Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
565-06-010 ISMRM Abstract

Deep learning-accelerated T2-weighted SPACE for cervical spine MRI: faster acquisition with preserved image quality

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Musculoskeletal - Other Musculoskeletal
565-06-010 · Diagnostic Utility of Advanced Techniques in MSK Applications · Wednesday, 13 May, 4:55 PM–5:50 PM · Digital Posters Row F
Keywords: Deep learning reconstruction Musculoskeletal MRI Image Quality Assessment Spine
Accepted
Thierno Diallo 1, Kai Falko Kästingschäfer2,3, Dominik Paul4,5,6,7,8, Marcel Dominik Nickel9, Ralph Strecker4,5,9, Matthias Jung10, Fabian Bamberg3,10, Jakob Weiß3,10, Alexander Rau1,11, Maximilian Frederik Russe2,3,10
1University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
2Division of Medical Physics, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Freiburg, Germany
3Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Freiburg, Germany
4Siemens Healthineers AG, Erlangen, Germany
5Research & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany
6Research & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany
7Research & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany
8MR Research & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany
9MR Application Predevelopment, Siemens Healthineers, Erlangen, Germany
10University Medical Center Freiburg — Department of Diagnostic and Interventional Radiology, Freiburg, Germany
11Department of Neuroradiology, University Medical Center Freiburg, Freiburg, Germany
Presenting Author: Thierno Diallo

Synopsis

Motivation:
Goals:
Approach:
Results:
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 in

References

1. Kwon JW, Yoon YC, Choi S-H. Three-dimensional isotropic T2-weighted cervical MRI at 3T: comparison with two-dimensional T2-weighted sequences. Clin Radiol. 2012;67(2):106-113. doi:10.1016/j.crad.2011.06.011. [doi]
2. Chokshi FH, Sadigh G, Carpenter W, Allen JW. Diagnostic Quality of 3D T2-SPACE Compared with T2-FSE in the Evaluation of Cervical Spine MRI Anatomy. AJNR Am J Neuroradiol. 2017 Apr;38(4):846-850. doi: 10.3174/ajnr.A5080. [doi]
3. Tins B, Cassar-Pullicino V, Haddaway M, Nachtrab U. Three-dimensional SPACE sequence for routine imaging of the spine: preliminary experience. Br J Radiol. 2012;85(1016):e480-e489. doi:10.1259/bjr/25760339. [doi]
4. Mugler JP III. Optimized three-dimensional fast-spin-echo MRI. J Magn Reson Imaging. 2014;39(4):745-767. doi:10.1002/jmri.24542. [doi]
5. Barnaure I, Galley J, Fritz B, Sutter R. MRI in the evaluation of cervical foraminal stenosis: 3D T2-SPACE vs sagittal oblique 2D T2-TSE. Skeletal Radiol. 2022;51:1453-1462. doi:10.1007/s00256-022-03988-9. [doi]
6. Almansour H, Herrmann J, Gassenmaier S, Afat S, Jacoby J, Koerzdoerfer G, et al. Deep learning reconstruction for accelerated spine MRI: prospective analysis of interchangeability. Radiology. 2023;306(3):e212922. doi:10.1148/radiol.212922. [doi]
7. Tang H, Zhang S, Zeng Z, Huang X, Li Y, Zhang L, et al. Deep learning reconstruction for lumbar spine MRI acceleration: a prospective study. Eur Radiol Exp. 2024;8:27. doi:10.1186/s41747-024-00470-0. [doi]
8. Jung HK, Choi Y, Kim S, Nickel D, Park JE, Kim HS. Image quality assessment and white matter hyperintensity quantification in two accelerated high-resolution 3D FLAIR techniques: Wave-CAIPI and deep learning-based SPACE. Clin Radiol. 2025;82:106783. doi:10.1016/j.crad.2024.106783. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/565-06-010