Thin slice positive source QSM improves deep learning based paramagnetic rim detection in multiple sclerosis lesions

Motivation: Rim lesions are important subset of chronic active MS lesions that show strong correlation to patient disability. Rim identification by experts is time consuming. Goal(s): Develop tool for supporting the expert in Rim identification using 1 mm QSM. Approach: We developed an automated deep learning-based network for PRL detection on thin-slice 1mm QSMp. We evaluated the improvement in performance compared with networks trained using 1mm QSM and 3mm QSMp. Results: Use of high-resolution positive susceptibility source maps improves detection of Rim in MS patients compared to 1mm QSM and 3mm QSMp. The network does not require a precise QSM lesion mask to operate. Impact: Using the Deep learning for detecting rim on 1mm QSMp, enabling reducing workload for human in detecting rim.