Longitudinal change detection: inference on the diffusion tensor along white matter pathways

Constrained Tensor Decomposition for Longitudinal Analysis of Diffusion Imaging Data

Analysis of complex data is still a challenge in medical image analysis. Due to the heterogeneous information that can be extracted from magnetic resonance imaging (MRI) it can be difficult to fuse such data in a proper way. One interesting case is given by the analysis of diffusion imaging (DI) data. DI techniques give an important variety of information about the status of microstructure in the brain. This is interesting information to use especially in longitudinal setting where the temporal evolution of the pathology is an important added value. In this paper, we propose a new tensor-based framework capable to detect longitudinal changes appearing in DI data in multiple sclerosis (MS) patients. We focus our attention to the analysis of longitudinal changes occurring along different white matter (WM) fiber-bundles. Our main goal is to detect which subset of fibers (within a bundle) and which sections of these fibers contain "pathological" longitudinal changes. The framework consists of three main parts: i) preprocessing of longitudinal diffusion acquisitions and WM fiber-bundles extraction, ii) data tensorization and rank selection, iii) application of a parallelized constrained tensor factorization algorithm to detect longitudinal "pathological" changes. The proposed method was applied on simulated longitudinal variations and on real MS data. High level of accuracy and precision were obtained in the detection of small longitudinal changes along the WM fiber-bundles.

A genetic algorithm-based model for longitudinal changes detection in white matter fiber-bundles of patient with multiple sclerosis

Analysis of white matter (WM) tissue is essential to understand the mechanisms of neurodegenerative pathologies like multiple sclerosis (MS). Recently longitudinal studies started to show how the temporal component is important to investigate temporal diffuse effects of neurodegenerative pathologies. Diffusion tensor imaging (DTI) constitutes one of the most sensitive techniques for the detection and characterization of brain related pathological processes and allows also the reconstruction of WM fibers. The analysis of spatial and temporal pathological changes along the fibers are thus possible by merging quantitative maps with structural information provided by DTI. In this work, we present a new genetic algorithm (GA) based method to analyze longitudinal changes occurring along WM fiber-bundles. In the first part of this paper, we describe the data processing pipeline, including data registration and fiber tract post-processing. In the second part, we focus our attention to the description of our GA model. In the last part, we show the tests we performed on simulated and real MS longitudinal data. Our method reached a high level of precision, recall and F-Measure in the detection of longitudinal pathological alterations occurring along different WM fiber-bundles.

Multiparametric Non-Negative Matrix Factorization for Longitudinal Variations Detection in White-Matter Fiber Bundles

Processing of longitudinal diffusion tensor imaging (DTI) data is a crucial challenge to better understand pathological mechanisms of complex brain diseases such as multiple sclerosis (MS) where white-matter (WM) fiber bundles are variably altered by inflammatory events. In this study, we propose a new fully automated method to detect longitudinal changes in diffusivity metrics along WM fiber bundles. The proposed method is divided in three main parts: 1) preprocessing of longitudinal diffusion acquisitions, 2) WM fiber-bundle extraction, and 3) application of nonnegative matrix factorization and density-based local outliers algorithms to detect and delineate longitudinal variations appearing in the cross section of the WM fiber bundle. In order to validate our method, we introduce a new model to simulate real longitudinal changes based on a generalized Gaussian probability density function. Moreover, we applied our method on longitudinal data. High level of performances were obtained for the detection of small longitudinal changes along the WM fiber bundles in MS patients.

A Sensitive and Automatic White Matter Fiber Tracts Model for Longitudinal Analysis of Diffusion Tensor Images in Multiple Sclerosis

Diffusion tensor imaging (DTI) is a sensitive tool for the assessment of microstructural alterations in brain white matter (WM). We propose a new processing technique to detect, local and global longitudinal changes of diffusivity metrics, in homologous regions along WM fiber-bundles. To this end, a reliable and automatic processing pipeline was developed in three steps: 1) co-registration and diffusion metrics computation, 2) tractography, bundle extraction and processing, and 3) longitudinal fiber-bundle analysis. The last step was based on an original Gaussian mixture model providing a fine analysis of fiber-bundle cross-sections, and allowing a sensitive detection of longitudinal changes along fibers. This method was tested on simulated and clinical data. High levels of F-Measure were obtained on simulated data. Experiments on cortico-spinal tract and inferior fronto-occipital fasciculi of five patients with Multiple Sclerosis (MS) included in a weekly follow-up protocol highlighted the greater sensitivity of this fiber scale approach to detect small longitudinal alterations.