Boosting the Signal-to-Noise of Low-Field MRI With Deep Learning Image Reconstruction

Abstract

Recent years have seen a resurgence of interest in inexpensive low-field (<0.3 T) MRI systems mainly due to advances in magnet, coil and gradient set designs. However, most of these advances are focused on hardware development and signal acquisition while far less attention has been given to how advanced image reconstruction can improve image quality at low field. We describe here the use of our end-to-end deep neural network approach (AUTOMAP) to improve the image quality of highly noise-corrupted low-field MRI data. We compare the performance of this approach to two additional state-of-the-art denoising pipelines. We find that AUTOMAP improves image reconstruction of data acquired on two very different low-field MRI systems: human brain data acquired at 6.5 mT, and plant root data acquired at 47 mT, demonstrating SNR gains above Fourier reconstruction by factors of 1.5- to 4.5-fold, and 3-fold, respectively. In these applications, AUTOMAP outperformed both contemporary denoising algorithms and suppressed noise-like spike artifacts in reconstructed images. The impact of domain-specific training corpora on the reconstruction performance is discussed. The AUTOMAP approach to image reconstruction will enable significant image quality improvements at low-field, especially in highly noise-corrupted environments.