Learn More
The success of diffusion magnetic resonance imaging (MRI) is deeply rooted in the powerful concept that during their random, diffusion-driven displacements molecules probe tissue structure at a microscopic scale well beyond the usual image resolution. As diffusion is truly a three-dimensional process, molecular mobility in tissues may be anisotropic, as in(More)
A landmark of corticostriatal connectivity in nonhuman primates is that cortical connections are organized into a set of discrete circuits. Each circuit is assumed to perform distinct behavioral functions. In animals, most connectivity studies are performed using invasive tracing methods, which are nonapplicable in humans. To test the proposal that(More)
Most of the approaches dedicated to fiber tracking from diffusion-weighted MR data rely on a tensor model. However, the tensor model can only resolve a single fiber orientation within each imaging voxel. New emerging approaches have been proposed to obtain a better representation of the diffusion process occurring in fiber crossing. In this paper, we adapt(More)
Many recent high angular resolution diffusion imaging (HARDI) reconstruction techniques have been introduced to infer an orientation distribution function (ODF) of the underlying tissue structure. These methods are more often based on a single-shell (one b-value) acquisition and can only recover angular structure information contained in the ensemble(More)
This paper presents a clustering method that detects the fiber bundles embedded in any MR-diffusion based tractography dataset. Our method can be seen as a compressing operation, capturing the most meaningful information enclosed in the fiber dataset. For the sake of efficiency, part of the analysis is based on clustering the white matter (WM) voxels rather(More)
Magnetic resonance (MR) diffusion imaging provides a valuable tool used for inferring structural anisotropy of brain white matter connectivity from diffusion tensor imaging. Recently, several high angular resolution diffusion models were introduced in order to overcome the inadequacy of the tensor model for describing fibre crossing within a single voxel.(More)
Magnetic resonance diffusion tensor imaging (DTI) provides information about fiber local directions in brain white matter. This paper addresses inference of the connectivity induced by fascicles made up of numerous fibers from such diffusion data. The usual fascicle tracking idea, which consists of following locally the direction of highest diffusion, is(More)
Although over the last 20 years diffusion MRI has become an established technique with a great impact on health care and neurosciences, like any other MRI technique it remains subject to artifacts and pitfalls. In addition to common MRI artifacts, there are specific problems that one may encounter when using MRI scanner gradient hardware for diffusion MRI,(More)
Diffusion magnetic resonance imaging (dMRI) has become an established research tool for the investigation of tissue structure and orientation. In this paper, we present a method for real-time processing of diffusion tensor and Q-ball imaging. The basic idea is to use Kalman filtering framework to fit either the linear tensor or Q-ball model. Because the(More)
This paper presents a new procedure to estimate the diffusion tensor from a sequence of diffusion-weighted images. The first step of this procedure consists of the correction of the distortions usually induced by eddy-current related to the large diffusion-sensitizing gradients. This correction algorithm relies on the maximization of mutual information to(More)