Automatic detection and measurement of WM lesions in MS patients using MR-STAT and a self-supervised bivariate Gaussian probabilistic model

Motivation: Radiology workflow around multiple sclerosis (MS) patients is time-consuming. Goal(s): To automatically count and measure individual white matter anomalies in MS patients from a five-minute Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT) scan. Approach: We imaged ten healthy volunteers (HV) and six MS patients using a five-minute MR-STAT protocol. Resulting quantitative data from seven HVs was fit to a multivariate Gaussian probabilistic model. The model was tested on three HVs and six MS patients. Results: Automatic anomaly detection was moderately accurate in MS patients. No anomalies were found in HVs. These results underline the potential for a shorter acquisition with automatic outlier detection. Impact: MRI protocols for MS patients are lengthy and the assessing the images is a time-consuming task for the radiologist. We combine a fast (five-minute) MR-STAT relaxometry scan with a data-driven, automatic outlier detection strategy to potentially accelerate the clinical workflow.