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    Quantitative assessment of the susceptibility artefact and its interaction with motion in diffusion MRI.

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    Author
    Graham, MS; Drobnjak, I; Jenkinson, M; Zhang, H
    Date
    2017
    Source Title
    PLoS One
    Publisher
    Public Library of Science (PLoS)
    University of Melbourne Author/s
    Jenkinson, Mark
    Affiliation
    Centre for Neuroscience
    Metadata
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    Document Type
    Journal Article
    Citations
    Graham, 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.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/256304
    DOI
    10.1371/journal.pone.0185647
    Open Access at PMC
    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5624609
    Abstract
    In 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.

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