ArticleMagnetic resonance in medicine2018
Super-resolution musculoskeletal MRI using deep learning.
Article in Magnetic resonance in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05820113 (Deep Learning Super Resolution Reconstruction for Fast and Motion Robust T2-weighted Prostate MRI), which is not on this map. Cited by 143 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Deep Learning Super Resolution Reconstruction for Fast and Motion Robust T2-weighted Prostate MRI
Who cites it
143 citing papers in PubMed.
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- Best fast MRI protocols for the knee: advantages and limitations.Skeletal radiology · 2026Review
- Cardiac MR function analysis with DL-based super resolution reconstruction: application in the clinical setting.The international journal of cardiovascular imaging · 2026Article
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- Performance of a GPU- and time-efficient pseudo-3D network for magnetic resonance image super-resolution and motion artifact reduction.Scientific reports · 2026Article
- Ultra-fast whole-brain T2-weighted imaging in 7 seconds using dual-type deep learning reconstruction with single-shot acquisition: clinical feasibility and comparison with conventional methods.Japanese journal of radiology · 2026Article
- Deep learning-enhanced super-resolution diffusion-weighted liver MRI: improved image quality, diagnostic performance, and acceleration.Insights into imaging · 2025Article
- Arthroscopy-validated diagnostic performance of sub-5-min deep learning super-resolution 3T knee MRI in children and adolescents.Skeletal radiology · 2025Article
- Utility of Thin-slice Single-shot T2-weighted MR Imaging with Deep Learning Reconstruction as a Protocol for Evaluating Pancreatic Cystic Lesions.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- Evaluation of high-resolution pituitary dynamic contrast-enhanced MRI using deep learning-based compressed sensing and super-resolution reconstruction.European radiology · 2025Article
- Deep Learning-Based Image Reconstruction in Musculoskeletal MRI.Journal of the Korean Society of Radiology · 2025Review
- Using deep feature distances for evaluating the perceptual quality of MR image reconstructions.Magnetic resonance in medicine · 2025Article
- Dual-type deep learning-based image reconstruction for advanced denoising and super-resolution processing in head and neck T2-weighted imaging.Japanese journal of radiology · 2025Article
- Deep learning-based acceleration of high-resolution compressed sense MR imaging of the hip.European journal of radiology open · 2025Article
- Artificial intelligence and its potential integration with the clinical practice of diagnostic imaging medical physicists: a review.Physical and engineering sciences in medicine · 2025Review
- Deep learning super-resolution reconstruction for fast and high-quality cine cardiovascular magnetic resonance.European radiology · 2025Article
- Patient-specific MRI super-resolution via implicit neural representations and knowledge transfer.Physics in medicine and biology · 2025Article
- Exploring the potential performance of 0.2 T low-field unshielded MRI scanner using deep learning techniques.Magma (New York, N.Y.) · 2025Article
83 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors.
Funding
Abstract
purposeTo 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.
methodsWe 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.
resultsDeepResolve 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).
conclusionDeepResolve 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.