Maxime Descoteaux

This paper reduces the dimensionality of diffusion MRI measures to identify biologically interpretable components that capture age-related changes in white matter microstructure.

The paper evaluates tractography reproducibility using empirical data from a single healthy volunteer scanned on two scanners, finding high consistency among top algorithms but variability due to tract volume containment.

The paper improves tractography by reducing false positives using anatomical priors and group-sparsity regularization in a convex optimization framework.

This study assesses the reproducibility of manual segmentation of the pyramidal tract in diffusion MRI tractography across experts and non-experts, finding voxel-wise measures more reproducible than streamline-wise ones.

The paper constructs a population-based atlas of the human pyramidal tract using diffusion MRI and a new tractography algorithm, showing improved resolution of cortico-lateral projections.

This study evaluates modern diffusion tractography methods using a standardized challenge, finding no significant improvement in anatomical accuracy despite methodological advances.

This paper introduces a bundle-specific tractography algorithm to improve fornix reconstruction in MRI, enabling combined tractometry with dual-tracer PET in MCI patients.

The paper evaluates tractography methods by analyzing a simulated dataset, finding that most algorithms generate many false-positive connections.

This paper introduces a machine learning-based fiber tractography method using random forest classification and evaluates it against state-of-the-art approaches using phantom and in vivo experiments.

This paper applies deep learning to fiber tractography, showing models can generate streamlines from DWI data and outperform traditional methods in accuracy and false connection control.