Super‐resolution musculoskeletal MRI using deep learning

作者：Akshay Chaudhari, Zhongnan Fang, Feliks Kogan, Jeff Wood, Kathryn J. Stevens, Eric K. Gibbons, Jin Hyung Lee, Garry E. Gold, Brian A. Hargreaves · 发表于：Magnetic Resonance in Medicine · 年份：2018 · DOI：10.1002/mrm.27178 · 被引用次数：407 · 研究领域：Advanced Image Processing Techniques、Ultrasound Imaging and Elastography、Total Knee Arthroplasty Outcomes

PURPOSE: To develop a super-resolution technique using convolutional neural networks for generating thin-slice knee MR images from thicker input slices, and compare this method with alternative through-plane interpolation methods. METHODS: We implemented a 3D convolutional neural network entitled DeepResolve to learn residual-based transformations between high-resolution thin-slice images and lower-resolution thick-slice images at the same center locations. DeepResolve was trained using 124 double echo in steady-state (DESS) data sets with 0.7-mm slice thickness and tested on 17 patients. Ground-truth images were compared with DeepResolve, clinically used tricubic interpolation, and Fourier interpolation methods, along with state-of-the-art single-image sparse-coding super-resolution. Comparisons were performed using structural similarity, peak SNR, and RMS error image quality metrics for a multitude of thin-slice downsampling factors. Two musculoskeletal radiologists ranked the 3 data sets and reviewed the diagnostic quality of the DeepResolve, tricubic interpolation, and ground-truth images for sharpness, contrast, artifacts, SNR, and overall diagnostic quality. Mann-Whitney U tests evaluated differences among the quantitative image metrics, reader scores, and rankings. Cohen's Kappa (κ) evaluated interreader reliability. RESULTS: DeepResolve had significantly better structural similarity, peak SNR, and RMS error than tricubic interpolation, Fourier interpolation, and spars...