Super-resolution MRI through Deep Learning

Abstract

Magnetic resonance imaging (MRI) is extensively used for diagnosis and image-guided therapeutics. Due to hardware, physical and physiological limitations, acquisition of high-resolution MRI data takes long scan time at high system cost, and could be limited to low spatial coverage and also subject to motion artifacts. Super-resolution MRI can be achieved with deep learning, which is a promising approach and has a great potential for preclinical and clinical imaging. Compared with polynomial interpolation or sparse-coding algorithms, deep learning extracts prior knowledge from big data and produces superior MRI images from a low-resolution counterpart. In this paper, we adapt two state-of-the-art neural network models for CT denoising and deblurring, transfer them for super-resolution MRI, and demonstrate encouraging super-resolution MRI results toward two-fold resolution enhancement.