Enhanced Spinal Cord Lesion Detection in Multiple Sclerosis Using White-Matter-Nulled 3D MPRAGE with Deep Learning Reconstruction

Motivation: Multiple sclerosis commonly presents spinal cord lesions, which can lead to disabling symptoms. Conventional MRI sequences often lack sensitivity for their detection. Goal(s): This study aimed to evaluate whether 3D WMn MPRAGE imaging combined with deep learning reconstruction could improve lesion visibility, detection accuracy, and confidence among neuroradiologists. Approach: Thirty-eight patients with RRMS or CIS underwent MRI scans, including conventional and 3D WMn MPRAGE sequences. Lesion detection, contrast-to-noise ratios, and artifact levels were assessed by four neuroradiologists. Results: The 3D WMn MPRAGE sequence significantly outperformed conventional sequences in lesion detection indicating its potential to improve MS spinal cord imaging. Impact: This study demonstrates that 3D WMn MPRAGE combined with deep learning reconstruction significantly improves spinal cord lesion detection in multiple sclerosis, offering superior contrast and fewer artifacts than conventional MRI, potentially enhancing early diagnosis and treatment outcomes.