High-Resolution Sodium MRI of Human Gliomas at 3T Using Physics-Based Generative AI

Abstract

Motivation: Sodium MRI is a promising technique for understanding the brain tumor microenvironment. However, sodium MRI at 3T suffers from extremely low SNR, resulting in compromised resolution and long acquisition times. Goal(s): Our goal is to create a high-resolution sodium MRI at 3T using generative AI to improve biological characterization, treatment monitoring, and surgical planning for brain tumor patients. Approach: We developed a physics-informed synthetic dataset to train an anatomically-constrained GAN for high-resolution neuroimaging of brain tumors. Results: When applied to brain tumor patients' images, the synthetic-sodium MRI improved resolution, SNR, and correlated with expression of sodium-proton exchanger (NHE1) on image-guided biopsy. Impact: High-resolution sodium neuroimaging at 3T using physics-informed anatomically-constrained GAN has the potential to make multinuclear MRI feasible in the clinical environment, leading to conceivable improvements in diagnosis, monitoring, treatment, and our understanding of the biology of brain tumors.