Synopsis
Motivation:
Goals:
Approach:
Results:
565-06-007
ISMRM Abstract
Lumbar Spine Imaging at 5.0 Tesla with Deep Learning-based Reconstruction: Improvement of Efficiency and Image Quality
Accepted
Full abstract & presentation
The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.
Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.
To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.
Log inReferences
1. Beattie, P. F., & Meyers, S. P. (1998). Magnetic Resonance Imaging in Low Back Pain: General Principles and Clinical Issues. Physical Therapy, 78(7), 738-753. https://doi.org/10.1093/ptj/78.7.738 [doi]
2. Chang, Y., Li, Z., Saju, G., Mao, H., & Liu, T. (2023). Deep learning-based rigid motion correction for magnetic resonance imaging: A survey. Meta-Radiology, 1(1), 100001. https://doi.org/10.1016/j.metrad.2023.100001 [doi]
3. Taber, K. H., Herrick, R. C., Weathers, S. W., Kumar, A. J., Schomer, D. F., & Hayman, L. A. (1998). Pitfalls and artifacts encountered in clinical MR imaging of the spine. RadioGraphics, 18(6), 1499–1521. https://doi.org/10.1148/radiographics.18.6.9821197 [doi]
4. Bratke, G., Rau, R., Weiss, K., Kabbasch, C., Sircar, K., Morelli, J. N., Persigehl, T., Maintz, D., Giese, D., & Haneder, S. (2018). Accelerated MRI of the Lumbar Spine Using Compressed Sensing: Quality and Efficiency. Journal of Magnetic Resonance Imaging, 49(7), e164–e175. https://doi.org/10.1002/jmri.26526 [doi]
5. Almansour, H., Herrmann, J., Gassenmaier, S., Afat, S., Jacoby, J., Koerzdoerfer, G., Nickel, D., Mostapha, M., Nadar, M., & Othman, A. E. (2022). Deep Learning Reconstruction for Accelerated Spine MRI: Prospective Analysis of Interchangeability. Radiology. https://doi.org/10.1148/radiol.212922 [doi]
6. Yoo, H., Yoo, R.-E., Seung Hong Choi, Hwang, I., Ji Ye Lee, June Young Seo, Seok Young Koh, Kyu Sung Choi, Koung Mi Kang, & Tae Jin Yun. (2023). Deep learning–based reconstruction for acceleration of lumbar spine MRI: a prospective comparison with standard MRI. European Radiology, 33(12), 8656–8668. https://doi.org/10.1007/s00330-023-09918-0 [doi]
7. Wen, Y., Ma, H., Xiang, S. et al. Accelerating brain T2-weighted imaging using artificial intelligence–assisted compressed sensing combined with deep learning-based reconstruction: a feasibility study at 5.0T MRI. BMC Med Imaging 25, 224 (2025). https://doi.org/10.1186/s12880-025-01763-5 [doi]
8. Yao H, Jia B, Pan X, et al. Validation and feasibility of ultrafast cervical spine MRI using a deep learning-assisted 3D iterative image enhancement system. J Multidiscip Health. 2024:2499-2509. https://doi.org/10.2147/JMDH.S465002 [doi]
9. Peng W, Wan L, Tong X, et al. Prospective and multi-reader evaluation of deep learning reconstruction-based accelerated rectal MRI: image quality, diagnostic performance, and reading time. Eur Radiol. 2024;34(11):7438-7449. https://doi.org/10.1007/s00330-024-10882-6 [doi]
10. 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. https://doi.org/10.1148/radiol.241351 [doi]
11. Herrmann J, Keller G, Gassenmaier S, et al. Feasibility of an accelerated 2D-multi-contrast knee MRI protocol using deep-learning image reconstruction: a prospective intraindividual comparison with a standard MRI protocol. Eur Radiol. 2022;32(9):6215-6229. https://doi.org/10.1007/s00330-022-08753-z [doi]
Cite this abstract
http://echo.ismrm.org/p/ISMRM2026/565-06-007