Empirical field mapping for gradient nonlinearity correction of multi-site diffusion weighted MRI
Colin B Hansen,
Baxter P. Rogers,
Kurt G. Schilling,
Justin A. Blaber,
Adam W Anderson,
Bennett A. Landman
Posted 20 May 2020
bioRxiv DOI: 10.1101/2020.05.18.102558
Posted 20 May 2020
Background: Achieving inter-site / inter-scanner reproducibility of diffusion weighted magnetic resonance imaging (DW-MRI) metrics has been challenging given differences in acquisition protocols, analysis models, and hardware factors. Purpose: Magnetic field gradients impart scanner-dependent spatial variations in the applied diffusion weighting that can be corrected if the gradient nonlinearities are known. However, retrieving manufacturer nonlinearity specifications is not well supported and may introduce errors in interpretation of units or coordinate systems. We propose an empirical approach to mapping the gradient nonlinearities with sequences that are supported across the major scanner vendors. Study Type: Prospective observational study. Subjects: A spherical isotropic diffusion phantom, and a single human control volunteer Field Strength/Sequence: 3T (two scanners). Stejskal-Tanner spin echo sequence with b-values of 1000, 2000 s/mm2 with 12, 32, and 384 diffusion gradient directions per shell. Assessment: We compare the proposed correction with the prior approach using manufacturer specifications against typical diffusion pre-processing pipelines (i.e., ignoring spatial gradient nonlinearities). In phantom data, we evaluate metrics against the ground truth. In human and phantom data, we evaluate reproducibility across scans, sessions, and hardware. Statistical Tests: Wilcoxon rank-sum test between uncorrected and corrected data. Results: In phantom data, our correction method reduces variation in mean diffusivity across sessions over uncorrected data (p<0.05). In human data, we show that this method can also reduce variation in mean diffusivity across scanners (p<0.05). Conclusion: Our method is relatively simple, fast, and can be applied retroactively. We advocate incorporating voxel-specific b-value and b-vector maps should be incorporated in DW-MRI harmonization preprocessing pipelines to improve quantitative accuracy of measured diffusion parameters. Keywords: Gradient Nonlinearity, Field Estimation, Pre-processing, DW-MRI ### Competing Interest Statement The authors have declared no competing interest.
- Downloaded 126 times
- Download rankings, all-time:
- Site-wide: 104,963 out of 118,977
- In neuroscience: 16,511 out of 18,697
- Year to date:
- Site-wide: 69,138 out of 118,977
- Since beginning of last month:
- Site-wide: 49,467 out of 118,977
Downloads over time
Distribution of downloads per paper, site-wide
- 27 Nov 2020: The website and API now include results pulled from medRxiv as well as bioRxiv.
- 18 Dec 2019: We're pleased to announce PanLingua, a new tool that enables you to search for machine-translated bioRxiv preprints using more than 100 different languages.
- 21 May 2019: PLOS Biology has published a community page about Rxivist.org and its design.
- 10 May 2019: The paper analyzing the Rxivist dataset has been published at eLife.
- 1 Mar 2019: We now have summary statistics about bioRxiv downloads and submissions.
- 8 Feb 2019: Data from Altmetric is now available on the Rxivist details page for every preprint. Look for the "donut" under the download metrics.
- 30 Jan 2019: preLights has featured the Rxivist preprint and written about our findings.
- 22 Jan 2019: Nature just published an article about Rxivist and our data.
- 13 Jan 2019: The Rxivist preprint is live!