Tomasz Pieciak

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.

The paper validates ten federated learning strategies for synthesizing multi-contrast MRI data from heterogeneous sources, finding that FedBAdam outperforms other methods in accuracy and stability.

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