{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T04:14:12Z","timestamp":1788840852958,"version":"build-2803163510"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T00:00:00Z","timestamp":1771459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Radiol."],"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aimed to evaluate the feasibility of a deep learning-based reconstruction (DLR) algorithm for optimizing conventional 5.0 Tesla knee joint MR protocols.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This prospective study enrolled 69 patients who underwent both knee arthroscopy and 5.0 Tesla knee joint MR examinations using the conventional protocols before and after a DLR process with different levels. The DLR technique was applied to original images to denoise and improve their quality. Two radiologists independently measured the signal-to-noise ratio (SNRs) in cartilage, meniscus, bone, ligament, and muscle, and graded image quality from the dimensions of different tissues' delineation clarity, global artifact severity, and overall image quality using a 5-point Likert scale. Moreover, the diagnostic performance was evaluated with different types of images, compared to the results of knee arthroscopy. Cohen's kappa test was employed to assess the agreement of image quality scoring and diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Compared to conventional images, those DLR ones demonstrated significant improvement in SNRs, with the increasement of 12.61% to 350.63% across various sequences. Two radiologists showed good-to-excellent agreement in image quality assessment, with kappa values ranging from 0.72 to 0.82. Regarding diagnostic performance, the DLR images moderately outperformed the non-DLR ones, as evidenced by a bit higher diagnostic agreement with the results of knee arthroscopy (DLR: kappa\u2009=\u20090.908\u20131; non-DLR: kappa\u2009=\u20090.882\u20130.963).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The DLR technique could improve 5.0 Tesla knee MR images' quality and obtain as least equal diagnostic efficiency without extra scan time, demonstrating its potential clinical applicability.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fradi.2026.1776035","type":"journal-article","created":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T06:40:51Z","timestamp":1771483251000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Validation and feasibility of a deep learning-based reconstruction technology in 5.0 tesla knee joint MR imaging"],"prefix":"10.3389","volume":"6","author":[{"given":"Pan","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Radiology, Shenzhen Longhua District Central Hospital","place":["Shenzhen, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhigang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiology, Shenzhen Longhua District Central Hospital","place":["Shenzhen, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Shenzhen Longhua District Central Hospital","place":["Shenzhen, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ran","family":"Mu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Shenzhen Longhua District Central Hospital","place":["Shenzhen, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Shenzhen Longhua District Central Hospital","place":["Shenzhen, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Yang","sequence":"additional","affiliation":[{"name":"United Imaging Healthcare","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lixin","family":"Du","sequence":"additional","affiliation":[{"name":"Department of Radiology, Shenzhen Longhua District Central Hospital","place":["Shenzhen, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,2,19]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1016\/j.csm.2021.05.006","article-title":"Knee cartilage imaging","volume":"40","author":"Cheng","year":"2021","journal-title":"Clin Sports Med"},{"key":"B2","doi-asserted-by":"publisher","first-page":"e0306257","DOI":"10.1371\/journal.pone.0306257","article-title":"MRI Analysis of and factors related to knee injuries in amateur marathon runners","volume":"19","author":"Shen","year":"2024","journal-title":"PLoS One"},{"key":"B3","doi-asserted-by":"publisher","first-page":"110336","DOI":"10.1016\/j.mri.2025.110336","article-title":"FDuDoCLNet: fully dual-domain contrastive learning network for parallel MRI reconstruction","volume":"117","author":"Zhang","year":"2025","journal-title":"Magn Reson Imaging"},{"key":"B4","doi-asserted-by":"publisher","first-page":"690","DOI":"10.1055\/s-0041-1736585","article-title":"How to report: knee MRI","volume":"25","author":"Griffith","year":"2021","journal-title":"Semin Musculoskelet Radiol"},{"key":"B5","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1002\/jmri.25620","article-title":"Magnetic resonance imaging of the knee: an overview and update of conventional and state of the art imaging","volume":"45","author":"Nacey","year":"2017","journal-title":"J Magn Reson Imaging"},{"key":"B6","doi-asserted-by":"publisher","first-page":"2733","DOI":"10.1002\/mrm.28830","article-title":"Removal of partial Fourier-induced Gibbs (RPG) ringing artifacts in MRI","volume":"86","author":"Lee","year":"2021","journal-title":"Magn Reson Med"},{"key":"B7","doi-asserted-by":"publisher","first-page":"1830","DOI":"10.1007\/s00247-020-04672-7","article-title":"Image-quality optimization and artifact reduction in fetal magnetic resonance imaging","volume":"50","author":"Machado-Rivas","year":"2020","journal-title":"Pediatr Radiol"},{"key":"B8","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.pnmrs.2017.04.002","article-title":"Recent advances in parallel imaging for MRI","volume":"101","author":"Hamilton","year":"2017","journal-title":"Prog Nucl Magn Reson Spectrosc"},{"key":"B9","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1186\/s41747-023-00374-5","article-title":"AI-assisted accelerated MRI of the ankle: clinical practice assessment","volume":"7","author":"Zhao","year":"2023","journal-title":"Eur Radiol Exp"},{"key":"B10","doi-asserted-by":"publisher","first-page":"109273","DOI":"10.1016\/j.ejrad.2020.109273","article-title":"Accelerated MRI of the knee. Quality and efficiency of compressed sensing","volume":"132","author":"Iuga","year":"2020","journal-title":"Eur J Radiol"},{"key":"B11","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1186\/s42490-019-0006-z","article-title":"Compressed sensing MRI: a review from signal processing perspective","volume":"1","author":"Ye","year":"2019","journal-title":"BMC Biomed Eng"},{"key":"B12","doi-asserted-by":"publisher","first-page":"e220425","DOI":"10.1148\/radiol.220425","article-title":"Deep learning reconstruction enables prospectively accelerated clinical knee MRI","volume":"307","author":"Johnson","year":"2023","journal-title":"Radiology"},{"key":"B13","doi-asserted-by":"publisher","first-page":"8656","DOI":"10.1007\/s00330-023-09918-0","article-title":"Deep learning-based reconstruction for acceleration of lumbar spine MRI: a prospective comparison with standard MRI","volume":"33","author":"Yoo","year":"2023","journal-title":"Eur Radiol"},{"key":"B14","doi-asserted-by":"publisher","first-page":"e232344","DOI":"10.1148\/radiol.232344","article-title":"The potential for deep learning reconstruction to improve the quality of T2-weighted prostate MRI","volume":"308","author":"Turkbey","year":"2023","journal-title":"Radiology"},{"key":"B15","doi-asserted-by":"publisher","first-page":"e241351","DOI":"10.1148\/radiol.241351","article-title":"Arthroscopy-validated diagnostic performance of 7-Minute five-sequence deep learning super-resolution 3-T shoulder MRI","volume":"314","author":"Vosshenrich","year":"2025","journal-title":"Radiology"},{"key":"B16","doi-asserted-by":"publisher","first-page":"e210059","DOI":"10.1148\/ryai.210059","article-title":"Clinical assessment of deep learning-based super-resolution for 3D volumetric brain MRI","volume":"4","author":"Rudie","year":"2022","journal-title":"Radiol Artif Intell"},{"key":"B17","doi-asserted-by":"publisher","first-page":"2648","DOI":"10.1109\/JLT.2018.2876909","article-title":"Real-Time denoising of brillouin optical time domain analyzer with high data fidelity using convolutional neural networks","volume":"37","author":"Wu","year":"2019","journal-title":"J Lightwave Technol"},{"key":"B18","doi-asserted-by":"publisher","first-page":"20220074","DOI":"10.1259\/bjr.20220074","article-title":"A deep learning-based reconstruction approach for accelerated magnetic resonance image of the knee with compressed sense: evaluation in healthy volunteers","volume":"96","author":"Iuga","year":"2023","journal-title":"Br J Radiol"},{"key":"B19","doi-asserted-by":"publisher","first-page":"1800113","DOI":"10.1109\/JTEHM.2021.3076152","article-title":"Autoencoder-Inspired convolutional network-based super-resolution method in MRI","volume":"9","author":"Park","year":"2021","journal-title":"IEEE J Transl Eng Health Med"},{"key":"B20","article-title":"How model accuracy and explanation fidelity influence user trust","author":"Papenmeier","year":"2019"},{"key":"B21","doi-asserted-by":"publisher","first-page":"1949","DOI":"10.3390\/tomography9050152","article-title":"Evaluation of a deep learning reconstruction for high-quality T2-weighted breast magnetic resonance imaging","volume":"9","author":"Allen","year":"2023","journal-title":"Tomography"},{"key":"B22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/EMBC40787.2023.10340709","article-title":"High-fidelity database-free deep learning reconstruction for real-time cine cardiac MRI","volume":"2023","author":"Demirel","year":"2023","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"key":"B23","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1111\/j.1699-0463.1989.tb00464.x","article-title":"Reproducibility of histomorphologic diagnoses with special reference to the kappa statistic","volume":"97","author":"Svanholm","year":"1989","journal-title":"APMIS"},{"key":"B24","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1615\/CritRevBiomedEng.2016016365","article-title":"Opportunities and challenges of 7 tesla magnetic resonance imaging: a review","volume":"44","author":"Karamat","year":"2016","journal-title":"Crit Rev Biomed Eng"},{"key":"B25","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1002\/mrm.25677","article-title":"Signal-to-noise ratio and MR tissue parameters in human brain imaging at 3, 7, and 9.4 tesla using current receive coil arrays","volume":"75","author":"Pohmann","year":"2016","journal-title":"Magn Reson Med"},{"key":"B26","doi-asserted-by":"publisher","first-page":"913707","DOI":"10.3389\/fcvm.2022.913707","article-title":"Bi-ventricular assessment with cardiovascular magnetic resonance at 5 Tesla: a pilot study","volume":"9","author":"Lin","year":"2022","journal-title":"Front Cardiovasc Med"},{"key":"B27","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/s10334-023-01142-7","article-title":"Improved reconstruction for highly accelerated propeller diffusion 1.5 T clinical MRI","volume":"37","author":"Yarach","year":"2024","journal-title":"MAGMA"},{"key":"B28","doi-asserted-by":"publisher","first-page":"e4458","DOI":"10.1002\/nbm.4458","article-title":"Minimizing echo and repetition times in magnetic resonance imaging using a double half-echo k-space acquisition and low-rank reconstruction","volume":"34","author":"Bydder","year":"2021","journal-title":"NMR Biomed"},{"key":"B29","doi-asserted-by":"publisher","first-page":"2899","DOI":"10.33425\/2771.9014.1011","article-title":"Gibbs artifacts removal with nonlinearity","volume":"2","author":"Zeng","year":"2023","journal-title":"J Biotechnol Appl"},{"key":"B30","doi-asserted-by":"publisher","first-page":"23021","DOI":"10.1038\/s41598-021-02279-y","article-title":"Direct pixel to pixel principal strain mapping from tagging MRI using end to end deep convolutional neural network (DeepStrain)","volume":"11","author":"Abd-Elmoniem","year":"2021","journal-title":"Sci Rep"},{"key":"B31","doi-asserted-by":"publisher","first-page":"410","DOI":"10.2463\/mrms.mp.2020-0073","article-title":"Introducing swish and parallelized blind removal improves the performance of a convolutional neural network in denoising MR images","volume":"20","author":"Sugai","year":"2021","journal-title":"Magn Reson Med Sci"},{"key":"B32","doi-asserted-by":"publisher","first-page":"5666","DOI":"10.3390\/s20195666","article-title":"Image compressive sensing via hybrid nonlocal sparsity regularization","volume":"20","author":"Li","year":"2020","journal-title":"Sensors (Basel)"},{"key":"B33","doi-asserted-by":"publisher","first-page":"045011","DOI":"10.1088\/1361-6560\/ac4561","article-title":"Group feature selection for enhancing information gain in MRI reconstruction","volume":"67","author":"Chang","year":"2022","journal-title":"Phys Med Biol"},{"key":"B34","doi-asserted-by":"publisher","first-page":"065006","DOI":"10.1088\/1361-6560\/ad2a95","article-title":"Radioport: a radiomics-reporting network for interpretable deep learning in BI-RADS classification of mammographic calcification","volume":"69","author":"Pang","year":"2024","journal-title":"Phys Med Biol"},{"key":"B35","doi-asserted-by":"publisher","first-page":"284","DOI":"10.3390\/jpm15070284","article-title":"Prostate MRI using deep learning reconstruction in response to cancer screening demands-A systematic review and meta-analysis","volume":"15","author":"Ursprung","year":"2025","journal-title":"J Pers Med"},{"key":"B36","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1007\/s11604-018-0758-8","article-title":"Percentage of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network","volume":"36","author":"Jung","year":"2018","journal-title":"Jpn J Radiol"}],"container-title":["Frontiers in Radiology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fradi.2026.1776035\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T06:40:52Z","timestamp":1771483252000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fradi.2026.1776035\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,19]]},"references-count":36,"alternative-id":["10.3389\/fradi.2026.1776035"],"URL":"https:\/\/doi.org\/10.3389\/fradi.2026.1776035","relation":{},"ISSN":["2673-8740"],"issn-type":[{"value":"2673-8740","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,19]]},"article-number":"1776035"}}