no code implementations • 4 Mar 2024 • Richard G. Carson, Alexander Leemans
A method is described, whereby an Akaike information weighted average of linear, Blackman and piecewise linear model predictions, may be used to compensate effectively for the dependence of FA (and other estimates of tissue microstructure) on streamline length, across the entire range of streamline lengths present in each specimen.
1 code implementation • 1 Oct 2019 • Samuel St-Jean, Max A. Viergever, Alexander Leemans
Results show that the effect size of the four studied diffusion metrics is preserved while removing variability attributable to the scanner.
1 code implementation • Magnetic resonance in medecine 2019 • Samuel St-Jean, Alberto De Luca, Chantal M. W. Tax, Max A. Viergever, Alexander Leemans
The proposed algorithms herein can estimate both parameters of the noise distribution, are robust to signal leakage artifacts and perform best when used on acquired noise maps.
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).
2 code implementations • 30 May 2018 • Samuel St-Jean, Alberto De Luca, Max A. Viergever, Alexander Leemans
Knowledge of the noise distribution in magnitude diffusion MRI images is the centerpiece to quantify uncertainties arising from the acquisition process.