Jean‐Christophe Houde

The paper evaluates the reliability and variability of diffusion- and myelin-based MRI measurements across multiple sessions and fiber populations, finding moderate to high reliability for diffusion measures and bundle-dependent variability.

TractoFlow-ABS improves tractography in pathological brains by using an atlas-based segmentation approach to avoid white matter mask holes caused by lesions.

TractoFlow is a diffusion MRI pipeline that ensures reproducibility and efficiency using Nextflow and Singularity, achieving high reproducibility and processing large datasets quickly.

This study uses diffusion imaging to identify structural abnormalities in thalamo-prefrontal white matter tracks that correlate with working memory deficits in concussed children.

The paper develops a free water (FW) processing pipeline to differentiate mild cognitive impairment (MCI) and Alzheimer's disease (AD) from controls using diffusion MRI data.

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

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.