Abstract
Tissue pathology in multiple sclerosis (MS) is highly complex, requiring multi-dimensional analysis. In this study, our goal was to test the feasibility of obtaining high angular resolution diffusion imaging (HARDI) metrics through single-shell modeling of diffusion tensor imaging (DTI) data, and investigate how advanced measures from single-shell HARDI and DTI tractography perform relative to classical DTI metrics in assessing MS pathology. We examined 52 relapsing-remitting MS patients who had 3T anatomical brain MRI and DTI. Single-shell HARDI modeling yielded 5 sub-voxel-based metrics, totalling 11 diffusion measures including 4 DTI and 2 tractography metrics. Based on machine learning of 3-dimensional regions of interest, we evaluated the importance of the measures through several tissue classification tasks. These included two within-subject comparisons: lesion versus normal appearing white matter (NAWM); and lesion core versus shell. Further, by stratifying patients as having high (above 75%ile) and low (below 25%ile) number of MS lesions, we also performed 2 classifications between subjects for lesions and NAWM respectively. Results showed that in lesion-NAWM analysis, HARDI orientation distribution function (ODF) energy, DTI fractional anisotropy (FA), and HARDI orientation dispersion index were the top three metrics, which together achieved 65.2% accuracy and 0.71 area under the receiver operating characteristic curve (AUROC). In core-shell analysis, DTI mean diffusivity (MD), radial diffusivity, and FA were the top three metrics, and MD dominated the classification, which achieved 59.3% accuracy and 0.59 AUROC alone. Between patients, FA was the leading feature in lesion comparisons, while ODF energy was the best in NAWM separation. Collectively, single-shell modeling of common diffusion data can provide robust orientation measures of lesion and NAWM pathology, and DTI metrics are most sensitive to intra-lesion abnormality. Combined analysis of both advanced and classical diffusion measures may be critical for improved understanding of MS pathology.
Highlights
Multiple sclerosis (MS) is a severe central nervous system disease impacting > 2.8 million people worldwide (Msif Tmsif., 2020)
The purpose of this study was to test the feasibility of single-shell HARDI (ssHARDI) using clinically available Diffusion magnetic resonance imaging (dMRI), and investigate how advanced metrics from ssHARDI and Diffusion tensor imaging (DTI) tractography compare to traditional DTI measures in assessing MS pathology
Using commonly available dMRI data, we showed the feasibility of conducting ssHARDI analysis and the complementary value of parameters from relatively simple and complex diffusion models for assessing MS pathology
Summary
Multiple sclerosis (MS) is a severe central nervous system disease impacting > 2.8 million people worldwide (Msif Tmsif., 2020). Focal lesions are the hallmark of MS pathology, characterized by several changes, including demyelination, axonal injury, and inflammation (Reich et al, 2018). Ongoing tissue damage in the latter is believed to play a major role in the relentless progression of patient disability in MS (Reich et al, 2018). Diffusion magnetic resonance imaging (dMRI) serves as a promising tool for in vivo assessment of tissue microstructure (Inglese and Bester, 2010). With advances in dMRI techniques, multi-dimensional analysis of tissue properties becomes possible, including localized analysis of lesion activities concerning specific white matter tracts (Inglese and Bester, 2010)
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