Antonio Tristán‐Vega

This study evaluates how retraining deep learning models affects the statistical consistency of diffusion tensor imaging parameters in bipolar disorder, showing significant variability across models.

The paper introduces a densely sampled longitudinal diffusion MRI dataset for validating dMRI techniques and assessing reproducibility.

This study evaluates the reproducibility and reliability of free-water-corrected diffusion tensor imaging (DTI) in healthy human brain white matter across different acquisition settings and correction methods.

This study shows that free-water volume fraction in white matter increases non-linearly with age, particularly after 60, with region-specific variations and reduced variability when using FW-compensated MR signals.

This study compares AMURA and DTI diffusion measures in migraine patients and controls, finding AMURA detects more regions and shows better discriminative power despite lower stability.

The paper introduces a method to estimate brain diffusion anisotropy using only 3 gradient directions, showing it works but with increased data variability and metric subestimation.

This paper introduces AMURA, a method to estimate diffusion scalar measures with reduced MRI acquisitions, showing improved performance over traditional techniques.

This paper introduces a method to reduce the data needed for Propagator Anisotropy calculation in diffusion MRI while maintaining comparable microstructural discrimination.