Magnetic Resonance Neurography: Evolution, Technical Foundations, and Future Directions

In the past 20 years, magnetic resonance neurography has evolved from an experimental technique into an essential diagnostic pillar for peripheral nerve evaluation. This review delineates the historical shift from 1.5T to 3T systems and the transition from two-dimensional acquisitions to three-dimensional isotropic volumetric sequences that allow for high-fidelity multiplanar reconstructions. We explore the principles of modern protocols, including Dixon-based fat suppression, contrast-enhanced imaging, and diffusion tensor imaging. Furthermore, the clinical usefulness of the Neuropathy Score Reporting and Data System in standardizing communication across multidisciplinary nerve boards is explored.

We focus specifically on recent developments highlighted by deep learning reconstruction and automated segmentation driven by artificial intelligence that enhance signal-to-noise ratios and diagnostic precision. By integrating quantitative biomarkers such as apparent diffusion coefficient values for tumor characterization, magnetic resonance neurography provides a definitive morphological and functional roadmap, bridging the gap between electrodiagnostic testing and surgical intervention.