Effectiveness of regional diffusion MRI measures in distinguishing multiple sclerosis abnormalities within the cervical spinal cord

TL;DR

This study evaluates the effectiveness of regional diffusion MRI measures, including novel Ball-and-Stick metrics, in detecting multiple sclerosis lesions in the cervical spinal cord, demonstrating improved prediction accuracy with combined metrics.

Contribution

The paper introduces a comprehensive analysis of diffusion MRI metrics, including Ball-and-Stick models, for detecting MS lesions in the cervical spinal cord, highlighting the benefit of combining specific metrics for better prediction.

Findings

Ball-and-Stick metrics provide new insights into MS tissue microstructure.

Combining FA, RD, MD with FWW, Stick-AD improves lesion detection accuracy.

Diffusion MRI metrics show sensitivity to microstructural changes in MS patients.

Abstract

Multiple sclerosis is an inflammatory disorder of the central nervous system. Quantitative MRI has huge potential to provide intrinsic and normative values of tissue properties useful for diagnosis, prognosis and ultimately clinical follow-up of this disease. However, there is a large discrepancy between the clinical observations and how the pathology is exhibited in MRI brain scans. Complementary to brain imaging, the study of multiple sclerosis lesions in the spinal cord has recently gained interest as a potential marker for early physical impairment. Therefore, investigating how the spinal cord is damaged using quantitative imaging, in particular, diffusion MRI, becomes an acute challenge. In this work, we extract average diffusion MRI metrics per vertebral level from spinal cord data acquired from multiple clinical sites. The diffusion-based metrics involved are extracted from the diffusion tensor imaging and Ball-and-Stick models and quantified for every cervical vertebral level using a collection of image processing methods and an atlas-based approach. Then, we perform a statistical analysis study to characterize the feasibility of these metrics to detect lesions. Specifically, we study the usefulness of combining different metrics to improve the accuracy prediction score associated with the presence of multiple sclerosis lesions. We demonstrate the grade of sensitivity to underlying microstructure changes in MS patients of each metric. Ball-and-Stick provides novel information about the MS damage to tissue microstructure. In addition, we show that choosing a subset of metrics: [FA, RD, MD] and [FWW, MD, Stick-AD, RD], which bring complementary information, has significantly increased the prediction score of the presence of the MS lesion in the cervical spinal cord.