{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/reliable-brain-morphometry-from-contrast","title":"Reliable brain morphometry from contrast‐enhanced T1w‐MRI in patients with multiple sclerosis","arxiv_id":null,"date":"2022-10-17","proceeding":"Human Brain Mapping 2022 10","authors":["Michael Rebsamen","Richard McKinley","Piotr Radojewski","Maximilian Pistor","Christoph Friedli","Robert Hoepner","Anke Salmen","Andrew Chan","Mauricio Reyes","Franca Wagner","Roland Wiest","Christian Rummel"],"abstract":"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.","url_abs":"https://doi.org/10.1002/hbm.26117","url_pdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.26117","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"reliable-brain-morphometry-from-contrast","repo_url":"https://github.com/SCAN-NRAD/DL-DiReCT","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3d-medical-imaging-segmentation","task_name":"3D Medical Imaging Segmentation"},{"task_slug":"anatomy","task_name":"Anatomy"},{"task_slug":"brain-morphometry","task_name":"Brain Morphometry"},{"task_slug":"brain-segmentation","task_name":"Brain Segmentation"},{"task_slug":"diagnostic","task_name":"Diagnostic"},{"task_slug":"diffeomorphic-medical-image-registration","task_name":"Diffeomorphic Medical Image Registration"},{"task_slug":"mri-segmentation","task_name":"MRI segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}