Quantifying cortical lesions in multiple sclerosis MRI datasets using multi-contrast post-processing and deep learning

BACKGROUND: Multiple sclerosis (MS) is a chronic neurological disease affecting both white and gray matter of the central nervous system. Despite the well-established involvement of cortical lesions in MS, feasibility limitations in their visualization on typical magnetic resonance imaging (MRI) protocols prevent their evaluation in nearly all clinical trials. Recently, several post-processing methods, including synthetic contrasts and artificial intelligence (AI)-based approaches, have shown potential for enhancing cortical lesion detection on conventional MRI data. These methods have the potential to reanalyze existing clinical-trial data to answer key mechanistic questions about both MS development and about treatment effects. METHODS: ), T1/T2 ratio, and artificial intelligence-derived double inversion recovery (AI-DIR) - and introduced a new combined contrast called multi-modal cortical lesion enhanced (MMCLE). We also harnessed transformer-based semantic segmentation to improve automated detection and delineation of these lesions. RESULTS: At baseline, we detected 14.8 + /-20.72 lesions per participant, with 86.0% true positive rate and 8.4% false positive rate across subjects for blinded MMCLE, using simultaneous review of all contrasts as the reference. High reproducibility was observed across field strengths and acquisition types (ICC 88.8-92.5%). CONCLUSIONS: We confirmed that cortical lesions can be clearly visualized and quantified with these methods. Using deep learning, we also confirmed that the simultaneous use of multiple contrasts improves quantification.