Empirical field mapping for gradient nonlinearity correction of multi-site diffusion weighted MRI |
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| Institution: | 1. Maternal & Fetal Health Research Centre, University of Manchester, UK;2. Centre for Imaging Science, Institute of Population Health, University of Manchester, UK;1. Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA;2. National Institute of Biomedical Imaging and Bioengineering (BESIP), National Institutes of Health, Bethesda, MD, USA;3. Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA;4. The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD, USA |
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| Abstract: | BackgroundAchieving 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.PurposeMagnetic 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 typeProspective observational study.SubjectsA spherical isotropic diffusion phantom, and a single human control volunteer.Field strength/sequence3 T (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.AssessmentWe 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 testsWilcoxon rank-sum test between uncorrected and corrected data.ResultsIn 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).ConclusionOur 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. |
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