Samuel St‐Jean

This paper introduces a scanner harmonization method for diffusion MRI data using adaptive dictionary learning, preserving biological variability while reducing scanner-induced variability.

This paper introduces automated methods to estimate noise distributions in diffusion MRI data using Gamma distribution moments and maximum likelihood, showing improved accuracy over Rician assumptions.

This paper introduces diffusion profile realignment to reduce variability in along-tract dMRI analysis by improving alignment across subjects, enhancing detection of regional changes.

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

This paper provides an open-source implementation of a two-compartment free water elimination model for diffusion tensor imaging and evaluates its performance using simulations.