Super-resolution musculoskeletal MRI using deep learning.

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 sparse-coding super-resolution for all downsampling factors (p < .05, except 4 × and 8 × sparse-coding super-resolution downsampling factors). In the reader study, DeepResolve significantly outperformed (p < .01) tricubic interpolation in all image quality categories and overall image ranking. Both readers had substantial scoring agreement (κ = 0.73). CONCLUSION: DeepResolve was capable of resolving high-resolution thin-slice knee MRI from lower-resolution thicker slices, achieving superior quantitative and qualitative diagnostic performance to both conventionally used and state-of-the-art methods.

MRI of the lumbar spine: comparison of 3D isotropic turbo spin-echo SPACE sequence versus conventional 2D sequences at 3.0 T.

BACKGROUND: Three-dimensional (3D) fast spin-echo sequence with variable flip-angle refocusing pulse allows retrospective alignments of magnetic resonance imaging (MRI) in any desired plane. PURPOSE: To compare isotropic 3D T2-weighted (T2W) turbo spin-echo sequence (TSE-SPACE) with standard two-dimensional (2D) T2W TSE imaging for evaluating lumbar spine pathology at 3.0 T MRI. MATERIAL AND METHODS: Forty-two patients who had spine surgery for disk herniation and had 3.0 T spine MRI were included in this study. In addition to standard 2D T2W TSE imaging, sagittal 3D T2W TSE-SPACE was obtained to produce multiplanar (MPR) images. Each set of MR images from 3D T2W TSE and 2D TSE-SPACE were independently scored for the degree of lumbar neural foraminal stenosis, central spinal stenosis, and nerve compression by two reviewers. These scores were compared with operative findings and the sensitivities were evaluated by McNemar test. Inter-observer agreements and the correlation with symptoms laterality were assessed with kappa statistics. RESULTS: The 3D T2W TSE and 2D TSE-SPACE had similar sensitivity in detecting foraminal stenosis (78.9% versus 78.9% in 32 foramen levels), spinal stenosis (100% versus 100% in 42 spinal levels), and nerve compression (92.9% versus 81.8% in 59 spinal nerves). The inter-observer agreements (κ = 0.849 vs. 0.451 for foraminal stenosis, κ = 0.809 vs. 0.503 for spinal stenosis, and κ = 0.681 vs. 0.429 for nerve compression) and symptoms correlation (κ = 0.449 vs. κ = 0.242) were better in 3D TSE-SPACE compared to 2D TSE. 3D TSE-SPACE with oblique coronal MPR images demonstrated better inter-observer agreements compared to 3D TSE-SPACE without oblique coronal MPR images (κ = 0.930 vs. κ = 0.681). CONCLUSION: Isotropic 3D T2W TSE-SPACE at 3.0 T was comparable to 2D T2W TSE for detecting foraminal stenosis, central spinal stenosis, and nerve compression with better inter-observer agreements and symptom correlation.