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Abstract #1629

Multi-parametric Diffusion MRI and Multiple Linear Regression Predict Regional Spinal Cord Damage

Feng Wang1,2, Junzhong Xu1,2, Tung-Lin Wu3, Pai-Feng Yang1,2, Li Min Chen1,2, and John C. Gore1,2,3
1Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States, 2Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States, 3Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States

Synopsis

Keywords: Large Animals, Nonhuman Primates, Spinal Cord, Multiple Linear Regression

Motivation: Diffusion MRI provides several quantitative parameters for evaluating structural alterations in spinal cord resulting from injuries.

Goal(s): Our primary objective is to comprehensively assess the sensitivity and specificity of measures derived from both the spherical mean technique and diffusion tensor imaging for assessing regional damage in the cervical spine of non-human primates after a targeted injury to a unilateral dorsal column.

Approach: We acquired diffusion MRI data and obtained silver-stained histological sections for validation.

Results: Our results suggest that diffusion MRI can detect and characterize axonal and cell body damage and assess the severity of spinal cord injury from multiple linear regression.

Impact: SMT and DTI offer sensitive and specific metrics to spinal cord injury (SCI). Vax correlates strongly with histologic assessments of SCI, independent of axonal orientation. Multiple linear regression provides better estimates of tissue damage after SCI than single linear regression.

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