On the down-sampling of diffusion MRI data along the angular dimension.

Journal Article (Journal Article)

BACKGROUND: It has been established that the diffusion gradient directions in diffusion MRI should be uniformly distributed in 3D spherical space, so that orientation-dependent diffusion properties (e.g., fractional anisotropy or FA) can be properly quantified. Sometimes the acquired data need to be down-sampled along the angular dimension before computing diffusion properties (e.g., to exclude data points corrupted by motion artifact; to harmonize data obtained with different protocols). It is important to quantitatively assess the impact of data down-sampling on measurement of diffusion properties. MATERIALS AND METHODS: Here we report 1) a numerical procedure for down-sampling diffusion MRI (e.g., for data harmonization), and 2) a spatial uniformity index of diffusion directions, aiming to predict the quality of the chosen down-sampling schemes (e.g., from data harmonization; or rejection of motion corrupted data points). We quantitatively evaluated human diffusion MRI data, which were down-sampled from 64 or 60 diffusion gradient directions to 30 directions, in terms of their 1) FA value accuracy (using fully-sampled data as the ground truth), 2) FA fitting residuals, and 3) spatial uniformity indices. RESULTS: Our experimental data show that the proposed spatial uniformity index is correlated with errors in FA obtained from down-sampled diffusion MRI data. The FA fitting residuals that are typically used to assess diffusion MRI quality are not correlated with either FA errors or spatial uniformity index. CONCLUSIONS: These results suggest that the spatial uniformity index could be more valuable in assessing quality of down-sampled diffusion MRI data, as compared with FA fitting residual measures. We expect that our implemented software procedure should prove valuable for 1) guiding data harmonization for multi-site diffusion MRI studies, and 2) assessing the impact of rejecting motion corrupted data points on the accuracy of diffusion measures.

Full Text

Duke Authors

Cited Authors

  • Chen, N-K; Bell, RP; Meade, CS

Published Date

  • October 2021

Published In

Volume / Issue

  • 82 /

Start / End Page

  • 104 - 110

PubMed ID

  • 34174330

Pubmed Central ID

  • PMC8289744

Electronic International Standard Serial Number (EISSN)

  • 1873-5894

Digital Object Identifier (DOI)

  • 10.1016/j.mri.2021.06.012


  • eng

Conference Location

  • Netherlands