no code implementations • 16 Aug 2023 • Tom Hendriks, Anna Vilanova, Maxime Chamberland
We present a novel way to model diffusion magnetic resonance imaging (dMRI) datasets, that benefits from the structural coherence of the human brain while only using data from a single subject.
no code implementations • MIDL 2019 • Maxime Chamberland, Sila Genc, Erika P. Raven, Greg D. Parker, Adam Cunningham, Joanne Doherty, Marianne van den Bree, Chantal M. W. Tax, Derek K. Jones
There is an urgent need for a paradigm shift from group-wise comparisons to individual diagnosis in diffusion MRI (dMRI) to enable the analysis of rare cases and clinically-heterogeneous groups.
1 code implementation • arXiv 2019 • Samuel St-Jean, Maxime Chamberland, Max A. Viergever, Alexander Leemans
In this work, we propose to address the issue of possible misalignment, which might be present even after resampling, by realigning the representative streamline of each subject in this 1D space with a new method, coined diffusion profile realignment (DPR).