A Potential Substitution for Gadolinium in Brachial Plexus Magnetic Resonance Neurography: Deep Learning-Based Virtual Enhancement.

Motivation: Contrast-enhanced Magnetic Resonance Neurography (MRN) improves visualization of brachial plexus, but gadolinium risks limit clinical use. To reduce reliance on gadolinium contrast in brachial plexus (BP) MRN, we explore deep learning's potential for virtual enhancement. Goal(s): To investigate the feasibility of virtually enhancing brachial plexus MRN without gadolinium. Approach: An image enhancement network based on 2.5D U-Net was trained to generate virtually enhanced BP images from non-enhancement BP images, achieving high image quality and nerve visualization. Results: The virtual enhancement BP images showed comparable vascular suppression and image quality to gadolinium-enhanced images, demonstrating the potential for gadolinium substitution in brachial plexus MRN. Impact: This work opens the door to safer and more accessible BP MRN by reducing reliance on gadolinium. It may lead to broader clinical adoption and facilitate research on non-contrast imaging methods, benefiting both clinicians and patients.