{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T15:11:48Z","timestamp":1760454708054,"version":"build-2065373602"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,14]]},"DOI":"10.7759\/cureus.94561","type":"journal-article","created":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T14:34:47Z","timestamp":1760452487000},"source":"Crossref","is-referenced-by-count":0,"title":["Examining the Effect of Deep Learning-Based Image Reconstruction on Accelerating Shoulder Magnetic Resonance Imaging (MRI) and Its Impact on Image Quality"],"prefix":"10.7759","author":[{"given":"Jordan Zheng Ting","family":"Sim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander Jiawei","family":"Yap","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong Han","family":"Ting","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shu Wen","family":"Goh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alvin Yong Quan","family":"Soon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minju","family":"Cho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sohyun","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Glen Chern Yue","family":"Ong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/s41568-018-0016-5","article-title":"Artificial intelligence in radiology","volume":"18","author":"Hosny A","year":"2018","unstructured":"Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJ. Artificial intelligence in radiology. Nat Rev Cancer. 2018, 18:500-10. 10.1038\/s41568-018-0016-5","journal-title":"Nat Rev Cancer"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare10010175","article-title":"Diagnostic performance of a deep learning model deployed at a national COVID-19 screening facility for detection of pneumonia on frontal chest radiographs","volume":"10","author":"Sim JZ","year":"2022","unstructured":"Sim JZ, Ting YH, Tang Y, et al.. Diagnostic performance of a deep learning model deployed at a national COVID-19 screening facility for detection of pneumonia on frontal chest radiographs. Healthcare (Basel). 2022, 10:175. 10.3390\/healthcare10010175","journal-title":"Healthcare (Basel)"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s11604-024-01552-0","article-title":"The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI","volume":"42","author":"Nakaura T","year":"2024","unstructured":"Nakaura T, Ito R, Ueda D, et al.. The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI. Jpn J Radiol. 2024, 42:685-96. 10.1007\/s11604-024-01552-0","journal-title":"Jpn J Radiol"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1148\/rg.220133","article-title":"Clinical impact of deep learning reconstruction in MRI","volume":"43","author":"Kiryu S","year":"2023","unstructured":"Kiryu S, Akai H, Yasaka K, et al.. Clinical impact of deep learning reconstruction in MRI. Radiographics. 2023, 43:e220133. 10.1148\/rg.220133","journal-title":"Radiographics"},{"key":"ref5","article-title":"All-in-one deep learning framework for MR image reconstruction","author":"Jeong G","year":"2024","unstructured":"Jeong G, Kim H, Yang J, Jang K, Kim J. All-in-one deep learning framework for MR image reconstruction. arXiv. 2024,","journal-title":"arXiv"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.230181","article-title":"Impact of deep learning image reconstruction methods on MRI throughput","volume":"6","author":"Yang A","year":"2024","unstructured":"Yang A, Finkelstein M, Koo C, Doshi AH. Impact of deep learning image reconstruction methods on MRI throughput. Radiol Artif Intell. 2024, 6:e230181. 10.1148\/ryai.230181","journal-title":"Radiol Artif Intell"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s00062-021-01121-2","article-title":"Deep learning image processing enables 40% faster spinal MR scans which match or exceed quality of standard of care: a\u00a0prospective multicenter multireader study","volume":"32","author":"Bash S","year":"2022","unstructured":"Bash S, Johnson B, Gibbs W, Zhang T, Shankaranarayanan A, Tanenbaum LN. Deep learning image processing enables 40% faster spinal MR scans which match or exceed quality of standard of care: a\u00a0prospective multicenter multireader study. Clin Neuroradiol. 2022, 32:197-203. 10.1007\/s00062-021-01121-2","journal-title":"Clin Neuroradiol"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics14111199","article-title":"Deep learning-based high-resolution magnetic resonance angiography (MRA) generation model for 4D time-resolved angiography with interleaved stochastic trajectories (TWIST) MRA in fast stroke imaging","volume":"14","author":"Kim BK","year":"2024","unstructured":"Kim BK, You SH, Kim B, Shin JH. Deep learning-based high-resolution magnetic resonance angiography (MRA) generation model for 4D time-resolved angiography with interleaved stochastic trajectories (TWIST) MRA in fast stroke imaging. Diagnostics (Basel). 2024, 14:1199. 10.3390\/diagnostics14111199","journal-title":"Diagnostics (Basel)"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-022-09151-1","article-title":"Application of deep learning-based image reconstruction in MR imaging of the shoulder joint to improve image quality and reduce scan time","volume":"33","author":"Kaniewska M","year":"2023","unstructured":"Kaniewska M, Deininger-Czermak E, Getzmann JM, Wang X, Lohezic M, Guggenberger R. Application of deep learning-based image reconstruction in MR imaging of the shoulder joint to improve image quality and reduce scan time. Eur Radiol. 2023, 33:1513-25. 10.1007\/s00330-022-09151-1","journal-title":"Eur Radiol"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejro.2024.100557","article-title":"Fast 5-minute shoulder MRI protocol with accelerated TSE-sequences and deep learning image reconstruction for the assessment of shoulder pain at 1.5 and 3\u202fTesla","volume":"12","author":"Herrmann J","year":"2024","unstructured":"Herrmann J, Feng YS, Gassenmaier S, et al.. Fast 5-minute shoulder MRI protocol with accelerated TSE-sequences and deep learning image reconstruction for the assessment of shoulder pain at 1.5 and 3\u202fTesla. Eur J Radiol Open. 2024, 12:100557. 10.1016\/j.ejro.2024.100557","journal-title":"Eur J Radiol Open"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.21037\/qims-23-1412","article-title":"Deep learning-based reconstruction enhances image quality and improves diagnosis in magnetic resonance imaging of the shoulder joint","volume":"14","author":"Liu Z","year":"2024","unstructured":"Liu Z, Wen B, Wang Z, et al.. Deep learning-based reconstruction enhances image quality and improves diagnosis in magnetic resonance imaging of the shoulder joint. Quant Imaging Med Surg. 2024, 14:2840-56. 10.21037\/qims-23-1412","journal-title":"Quant Imaging Med Surg"},{"key":"ref12","article-title":"U-Net: convolutional networks for biomedical image segmentation","author":"Ronneberger O","year":"2015","unstructured":"Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. arXiv. 2015,","journal-title":"arXiv"},{"key":"ref13","article-title":"Adam: a method for stochastic optimization","author":"Kingma DP","year":"2015","unstructured":"Kingma DP, Ba JL. Adam: a method for stochastic optimization. arXiv. 2015,","journal-title":"arXiv"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s00256-025-04969-4","article-title":"Arthroscopy-validated diagnostic performance of sub-5-min deep learning super-resolution 3T knee MRI in children and adolescents","author":"Vosshenrich J","year":"2025","unstructured":"Vosshenrich J, Breit HC, Donners R, et al.. Arthroscopy-validated diagnostic performance of sub-5-min deep learning super-resolution 3T knee MRI in children and adolescents. Skeletal Radiol. 2025, 10.1007\/s00256-025-04969-4","journal-title":"Skeletal Radiol"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.3345\/kjp.2012.55.11.403","article-title":"Understanding noninferiority trials","volume":"55","author":"Hahn S","year":"2012","unstructured":"Hahn S. Understanding noninferiority trials. Korean J Pediatr. 2012, 55:403-7. 10.3345\/kjp.2012.55.11.403","journal-title":"Korean J Pediatr"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.5114\/pjr\/192822","article-title":"Deep learning and AI in reducing magnetic resonance imaging scanning time: advantages and pitfalls in clinical practice","volume":"89","author":"Foti G","year":"2024","unstructured":"Foti G, Longo C. Deep learning and AI in reducing magnetic resonance imaging scanning time: advantages and pitfalls in clinical practice. Pol J Radiol. 2024, 89:e443-51. 10.5114\/pjr\/192822","journal-title":"Pol J Radiol"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3075856","article-title":"Results of the 2020 fastMRI challenge for machine learning MR image reconstruction","volume":"40","author":"Muckley MJ","year":"2021","unstructured":"Muckley MJ, Riemenschneider B, Radmanesh A, et al.. Results of the 2020 fastMRI challenge for machine learning MR image reconstruction. IEEE Trans Med Imaging. 2021, 40:2306-17. 10.1109\/TMI.2021.3075856","journal-title":"IEEE Trans Med Imaging"},{"key":"ref18","unstructured":"Deep Resolve. unrivaled speed in MRI. (2025). Accessed: 2025: https:\/\/www.siemens-healthineers.com\/en-ph\/magnetic-resonance-imaging\/technologies-and-innovations\/deep-resolve."},{"key":"ref19","unstructured":"The clinical benefits of AIR\u2122 Recon DL for MR image reconstruction. (2020). Accessed. 2025: https:\/\/www.gehealthcare.com\/-\/jssmedia\/gehc\/us\/files\/products\/molecular-resonance-imaging\/air\/mr_gbl_air-recon-dl_09...."},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.20.22902","article-title":"Rapid musculoskeletal MRI in 2021: clinical application of advanced accelerated techniques","volume":"216","author":"Fritz J","year":"2021","unstructured":"Fritz J, Guggenberger R, Del Grande F. Rapid musculoskeletal MRI in 2021: clinical application of advanced accelerated techniques. AJR Am J Roentgenol. 2021, 216:718-33. 10.2214\/AJR.20.22902","journal-title":"AJR Am J Roentgenol"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2022.3192309","article-title":"Synthesizing MR image contrast enhancement using 3D high-resolution ConvNets","volume":"70","author":"Chen C","year":"2023","unstructured":"Chen C, Raymond C, Speier W, et al.. Synthesizing MR image contrast enhancement using 3D high-resolution ConvNets. IEEE Trans Biomed Eng. 2023, 70:401-12. 10.1109\/TBME.2022.3192309","journal-title":"IEEE Trans Biomed Eng"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.3174\/ajnr.A7920","article-title":"Deep learning-generated synthetic MR imaging STIR spine images are superior in image quality and diagnostically equivalent to conventional STIR: a multicenter, multireader trial","volume":"44","author":"Tanenbaum LN","year":"2023","unstructured":"Tanenbaum LN, Bash SC, Zaharchuk G, et al.. Deep learning-generated synthetic MR imaging STIR spine images are superior in image quality and diagnostically equivalent to conventional STIR: a multicenter, multireader trial. AJNR Am J Neuroradiol. 2023, 44:987-93. 10.3174\/ajnr.A7920","journal-title":"AJNR Am J Neuroradiol"}],"container-title":["Cureus"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cureus.com\/articles\/421850-examining-the-effect-of-deep-learning-based-image-reconstruction-on-accelerating-shoulder-magnetic-resonance-imaging-mri-and-its-impact-on-image-quality","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T14:34:49Z","timestamp":1760452489000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cureus.com\/articles\/421850-examining-the-effect-of-deep-learning-based-image-reconstruction-on-accelerating-shoulder-magnetic-resonance-imaging-mri-and-its-impact-on-image-quality"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,14]]},"references-count":22,"URL":"https:\/\/doi.org\/10.7759\/cureus.94561","relation":{},"ISSN":["2168-8184"],"issn-type":[{"value":"2168-8184","type":"print"}],"subject":[],"published":{"date-parts":[[2025,10,14]]}}}