Advanced Analysis of Diffusion Tensor Imaging Along With Machine Learning Provides New Sensitive Measures of Tissue Pathology and Intra-Lesion Activity in Multiple Sclerosis
| Autor: | Olayinka Oladosu, Wei-Qiao Liu, Bruce G. Pike, Marcus Koch, Luanne M. Metz, Yunyan Zhang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Pathology
medicine.medical_specialty tractography Neurosciences. Biological psychiatry. Neuropsychiatry Machine learning computer.software_genre lesions White matter Lesion intra-lesion pathology Fractional anisotropy medicine support vector machine Original Research Receiver operating characteristic Orientation (computer vision) business.industry General Neuroscience single-shell high angular resolution diffusion imaging diffusion tensor imaging medicine.anatomical_structure Feature (computer vision) Artificial intelligence medicine.symptom business computer Diffusion MRI Tractography RC321-571 Neuroscience |
| Zdroj: | Frontiers in Neuroscience Frontiers in Neuroscience, Vol 15 (2021) |
| ISSN: | 1662-4548 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|