Super-Resolution Musculoskeletal MRI Using Deep Learning
In this manuscript, we have demonstrated a method termed ‘DeepResolve’, which can transform low-resolution magnetic resonance images (MRI) into higher-resolution images. In MRI high-resolution images are beneficial in order to better delineate anatomical detail, however, the acquisition of such high-resolution data is time consuming and uncomfortable for patients. To overcome this inefficiency, we trained a convolutional neural network in order to learn features between low and high-resolution representation of the same images in order to teach the network to enhance the quality of arbitrary low-resolution images. Specifically, we showed that DeepResolve was able to outperform (using quantitative image quality metrics and a qualitative radiologist reader study) commonly used interpolation methods for enhancing the through-plane resolution for a variety of downsampling factors.
Chaudhari AS, Fang Z, Kogan F, Wood J, Stevens KJ, Gibbons EK, Lee JH, Gold GE, Hargreaves BA. Super-resolution musculoskeletal MRI using deep learning. Magn Reson Med. 2018 Mar 26. doi: 10.1002/mrm.27178.