Search Results for author: Alexander Leemans

Found 5 papers, 4 papers with code

Tractography derived quantitative estimates of tissue microstructure depend on streamline length: A characterization and method of adjustment

no code implementations4 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.

Harmonization of diffusion MRI datasets with adaptive dictionary learning

1 code implementation1 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.

Dictionary Learning

Automated characterization of noise distributions in diffusion MRI data

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.

Denoising

Reducing variability in along-tract analysis with diffusion profile realignment

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).

Anatomy Specificity

Automatic, fast and robust characterization of noise distributions for diffusion MRI

2 code implementations30 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.

Noise Estimation

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