Noninvasive high resolution mechanical strain maps of the spine intervertebral disc using nonrigid registration of magnetic resonance images

High resolution strain measurements are of particular interest in load bearing tissues such as the intervertebral disc (IVD), permitting characterization of biomechanical conditions which could lead to injury and degenerative outcomes. Magnetic resonance (MR) imaging produces excellent image contrast in cartilaginous tissues, allowing for image-based strain determination. Nonrigid registration (NRR) of MR images has previously demonstrated sub-voxel registration accuracy although its accuracy and precision in determining strain has not been evaluated. Accuracy and precision of NRR-derived strain measurements were evaluated using computer generated deformations applied to both computer generated images and MR images. Two different measures of registration similarity--the cost function which drives the registration algorithm--were compared: Mutual Information (MI) and Least Squares (LS). Strain error was evaluated with respect to signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and strain heterogeneity. Additionally, the creep strain response from an in vitro loaded porcine IVD is shown and comparisons between similarity measures are presented. MI showed a decrease in strain precision with increasing CNR and decreasing SNR while LS was insensitive to both. Both similarity measures showed a decrease in strain precision with increasing strain heterogeneity. When computer generated heterogeneous strains were applied to MR images of the IVD, LS showed substantially lower strain error in comparison to MI. Results suggest that LS-driven NRR provides a more accurate image-based method for mapping large and heterogeneous strain fields and this method can be applied to studies of the IVD and, potentially, other soft tissues which present sufficient image texture.     

Evaluation of radiomic texture feature error due to MRI acquisition and reconstruction: A simulation study utilizing ground truth

The purpose of this study was to examine the dependence of image texture features on MR acquisition parameters and reconstruction using a digital MR imaging phantom. MR signal was simulated in a parallel imaging radiofrequency coil setting as well as a single element volume coil setting, with varying levels of acquisition noise, three acceleration factors, and four image reconstruction algorithms. Twenty-six texture features were measured on the simulated images, ground truth images, and clinical brain images. Subtle algorithm-dependent errors were observed on reconstructed phantom images, even in the absence of added noise. Sources of image error include Gibbs ringing at image edge gradients (tissue interfaces) and well-known artifacts due to high acceleration; two of the iterative reconstruction algorithms studied were able to mitigate these image errors. The difference of the texture features from ground truth, and their variance over reconstruction algorithm and parallel imaging acceleration factor, were compared to the clinical “effect size”, i.e., the feature difference between high- and low-grade tumors on T1- and T2-weighted brain MR images of twenty glioma patients. The measured feature error (difference from ground truth) was small for some features, but substantial for others. The feature variance due to reconstruction algorithm and acceleration factor were generally smaller than the clinical effect size. Certain texture features may be preserved by MR imaging, but adequate precautions need to be taken regarding their validity and reliability. We present a general simulation framework for assessing the robustness and accuracy of radiomic textural features under various MR acquisition/reconstruction scenarios.     

Detection of Mild Cognitive Impairment Using Kernel Density Estimation Based Texture Analysis of the Corpus Callosum in Brain MR Images

ObjectivesMild Cognitive Impairment (MCI) is the prodromal stage of Alzheimer's disease (AD), which is a progressive and fatal neurodegenerative disorder. Detection of MCI condition can enable early diagnosis resulting in timely intervention to delay the disease progression. Onset of MCI causes tissue alterations in Corpus Callosum (CC) of the brain. Texture analysis of brain Magnetic Resonance (MR) images aids in characterising these imperceptible changes. In this study, Kernel Density Estimation (KDE) technique is used to analyse the textural variations in CC to detect MCI condition.Materials and methodThe pre-processed brain MR images are obtained from a public access database. Reaction Diffusion level set is employed to segment CC from sagittal slices of the images. Kernel density estimation method is applied to study the local intensity variations within the segmented CC. Statistical features quantifying these variations are extracted from the KDE values. These features are used to differentiate MCI condition using linear classifiers based on discriminant analysis and support vector machine. The results are compared with conventional Grey Level Co-occurrence Matrix (GLCM) features for validation.ResultsThe KDE-based texture features extracted from CC show significant variation between normal and MCI classes. Results demonstrate that this approach can differentiate MCI condition with high accuracy and specificity of 81.3% and 82.7%, respectively. The KDE-based features perform better when compared with GLCM features for distinguishing MCI.ConclusionsThe KDE-based texture features are able to capture the subtle changes occurring in CC at the MCI stage. This technique achieves comparable performance to other state-of-the-art methods with reduced number of features. Efficiency of the KDE-based texture analysis confirms that the proposed computer assisted technique can be used for mass screening of MCI, which can aid in handling the disease severity.     

Tendon strain measurements with dynamic ultrasound images: evaluation of digital image correlation

Strain is an essential metric in tissue mechanics. Strains and strain distributions during functional loads can help identify damaged and pathologic regions as well as quantify functional compromise. Noninvasive strain measurement in vivo is difficult to perform. The goal of this in vitro study is to determine the efficacy of digital image correlation (DIC) methods to measure strain in B-mode ultrasound images. The Achilles tendons of eight male Wistar rats were removed and mechanically cycled between 0 and 1% strain. Three cine video images were captured for each specimen: (1) optical video for manual tracking of optical markers; (2) optical video for DIC tracking of optical surface markers; and (3) ultrasound video for DIC tracking of image texture within the tissue. All three imaging modalities were similarly able to measure tendon strain during cyclic testing. Manual/ImageJ-based strain values linearly correlated with DIC (optical marker)-based strain values for all eight tendons with a slope of 0.970. DIC (optical marker)-based strain values linearly correlated with DIC (ultrasound texture)-based strain values for all eight tendons with a slope of 1.003. Strain measurement using DIC was as accurate as manual image tracking methods, and DIC tracking was equally accurate when tracking ultrasound texture as when tracking optical markers. This study supports the use of DIC to calculate strains directly from the texture present in standard B-mode ultrasound images and supports the use of DIC for in vivo strain measurement using ultrasound images without additional markers, either artificially placed (for optical tracking) or anatomically in view (i.e., bony landmarks and/or muscle-tendon junctions).     

Texture Analysis of T2-Weighted MR Images to Assess Acute Inflammation in Brain MS Lesions

Brain blood barrier breakdown as assessed by contrast-enhanced (CE) T1-weighted MR imaging is currently the standard radiological marker of inflammatory activity in multiple sclerosis (MS) patients. Our objective was to evaluate the performance of an alternative model assessing the inflammatory activity of MS lesions by texture analysis of T2-weighted MR images. Twenty-one patients with definite MS were examined on the same 3.0T MR system by T2-weighted, FLAIR, diffusion-weighted and CE-T1 sequences. Lesions and mirrored contralateral areas within the normal appearing white matter (NAWM) were characterized by texture parameters computed from the gray level co-occurrence and run length matrices, and by the apparent diffusion coefficient (ADC). Statistical differences between MS lesions and NAWM were analyzed. ROC analysis and leave-one-out cross-validation were performed to evaluate the performance of individual parameters, and multi-parametric models using linear discriminant analysis (LDA), partial least squares (PLS) and logistic regression (LR) in the identification of CE lesions. ADC and all but one texture parameter were significantly different within white matter lesions compared to within NAWM (p < 0.0167). Using LDA, an 8-texture parameter model identified CE lesions with a sensitivity Se = 70% and a specificity Sp = 76%. Using LR, a 10-texture parameter model performed better with Se = 86% / Sp = 84%. Using PLS, a 6-texture parameter model achieved the highest accuracy with Se = 88% / Sp = 81%. Texture parameter from T2-weighted images can assess brain inflammatory activity with sufficient accuracy to be considered as a potential alternative to enhancement on CE T1-weighted images.     

Color image analysis for quantifying renal tumor angiogenesis

OBJECTIVE: To segment and quantify microvessels in renal tumor angiogenesis based on a color image analysis method and to improve the accuracy and reproducibility of quantifying microvessel density. STUDY DESIGN: The segmentation task was based on a supervised learning scheme. First, 12 color features (RGB, HSI, I1I2I3 and L*a*b*) were extracted from a training set. The feature selection procedure selected I2L*S features as the best color feature vector. Then we segmented microvessels using the discriminant function made using the minimum error rate classification rule of Bayesian decision theory. In the quantification step, after applying a connected component-labeling algorithm, microvessels with discontinuities were connected and touching microvessels separated. We tested the proposed method on 23 images. RESULTS: The results were evaluated by comparing them with manual quantification of the same images. The comparison revealed that our computerized microvessel counting correlated highly with manual counting by an expert (r = 0.95754). The association between the number of microvessels after the initial segmentation and manual quantification was also assessed using Pearson's correlation coefficient (r = 0.71187). The results indicate that our method is better than conventional computerized image analysis methods. CONCLUSION: Our method correlated highly with quantification by an expert and could become a way to improve the accuracy, feasibility and reproducibility of quantifying microvessel density. We anticipate that it will become a useful diagnostic tool for angiogenesis studies.     

Quantification of meniscal volume by segmentation of 3T magnetic resonance images

Meniscal injuries place the knee at risk for early osteoarthritis (OA) because they disrupt their load-bearing capabilities. Partial resection is routinely performed to alleviate symptomatic meniscal tears. While the removal of meniscal tissue may not be the only factor associated with partial meniscectomy outcome, the amount removed certainly contributes to functional loss. It is unknown, however, whether there is a critical amount of meniscal tissue that can be removed without diminishing the structure's chondroprotective role. In order to examine the existence of such a threshold, it is necessary to accurately quantify meniscal volume both before and after partial meniscectomy to determine the amount of meniscal tissue removed. Therefore, our goal was to develop and validate an MR-based method for assessing meniscal volume. The specific aims were: (1) to evaluate the feasibility of the MR-based segmentation method; (2) to determine the method's reliability for repeated measurements; and (3) to validate its accuracy in situ. MR images were obtained on a 3T magnet, and each scan was segmented using a biplanar approach. The MR-based volumes for each specimen were compared to those measured by water displacement. The results indicate that the biplanar approach of measuring meniscal volumes is accurate and reliable. The calculated volumes of the menisci were within 5% of the true values, the coefficients of variation were 4%, and the intraclass correlation coefficients were greater than 0.96. These data demonstrate that this method could be used to measure the amount of meniscal tissue excised during partial meniscectomy to within 125.7 mm(3).     

Glioblastomas and brain metastases differentiation following an MRI texture analysis-based radiomics approach

PurposeTo evaluate the potential of 2D texture features extracted from magnetic resonance (MR) images for differentiating brain metastasis (BM) and glioblastomas (GBM) following a radiomics approach.MethodsThis retrospective study included 50 patients with BM and 50 with GBM who underwent T1-weighted MRI between December 2010 and January 2017. Eighty-eight rotation-invariant texture features were computed for each segmented lesion using six texture analysis methods. These features were also extracted from the four images obtained after applying the discrete wavelet transform (88 features × 4 images). Three feature selection methods and five predictive models were evaluated. A 5-fold cross-validation scheme was used to randomly split the study group into training (80 patients) and testing (20 patients), repeating the process ten times. Classification was evaluated computing the average area under the receiver operating characteristic curve. Sensibility, specificity and accuracy were also computed. The whole process was tested quantizing the images with different gray-level values to evaluate their influence in the final results.ResultsHighest classification accuracy was obtained using the original images quantized with 128 gray-levels and a feature selection method based on the p-value. The best overall performance was achieved using a support vector machine model with a subset of 32 features (AUC = 0.896 ± 0.067, sensitivity of 82% and specificity of 80%). Naïve Bayes and k-nearest neighbors models showed also valuable results (AUC ≈ 0.8) with a lower number of features (<13), thus suggesting that these models may be more generalizable when using external validations.ConclusionThe proposed radiomics MRI approach is able to discriminate between GBM and BM with high accuracy employing a set of 2D texture features, thus helping in the diagnosis of brain lesions in a fast and non-invasive way.     

Measurement of in vivo anterior cruciate ligament strain during dynamic jump landing

Despite recent attention in the literature, anterior cruciate ligament (ACL) injury mechanisms are controversial and incidence rates remain high. One explanation is limited data on in vivo ACL strain during high-risk, dynamic movements. The objective of this study was to quantify ACL strain during jump landing. Marker-based motion analysis techniques were integrated with fluoroscopic and magnetic resonance (MR) imaging techniques to measure dynamic ACL strain non-invasively. First, eight subjects' knees were imaged using MR. From these images, the cortical bone and ACL attachment sites of the tibia and femur were outlined to create 3D models. Subjects underwent motion analysis while jump landing using reflective markers placed directly on the skin around the knee. Next, biplanar fluoroscopic images were taken with the markers in place so that the relative positions of each marker to the underlying bone could be quantified. Numerical optimization allowed jumping kinematics to be superimposed on the knee model, thus reproducing the dynamic in vivo joint motion. ACL length, knee flexion, and ground reaction force were measured. During jump landing, average ACL strain peaked 55±14 ms (mean and 95% confidence interval) prior to ground impact, when knee flexion angles were lowest. The peak ACL strain, measured relative to its length during MR imaging, was 12±7%. The observed trends were consistent with previously described neuromuscular patterns. Unrestricted by field of view or low sampling rate, this novel approach provides a means to measure kinematic patterns that elevate ACL strains and that provide new insights into ACL injury mechanisms.     

Effect of slice thickness on brain magnetic resonance image texture analysis

Background  

The accuracy of texture analysis in clinical evaluation of magnetic resonance images depends considerably on imaging arrangements and various image quality parameters. In this paper, we study the effect of slice thickness on brain tissue texture analysis using a statistical approach and classification of T1-weighted images of clinically confirmed multiple sclerosis patients.     

In vivo assessment of nodularin-induced hepatotoxicity in the rat using magnetic resonance techniques (MRI, MRS and EPR oximetry)

Acute nodularin-induced hepatotoxicity was assessed in vivo, in rats using magnetic resonance (MR) techniques, including MR imaging (MRI), MR spectroscopy (MRS), and electron paramagnetic resonance (EPR) oximetry. Nodularin is a cyclic hepatotoxin isolated from the cyanobacterium Nodularia spumigena. Three hours following the intraperitoneal (i.p.) administration of nodularin (LD50), a region of 'damage', characterized by an increase in signal intensity, was observed proximal to the porta hepatis (PH) region in T2-weighted MR images of rat liver. Image analysis of these regions of apparent 'damage' indicated a statistically significant increase in signal intensity around the PH region following nodularin administration, in comparison with controls and regions peripheral to the PH region. An increase in signal intensity was also observed proximal to the PH region in water chemical shift selective images (CSSI) of nodularin-treated rat livers, indicating that the increased signal observed by MRI is an oedematous response to the toxin. Microscopic assessment (histology and electron microscopy) and serum liver enzyme function tests (aminotransferase (ALT) and aspartate ALT (AST)) confirmed the nodularin-induced tissue injury observed by MRI. In vivo and in vitro MRS was used to detect alterations in metabolites, such as lipids, Glu+Gln, and choline, during the hepatotoxic response (2-3 h post-exposure). Biochemical assessment of perchloric acid extracts of nodularin-treated rat livers were used to confirm the MRS results. In vivo EPR oximetry was used to monitor decreasing hepatic pO2 (approximately 2-fold from controls) 2-3 h following nodularin exposure. In vivo MR techniques (MRI, MRS and EPR oximetry) are able to highlight effects that may not have been evident in single end point studies, and are ideal methods to follow tissue injury progression in longitudinally, increasing the power of a study through repeated measures, and decreasing the number of animals to perform a similar study using histological or biochemical techniques.     

Colorization and automated segmentation of human T2 MR brain images for characterization of soft tissues

Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segmentation method (both hard and soft segmentation) to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using prior anatomical knowledge). Results have been successfully validated on human T2-weighted (T2) brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described.     

Comparison of methods for assessing abdominal adipose tissue from magnetic resonance images

Objective: To compare the inter‐rater and intra‐rater reliability and analysis time of two methods for quantifying visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) volumes from magnetic resonance (MR) images. Research Methods and Procedures: Ten subjects (BMI, 27.0 ± 2.1 kg/m²; 56 years of age ± 4 years) underwent MR imaging of the abdomen. Ten transverse T1‐weighted images were selected from each scan and analyzed using two software packages that differ in principle. The first method, ANALYZE version 5.0, represents the manual threshold method, and the second, HIPPO version 1.3, is based on the fuzzy clustering approach. Inter‐rater reliability for each method was assessed by comparing the intra‐class correlation coefficients (ICCs) for VAT and SAT results from two evaluators, and intra‐rater reliability for each method was assessed by comparing ICCs for VAT and SAT analyses performed 1 week apart by the same evaluator. The total time for analysis also was compared between methods. Results: The inter‐rater reliability for VAT was greater with HIPPO than with ANALYZE (ICC = 0.996 vs. 0.828), whereas inter‐rater reliability for SAT did not differ between methods (ICC = 0.975 and 0.987). The intra‐rater reliability was equally high with HIPPO and ANALYZE for both VAT (ICC = 0.998 vs. 0.992) and SAT (ICC = 0.996 vs. 0.992). HIPPO required less than one‐half as much analysis time as ANALYZE (15.9 ± 4.4 vs. 36.5 ± 8.2 minutes, p < 0.0001). Discussion: HIPPO software appears advantageous for the quantification of VAT from multislice MR images because inter‐rater results are more reliable, and it is more time‐efficient than less automated methods.     

Using X-ray in-line phase-contrast imaging for the investigation of nude mouse hepatic tumors

The purpose of this paper is to report the noninvasive imaging of hepatic tumors without contrast agents. Both normal tissues and tumor tissues can be detected, and tumor tissues in different stages can be classified quantitatively. We implanted BEL-7402 human hepatocellular carcinoma cells into the livers of nude mice and then imaged the livers using X-ray in-line phase-contrast imaging (ILPCI). The projection images' texture feature based on gray level co-occurrence matrix (GLCM) and dual-tree complex wavelet transforms (DTCWT) were extracted to discriminate normal tissues and tumor tissues. Different stages of hepatic tumors were classified using support vector machines (SVM). Images of livers from nude mice sacrificed 6 days after inoculation with cancer cells show diffuse distribution of the tumor tissue, but images of livers from nude mice sacrificed 9, 12, or 15 days after inoculation with cancer cells show necrotic lumps in the tumor tissue. The results of the principal component analysis (PCA) of the texture features based on GLCM of normal regions were positive, but those of tumor regions were negative. The results of PCA of the texture features based on DTCWT of normal regions were greater than those of tumor regions. The values of the texture features in low-frequency coefficient images increased monotonically with the growth of the tumors. Different stages of liver tumors can be classified using SVM, and the accuracy is 83.33%. Noninvasive and micron-scale imaging can be achieved by X-ray ILPCI. We can observe hepatic tumors and small vessels from the phase-contrast images. This new imaging approach for hepatic cancer is effective and has potential use in the early detection and classification of hepatic tumors.     

Plant species identification using shortest paths in graphs applied to color images of palisade parenchyma

Aiming to identify plants, we propose to evaluate the color texture of the palisade parenchyma, from microscopic images of leaf cross-sections, using a graph based approach. Our texture analysis approach models the image texture as a graph and uses measurements computed from the shortest paths between specific vertices to provide a feasible texture signature. For a more consistent evaluation, we compared our approach to different methods for color texture analysis in a texture classification experiment. The results obtained indicate that our approach is the most suitable for this histological analysis as it surpassed all the other texture approaches using Linear Discriminant Analysis, and obtained the second best accuracy using 1-Nearest Neighbor. These results also corroborate the feasibility of using both histological (as the palisade parenchyma) and computer analysis for identification and delimitation of plant taxa.     

Using remote sensing to quantify fishing effort and predict shorebird conflicts in an intertidal fishery

Accurate estimates of fishing effort are necessary in order to assess interactions with the wider ecosystem and for defining and implementing appropriate management. In intertidal and inshore fisheries in which vessel monitoring systems (VMS) or logbook programmes may not be implemented, quantifying the distribution and intensity of fishing can be difficult. The most obvious effects of bottom-contact fishing are often physical changes to the habitat, such as scarring of the sediment following dredging or trawling. We explored the potential of applying remote sensing techniques to aerial imagery collected by an unmanned aerial vehicle, or drone, in an area of intertidal mud flat (0.52 km²) in Poole Harbour, UK, where shellfish dredging is widely carried out and conflicts between commercial fishing interests and the conservation of internationally important shorebird populations are a concern. Image classification and image texture analysis were performed on imagery collected during the open dredge season in November 2015, in order to calculate measures of fishing intensity across three areas of the harbour subject to different management measures. We found a significant correlation between results of the image texture analysis and official sightings records collected during the dredging season, indicating that this method most accurately quantified dredging disturbance. The relationship between shorebird densities and food intake rates and the results of this analysis method were then investigated to assess the potential for using remotely sensed measures of fishing effort to assess responses of overwintering shorebird populations to intertidal shellfish dredging. Our work highlights the application of such methods, providing a low-cost tool for quantifying fishing effort and predicting wildlife conflicts.     

Non-uniform strain distribution within rat cartilaginous growth plate under uniaxial compression

Growth plates are highly inhomogeneous in morphology and composition. Mechanical loading can modulate longitudinal bone growth, though the mechanisms underlying this mechanobiology are poorly understood. The proximal tibial growth plates of six rats were tested in vitro under uniaxial compression to 5% strain, and confocal microscopy was used to track and capture images of fluorescently labeled cell nuclei with increasing applied strains. The local strain patterns through the growth plate thickness were quantified using texture correlation analysis. The technique of texture correlation analysis was first validated by comparing theoretical simulated strain maps generated from numerically distorted images. The texture correlation algorithm was sensitive to the grid size superimposed on the original image, but remained insensitive to parameters related to the size of the final image mask, which was searched by the correlation algorithm for each grid point of the original image. Within the growth plate, experimental strain distributions were non-uniform in all six specimens. Growth plates were mostly under compression strains. The strain distributions differed among the histomorphological zones of the growth plate, which was most obvious in specimens with regular growth plate shape: higher compressive strains (4-8 times higher than the applied 5% strain) were located mainly in regions overlapping the reserve and hypertrophic zones with lower compressive strains in the proliferative zone. This study documents the non-uniform mechanical behavior of growth plate across its three histological zones when exposed to compression. Further investigation is required to establish the significance of non-uniform strain fields during growth in vivo.     

Combined Metaheuristic Algorithm and Radiomics Strategy for the Analysis of Neuroanatomical Structures in Schizophrenia and Schizoaffective Disorders

ObjectivesSchizophrenia (SZ) is the most chronic disabling psychotic brain disorder. It is characterized by delusions and auditory hallucinations, as well as impairments in memory. Schizoaffective (SA) signs are co-morbid with SZ and are characterized by symptoms of SZ and mood disorder. Various researches suggest that SZ and SA share a number of equally severe cognitive deficits, but the pathophysiology has not yet been addressed in a comprehensive way. In this work, the heterogeneity in whole brain, ventricle and cerebellum region from psychotic MR brain images is examined using Machine learning and radiomic features.Materials and methodsT1 weighted MR brain images are obtained from Schizconnect database for the analysis. The shape prior level set method is used to segment the ventricle and cerebellum structures. The radiomic features which include shape and texture are extracted from these regions to discriminate the SZ and SA subjects. The performance of these features is evaluated with Binary Particle Swarm Optimization (BPSO) based Fuzzy Support Vector Machine (FSVM) classifier.ResultsThe shape constrained Level Set method is able to better segment ventricles and cerebellum regions from the images. The significant features that are extracted from whole brain, ventricle and cerebellum are identified by the BPSO based FSVM. The combination of radiomic features extracted from cerebellum region achieved high classification accuracy (90.09%) using metaheuristic algorithm. The extracted features from cerebellum are correlated with PANSS score. The causal analysis shows that there is an association been the tissue texture variation in identifying the disease severity. The symmetry analysis shows that left brain mean area is larger than the right side area. In particular SA has low cerebellum area compared to SZ. The radiomic features such as Hermite, Laws and tensor extracted from the left cerebellum show a significant texture variation in all the considered subjects (p<0.0001).ConclusionsThe results are clinically relevant in discriminating the pattern change in the structure, hence this biomarker and frame work could be used for the severity study of psychotic disorders.     

表达谱基因芯片的可靠性验证分析

cDNA芯片是一项新兴的能评估检测全范围mRNA表达水平变化的技术。通过同种组织RNA自身比较实验及不同组织RNA的差异分析实验对cDNA芯片实验的重复性进行检验,利用相关系数(correlation coefficient,R)、变异系数(coefficient of variation,CV)和假阳性率(false positiver ate,FPR)分析eDNA芯片数据的可靠程度,对cDNA芯片实验数据作了整体的评估。结果证实,该芯片系统得到的cDNA表达谱数据相关系数一般大于0．9,平均变异系数15％左右,假阳性率控制在3％以内。还提出一致率(consistence rate,CR)的概念,作为衡量cDNA芯片系统重复性的新参数,同时阐述了该参数优于目前常用的相关系数及变异系数的特点。另外,通过比较芯片制备中点样浓度、mRNA和总RNA以及不同批次芯片和不同标记过程对实验的影响,来分析芯片数据的系统误差来源。并提出重复两次实验,可以克服绝大部分实验系统引入的假阳性。     

Modeling concordance correlation via GEE to evaluate reproducibility

Clinical studies are often concerned with assessing whether different raters/methods produce similar values for measuring a quantitative variable. Use of the concordance correlation coefficient as a measure of reproducibility has gained popularity in practice since its introduction by Lin (1989, Biometrics 45, 255-268). Lin's method is applicable for studies evaluating two raters/two methods without replications. Chinchilli et al. (1996, Biometrics 52, 341-353) extended Lin's approach to repeated measures designs by using a weighted concordance correlation coefficient. However, the existing methods cannot easily accommodate covariate adjustment, especially when one needs to model agreement. In this article, we propose a generalized estimating equations (GEE) approach to model the concordance correlation coefficient via three sets of estimating equations. The proposed approach is flexible in that (1) it can accommodate more than two correlated readings and test for the equality of dependent concordant correlation estimates; (2) it can incorporate covariates predictive of the marginal distribution; (3) it can be used to identify covariates predictive of concordance correlation; and (4) it requires minimal distribution assumptions. A simulation study is conducted to evaluate the asymptotic properties of the proposed approach. The method is illustrated with data from two biomedical studies.