Exploring the precision of robust modelling methods for diffusion-weighted MRI

Motivation: Clinical research with infants, subject motion can cause many subjects being excluded from analyses due to large parts of their data is corrupted by outliers. While robust modelling methods can mitigate this problem, how they affect dMRI model estimate precision is not well known. Goal(s): We demonstrate how dMRI model precision can be evaluated with two robust modelling strategies. Approach: We used white-matter simulation to compare multi-tensor model precision between 1) Gaussian Process outlier replacements and ordinary model estimator to 2) robustly weighted model estimation. Results: Model precision estimation is possible with both robust approaches, but outlier replacement can cause inflated precision estimates. Impact: Our aim is to enable larger sample sizes for clinical dMRI research by decreasing the need to exclude subject due to subject motion. Additionally, we provide new robust tools to evaluate the precision of dMRI model estimates.