Denoising and Image Enhancement in Low-Field MRI

Deep learning has proven successful in a variety of medical image processing applications, including denoising and re- moving artifacts. This is of particular interest for low-field Magnetic Resonance Imaging (MRI), which is promising for its affordability, compact footprint, and reduced shielding requirements, but inherently suffers from low signal-to-noise ratio. In this work, we propose a method of simulating scanner-specific images from publicly available, 1.5T and 3T database of MR images, using a signal encoding matrix in- corporating explicitly modeled imaging gradients and fields. We apply a stacked, U-Net architecture to reduce noise from the system and remove artifacts due to the inhomogeneous B0 field, nonlinear gradients, undersampling of k-space and image reconstruction to enhance low-field MR images. The final network is applied as a post-processing step following image reconstruction to phantom images acquired on a 60- 67mT MR scanner and demonstrates promising qualitative and quantitative improvements to overall image quality.