Characterization of a robust probabilistic framework for brain magnetic resonance image data distributions

Probabilistic characterisation of image data for accurate prognosis and treatment planning remains a long-standing problem in medical research, especially when the data distribution depicts flat-top and high-order contact. Such flat-top distributions are quite common in brain magnetic resonance (MR) image data, where the density drops sharply beyond the flat interval. Intuitively, it would indicate a bipartition of data into positive region containing observations definitely belonging to the image class and boundary region with observations possibly belonging to it. The flat peak would also imply that multiple values are equally most likely to belong to that class. However, the popular probability distributions used in such cases are unimodal, creating ambiguity about the positive region. In this work, we study the statistical properties and develop likelihood-based iterative estimation method for the parameters of a novel class of platykurtic probability distributions containing normal, called the stomped normal distribution, that provides more accurate modelling to the flat-top data distributions. The robustness of the proposed stomped normal model has been illustrated with six simulated and nine real brain MR volumes. Our analysis shows substantial improvement in explaining a variety of shapes of data distributions using the proposed probability model

Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM

We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known. To solve this semi-blind deconvolution problem, prior distributions are specified for the PSF and the 3D image. Joint image reconstruction and PSF estimation is then performed within a Bayesian framework, using a variational algorithm to estimate the posterior distribution. The image prior distribution imposes an explicit atomic measure that corresponds to image sparsity. Importantly, the proposed Bayesian deconvolution algorithm does not require hand tuning. Simulation results clearly demonstrate that the semi-blind deconvolution algorithm compares favorably with previous Markov chain Monte Carlo (MCMC) version of myopic sparse reconstruction. It significantly outperforms mismatched non-blind algorithms that rely on the assumption of the perfect knowledge of the PSF. The algorithm is illustrated on real data from magnetic resonance force microscopy (MRFM)

Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM

We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known. To solve this semi-blind deconvolution problem, prior distributions are specified for the PSF and the 3D image. Joint image reconstruction and PSF estimation is then performed within a Bayesian framework, using a variational algorithm to estimate the posterior distribution. The image prior distribution imposes an explicit atomic measure that corresponds to image sparsity. Importantly, the proposed Bayesian deconvolution algorithm does not require hand tuning. Simulation results clearly demonstrate that the semi-blind deconvolution algorithm compares favorably with previous Markov chain Monte Carlo (MCMC) version of myopic sparse reconstruction. It significantly outperforms mismatched non-blind algorithms that rely on the assumption of the perfect knowledge of the PSF. The algorithm is illustrated on real data from magnetic resonance force microscopy (MRFM)

Computer assisted enhanced volumetric segmentation magnetic imaging data using a mixture of artificial neural networks

An accurate computer-assisted method able to perform regional segmentation on 3D single modality images and measure its volume is designed using a mixture of unsupervised and supervised artificial neural networks. Firstly, an unsupervised artificial neural network is used to estimate representative textures that appear in the images. The region of interest of the resultant images is selected by means of a multi-layer perceptron after a training using a single sample slice, which contains a central portion of the 3D region of interest. The method was applied to magnetic resonance imaging data collected from an experimental acute inflammatory model (T(2) weighted) and from a clinical study of human Alzheimer's disease (T(1) weighted) to evaluate the proposed method. In the first case, a high correlation and parallelism was registered between the volumetric measurements, of the injured and healthy tissue, by the proposed method with respect to the manual measurements (r = 0.82 and p < 0.05) and to the histopathological studies (r = 0.87 and p < 0.05). The method was also applied to the clinical studies, and similar results were derived of the manual and semi-automatic volumetric measurement of both hippocampus and the corpus callosum (0.95 and 0.88

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE

Partial‐volume modeling reveals reduced gray matter in specific thalamic nuclei early in the time course of psychosis and chronic schizophrenia

The structural complexity of the thalamus, due to its mixed composition of gray and white matter, make it challenging to disjoint and quantify each tissue contribution to the thalamic anatomy. This work promotes the use of partial-volume-based over probabilistic-based tissue segmentation approaches to better capture thalamic gray matter differences between patients at different stages of psychosis (early and chronic) and healthy controls. The study was performed on a cohort of 23 patients with schizophrenia, 41 with early psychosis and 69 age and sex-matched healthy subjects. Six tissue segmentation approaches were employed to obtain the gray matter concentration/probability images. The statistical tests were applied at three different anatomical scales: whole thalamus, thalamic subregions and voxel-wise. The results suggest that the partial volume model estimation of gray matter is more sensitive to detect atrophies within the thalamus of patients with psychosis. However all the methods detected gray matter deficit in the pulvinar, particularly in early stages of psychosis. This study demonstrates also that the gray matter decrease varies nonlinearly with age and between nuclei. While a gray matter loss was found in the pulvinar of patients in both stages of psychosis, reduced gray matter in the mediodorsal was only observed in early psychosis subjects. Finally, our analyses point to alterations in a sub-region comprising the lateral posterior and ventral posterior nuclei. The obtained results reinforce the hypothesis that thalamic gray matter assessment is more reliable when the tissues segmentation method takes into account the partial volume effect

COMIC: An Unsupervised Change Detection Method for Heterogeneous Remote Sensing Images Based on Copula Mixtures and Cycle-Consistent Adversarial Networks

In this paper, we consider the problem of change detection (CD) with two heterogeneous remote sensing (RS) images. For this problem, an unsupervised change detection method has been proposed recently based on the image translation technique of Cycle-Consistent Adversarial Networks (CycleGANs), where one image is translated from its original modality to the modality of the other image so that the difference map can be obtained by performing arithmetical subtraction. However, the difference map derived from subtraction is susceptible to image translation errors, in which case the changed area and the unchanged area are less distinguishable. To overcome the above shortcoming, we propose a new unsupervised copula mixture and CycleGAN-based CD method (COMIC), which combines the advantages of copula mixtures on statistical modeling and the advantages of CycleGANs on data mining. In COMIC, the pre-event image is first translated from its original modality to the post-event image modality. After that, by constructing a copula mixture, the joint distribution of the features from the heterogeneous images can be learnt according to quantitive analysis of the dependence structure based on the translated image and the original pre-event image, which are of the same modality and contain totally the same objects. Then, we model the CD problem as a binary hypothesis testing problem and derive its test statistics based on the constructed copula mixture. Finally, the difference map can be obtained from the test statistics and the binary change map (BCM) is generated by K-means clustering. We perform experiments on real RS datasets, which demonstrate the superiority of COMIC over the state-of-the-art methods

Renal Cell Carcinoma Metastatic to the Liver: Early Response Assessment after Intraarterial Therapy Using 3D Quantitative Tumor Enhancement Analysis

AbstractPURPOSELiver metastases from renal cell carcinoma (RCC) are not uncommon in the course of disease. However, data about tumor response to intraarterial therapy (IAT) are scarce. This study assessed whether changes of enhancing tumor volume using quantitative European Association for the Study of the Liver (qEASL) on magnetic resonance imaging (MRI) and computed tomography (CT) can evaluate tumor response and predict overall survival (OS) early after therapy.METHODS AND MATERIALSFourteen patients with liver metastatic RCC treated with IAT (transarterial chemoembolization: n= 9 and yttrium-90: n= 5) were retrospectively included. All patients underwent contrast-enhanced imaging (MRI: n= 10 and CT: n= 4) 3 to 4 weeks pre- and posttreatment. Response to treatment was evaluated on the arterial phase using Response Evaluation Criteria in Solid Tumors (RECIST), World Health Organization, modified RECIST, EASL, tumor volume, and qEASL. Paired t test was used to compare measurements pre- and post-IAT. Patients were stratified into responders (≥65% decrease in qEASL) and nonresponders (<65% decrease in qEASL). OS was evaluated using Kaplan-Meier curves with log-rank test and the Cox proportional hazard model.RESULTSMean qEASL (cm3) decreased from 93.5 to 67.2 cm3 (P= .004) and mean qEASL (%) from 63.1% to 35.6% (P= .001). No significant changes were observed using other response criteria. qEASL was the only significant predictor of OS when used to stratify patients into responders and nonresponders with median OS of 31.9 versus 11.1 months (hazard ratio [HR], 0.43; 95% confidence interval [CI], 0.19-0.97; P= .042) for qEASL (cm3) and 29.9 versus 10.2 months (HR, 0.09; 95% CI, 0.01-0.74; P= .025) for qEASL (%).CONCLUSIONThree-dimensional (3D) quantitative tumor analysis is a reliable predictor of OS when assessing treatment response after IAT in patients with RCC metastatic to the liver. qEASL outperforms conventional non-3D methods and can be used as a surrogate marker for OS early after therapy

Early Response Assessment after Intraarterial Therapy Using 3D Quantitative Tumor Enhancement Analysis

PURPOSE Liver metastases from renal cell carcinoma (RCC) are not uncommon in the course of disease. However, data about tumor response to intraarterial therapy (IAT) are scarce. This study assessed whether changes of enhancing tumor volume using quantitative European Association for the Study of the Liver (qEASL) on magnetic resonance imaging (MRI) and computed tomography (CT) can evaluate tumor response and predict overall survival (OS) early after therapy. METHODS AND MATERIALS Fourteen patients with liver metastatic RCC treated with IAT (transarterial chemoembolization: n= 9 and yttrium-90: n= 5) were retrospectively included. All patients underwent contrast-enhanced imaging (MRI: n= 10 and CT: n= 4) 3 to 4 weeks pre- and posttreatment. Response to treatment was evaluated on the arterial phase using Response Evaluation Criteria in Solid Tumors (RECIST), World Health Organization, modified RECIST, EASL, tumor volume, and qEASL. Paired t test was used to compare measurements pre- and post-IAT. Patients were stratified into responders (≥65% decrease in qEASL) and nonresponders (<65% decrease in qEASL). OS was evaluated using Kaplan-Meier curves with log-rank test and the Cox proportional hazard model. RESULTS Mean qEASL (cm3) decreased from 93.5 to 67.2 cm3 (P= .004) and mean qEASL (%) from 63.1% to 35.6% (P= .001). No significant changes were observed using other response criteria. qEASL was the only significant predictor of OS when used to stratify patients into responders and nonresponders with median OS of 31.9 versus 11.1 months (hazard ratio [HR], 0.43; 95% confidence interval [CI], 0.19-0.97; P= .042) for qEASL (cm3) and 29.9 versus 10.2 months (HR, 0.09; 95% CI, 0.01-0.74; P= .025) for qEASL (%). CONCLUSION Three-dimensional (3D) quantitative tumor analysis is a reliable predictor of OS when assessing treatment response after IAT in patients with RCC metastatic to the liver. qEASL outperforms conventional non- 3D methods and can be used as a surrogate marker for OS early after therapy

Body Mass Index in Multiple Sclerosis modulates ceramide-induced DNA methylation and disease course

Background: Multiple Sclerosis (MS) results from genetic predisposition and environmental variables, including elevated Body Mass Index (BMI) in early life. This study addresses the effect of BMI on the epigenome of monocytes and disease course in MS. Methods: Fifty-four therapy-naive Relapsing Remitting (RR) MS patients with high and normal BMI received clinical and MRI evaluation. Blood samples were immunophenotyped, and processed for unbiased plasma lipidomic profiling and genome-wide DNA methylation analysis of circulating monocytes. The main findings at baseline were validated in an independent cohort of 91 therapy-na\uefve RRMS patients. Disease course was evaluated by a two-year longitudinal follow up and mechanistic hypotheses tested in human cell cultures and in animal models of MS. Findings: Higher monocytic counts and plasma ceramides, and hypermethylation of genes involved in negative regulation of cell proliferation were detected in the high BMI group of MS patients compared to normal BMI. Ceramide treatment of monocytic cell cultures increased proliferation in a dose-dependent manner and was prevented by DNA methylation inhibitors. The high BMI group of MS patients showed a negative correlation between monocytic counts and brain volume. Those subjects at a two-year follow-up showed increased T1 lesion load, increased disease activity, and worsened clinical disability. Lastly, the relationship between body weight, monocytic infiltration, DNA methylation and disease course was validated in mouse models of MS. Interpretation: High BMI negatively impacts disease course in Multiple Sclerosis by modulating monocyte cell number through ceramide-induced DNA methylation of anti-proliferative genes. Fund: This work was supported by funds from the Friedman Brain Institute, NIH, and Multiple Sclerosis Society