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 negl...

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Autores principales: Rudrapatna, Umesh, Parker, Greg D., Roberts, Jamie, Jones, Derek K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8103165/
https://www.ncbi.nlm.nih.gov/pubmed/33009875
http://dx.doi.org/10.1002/mrm.28464
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author Rudrapatna, Umesh
Parker, Greg D.
Roberts, Jamie
Jones, Derek K.
author_facet Rudrapatna, Umesh
Parker, Greg D.
Roberts, Jamie
Jones, Derek K.
author_sort Rudrapatna, Umesh
collection PubMed
description 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 [Formula: see text] 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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spelling pubmed-81031652021-05-10 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 K. Magn Reson Med Notes—Computer Processing and Modeling 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 [Formula: see text] 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. John Wiley and Sons Inc. 2020-10-03 2021-02 /pmc/articles/PMC8103165/ /pubmed/33009875 http://dx.doi.org/10.1002/mrm.28464 Text en © 2020 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Notes—Computer Processing and Modeling
Rudrapatna, Umesh
Parker, Greg D.
Roberts, Jamie
Jones, Derek K.
A comparative study of gradient nonlinearity correction strategies for processing diffusion data obtained with ultra‐strong gradient MRI scanners
title A comparative study of gradient nonlinearity correction strategies for processing diffusion data obtained with ultra‐strong gradient MRI scanners
title_full A comparative study of gradient nonlinearity correction strategies for processing diffusion data obtained with ultra‐strong gradient MRI scanners
title_fullStr A comparative study of gradient nonlinearity correction strategies for processing diffusion data obtained with ultra‐strong gradient MRI scanners
title_full_unstemmed A comparative study of gradient nonlinearity correction strategies for processing diffusion data obtained with ultra‐strong gradient MRI scanners
title_short A comparative study of gradient nonlinearity correction strategies for processing diffusion data obtained with ultra‐strong gradient MRI scanners
title_sort comparative study of gradient nonlinearity correction strategies for processing diffusion data obtained with ultra‐strong gradient mri scanners
topic Notes—Computer Processing and Modeling
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8103165/
https://www.ncbi.nlm.nih.gov/pubmed/33009875
http://dx.doi.org/10.1002/mrm.28464
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