A comparative study of gradient nonlinearity correction strategies for processing diffusion data obtained with ultra‐strong gradient MRI scanners

Purpose The analysis of diffusion data obtained under large gradient nonlinearities necessitates corrections during data reconstruction and analysis. While two such preprocessing pipelines have been proposed, no comparative studies assessing their performance exist. Furthermore, both pipelines negle...

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Veröffentlicht in:Magnetic resonance in medicine Jg. 85; H. 2; S. 1104 - 1113
Hauptverfasser: Rudrapatna, Umesh, Parker, Greg D., Roberts, Jamie, Jones, Derek K.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Wiley Subscription Services, Inc 01.02.2021
John Wiley and Sons Inc
Schlagworte:
Comparative studies
connectom
Data acquisition
Data analysis
Diffusion
Diffusion Magnetic Resonance Imaging
diffusion MRI
diffusion preprocessing
Diffusion Tensor Imaging
Eddy currents
Estimates
gradient nonlinearity
human connectome project
Humans
Image Processing, Computer-Assisted
Inhomogeneity
Magnetic Resonance Imaging
Motion
Nonlinear systems
Nonlinearity
Notes—Computer Processing and Modeling
Parameter estimation
Pipelines
Preprocessing
Scanners
Small mammals
Tensors
ultra‐strong gradients
diffusion MRI
connectom
ultra-strong gradients
diffusion preprocessing
gradient nonlinearity
human connectome project
ISSN:0740-3194, 1522-2594, 1522-2594
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Zusammenfassung:Purpose The analysis of diffusion data obtained under large gradient nonlinearities necessitates corrections during data reconstruction and analysis. While two such preprocessing pipelines have been proposed, no comparative studies assessing their performance exist. Furthermore, both pipelines neglect the impact of subject motion during acquisition, which, in the presence of gradient nonlinearities, induces spatio‐temporal B‐matrix variations. Here, spatio‐temporal B‐matrix tracking (STB) is proposed and its performance compared to established pipelines. Methods Diffusion tensor MRI (DT‐MRI) was performed using a 300 mT/m gradient system. Data were acquired with volunteers positioned in regions with pronounced gradient nonlinearities, and used to compare the performance of six different processing pipelines, including STB. Results Up to 30% errors were observed in DT‐MRI parameter estimates when neglecting gradient nonlinearities. Moreover, the order in which B 0 inhomogeneity, eddy current and gradient nonlinearity corrections were performed was found to impact the consistency of parameter estimates significantly. Although, no pipeline emerged as a clear winner, the STB approach seemed to yield the most consistent parameter estimates under large gradient nonlinearities. Conclusions Under large gradient nonlinearities, the choice of preprocessing pipeline significantly impacts the estimated diffusion parameters. Motion‐induced spatio‐temporal B‐matrix variations can lead to systematic bias in the parameter estimates, that can be ameliorated using the proposed STB framework.
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ISSN:0740-3194
1522-2594
1522-2594
DOI:10.1002/mrm.28464