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Cited by 19Advanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and ApplicationsMedical Image Segmentation Techniques

Image registration to compensate for EPI distortion in patients with brain tumors: an evaluation of tract-specific effects

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Albi, Angela, Meola, Antonio, Zhang, Fan, Kahali, Pegah, Rigolo, Laura, Tax, Chantal M.W., Ciris, Pelin Aksit, Essayed, Walid I. +6 more·2018·Journal of Neuroimaging
DOI 10.1111/jon.12485PMC PMC5844838
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Abstract

BACKGROUND AND PURPOSE: Diffusion magnetic resonance imaging (dMRI) provides preoperative maps of neurosurgical patients’ white matter tracts, but these maps suffer from echo‐planar imaging (EPI) distortions caused by magnetic field inhomogeneities. In clinical neurosurgical planning, these distortions are generally not corrected and thus contribute to the uncertainty of fiber tracking. Multiple image processing pipelines have been proposed for image‐registration‐based EPI distortion correction in healthy subjects. In this article, we perform the first comparison of such pipelines in neurosurgical patient data. METHODS: Five pipelines were tested in a retrospective clinical dMRI dataset of 9 patients with brain tumors. Pipelines differed in the choice of fixed and moving images and the similarity metric for image registration. Distortions were measured in two important tracts for neurosurgery, the arcuate fasciculus and corticospinal tracts. RESULTS: Significant differences in distortion estimates were found across processing pipelines. The most successful pipeline used dMRI baseline and T2‐weighted images as inputs for distortion correction. This pipeline gave the most consistent distortion estimates across image resolutions and brain hemispheres. CONCLUSIONS: Quantitative results of mean tract distortions on the order of 1–2 mm are in line with other recent studies, supporting the potential need for distortion correction in neurosurgical planning. Novel results include significantly higher distortion estimates in the tumor hemisphere and greater effect of image resolution choice on results in the tumor hemisphere. Overall, this study demonstrates possible pitfalls and indicates that care should be taken when implementing EPI distortion correction in clinical settings.

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  • Intermodality registration between dMRI and T1-weighted images can be affected by differences in image resolutions and echo-planar imaging (EPI) distortion in dMRI data.

    “For example, the intermodality registration between dMRI and T1-weighted images can be affected by differences in image resolutions (Malinsky et al., 2013) and echo-planar imaging (EPI) distortion in dMRI data (Albi et al., 2018).”

    From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Albi et al., 2018

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Authors
Albi, A., Meola, A., Zhang, F., Kahali, P., Rigolo, L., Tax, C. M. W., Ciris, P. A., Essayed, W. I., Unadkat, P., Norton, I., Rathi, Y., Olubiyi, O., Golby, A. J., & O'Donnell, L. J.
Affiliations
  • Brigham and Women's Hospital (US)
  • Brigham and Women's Hospital Harvard Medical School Boston MA
ORCID
Venue
Journal of Neuroimaging
Publisher
Wiley
Published
2018-01-10
Volume
28
Issue
2
Pages
173-182
ISSN
  • 1051-2284 (ISSN-L)
  • 1552-6569
DOI
10.1111/jon.12485
PMC
PMC5844838
OpenAlex
W2783918318
Cited by
19
References
1
Citations by year
2026: 1 · 2025: 3 · 2024: 1 · 2023: 3 · 2022: 3 · 2021: 1 · 2020: 2 · 2019: 4 · 2018: 1
FWCI
1.38
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • Journal of Neuroimaging (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
  • MPG.PuRe (Max Planck Society) (submittedVersion)
Funders
  • Nederlandse Organisatie voor Wetenschappelijk Onderzoek (612.001.104)
  • National Institutes of Health (P41 EB015898, P41 EB015902, R01 MH074794, R01 MH097979, R03 NS088301, R21 CA198740, R25 CA089017, U01 CA199459, U01 NS083223)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Keywords
diffusion MRI, brain tumors, image registration, white matter tracts, arcuate fasciculus, corticospinal tract, neurosurgical planning, T2-weighted imaging, image resolution, tractography
MeSH terms
Adult, Aged, Algorithms, Brain, Brain Neoplasms, Female, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Retrospective Studies, Artifacts, Echo-Planar Imaging, Neurosurgical Procedures, Diffusion Magnetic Resonance Imaging, White Matter

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