# Diffusion MRI sampling schemes bias diffusion metrics and tractography

## TL;DR

Different MRI scanning methods can lead to biased results in brain imaging studies, affecting how we understand white matter structures.

## Contribution

The study reveals systematic differences in diffusion metrics and tractography outcomes across four common diffusion MRI sampling schemes.

## Key findings

- Fractional anisotropy and mean diffusivity showed moderate cross-scheme correlations but rare matched means.
- Tract volumes varied significantly between schemes, with DSI and HCPms yielding similar results in most regions.
- Harmonization methods could reduce residual bias and enable pooled analyses across diverse protocols.

## Abstract

Diffusion MRI is increasingly used to study white-matter architecture, but tractography and diffusion metrics can be biased by different sampling schemes. We assessed systematic differences across four common protocols—single-shell high-angular resolution diffusion imaging (HARDI), Siemens clinical multi-shell (Sms), diffusion spectrum imaging (DSI), and Human Connectome Project multi-shell (HCPms)—in healthy adults and individuals with corpus callosum dysgenesis (CCD).

All data were acquired on a single 3 T scanner and processed uniformly to extract fractional anisotropy (FA), mean diffusivity (MD), effective contrast-to-noise ratio (eCNR), and orientation dispersion within the corpus callosum (CC), corona radiata (CR), and centrum semiovale (CSO). In controls, we measured tract volumes for CC, bilateral CR, anterior commissure (AC) and posterior commissure (PC), and streamline counts for AC and PC; in CCD, we quantified volumes of the Probst and sigmoid bundles.

Across participants, FA and MD showed moderate cross-scheme correlations for most ROIs, but matched means were rare (only Sms–HARDI in CC). eCNR and dispersion exhibited few cross-scheme correlations; however, means were similar for eCNR between Sms and HCPms and for dispersion among HARDI, DSI, and HCPms. Tract-based volumes correlated across Sms, DSI, and HCPms for CC in controls and for the right sigmoid and both Probst bundles in CCD. DSI and HCPms yielded similar volumes in all ROIs (controls and CCD). In controls, Sms volumes agreed with DSI/HCPms in CR, but were lower in CC and in all CCD ROIs. HARDI produced higher volumes in CC and bilateral CR in controls and in all CCD ROIs. For AC and PC in controls, tract-based means (volumes, streamlines, streamlines/volume) were consistent across schemes; nonetheless, correlations were limited—streamlines and streamlines/volume correlated for Sms, DSI, and HARDI in AC, and for DSI and HCPms in PC.

These findings demonstrate systematic differences in voxel-wise metrics and tractography outcomes from four diffusion-sampling schemes. In addition to qualitatively informing attempts to consolidate or contrast data across schemes, future work could explore regression-based harmonization—and other methods—to reduce residual bias and enable pooled analyses across diverse protocols.