Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026
· Cape Town, South Africa
565-06-008
ISMRM Abstract
Deep Learning-based Reconstruction Enhances Image Quality and Diagnostic Performance in 5.0 Tesla Knee MRI
Primary:
Acquisition & Reconstruction - Image Reconstruction: AI
Secondary:
Musculoskeletal - Whole Joint
565-06-008 · Diagnostic Utility of Advanced Techniques in MSK Applications
· Wednesday, 13 May, 4:55 PM–5:50 PM · Digital Posters Row F
Keywords:Image Quality AssessmentDiagnostic performanceDeep-learning-based image reconstructionKnee imagingUltra-high field (UHF) MRI
Accepted
Lixin Du1, Pan Wang 1, Jing Yang2, Hai Lin2
1Shenzhen Longhua District Central Hospital, Shenzhen, China
2Collaborative Innovation Department, United Imaging Healthcare, Shanghai, China
Presenting Author: Pan Wang
Synopsis
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1. Lee HH, Novikov DS, Fieremans E. Removal of partial Fourier-induced Gibbs (RPG) ringing artifacts in MRI. Magn Reson Med. 2021;86(5):2733-2750. doi:10.1002/mrm.28830 [doi]
2. Machado-Rivas F, Jaimes C, Kirsch JE, Gee MS. Image-quality optimization and artifact reduction in fetal magnetic resonance imaging. Pediatr Radiol. 2020;50(13):1830-1838. doi:10.1007/s00247-020-04672-7 [doi]
3. Fritz J, Guggenberger R, Del Grande F. Rapid Musculoskeletal MRI in 2021: Clinical Application of Advanced Accelerated Techniques. AJR Am J Roentgenol. 2021;216(3):718-733. doi: 10.2214/AJR.20.22902 [doi]
4. Kijowski R, Fritz J. Emerging Technology in Musculoskeletal MRI and CT. Radiology. 2023;306(1):6-19. doi: 10.1148/radiol.220634 [doi]
5. Hamilton J, Franson D, Seiberlich N. Recent advances in parallel imaging for MRI. Prog Nucl Magn Reson Spectrosc. 2017;101:71-95. doi:10.1016/j.pnmrs.2017.04.002 [doi]
6. Zhao Q, Xu J, Yang YX, Yu D, Zhao Y, Wang Q, Yuan H. AI-assisted accelerated MRI of the ankle: clinical practice assessment. Eur Radiol Exp. 2023;7(1):62. doi: 10.1186/s41747-023-00374-5 [doi]
7. Iuga AI, Abdullayev N, Weiss K, et al. Accelerated MRI of the knee. Quality and efficiency of compressed sensing. Eur J Radiol. 2020;132:109273. doi:10.1016/j.ejrad.2020.109273 [doi]
8. Ye JC. Compressed sensing MRI: a review from signal processing perspective. BMC Biomed Eng. 2019;1:8. doi: 10.1186/s42490-019-0006-z [doi]
9. Johnson PM, Lin DJ, Zbontar J, et al. Deep Learning Reconstruction Enables Prospectively Accelerated Clinical Knee MRI. Radiology. 2023;307(2):e220425. doi:10.1148/radiol.220425 [doi]
10. Yoo H, Yoo RE, Choi SH, et al. Deep learning-based reconstruction for acceleration of lumbar spine MRI: a prospective comparison with standard MRI. Eur Radiol. 2023;33(12):8656-8668. doi:10.1007/s00330-023-09918-0 [doi]
11. Turkbey B. The Potential for Deep Learning Reconstruction to Improve the Quality of T2-weighted Prostate MRI. Radiology. 2023;308(3):e232344. doi:10.1148/radiol.232344 [doi]
12. Vosshenrich J, Bruno M, Cantarelli Rodrigues T, et al. Arthroscopy-validated Diagnostic Performance of 7-Minute Five-Sequence Deep Learning Super-Resolution 3-T Shoulder MRI. Radiology. 2025;314(2):e241351. doi:10.1148/radiol.241351 [doi]