A multi-b-value test-retest diffusion MRI brain dataset for model validation and reproducibility assessment
Transparent assessment of diffusion magnetic resonance imaging (dMRI) techniques with empirical verification of confounding factors requires adequately designed protocols and collected datasets. Publicly available diffusion-weighted MR datasets often provide limited sampling across b-values, making it difficult to study optimal acquisition protocols or the relationships between different…
A comprehensive multi-value test-retest diffusion MRI brain dataset has been developed for model validation and reproducibility assessment. This dataset, comprising raw and preprocessed data, was gathered from eleven healthy volunteers who underwent four scanning sessions. The first two sessions were conducted on consecutive days, serving as the test data, followed by two additional sessions a week later, constituting the retest data.
The imaging was performed using twenty-two distinct b-values, ranging from 10 to 3000 s/mm2, in conjunction with structural T1-weighted scans. The primary objectives of this dataset are to facilitate the assessment of longitudinal reproducibility and reliability of quantitative metrics, evaluate robust and outlier-resistant estimation techniques, investigate factors influencing estimation procedures, and verify optimal acquisition protocols for various signal models.
By making this dataset publicly accessible, researchers can utilize it to enhance the validation and reproducibility of diffusion MRI techniques.
Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
