31 research outputs found
Fiber-Flux Diffusion Density for White Matter Tracts Analysis: Application to Mild Anomalies Localization in Contact Sports Players
We present the concept of fiber-flux density for locally quantifying white
matter (WM) fiber bundles. By combining scalar diffusivity measures (e.g.,
fractional anisotropy) with fiber-flux measurements, we define new local
descriptors called Fiber-Flux Diffusion Density (FFDD) vectors. Applying each
descriptor throughout fiber bundles allows along-tract coupling of a specific
diffusion measure with geometrical properties, such as fiber orientation and
coherence. A key step in the proposed framework is the construction of an FFDD
dissimilarity measure for sub-voxel alignment of fiber bundles, based on the
fast marching method (FMM). The obtained aligned WM tract-profiles enable
meaningful inter-subject comparisons and group-wise statistical analysis. We
demonstrate our method using two different datasets of contact sports players.
Along-tract pairwise comparison as well as group-wise analysis, with respect to
non-player healthy controls, reveal significant and spatially-consistent FFDD
anomalies. Comparing our method with along-tract FA analysis shows improved
sensitivity to subtle structural anomalies in football players over standard FA
measurements
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.status: publishe
Integrating quickbundles into a model-guided approach for extracting âanatomically-coherentâ and âsymmetry-awareâ white matter fiber-bundles.
International audienc
Non-Negative Matrix Factorization for White-Matter Fiber-Bundles Longitudinal Analysis
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Caractérisation de la connectivité structurelle cérébrale par la théorie des graphes dans différentes formes cliniques de Sclérose en Plaques
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