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dc.contributor.authorGraham, MS
dc.contributor.authorDrobnjak, I
dc.contributor.authorJenkinson, M
dc.contributor.authorZhang, H
dc.date.accessioned2020-12-18T04:40:25Z
dc.date.available2020-12-18T04:40:25Z
dc.date.issued2017
dc.identifierpii: PONE-D-17-16065
dc.identifier.citationGraham, M. S., Drobnjak, I., Jenkinson, M. & Zhang, H. (2017). Quantitative assessment of the susceptibility artefact and its interaction with motion in diffusion MRI.. PLoS One, 12 (10), pp.e0185647-. https://doi.org/10.1371/journal.pone.0185647.
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/11343/256304
dc.description.abstractIn this paper we evaluate the three main methods for correcting the susceptibility-induced artefact in diffusion-weighted magnetic-resonance (DW-MR) data, and assess how correction is affected by the susceptibility field's interaction with motion. The susceptibility artefact adversely impacts analysis performed on the data and is typically corrected in post-processing. Correction strategies involve either registration to a structural image, the application of an acquired field-map or the use of additional images acquired with different phase-encoding. Unfortunately, the choice of which method to use is made difficult by the absence of any systematic comparisons of them. In this work we quantitatively evaluate these methods, by extending and employing a recently proposed framework that allows for the simulation of realistic DW-MR datasets with artefacts. Our analysis separately evaluates the ability for methods to correct for geometric distortions and to recover lost information in regions of signal compression. In terms of geometric distortions, we find that registration-based methods offer the poorest correction. Field-mapping techniques are better, but are influenced by noise and partial volume effects, whilst multiple phase-encode methods performed best. We use our simulations to validate a popular surrogate metric of correction quality, the comparison of corrected data acquired with AP and LR phase-encoding, and apply this surrogate to real datasets. Furthermore, we demonstrate that failing to account for the interaction of the susceptibility field with head movement leads to increased errors when analysing DW-MR data. None of the commonly used post-processing methods account for this interaction, and we suggest this may be a valuable area for future methods development.
dc.languageeng
dc.publisherPublic Library of Science (PLoS)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleQuantitative assessment of the susceptibility artefact and its interaction with motion in diffusion MRI.
dc.typeJournal Article
dc.identifier.doi10.1371/journal.pone.0185647
melbourne.affiliation.departmentCentre for Neuroscience
melbourne.source.titlePLoS One
melbourne.source.volume12
melbourne.source.issue10
melbourne.source.pagese0185647-
dc.rights.licenseCC BY
melbourne.elementsid1318139
melbourne.openaccess.pmchttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC5624609
melbourne.contributor.authorJenkinson, Mark
dc.identifier.eissn1932-6203
melbourne.accessrightsOpen Access


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