Benoit Scherrer

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Diffusion-weighted imaging (DWI) enables non-invasive investigation and characterization of the white matter but suffers from a relatively poor spatial resolution. Increasing the spatial resolution in DWI is challenging with a single-shot EPI acquisition due to the decreased signal-to-noise ratio and T2(∗) relaxation effect amplified with increased echo(More)
Accurate tissue and structure segmentation of magnetic resonance (MR) brain scans is critical in several applications. In most approaches this task is handled through two sequential steps. We propose to carry out cooperatively both tissue and subcortical structure segmentation by distributing a set of local and cooperative Markov random field (MRF) models(More)
Multi-tensor models have been proposed to assess multiple fiber orientations but are known to be numerically challenging. We show that the estimation cannot be performed with a single-shell HARDI acquisition because the fitting procedure leads to an infinite number of solutions ; multiple-shell HARDI acquisitions are required. Additionally, we propose a new(More)
The characterization of the complex diffusion signal arising from the brain remains an open problem. Many representations focus on characterizing the global shape of the diffusion profile at each voxel and are limited to the assessment of connectivity. In contrast, Multiple Fascicle Models (MFM) seek to represent the contribution from each white matter(More)
Diffusion tensor imaging (DTI) is unable to represent the diffusion signal arising from multiple crossing fascicles and freely diffusing water molecules. Generative models of the diffusion signal, such as multi-fascicle models, overcome this limitation by providing a parametric representation for the signal contribution of each population of water(More)
Multi-fiber models have been introduced to leverage the accuracy of the diffusion representation in crossing fiber areas. The improved accuracy may, however, be impaired by poor processing of the multi-fiber models. In particular, interpolating multi-fiber models proves challenging, while it is a pervasive and recurrent task in many processes. The error(More)
This work proposes a novel approach for motion-robust diffusion-weighted (DW) brain MRI reconstruction through tracking temporal head motion using slice-to-volume registration. The slice-level motion is estimated through a filtering approach that allows tracking the head motion during the scan and correcting for out-of-plane inconsistency in the acquired(More)
Diffusion weighted magnetic resonance imaging, or DWI, is one of the most promising tools for the analysis of neural microstructure and the structural connectome of the human brain. The application of DWI to map early development of the human connectome in-utero, however, is challenged by intermittent fetal and maternal motion that disrupts the spatial(More)
RATIONALE AND OBJECTIVES Tuberous sclerosis complex (TSC) is a genetic neurocutaneous syndrome in which cognitive and social-behavioral outcomes for patients vary widely in an unpredictable manner. The cause of adverse neurologic outcome remains unclear. The aim of this study was to investigate the hypothesis that disordered white matter and abnormal neural(More)
PURPOSE To compare and evaluate the use of super-resolution reconstruction (SRR), in frequency, image, and wavelet domains, to reduce through-plane partial voluming effects in magnetic resonance imaging. METHODS The reconstruction of an isotropic high-resolution image from multiple thick-slice scans has been investigated through techniques in frequency,(More)