Applying Deep Learning to Sodium MRI Reconstruction Using Anatomically-Guided Neural Networks

Motivation: Sodium Magnetic Resonance Imaging (23Na MRI) provides unique metabolic information but suffers from low signal-to-noise ratio (SNR). Iterative anatomically guided reconstructions (AGR) can improve SNR and resolution but are limited in practice by their long computational times. Goal(s): To address these limitations, we explore the use of neural networks to approximate the AGR sodium MRI reconstruction and reduce computational time. Approach: A U-Net convolutional neural network (CNN) was trained to approximate the AGR iterative reconstruction using data from normal human volunteers. Results: Our results indicate that the neural network implementation achieves comparable image quality while significantly reducing reconstruction time. Impact: The improved SNR accuracy and spatial resolution of the CNN AGR reconstructions make the use of Sodium MRI more feasible within the confines of a clinical examination.