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

Rebsamen, Michael; McKinley, Richard; Radojewski, Piotr; Pistor, Maximilian; Friedli, Christoph; Hoepner, Robert; Salmen, Anke; Chan, Andrew; Reyes, Mauricio; Wagner, Franca; Wiest, Roland; Rummel, Christian (2023). Reliable brain morphometry from contrast-enhanced T1w-MRI in patients with multiple sclerosis. Human brain mapping, 44(3), pp. 970-979. Wiley-Blackwell 10.1002/hbm.26117

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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.

Item Type:

Journal Article (Original Article)


04 Faculty of Medicine > Department of Haematology, Oncology, Infectious Diseases, Laboratory Medicine and Hospital Pharmacy (DOLS) > Clinic of Radiation Oncology
04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurology
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Rebsamen, Michael Andreas, McKinley, Richard Iain, Radojewski, Piotr, Pistor, Maximilian, Friedli, Christoph Daniel, Hoepner, Robert, Salmen, Anke, Chan, Andrew Hao-Kuang, Reyes, Mauricio, Wagner, Franca, Wiest, Roland Gerhard Rudi, Rummel, Christian


600 Technology > 610 Medicine & health
500 Science > 570 Life sciences; biology








Pubmed Import

Date Deposited:

18 Oct 2022 10:30

Last Modified:

24 Nov 2023 10:40

Publisher DOI:


PubMed ID:


Uncontrolled Keywords:

MRI brain morphometry cortical thickness deep learning post-contrast imaging




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