furthermore, we find a population of low mass red galaxies in which only weak, uniform, circumgalactic OVI is found. Incorporating our multivariate results for circumgalactic MgII, including evidence for quenching of star formation via weak circumgalactic MgII preferentially found along the minor axes of redder galaxies, and invoking the similarity of OVI column densities and kinematic spreads along the major and minor axes, we infer that OVI is ancient gas in the CGM.

Clinical usefulness of novel tag-less Agile patency capsule prior to capsule endoscopy for patients with suspected small bowel stenosis.

PubMed

Nakamura, Masanao; Hirooka, Yoshiki; Yamamura, Takeshi; Miyahara, Ryoji; Watanabe, Osamu; Ando, Takafumi; Ohmiya, Naoki; Goto, Hidemi

2015-01-01

The Agile patency capsule (PC; Given Imaging Ltd, Yoqneam, Israel) is used as a dummy prior to capsule endoscopy (CE) to avoid retention of the CE capsule. However, impaction of the PC's inner radio frequency identification (RFID) tag in a stricture could cause small-bowel ileus. Recently, the RFID tag-less PC was introduced into clinical practice. Herein, we aimed to retrospectively evaluate the usefulness of the tag-less PC. Of 154 patients who were scheduled to undergo CE, 100 consecutive patients (65%) who underwent PC evaluation were enrolled in the present study. Primary study end point was the retention rate of the CE capsule after successful passage of the PC. Secondary end point was analysis of the significant factors affecting the passage of the PC. In total, 87 patients (87%) had bowel patency confirmed by PC evaluation. There was no capsule retention in any of these 87 patients during CE. Abnormal findings were obtained from 60 CE, and 41 patients received new or modified treatment. Multivariate analysis of factors related to the confirmation of patency demonstrated that stenosis on imaging was the most influential factor (P = 0.002, odds ratio 16.387). The results confirmed that passage of the PC depends on stenosis on imaging. Use of the tag-less PC confirmed gastrointestinal tract patency for most of the patients who did not have stenosis on imaging and allowed estimation of the patency for patients who did have stenosis on imaging. © 2014 The Authors. Digestive Endoscopy © 2014 Japan Gastroenterological Endoscopy Society.

Pyrcca: Regularized Kernel Canonical Correlation Analysis in Python and Its Applications to Neuroimaging.

PubMed

Bilenko, Natalia Y; Gallant, Jack L

2016-01-01

In this article we introduce Pyrcca, an open-source Python package for performing canonical correlation analysis (CCA). CCA is a multivariate analysis method for identifying relationships between sets of variables. Pyrcca supports CCA with or without regularization, and with or without linear, polynomial, or Gaussian kernelization. We first use an abstract example to describe Pyrcca functionality. We then demonstrate how Pyrcca can be used to analyze neuroimaging data. Specifically, we use Pyrcca to implement cross-subject comparison in a natural movie functional magnetic resonance imaging (fMRI) experiment by finding a data-driven set of functional response patterns that are similar across individuals. We validate this cross-subject comparison method in Pyrcca by predicting responses to novel natural movies across subjects. Finally, we show how Pyrcca can reveal retinotopic organization in brain responses to natural movies without the need for an explicit model.

Pyrcca: Regularized Kernel Canonical Correlation Analysis in Python and Its Applications to Neuroimaging

PubMed Central

Bilenko, Natalia Y.; Gallant, Jack L.

2016-01-01

In this article we introduce Pyrcca, an open-source Python package for performing canonical correlation analysis (CCA). CCA is a multivariate analysis method for identifying relationships between sets of variables. Pyrcca supports CCA with or without regularization, and with or without linear, polynomial, or Gaussian kernelization. We first use an abstract example to describe Pyrcca functionality. We then demonstrate how Pyrcca can be used to analyze neuroimaging data. Specifically, we use Pyrcca to implement cross-subject comparison in a natural movie functional magnetic resonance imaging (fMRI) experiment by finding a data-driven set of functional response patterns that are similar across individuals. We validate this cross-subject comparison method in Pyrcca by predicting responses to novel natural movies across subjects. Finally, we show how Pyrcca can reveal retinotopic organization in brain responses to natural movies without the need for an explicit model. PMID:27920675

Development and implementation of a low cost micro computer system for LANDSAT analysis and geographic data base applications

NASA Technical Reports Server (NTRS)

Faust, N.; Jordon, L.

1981-01-01

Since the implementation of the GRID and IMGRID computer programs for multivariate spatial analysis in the early 1970's, geographic data analysis subsequently moved from large computers to minicomputers and now to microcomputers with radical reduction in the costs associated with planning analyses. Programs designed to process LANDSAT data to be used as one element in a geographic data base were used once NIMGRID (new IMGRID), a raster oriented geographic information system, was implemented on the microcomputer. Programs for training field selection, supervised and unsupervised classification, and image enhancement were added. Enhancements to the color graphics capabilities of the microsystem allow display of three channels of LANDSAT data in color infrared format. The basic microcomputer hardware needed to perform NIMGRID and most LANDSAT analyses is listed as well as the software available for LANDSAT processing.

Multivariate statistical analysis: Principles and applications to coorbital streams of meteorite falls

NASA Technical Reports Server (NTRS)

Wolf, S. F.; Lipschutz, M. E.

1993-01-01

Multivariate statistical analysis techniques (linear discriminant analysis and logistic regression) can provide powerful discrimination tools which are generally unfamiliar to the planetary science community. Fall parameters were used to identify a group of 17 H chondrites (Cluster 1) that were part of a coorbital stream which intersected Earth's orbit in May, from 1855 - 1895, and can be distinguished from all other H chondrite falls. Using multivariate statistical techniques, it was demonstrated that a totally different criterion, labile trace element contents - hence thermal histories - or 13 Cluster 1 meteorites are distinguishable from those of 45 non-Cluster 1 H chondrites. Here, we focus upon the principles of multivariate statistical techniques and illustrate their application using non-meteoritic and meteoritic examples.

Magnetic Resonance Imaging as a Predictor of Survival Free of Life-Threatening Arrhythmias and Transplantation in Cardiac Sarcoidosis.

PubMed

Ekström, Kaj; Lehtonen, Jukka; Hänninen, Helena; Kandolin, Riina; Kivistö, Sari; Kupari, Markku

2016-05-02

Cardiac magnetic resonance imaging has a key role in today's diagnosis of cardiac sarcoidosis. We set out to investigate whether cardiac magnetic resonance imaging also helps predict outcome in cardiac sarcoidosis. Our work involved 59 patients with cardiac sarcoidosis (38 female, mean age 46±10 years) seen at our hospital since February 2004 and followed up after contrast-enhanced cardiac magnetic resonance imaging. The extent of myocardial late gadolinium enhancement (measured as percentage of left ventricular mass), the volumes and ejection fractions of the left and right ventricles, and the thickness of the basal interventricular septum were determined and analyzed for prognostic significance. By April 2015, 23 patients had reached the study's end point, consisting of a composite of cardiac death (n=3), cardiac transplantation (n=1), and occurrence of life-threatening ventricular tachyarrhythmias (n=19; ventricular fibrillation in 5 and sustained ventricular tachycardia in 14 patients). In univariate analysis, myocardial extent of late gadolinium enhancement predicted event-free survival, as did scar-like thinning (<4 mm) of the basal interventricular septum and the ejection fraction of the right ventricle (P<0.05 for all). In multivariate Cox regression analysis, extent of late gadolinium enhancement was the only independent predictor of outcome events on cardiac magnetic resonance imaging, with a hazard ratio of 2.22 per tertile (95% CI 1.07-4.59). An extent of late gadolinium enhancement >22% (third tertile) had positive and negative predictive values for serious cardiac events of 75% and 76%, respectively. Findings on cardiac magnetic resonance imaging and the extent of myocardial late gadolinium enhancement in particular help predict serious cardiac events in cardiac sarcoidosis. © 2016 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

Effects of silicone expanders and implants on echocardiographic image quality after breast reconstruction.

PubMed

Pignatti, Marco; Mantovani, Francesca; Bertelli, Luca; Barbieri, Andrea; Pacchioni, Lucrezia; Loschi, Pietro; De Santis, Giorgio

2013-08-01

Use of silicone expanders and implants is the most common breast reconstruction technique after mastectomy. Postmastectomy patients often need echocardiographic monitoring of potential cardiotoxicity induced by cancer chemotherapy. The impairment of the echocardiographic acoustic window caused by silicone implants for breast augmentation has been reported. This study investigates whether the echocardiographic image quality was impaired in women reconstructed with silicone expanders and implants. The records of 44 consecutive women who underwent echocardiographic follow-up after breast reconstruction with expanders and implants at the authors' institution from January of 2000 to August of 2012 were reviewed. The population was divided into a study group (left or bilateral breast expanders/implants, n=30) and a control group (right breast expanders/implants, n=14). The impact of breast expanders/implants on echocardiographic image quality was tested (analysis of covariance model). Patients with a breast expander/implant (left or bilateral and right breast expanders/implants) were included. The mean volume of the breast devices was 353.2±125.5 cc. The quality of the echocardiographic images was good or sufficient in the control group; in the study group, it was judged as adequate in only 50 percent of cases (15 patients) and inadequate in the remaining 15 patients (p<0.001). At multivariable analysis, a persistent relationship between device position (left versus right) and image quality (p=0.001) was shown, independent from other factors. Silicone expanders and implants in postmastectomy left breast reconstruction considerably reduce the image quality of echocardiography. This may have important clinical implications, given the need for periodic echocardiographic surveillance before and during chemotherapy. Therapeutic, III.

Comparing near-infrared conventional diffuse reflectance spectroscopy and hyperspectral imaging for determination of the bulk properties of solid samples by multivariate regression: determination of Mooney viscosity and plasticity indices of natural rubber.

PubMed

Juliano da Silva, Carlos; Pasquini, Celio

2015-01-21

Conventional reflectance spectroscopy (NIRS) and hyperspectral imaging (HI) in the near-infrared region (1000-2500 nm) are evaluated and compared, using, as the case study, the determination of relevant properties related to the quality of natural rubber. Mooney viscosity (MV) and plasticity indices (PI) (PI0 - original plasticity, PI30 - plasticity after accelerated aging, and PRI - the plasticity retention index after accelerated aging) of rubber were determined using multivariate regression models. Two hundred and eighty six samples of rubber were measured using conventional and hyperspectral near-infrared imaging reflectance instruments in the range of 1000-2500 nm. The sample set was split into regression (n = 191) and external validation (n = 95) sub-sets. Three instruments were employed for data acquisition: a line scanning hyperspectral camera and two conventional FT-NIR spectrometers. Sample heterogeneity was evaluated using hyperspectral images obtained with a resolution of 150 × 150 μm and principal component analysis. The probed sample area (5 cm(2); 24,000 pixels) to achieve representativeness was found to be equivalent to the average of 6 spectra for a 1 cm diameter probing circular window of one FT-NIR instrument. The other spectrophotometer can probe the whole sample in only one measurement. The results show that the rubber properties can be determined with very similar accuracy and precision by Partial Least Square (PLS) regression models regardless of whether HI-NIR or conventional FT-NIR produce the spectral datasets. The best Root Mean Square Errors of Prediction (RMSEPs) of external validation for MV, PI0, PI30, and PRI were 4.3, 1.8, 3.4, and 5.3%, respectively. Though the quantitative results provided by the three instruments can be considered equivalent, the hyperspectral imaging instrument presents a number of advantages, being about 6 times faster than conventional bulk spectrometers, producing robust spectral data by ensuring sample representativeness, and minimizing the effect of the presence of contaminants.

Prognostic Significance of Hyponatremia in Acute Intracerebral Hemorrhage: Pooled Analysis of the Intensive Blood Pressure Reduction in Acute Cerebral Hemorrhage Trial Studies.

PubMed

Carcel, Cheryl; Sato, Shoichiro; Zheng, Danni; Heeley, Emma; Arima, Hisatomi; Yang, Jie; Wu, Guojun; Chen, Guofang; Zhang, Shihong; Delcourt, Candice; Lavados, Pablo; Robinson, Thompson; Lindley, Richard I; Wang, Xia; Chalmers, John; Anderson, Craig S

2016-07-01

To determine the association of hyponatremia at presentation with clinical and imaging outcomes in patients with acute intracerebral hemorrhage. Retrospective pooled analysis of prospectively collected data from 3,243 participants of the pilot and main phases of the Intensive Blood Pressure Reduction in Acute Cerebral Hemorrhage Trials 1 and 2 (international, multicenter, open, blinded endpoint, randomized controlled trials designed to assess the effects of early intensive blood pressure lowering in patients with acute intracerebral hemorrhage). Clinical hospital sites in 21 countries. Patients with predominantly mild-moderate severity of spontaneous intracerebral hemorrhage within 6 hours of onset and elevated systolic blood pressure (150-220 mm Hg) were included in the study. Patients were assigned to receive intensive (target systolic blood pressure, < 140 mm Hg within 1 hr) or guideline-recommended (target systolic blood pressure, < 180 mm Hg) blood pressure-lowering therapy. Presentation hyponatremia was defined as serum sodium less than 135 mEq/L. The primary outcome was death at 90 days. Multivariable logistic regression was used to assess the association of hyponatremia with important clinical events. Of 3,002 patients with available data, 349 (12%) had hyponatremia. Hyponatremia was associated with death (18% vs 11%; multivariable-adjusted odds ratio, 1.81; 95% CI, 1.28-2.57; p < 0.001) and larger baseline intracerebral hemorrhage volume (multivariable adjusted, p = 0.046) but not with baseline perihematomal edema volume nor with growth of intracerebral hemorrhage or perihematomal edema during the initial 24 hours. Hyponatremia at presentation is associated with increased mortality in patients with predominantly deep and modest volume intracerebral hemorrhage through mechanisms that seem independent of growth in intracerebral hemorrhage or perihematomal edema.

The Multivariate Temporal Response Function (mTRF) Toolbox: A MATLAB Toolbox for Relating Neural Signals to Continuous Stimuli.

PubMed

Crosse, Michael J; Di Liberto, Giovanni M; Bednar, Adam; Lalor, Edmund C

2016-01-01

Understanding how brains process sensory signals in natural environments is one of the key goals of twenty-first century neuroscience. While brain imaging and invasive electrophysiology will play key roles in this endeavor, there is also an important role to be played by noninvasive, macroscopic techniques with high temporal resolution such as electro- and magnetoencephalography. But challenges exist in determining how best to analyze such complex, time-varying neural responses to complex, time-varying and multivariate natural sensory stimuli. There has been a long history of applying system identification techniques to relate the firing activity of neurons to complex sensory stimuli and such techniques are now seeing increased application to EEG and MEG data. One particular example involves fitting a filter-often referred to as a temporal response function-that describes a mapping between some feature(s) of a sensory stimulus and the neural response. Here, we first briefly review the history of these system identification approaches and describe a specific technique for deriving temporal response functions known as regularized linear regression. We then introduce a new open-source toolbox for performing this analysis. We describe how it can be used to derive (multivariate) temporal response functions describing a mapping between stimulus and response in both directions. We also explain the importance of regularizing the analysis and how this regularization can be optimized for a particular dataset. We then outline specifically how the toolbox implements these analyses and provide several examples of the types of results that the toolbox can produce. Finally, we consider some of the limitations of the toolbox and opportunities for future development and application.

The Multivariate Temporal Response Function (mTRF) Toolbox: A MATLAB Toolbox for Relating Neural Signals to Continuous Stimuli

PubMed Central

Crosse, Michael J.; Di Liberto, Giovanni M.; Bednar, Adam; Lalor, Edmund C.

2016-01-01

Understanding how brains process sensory signals in natural environments is one of the key goals of twenty-first century neuroscience. While brain imaging and invasive electrophysiology will play key roles in this endeavor, there is also an important role to be played by noninvasive, macroscopic techniques with high temporal resolution such as electro- and magnetoencephalography. But challenges exist in determining how best to analyze such complex, time-varying neural responses to complex, time-varying and multivariate natural sensory stimuli. There has been a long history of applying system identification techniques to relate the firing activity of neurons to complex sensory stimuli and such techniques are now seeing increased application to EEG and MEG data. One particular example involves fitting a filter—often referred to as a temporal response function—that describes a mapping between some feature(s) of a sensory stimulus and the neural response. Here, we first briefly review the history of these system identification approaches and describe a specific technique for deriving temporal response functions known as regularized linear regression. We then introduce a new open-source toolbox for performing this analysis. We describe how it can be used to derive (multivariate) temporal response functions describing a mapping between stimulus and response in both directions. We also explain the importance of regularizing the analysis and how this regularization can be optimized for a particular dataset. We then outline specifically how the toolbox implements these analyses and provide several examples of the types of results that the toolbox can produce. Finally, we consider some of the limitations of the toolbox and opportunities for future development and application. PMID:27965557

SU-F-R-07: Radiomics of CT Features and Associations and Correlation with Outcomes Following Lung SBRT

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

Schreibmann, E; Iwinski Sutter, A; Whitaker, D

Objective: To investigate the prognostic significance of image gradients and in predicting clinical outcomes in a patients with non-small cell lung cancer treated with stereotactic body radiotherapy (SBRT) on 71 patients with 83 treated lesions. Methods: The records of patients treated with lung SBRT were retrospectively reviewed. When applicable, SBRT target volumes were modified to exclude any overlap with pleura, chestwall, or mediastinum. The ITK software package was utilized to generate quantitative measures of image intensity, inhomogeneity, shape morphology and first and second-order CT textures. Multivariate and univariate models containing CT features were generated to assess associations with clinicopathologic factors.more » Results: On univariate analysis, tumor size (HR 0.54, p=0.045) sumHU (HR 0.31, p=0.044) and short run grey level emphasis STD (HR 0.22, p=0.019) were associated with regional failure-free survival; meanHU (HR 0.30, p=0.035), long run emphasis (HR 0.21, p=0.011) and long run low grey level emphasis (HR 0.14, p=0.005) was associated with distant failure-free survival (DFFS). No features were significant on multivariate modeling however long run low grey level emphasis had a hazard ratio of 0.12 (p=0.061) for DFFS. Adenocarcinoma and squamous cell carcinoma differed with respect to long run emphasis STD (p=0.024), short run low grey level emphasis STD (p<0.001), and long run low grey level emphasis STD (p=0.024). Multivariate modeling of texture features associated with tumor histology was used to estimate histologies of 18 lesions treated without histologic confirmation. Of these, MVA suggested the same histology as a prior metachronous lung malignancy in 3/7 patients. Conclusion: Extracting radiomics features on clinical datasets was feasible with the ITK package with minimal effort to identify pre-treatment quantitative CT features with prognostic factors for distant control after lung SBRT.« less

Outcomes and resource utilization associated with underage drinking at a level I trauma center.

PubMed

Psoter, Kevin J; Roudsari, Bahman S; Mack, Christopher; Vavilala, Monica S; Jarvik, Jeffrey G

2014-08-01

To examine the association of blood alcohol content (BAC) on hospital-based outcomes and imaging utilization for patients <21 years admitted to a level I trauma center. Retrospective analysis of alcohol-involved injuries in patients 13-20 years, admitted to a level I trauma center from 1996 to 2010. An injury was considered alcohol involved if the patient had a BAC > 0. Multivariable logistic regression was used to compare mortality, discharge destination (home and skilled nursing facility), intensive care unit admission, and operating room use between patients with and without positive BAC for patients 13-15, 16-17, and 18-20 years. Multivariable linear regression was used to compare length of hospitalization. Finally, multivariable negative binomial regression evaluated radiology resource utilization (x-ray, computed tomography [CT], and magnetic resonance imaging). A total of 7,663 patients, 13-20 years old, were admitted over the study period. A positive BAC was reported in 19% of these patients. In general, the presence of alcohol was not associated with mortality rate, length of hospitalization, intensive care unit, and operating room use or discharge status for any age group. However, the presence of alcohol was associated with higher utilization of head (incidence rate ratio [IRR] 1.13, 95% confidence interval [CI] 1.02-1.26), cervical spine (IRR 1.10, 95% CI 1.01-1.22), and thoracic (IRR 1.30, 95% CI 1.05-1.63) CTs in young adults 18-20 years. No differences in CT use were observed in patients 13-15 or 16-17 years. Positive BAC was not significantly associated with adverse outcomes or resource utilization in younger trauma patients. However, the use of certain body region CTs was associated with positive BAC in patients 18-20 years. Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Combined high-field intraoperative magnetic resonance imaging and endoscopy increase extent of resection and progression-free survival for pituitary adenomas

PubMed Central

Sylvester, Peter T.; Evans, John A.; Zipfel, Gregory J.; Chole, Richard A.; Uppaluri, Ravindra; Haughey, Bruce H.; Getz, Anne E.; Silverstein, Julie; Rich, Keith M.; Kim, Albert H.; Dacey, Ralph G.

2014-01-01

Purpose The clinical benefit of combined intraoperative magnetic resonance imaging (iMRI) and endoscopy for transsphenoidal pituitary adenoma resection has not been completely characterized. This study assessed the impact of microscopy, endoscopy, and/or iMRI on progression-free survival, extent of resection status (gross-, near-, and subtotal resection), and operative complications. Methods Retrospective analyses were performed on 446 transsphenoidal pituitary adenoma surgeries at a single institution between 1998 and 2012. Multivariate analyses were used to control for baseline characteristics, differences during extent of resection status, and progression-free survival analysis. Results Additional surgery was performed after iMRI in 56/156 cases (35.9 %), which led to increased extent of resection status in 15/156 cases (9.6 %). Multivariate ordinal logistic regression revealed no increase in extent of resection status following iMRI or endoscopy alone; however, combining these modalities increased extent of resection status (odds ratio 2.05, 95 % CI 1.21–3.46) compared to conventional transsphenoidal microsurgery. Multivariate Cox regression revealed that reduced extent of resection status shortened progression-free survival for near- versus gross-total resection [hazard ratio (HR) 2.87, 95 % CI 1.24–6.65] and sub- versus near-total resection (HR 2.10; 95 % CI 1.00–4.40). Complication comparisons between microscopy, endoscopy, and iMRI revealed increased perioperative deaths for endoscopy versus microscopy (4/209 and 0/237, respectively), but this difference was non-significant considering multiple post hoc comparisons (Fisher exact, p = 0.24). Conclusions Combined use of endoscopy and iMRI increased pituitary adenoma extent of resection status compared to conventional transsphenoidal microsurgery, and increased extent of resection status was associated with longer progression-free survival. Treatment modality combination did not significantly impact complication rate. PMID:24599833

Brain MR image segmentation based on an improved active contour model

PubMed Central

Meng, Xiangrui; Gu, Wenya; Zhang, Jianwei

2017-01-01

It is often a difficult task to accurately segment brain magnetic resonance (MR) images with intensity in-homogeneity and noise. This paper introduces a novel level set method for simultaneous brain MR image segmentation and intensity inhomogeneity correction. To reduce the effect of noise, novel anisotropic spatial information, which can preserve more details of edges and corners, is proposed by incorporating the inner relationships among the neighbor pixels. Then the proposed energy function uses the multivariate Student's t-distribution to fit the distribution of the intensities of each tissue. Furthermore, the proposed model utilizes Hidden Markov random fields to model the spatial correlation between neigh-boring pixels/voxels. The means of the multivariate Student's t-distribution can be adaptively estimated by multiplying a bias field to reduce the effect of intensity inhomogeneity. In the end, we reconstructed the energy function to be convex and calculated it by using the Split Bregman method, which allows our framework for random initialization, thereby allowing fully automated applications. Our method can obtain the final result in less than 1 second for 2D image with size 256 × 256 and less than 300 seconds for 3D image with size 256 × 256 × 171. The proposed method was compared to other state-of-the-art segmentation methods using both synthetic and clinical brain MR images and increased the accuracies of the results more than 3%. PMID:28854235

18F-Fluoride PET/CT tumor burden quantification predicts survival in breast cancer.

PubMed

Brito, Ana E; Santos, Allan; Sasse, André Deeke; Cabello, Cesar; Oliveira, Paulo; Mosci, Camila; Souza, Tiago; Amorim, Barbara; Lima, Mariana; Ramos, Celso D; Etchebehere, Elba

2017-05-30

In bone-metastatic breast cancer patients, there are no current imaging biomarkers to identify which patients have worst prognosis. The purpose of our study was to investigate if skeletal tumor burden determined by 18F-Fluoride PET/CT correlates with clinical outcomes and may help define prognosis throughout the course of the disease. Bone metastases were present in 49 patients. On multivariable analysis, skeletal tumor burden was significantly and independently associated with overall survival (p < 0.0001) and progression free-survival (p < 0.0001). The simple presence of bone metastases was associated with time to bone event (p = 0.0448). We quantified the skeletal tumor burden on 18F-Fluoride PET/CT images of 107 female breast cancer patients (40 for primary staging and the remainder for restaging after therapy). Clinical parameters, primary tumor characteristics and skeletal tumor burden were correlated to overall survival, progression free-survival and time to bone event. The median follow-up time was 19.5 months. 18F-Fluoride PET/CT skeletal tumor burden is a strong independent prognostic imaging biomarker in breast cancer patients.

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

Miller, Michael K; Parish, Chad M

Helium accumulation negatively impacts structural materials used in neutron-irradiated environments, such as fission and fusion reactors. Next-generation fission and fusion reactors will require structural materials, such as steels, resistant to large neutron doses yet see service temperatures in the range most affected by helium embrittlement. Previous work has indicated the difficulty of experimentally differentiating nanometer-sized helium bubbles from the Ti-Y-O rich nanoclustsers (NCs) in radiation-tolerant nanostructured ferritic alloys (NFAs). Because the NCs are expected to sequester helium away from grain boundaries and reduce embrittlement, experimental methods to study simultaneously the NC and bubble populations are needed. In this study, aberration-correctedmore » scanning transmission electron microscopy (STEM) results combining high-collection-efficiency X-ray spectrum images (SIs), multivariate statistical analysis (MVSA), and Fresnel-contrast bright-field STEM imaging have been used for such a purpose. Results indicate that Fresnel-contrast imaging, with careful attention to TEM-STEM reciprocity, differentiates bubbles from NCs, and MVSA of X-ray SIs unambiguously identifies NCs. Therefore, combined Fresnel-contrast STEM and X-ray SI is an effective STEM-based method to characterize helium-bearing NFAs.« less

Multi-object segmentation using coupled nonparametric shape and relative pose priors

NASA Astrophysics Data System (ADS)

Uzunbas, Mustafa Gökhan; Soldea, Octavian; Çetin, Müjdat; Ünal, Gözde; Erçil, Aytül; Unay, Devrim; Ekin, Ahmet; Firat, Zeynep

2009-02-01

We present a new method for multi-object segmentation in a maximum a posteriori estimation framework. Our method is motivated by the observation that neighboring or coupling objects in images generate configurations and co-dependencies which could potentially aid in segmentation if properly exploited. Our approach employs coupled shape and inter-shape pose priors that are computed using training images in a nonparametric multi-variate kernel density estimation framework. The coupled shape prior is obtained by estimating the joint shape distribution of multiple objects and the inter-shape pose priors are modeled via standard moments. Based on such statistical models, we formulate an optimization problem for segmentation, which we solve by an algorithm based on active contours. Our technique provides significant improvements in the segmentation of weakly contrasted objects in a number of applications. In particular for medical image analysis, we use our method to extract brain Basal Ganglia structures, which are members of a complex multi-object system posing a challenging segmentation problem. We also apply our technique to the problem of handwritten character segmentation. Finally, we use our method to segment cars in urban scenes.

Potential of Near-Infrared Chemical Imaging as Process Analytical Technology Tool for Continuous Freeze-Drying.

PubMed

Brouckaert, Davinia; De Meyer, Laurens; Vanbillemont, Brecht; Van Bockstal, Pieter-Jan; Lammens, Joris; Mortier, Séverine; Corver, Jos; Vervaet, Chris; Nopens, Ingmar; De Beer, Thomas

2018-04-03

Near-infrared chemical imaging (NIR-CI) is an emerging tool for process monitoring because it combines the chemical selectivity of vibrational spectroscopy with spatial information. Whereas traditional near-infrared spectroscopy is an attractive technique for water content determination and solid-state investigation of lyophilized products, chemical imaging opens up possibilities for assessing the homogeneity of these critical quality attributes (CQAs) throughout the entire product. In this contribution, we aim to evaluate NIR-CI as a process analytical technology (PAT) tool for at-line inspection of continuously freeze-dried pharmaceutical unit doses based on spin freezing. The chemical images of freeze-dried mannitol samples were resolved via multivariate curve resolution, allowing us to visualize the distribution of mannitol solid forms throughout the entire cake. Second, a mannitol-sucrose formulation was lyophilized with variable drying times for inducing changes in water content. Analyzing the corresponding chemical images via principal component analysis, vial-to-vial variations as well as within-vial inhomogeneity in water content could be detected. Furthermore, a partial least-squares regression model was constructed for quantifying the water content in each pixel of the chemical images. It was hence concluded that NIR-CI is inherently a most promising PAT tool for continuously monitoring freeze-dried samples. Although some practicalities are still to be solved, this analytical technique could be applied in-line for CQA evaluation and for detecting the drying end point.

Identification of species and geographical strains of Sitophilus oryzae and Sitophilus zeamais using the visible/near-infrared hyperspectral imaging technique.

PubMed

Cao, Yang; Zhang, Chaojie; Chen, Quansheng; Li, Yanyu; Qi, Shuai; Tian, Lin; Ren, YongLin

2015-08-01

Identifying stored-product insects is essential for granary management. Automated, computer-based classification methods are rapidly developing in many areas. A hyperspectral imaging technique could potentially be developed to identify stored-product insect species and geographical strains. This study tested and adapted the technique using four geographical strains of each of two insect species, the rice weevil and maize weevil, to collect and analyse the resultant hyperspectral data. Three characteristic images that corresponded to the dominant wavelengths, 505, 659 and 955 nm, were selected by multivariate image analysis. Each image was processed, and 22 morphological and textural features from regions of interest were extracted as the inputs for an identification model. We found the backpropagation neural network model to be the superior method for distinguishing between the insect species and geographical strains. The overall recognition rates of the classification model for insect species were 100 and 98.13% for the calibration and prediction sets respectively, while the rates of the model for geographical strains were 94.17 and 86.88% respectively. This study has demonstrated that hyperspectral imaging, together with the appropriate recognition method, could provide a potential instrument for identifying insects and could become a useful tool for identification of Sitophilus oryzae and Sitophilus zeamais to aid in the management of stored-product insects. © 2014 Society of Chemical Industry.

Left ventricular eccentricity index measured with SPECT myocardial perfusion imaging: An additional parameter of adverse cardiac remodeling.

PubMed

Gimelli, Alessia; Liga, Riccardo; Clemente, Alberto; Marras, Gavino; Kusch, Annette; Marzullo, Paolo

2017-01-12

Single-photon emission computed-tomography (SPECT) allows the quantification of LV eccentricity index (EI), a measure of cardiac remodeling. We sought to evaluate the feasibility of EI measurement with SPECT myocardial perfusion imaging and its interactions with relevant LV functional and structural parameters. Four-hundred and fifty-six patients underwent myocardial perfusion imaging on a Cadmium-Zinc-Telluride (CZT) camera. The summed rest, stress, and difference scores were calculated. From rest images, the LV end-diastolic (EDV) and end-systolic volumes, ejection fraction (EF), and peak filling rate (PFR) were calculated. In every patient, the EI, ranging from 0 (sphere) to 1 (line), was computed using a dedicated software (QGS/QPS; Cedars-Sinai Medical Center). Three-hundred and thirty-eight/456 (74%) patients showed a normal EF (>50%), while 26% had LV systolic dysfunction. The EI was computed from CZT images with excellent reproducibility (interclass correlation coefficient: 0.99, 95% CI 0.98-0.99). More impaired EI values correlated with the presence of a more abnormal LV perfusion (P < .001), function (EF and PFR, P < .001), and structure (EDV, P < .001). On multivariate analysis, higher EDV (P < .001) and depressed EF (P = .014) values were independent predictors of abnormal EI. The evaluation of LV eccentricity is feasible on gated CZT images. Abnormal EI associates with significant cardiac structural and functional abnormalities.

Multivariate analysis in the pharmaceutical industry: enabling process understanding and improvement in the PAT and QbD era.

PubMed

Ferreira, Ana P; Tobyn, Mike

2015-01-01

In the pharmaceutical industry, chemometrics is rapidly establishing itself as a tool that can be used at every step of product development and beyond: from early development to commercialization. This set of multivariate analysis methods allows the extraction of information contained in large, complex data sets thus contributing to increase product and process understanding which is at the core of the Food and Drug Administration's Process Analytical Tools (PAT) Guidance for Industry and the International Conference on Harmonisation's Pharmaceutical Development guideline (Q8). This review is aimed at providing pharmaceutical industry professionals an introduction to multivariate analysis and how it is being adopted and implemented by companies in the transition from "quality-by-testing" to "quality-by-design". It starts with an introduction to multivariate analysis and the two methods most commonly used: principal component analysis and partial least squares regression, their advantages, common pitfalls and requirements for their effective use. That is followed with an overview of the diverse areas of application of multivariate analysis in the pharmaceutical industry: from the development of real-time analytical methods to definition of the design space and control strategy, from formulation optimization during development to the application of quality-by-design principles to improve manufacture of existing commercial products.

Quantitative analysis of thyroid tumors vascularity: A comparison between 3-D contrast-enhanced ultrasound and 3-D Power Doppler on benign and malignant thyroid nodules.

PubMed

Caresio, Cristina; Caballo, Marco; Deandrea, Maurilio; Garberoglio, Roberto; Mormile, Alberto; Rossetto, Ruth; Limone, Paolo; Molinari, Filippo

2018-05-15

To perform a comparative quantitative analysis of Power Doppler ultrasound (PDUS) and Contrast-Enhancement ultrasound (CEUS) for the quantification of thyroid nodules vascularity patterns, with the goal of identifying biomarkers correlated with the malignancy of the nodule with both imaging techniques. We propose a novel method to reconstruct the vascular architecture from 3-D PDUS and CEUS images of thyroid nodules, and to automatically extract seven quantitative features related to the morphology and distribution of vascular network. Features include three tortuosity metrics, the number of vascular trees and branches, the vascular volume density, and the main spatial vascularity pattern. Feature extraction was performed on 20 thyroid lesions (ten benign and ten malignant), of which we acquired both PDUS and CEUS. MANOVA (multivariate analysis of variance) was used to differentiate benign and malignant lesions based on the most significant features. The analysis of the extracted features showed a significant difference between the benign and malignant nodules for both PDUS and CEUS techniques for all the features. Furthermore, by using a linear classifier on the significant features identified by the MANOVA, benign nodules could be entirely separated from the malignant ones. Our early results confirm the correlation between the morphology and distribution of blood vessels and the malignancy of the lesion, and also show (at least for the dataset used in this study) a considerable similarity in terms of findings of PDUS and CEUS imaging for thyroid nodules diagnosis and classification. © 2018 American Association of Physicists in Medicine.

Multi-variant study of obesity risk genes in African Americans: The Jackson Heart Study.

PubMed

Liu, Shijian; Wilson, James G; Jiang, Fan; Griswold, Michael; Correa, Adolfo; Mei, Hao

2016-11-30

Genome-wide association study (GWAS) has been successful in identifying obesity risk genes by single-variant association analysis. For this study, we designed steps of analysis strategy and aimed to identify multi-variant effects on obesity risk among candidate genes. Our analyses were focused on 2137 African American participants with body mass index measured in the Jackson Heart Study and 657 common single nucleotide polymorphisms (SNPs) genotyped at 8 GWAS-identified obesity risk genes. Single-variant association test showed that no SNPs reached significance after multiple testing adjustment. The following gene-gene interaction analysis, which was focused on SNPs with unadjusted p-value<0.10, identified 6 significant multi-variant associations. Logistic regression showed that SNPs in these associations did not have significant linear interactions; examination of genetic risk score evidenced that 4 multi-variant associations had significant additive effects of risk SNPs; and haplotype association test presented that all multi-variant associations contained one or several combinations of particular alleles or haplotypes, associated with increased obesity risk. Our study evidenced that obesity risk genes generated multi-variant effects, which can be additive or non-linear interactions, and multi-variant study is an important supplement to existing GWAS for understanding genetic effects of obesity risk genes. Copyright © 2016 Elsevier B.V. All rights reserved.

Brain shaving: adaptive detection for brain PET data

NASA Astrophysics Data System (ADS)

Grecchi, Elisabetta; Doyle, Orla M.; Bertoldo, Alessandra; Pavese, Nicola; Turkheimer, Federico E.

2014-05-01

The intricacy of brain biology is such that the variation of imaging end-points in health and disease exhibits an unpredictable range of spatial distributions from the extremely localized to the very diffuse. This represents a challenge for the two standard approaches to analysis, the mass univariate and the multivariate that exhibit either strong specificity but not as good sensitivity (the former) or poor specificity and comparatively better sensitivity (the latter). In this work, we develop an analytical methodology for positron emission tomography that operates an extraction (‘shaving’) of coherent patterns of signal variation while maintaining control of the type I error. The methodology operates two rotations on the image data, one local using the wavelet transform and one global using the singular value decomposition. The control of specificity is obtained by using the gap statistic that selects, within each eigenvector, a subset of significantly coherent elements. Face-validity of the algorithm is demonstrated using a paradigmatic data-set with two radiotracers, [11C]-raclopride and [11C]-(R)-PK11195, measured on the same Huntington's disease patients, a disorder with a genetic based diagnosis. The algorithm is able to detect the two well-known separate but connected processes of dopamine neuronal loss (localized in the basal ganglia) and neuroinflammation (diffusive around the whole brain). These processes are at the two extremes of the distributional envelope, one being very sparse and the latter being perfectly Gaussian and they are not adequately detected by the univariate and the multivariate approaches.

Image guided radiation therapy may result in improved local control in locally advanced lung cancer patients.

PubMed

Kilburn, Jeremy M; Soike, Michael H; Lucas, John T; Ayala-Peacock, Diandra; Blackstock, William; Isom, Scott; Kearns, William T; Hinson, William H; Miller, Antonius A; Petty, William J; Munley, Michael T; Urbanic, James J

2016-01-01

Image guided radiation therapy (IGRT) is designed to ensure accurate and precise targeting, but whether improved clinical outcomes result is unknown. A retrospective comparison of locally advanced lung cancer patients treated with and without IGRT from 2001 to 2012 was conducted. Median local failure-free survival (LFFS), regional, locoregional failure-free survival (LRFFS), distant failure-free survival, progression-free survival, and overall survival (OS) were estimated. Univariate and multivariate models assessed the association between patient- and treatment-related covariates and local failure. A total of 169 patients were treated with definitive radiation therapy and concurrent chemotherapy with a median follow-up of 48 months in the IGRT cohort and 96 months in the non-IGRT cohort. IGRT was used in 36% (62 patients) of patients. OS was similar between cohorts (2-year OS, 47% vs 49%, P = .63). The IGRT cohort had improved 2-year LFFS (80% vs 64%, P = .013) and LRFFS (75% and 62%, P = .04). Univariate analysis revealed IGRT and treatment year improved LFFS, whereas group stage, dose, and positron emission tomography/computed tomography planning had no impact. IGRT remained significant in the multivariate model with an adjusted hazard ratio of 0.40 (P = .01). Distant failure-free survival (58% vs 59%, P = .67) did not differ significantly. IGRT with daily cone beam computed tomography confers an improvement in the therapeutic ratio relative to patients treated without this technology. Copyright © 2015 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.

Magnetic Resonance Imaging Findings Predict the Recurrence of Chronic Subdural Hematoma

PubMed Central

GOTO, Haruo; ISHIKAWA, Osamu; NOMURA, Masashi; TANAKA, Kentaro; NOMURA, Seiji; MAEDA, Keiichiro

2015-01-01

The exact predictive factors for postoperative recurrence of chronic subdural hematoma (CSDH) are still unknown. Based on the preoperative magnetic resonance imaging (MRI), low recurrence rate of T1-hyperintensity hematoma was previously reported. We investigated the other types of radiological findings which are related to the recurrence rate of CSDH in large number of patients analyzed by multivariate logistic regression model. Preoperative MRI and postoperative computed tomography (CT) were performed and the influence of the preoperative use of antiplatelet or anticoagulant drugs was also studied. The overall recurrence rate was 9.3% (47 of 505 hematomas). The MRI T1-iso/hypointensity group showed a significantly higher recurrence rate (18.2%, 29 of 159) compared to the other groups (5.2%, 18 of 346; p < 0.001). Multivariate logistic regression analysis showed T1 classification was the solo significant prognostic predictor among various factors such as bilateral hematoma, antiplatelet or anticoagulant drug usage, residual hematoma on postoperative CT, and MRI classification (p < 0.001): adjusted odds ratio for the recurrence in T1-iso/hypointensity group relative to the T1-hyperintensity group was 5.58 [95% confidence interval (CI), 2.09–14.86] (p = 0.001). Postoperative residual hematoma and antiplatelet or anticoagulant drug usage did not increase the recurrence risk. The preoperative MRI findings, especially T1WI findings, have predictive value for postoperative recurrence of CSDH and the T1-iso/hypointensity group can be assumed to be a high recurrence risk group. PMID:25746312

A new method for computer-assisted detection, definition and differentiation of the urinary calculi.

PubMed

Yildirim, Duzgun; Ozturk, Ovunc; Tutar, Onur; Nurili, Fuad; Bozkurt, Halil; Kayadibi, Huseyin; Karaarslan, Ercan; Bakan, Selim

2014-09-01

Urinary stones are common and can be diagnosed with computed tomography (CT) easily. In this study, we aimed to specify the opacity characteristics of various types of calcified foci that develop through the urinary system by using an image analysis program. With this method, we try to differentiate the calculi from the non-calculous opacities and also we aimed to present how to identify the characteristic features of renal and ureteral calcules. We obtained the CT studies of the subjects (n = 48, mean age = 41 years) by using a dual source CT imaging system. We grouped the calculi detected in the dual-energy CT sections as renal (n = 40) or ureteric (n = 45) based on their locations. Other radio-opaque structures that were identified outside but within close proximity of the urinary tract were recorded as calculi "mimickers". We used ImageJ program for morphological analysis. All the acquired data were analyzed statistically. According to thorough morphological parameters, there were statistically significant differences in the angle and Feret angle values between calculi and mimickers (p < 0.001). Multivariate logistical regression analysis showed that Minor Axis and Feret angle parameters can be used to distinguish between ureteric (p = 0.003) and kidney (p = 0.001) stones. Computer-based morphologic parameters can be used simply to differentiate between calcular and noncalcular densities on CT and also between renal and ureteric stones.

Matrix assisted laser desorption ionization mass spectrometry imaging identifies markers of ageing and osteoarthritic cartilage

PubMed Central

2014-01-01

Introduction Cartilage protein distribution and the changes that occur in cartilage ageing and disease are essential in understanding the process of cartilage ageing and age related diseases such as osteoarthritis. The aim of this study was to investigate the peptide profiles in ageing and osteoarthritic (OA) cartilage sections using matrix assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI). Methods The distribution of proteins in young, old and OA equine cartilage was compared following tryptic digestion of cartilage slices and MALDI-MSI undertaken with a MALDI SYNAPT™ HDMS system. Protein identification was undertaken using database searches following multivariate analysis. Peptide intensity differences between young, ageing and OA cartilage were imaged with Biomap software. Analysis of aggrecanase specific cleavage patterns of a crude cartilage proteoglycan extract were used to validate some of the differences in peptide intensity identified. Immunohistochemistry studies validated the differences in protein abundance. Results Young, old and OA equine cartilage was discriminated based on their peptide signature using discriminant analysis. Proteins including aggrecan core protein, fibromodulin, and cartilage oligomeric matrix protein were identified and localised. Fibronectin peptides displayed a stronger intensity in OA cartilage. Age-specific protein markers for collectin-43 and cartilage oligomeric matrix protein were identified. In addition potential fibromodulin and biglycan peptides targeted for degradation in OA were detected. Conclusions MALDI-MSI provided a novel platform to study cartilage ageing and disease enabling age and disease specific peptides in cartilage to be elucidated and spatially resolved. PMID:24886698

Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

NASA Astrophysics Data System (ADS)

Glascock, M. D.; Neff, H.; Vaughn, K. J.

2004-06-01

The application of instrumental neutron activation analysis and multivariate statistics to archaeological studies of ceramics and clays is described. A small pottery data set from the Nasca culture in southern Peru is presented for illustration.

A Study of Effects of MultiCollinearity in the Multivariable Analysis

PubMed Central

Yoo, Wonsuk; Mayberry, Robert; Bae, Sejong; Singh, Karan; (Peter) He, Qinghua; Lillard, James W.

2015-01-01

A multivariable analysis is the most popular approach when investigating associations between risk factors and disease. However, efficiency of multivariable analysis highly depends on correlation structure among predictive variables. When the covariates in the model are not independent one another, collinearity/multicollinearity problems arise in the analysis, which leads to biased estimation. This work aims to perform a simulation study with various scenarios of different collinearity structures to investigate the effects of collinearity under various correlation structures amongst predictive and explanatory variables and to compare these results with existing guidelines to decide harmful collinearity. Three correlation scenarios among predictor variables are considered: (1) bivariate collinear structure as the most simple collinearity case, (2) multivariate collinear structure where an explanatory variable is correlated with two other covariates, (3) a more realistic scenario when an independent variable can be expressed by various functions including the other variables. PMID:25664257

A Study of Effects of MultiCollinearity in the Multivariable Analysis.

PubMed

Yoo, Wonsuk; Mayberry, Robert; Bae, Sejong; Singh, Karan; Peter He, Qinghua; Lillard, James W

2014-10-01

A multivariable analysis is the most popular approach when investigating associations between risk factors and disease. However, efficiency of multivariable analysis highly depends on correlation structure among predictive variables. When the covariates in the model are not independent one another, collinearity/multicollinearity problems arise in the analysis, which leads to biased estimation. This work aims to perform a simulation study with various scenarios of different collinearity structures to investigate the effects of collinearity under various correlation structures amongst predictive and explanatory variables and to compare these results with existing guidelines to decide harmful collinearity. Three correlation scenarios among predictor variables are considered: (1) bivariate collinear structure as the most simple collinearity case, (2) multivariate collinear structure where an explanatory variable is correlated with two other covariates, (3) a more realistic scenario when an independent variable can be expressed by various functions including the other variables.