Loading...
A comparative study of gradient nonlinearity correction strategies for processing diffusion data obtained with ultra-strong gradient MRI scanners
Rudrapatna, Umesh Parker, Greg D. Roberts, Jamie Jones, Derek
Rudrapatna, Umesh
Parker, Greg D.
Roberts, Jamie
Jones, Derek
Abstract
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 urn:x-wiley:07403194:media:mrm28464:mrm28464-math-0001 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.
Keywords
connectom, diffusion MRI, diffusion preprocessing, gradient nonlinearity, human connectome project, ultra-strong gradients
Date
2020
Type
Journal article
Journal
Magnetic Resonance in Medicine
Book
Volume
85
Issue
2
Page Range
1104-1113
Article Number
ACU Department
Collections
Relation URI
Source URL
Event URL
Open Access Status
Published as ‘gold’ (paid) open access
License
CC BY 4.0
File Access
Open