qMRI-based classification of active vs. inactive multiple sclerosis lesions – towards eliminating the need for contrast agent injections

Motivation: The gold standard way for assessing Multiple sclerotic (MS) disease activity is by identifying new active lesions using contrast enhanced imaging. The repeated use of gadolinium injections for MS patients constitute a major concern due to long-term accumulation and even breakdown of this agent in the brain and body without efficient clearance. Goal(s): Classify active vs. inactive MS lesions using quantitative MRI (qMRI) without the need for contrast-enhanced imaging. Approach: Machine learning classifier trained on qMRI features of MS lesions. Results: qMRI profiling has the potential to classify MS lesions into active/inactive state with accuracy of 81.7 ± 10 %. Impact: Multiple sclerosis disease activity is assessed using contrast-enhanced MRI. Recently, concerns have been raised regarding the long-term accumulation and breakdown of contrast agents in the brain. This study introduces a qMRI-based and contrast-free approach for assessing multiple sclerosis disease activity.