Reliable brain morphometry from contrast-enhanced T1w-MRI in patients with multiple sclerosis.


Journal

Human brain mapping
ISSN: 1097-0193
Titre abrégé: Hum Brain Mapp
Pays: United States
ID NLM: 9419065

Informations de publication

Date de publication:
15 02 2023
Historique:
revised: 12 08 2022
received: 03 06 2022
accepted: 26 09 2022
pubmed: 18 10 2022
medline: 28 1 2023
entrez: 17 10 2022
Statut: ppublish

Résumé

Brain morphometry is usually based on non-enhanced (pre-contrast) T1-weighted MRI. However, such dedicated protocols are sometimes missing in clinical examinations. Instead, an image with a contrast agent is often available. Existing tools such as FreeSurfer yield unreliable results when applied to contrast-enhanced (CE) images. Consequently, these acquisitions are excluded from retrospective morphometry studies, which reduces the sample size. We hypothesize that deep learning (DL)-based morphometry methods can extract morphometric measures also from contrast-enhanced MRI. We have extended DL+DiReCT to cope with contrast-enhanced MRI. Training data for our DL-based model were enriched with non-enhanced and CE image pairs from the same session. The segmentations were derived with FreeSurfer from the non-enhanced image and used as ground truth for the coregistered CE image. A longitudinal dataset of patients with multiple sclerosis (MS), comprising relapsing remitting (RRMS) and primary progressive (PPMS) subgroups, was used for the evaluation. Global and regional cortical thickness derived from non-enhanced and CE images were contrasted to results from FreeSurfer. Correlation coefficients of global mean cortical thickness between non-enhanced and CE images were significantly larger with DL+DiReCT (r = 0.92) than with FreeSurfer (r = 0.75). When comparing the longitudinal atrophy rates between the two MS subgroups, the effect sizes between PPMS and RRMS were higher with DL+DiReCT both for non-enhanced (d = -0.304) and CE images (d = -0.169) than for FreeSurfer (non-enhanced d = -0.111, CE d = 0.085). In conclusion, brain morphometry can be derived reliably from contrast-enhanced MRI using DL-based morphometry tools, making additional cases available for analysis and potential future diagnostic morphometry tools.

Identifiants

pubmed: 36250711
doi: 10.1002/hbm.26117
pmc: PMC9875932
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

970-979

Informations de copyright

© 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.

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Auteurs

Michael Rebsamen (M)

Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland.
Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland.

Richard McKinley (R)

Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland.

Piotr Radojewski (P)

Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland.
Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland.

Maximilian Pistor (M)

Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland.

Christoph Friedli (C)

Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland.

Robert Hoepner (R)

Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland.

Anke Salmen (A)

Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland.

Andrew Chan (A)

Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland.

Mauricio Reyes (M)

ARTORG Center for Biomedical Research, University of Bern, Bern, Switzerland.

Franca Wagner (F)

Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland.

Roland Wiest (R)

Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland.
Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland.

Christian Rummel (C)

Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland.

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Classifications MeSH