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 Nov;80(5):2139-54.

Online Journal Article

In the above image, DeepResolve enhanced the tricubic interpolation image. Compared to the ground-truth, the interpolated image had 3x lower resolution in the left-right direction. The medial collateral ligament (solid arrow), an osteophyte (dashed arrow) and small blood vessels (dotted arrow) had better delineation in the DeepResolve image than the tricubic interpolation image. The DeepResolve images were comparable to the original ground-truth images.

Akshay Chaudhari
Assistant Professor (Research) of Radiology (Integrative Biomedical Imaging Informatics at Stanford) and, by courtesy, of Biomedical Data Science
Feliks Kogan
Assistant Professor (Research) of Radiology (Musculoskeletal Imaging)
Kate Stevens
Associate Professor of Radiology (Musculoskeletal Imaging)
Jin Hyung Lee
Associate Professor of Neurology (Neurology Research Faculty), of Neurosurgery and of Bioengineering and, by courtesy, of Electrical Engineering
Garry Gold
Stanford Medicine Professor of Radiology and Biomedical Imaging
Brian A. Hargreaves
Professor of Radiology (Radiological Sciences Laboratory) and, by courtesy, of Electrical Engineering and of Bioengineering