Fluid and White Matter Suppression Contrasts MRI Improves Deep Learning Detection of Multiple Sclerosis Cortical Lesions

Purpose To investigate, for the first time, the efficacy of Fluid and White Matter Suppression (FLAWS) MRI sequence in improving Deep Learning (DL)-based detection and segmentation of cortical lesions in Multiple Sclerosis (MS) patients even, with applicability to clinical settings where only standard T1-weighted images are available.

In this retrospective multi-site study, we analyzed 204 MS patients using DL models developed with FLAWS and Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) sequences. Reference standard annotations were established through two approaches: (1) consensus of three expert raters across all contrasts, and (2) single-rater annotations for individual modalities. Models were validated on both internal and external datasets, with performance assessed using F1-score for detection and DSC for segmentation accuracy.

Models involving FLAWS demonstrated superior performance over MP2RAGE-only models. The combined MP2RAGE+FLAWS model achieved CL detection with median F1-score of 0.667[0.339−0.840] compared to multirater consensus. Models trained on comprehensive consensus annotations outperformed those trained on single-modality annotations. Notably, a model exclusively based on MP2RAGE images and trained with FLAWS-derived annotations showed, showed strong generalization to external Magnetization Prepared Rapid Gradient-Echo (MPRAGE) clinical datasets (median F1-score: 0.55[0.211 − 0.998]).

Integration of FLAWS-derived contrasts and annotations significantly improves DL-based CL detection and segmentation. The models demonstrate capability in identifying lesions missed by individual raters and maintain robust performance even without FLAWS sequences in standard clinical settings. This advancement facilitates clinical translation, supported by publicly available inference models on DockerHub. Highlights MP2RAGE+FLAWS MRI sequences achieves superior cortical lesion detection performance Quantitative validation shows median F1 of 0.667[0.339 − 0.840] with combined sequences FLAWS-trained model generalizes well to standard MPRAGE images (F1: 0.55[0.211 − 0.998) Model demonstrates immediate clinical applicability on standard MPRAGE without FLAWS input Implementation publicly accessible via DockerHub for widespread clinical adoption Competing Interest Statement Dr. Alessandro Cagol is supported by EUROSTAR E!113682 HORIZON2020 and has received speaker honoraria from Novartis and Roche. Dr. Cristina Granziera, as an employee of the University Hospital Basel, has received advisory board and consultancy fees from Actelion, Novartis, Genzyme, and F. Hoffmann-La Roche Ltd; speaker fees from Biogen and Genzyme-Sanofi; and research support from F. Hoffmann-La Roche Ltd. These fees were used exclusively for research support. No other authors have conflicts to declare. Funding Statement This study did not receive any funding Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study was approved by the institutional review board (Ethikkommission Nordwest- und Zentralschweiz (EKNZ) University Hospital Basel, Switzerland) on July 30, 2018 (Project ID: 2018-01174). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability The authors do not have permission to share the data