ArticleJournal of magnetic resonance imaging : JMRI2020
Utility of deep learning super-resolution in the context of osteoarthritis MRI biomarkers.
Article in Journal of magnetic resonance imaging : JMRI, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed, 70 citations in OpenAlex.
- Quantification of edge-enhancing effects using accelerated deep learning reconstructed orbital MRI sequences.European journal of radiology open · 2026Article
- Cardiac MR function analysis with DL-based super resolution reconstruction: application in the clinical setting.The international journal of cardiovascular imaging · 2026Article
- Advancing deep learning based knee cartilage segmentation in MRI: Innovations, challenges and applications.Osteoarthritis and cartilage open · 2026Article
- Prospective study assessing the validity of accelerated 2D Fast Spin Echo (2D FSE) based high-resolution knee MRI and T2 mapping using deep learning reconstruction.BMC musculoskeletal disorders · 2026Article
- Evaluation of high-resolution pituitary dynamic contrast-enhanced MRI using deep learning-based compressed sensing and super-resolution reconstruction.European radiology · 2025Article
- Deep learning-driven abbreviated knee MRI protocols: diagnostic accuracy in clinical practice.La Radiologia medica · 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
- Effects of Deep Learning-Based Reconstruction on the Quality of Accelerated Contrast-Enhanced Neck MRI.Korean journal of radiology · 2025Article
- Artificial intelligence demonstrates potential to enhance orthopaedic imaging across multiple modalities: A systematic review.Journal of experimental orthopaedics · 2025Review
- AI-assisted compressed sensing MRI improves imaging quality in rectal cancer: a comparative study with conventional acceleration techniques.Quantitative imaging in medicine and surgery · 2025Article
- Super-resolution of clinical CT: Revealing microarchitecture in whole bone clinical CT image data.Bone · 2024Article
- 3D-DESS MRI with CAIPIRINHA two- and fourfold acceleration for quantitatively assessing knee cartilage morphology.Skeletal radiology · 2024Article
- Clinical Super-Resolution Computed Tomography of Bone Microstructure: Application in Musculoskeletal and Dental Imaging.Annals of biomedical engineering · 2024Article
- Exploring the impact of super-resolution deep learning on MR angiography image quality.Neuroradiology · 2024Article
- Clinical validation of enhanced CT imaging for distal radius fractures through conditional Generative Adversarial Networks (cGAN).PloS one · 2024Article
- 2D versus 3D MRI of osteoarthritis in clinical practice and research.Skeletal radiology · 2023Review
- Machine Learning Approaches to the Prediction of Osteoarthritis Phenotypes and Outcomes.Current rheumatology reports · 2023Review
- Feasibility study of super-resolution deep learning-based reconstruction using k-space data in brain diffusion-weighted images.Neuroradiology · 2023Article
- Deep learning-assisted knee osteoarthritis automatic grading on plain radiographs: the value of multiview X-ray images and prior knowledge.Quantitative imaging in medicine and surgery · 2023Article
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundSuper-resolution is an emerging method for enhancing MRI resolution; however, its impact on image quality is still unknown. PURPOSE: To evaluate MRI super-resolution using quantitative and qualitative metrics of cartilage morphometry, osteophyte detection, and global image blurring. STUDY TYPE: Retrospective. POPULATION: In all, 176 MRI studies of subjects at varying stages of osteoarthritis. FIELD STRENGTH/SEQUENCE: Original-resolution 3D double-echo steady-state (DESS) and DESS with 3× thicker slices retrospectively enhanced using super-resolution and tricubic interpolation (TCI) at 3T. ASSESSMENT: A quantitative comparison of femoral cartilage morphometry was performed for the original-resolution DESS, the super-resolution, and the TCI scans in 17 subjects. A reader study by three musculoskeletal radiologists assessed cartilage image quality, overall image sharpness, and osteophytes incidence in all three sets of scans. A referenceless blurring metric evaluated blurring in all three image dimensions for the three sets of scans. STATISTICAL TESTS: Mann-Whitney U-tests compared Dice coefficients (DC) of segmentation accuracy for the DESS, super-resolution, and TCI images, along with the image quality readings and blurring metrics. Sensitivity, specificity, and diagnostic odds ratio (DOR) with 95% confidence intervals compared osteophyte detection for the super-resolution and TCI images, with the original-resolution as a reference.
resultsDC for the original-resolution (90.2 ± 1.7%) and super-resolution (89.6 ± 2.0%) were significantly higher (P < 0.001) than TCI (86.3 ± 5.6%). Segmentation overlap of super-resolution with the original-resolution (DC = 97.6 ± 0.7%) was significantly higher (P < 0.0001) than TCI overlap (DC = 95.0 ± 1.1%). Cartilage image quality for sharpness and contrast levels, and the through-plane quantitative blur factor for super-resolution images, was significantly (P < 0.001) better than TCI. Super-resolution osteophyte detection sensitivity of 80% (76-82%), specificity of 93% (92-94%), and DOR of 32 (22-46) was significantly higher (P < 0.001) than TCI sensitivity of 73% (69-76%), specificity of 90% (89-91%), and DOR of 17 (13-22). DATA
conclusionSuper-resolution appears to consistently outperform naïve interpolation and may improve image quality without biasing quantitative biomarkers. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:768-779.
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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.