Unique MS Lesion Identification from MRI

TL;DR

This paper presents a novel MRI-based method for accurately identifying and quantifying individual MS white matter lesions, improving lesion detection, separation, and volume estimation over previous techniques.

Contribution

The method integrates Hessian matrix analysis with a random walker algorithm to enhance lesion identification and volume measurement in MS MRI scans.

Findings

Accurately counts the number of lesions in synthetic images.

Improves separation of confluent lesions.

Provides precise total lesion volume estimation.

Abstract

Unique identification of multiple sclerosis (MS) white matter lesions (WMLs) is important to help characterize MS progression. WMLs are routinely identified from magnetic resonance images (MRIs) but the resultant total lesion load does not correlate well with EDSS; whereas mean unique lesion volume has been shown to correlate with EDSS. Our approach builds on prior work by incorporating Hessian matrix computation from lesion probability maps before using the random walker algorithm to estimate the volume of each unique lesion. Synthetic images demonstrate our ability to accurately count the number of lesions present. The takeaways, are: 1) that our method correctly identifies all lesions including many that are missed by previous methods; 2) we can better separate confluent lesions; and 3) we can accurately capture the total volume of WMLs in a given probability map. This work will allow new more meaningful statistics to be computed from WMLs in brain MRIs