Magnetization transfer MRI (MT-MRI) detects white matter damage beyond the primary site of compression in degenerative cervical myelopathy using a novel semi-automated analysis

• Published in: Computers in biology and medicine Vol. 197; no. Pt B; p. 111083
• Main Authors: Muhammad, Fauziyya, Weber II, Kenneth A., Haynes, Grace, Villeneuve, Lance, Smith, Lonnie, Baha, Alaa, Hameed, Sanaa, Khan, Ali F., Dhaher, Yasin, Parrish, Todd, Rohan, Michael, Smith, Zachary A.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.10.2025
• Subjects: Adult; Aged; Cervical Vertebrae - diagnostic imaging; CNN; Deep Learning; Degenerative cervical myelopathy; Dexterity; Female; Global curve optimization algorithm; Humans; Image Processing, Computer-Assisted - methods; Internal Medicine; Machine learning; Magnetic Resonance Imaging - methods; Magnetization transfer ratio; Male; Middle Aged; Neural Networks, Computer; Other; Spinal Cord Compression - diagnostic imaging; Spinal Cord Diseases - diagnostic imaging; Spinal cord toolbox; White Matter - diagnostic imaging; Degenerative cervical myelopathy; Dexterity; CNN; Machine learning; Spinal cord toolbox; Global curve optimization algorithm; Magnetization transfer ratio
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2025.111083

Degenerative cervical myelopathy (DCM) is the leading cause of spinal cord disorder in adults, yet conventional MRI cannot detect microstructural damage beyond the compression site. Current application of magnetization transfer ratio (MTR), while promising, suffer from limited standardization, operator dependence, and unclear added value over traditional metrics such as cross-sectional area (CSA). To address these limitations, we utilized our semi-automated analysis pipeline built on the Spinal Cord Toolbox (SCT) platform to automate MTR extraction. Our method integrates deep learning-based convolutional neural networks (CNNs) for spinal cord segmentation, vertebral labeling via the global curve optimization algorithm and PAM50 template registration to enable automated MTR extraction. Using the Generic Spine Protocol, we acquired 3T T2w- and MT-MRI images from 30 patients with DCM and 15 age-matched healthy controls (HC). We computed MTR and CSA at the maximal compression level (C5-C6) and a distant, uncompressed region (C2-C3). We extracted regional and tract-specific MTR using probabilistic maps in template space. Diagnostic accuracy was assessed with ROC analysis, and k-means clustering reveal patients subgroups based on neurological impairments. Correlation analysis assessed associations between MTR measures and DCM deficits. Patients with DCM showed significant MTR reductions in both compressed and uncompressed regions (p < 0.05). At C2-C3, MTR outperformed CSA (AUC 0.74 vs 0.69) in detecting spinal cord pathology. Tract-specific MTR were correlated with dexterity, grip strength, and balance deficits. Our reproducible, computationally robust pipeline links microstructural injury to clinical outcomes in DCM and provides a scalable framework for multi-site quantitative MRI analysis of the spinal cord. •MTR detects white matter damage beyond the compression site in DCM.•Semi-automated SCT pipeline enables the reproducible analysis of tract-specific MTR.•MTR outperforms CSA in detecting microstructural damage in uncompressed regions.•Tract-specific MTR reductions correlate with specific neurological deficits in DCM.•MTR is a non-invasive biomarker that clinicians can use to assess and monitor DCM.