Keywords: Diagnosis/Prediction, Quantitative Susceptibility mapping
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.
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