Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
351-02-010 / 351-02-010 ISMRM Abstract

CAIPIRINHA & Compressed Sensing with Deep Learning Reconstruction for Slice Encoding for Metal Artifact Reduction (SEMAC)

Primary: Musculoskeletal - Other Musculoskeletal
Secondary: Acquisition & Reconstruction - Subsampling: Parallel imaging, compressed sensing and low-rank modeling
351-02-010 · MSK: Everything, Everywhere, and All at Once · Monday, 11 May, 1:50 PM–3:26 PM · Power Pitch Theatre 1
351-02-010 · MSK: Everything, Everywhere, and All at Once · Monday, 11 May, 1:50 PM–3:26 PM · Power Pitch Theatre 1
Keywords: Compressed Sensing Deep learning reconstruction SEMAC Metal Artifacts CAIPIRINHA
Accepted
Constantin von Deuster 1,2, Thomas Yu2,3,4, Jeanette C Deck2,5,6, Marcel Dominik Nickel7, Mathias Nittka7, Dominik Paul7, Daniel Nanz1,5, Reto Sutter5,6
1Swiss Center for Musculoskeletal Imaging (SCMI), Balgrist Campus AG, Zurich, Switzerland
2Swiss Innovation Hub (SIH), Siemens Healthineers International AG, Lausanne, Switzerland
3LTS5, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
4Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
5Faculty of Medicine, University of Zurich, Zurich, Switzerland
6Department of Radiology, Balgrist University Hospital, Zurich, Switzerland
7Research & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany
Presenting Author: Constantin von Deuster

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. Lu W, Pauly KB, Gold GE, Pauly JM, Hargreaves BA. SEMAC: Slice encoding for metal artifact correction in MRI. Magn Reson Med 2009;62:66–76. https://doi.org/10.1002/mrm.21967. [doi]
2. Koch KM, Lorbiecki JE, Hinks RS, King KF. A multispectral three-dimensional acquisition technique for imaging near metal implants. Magn Reson Med 2009;61:381–90. https://doi.org/10.1002/mrm.21856. [doi]
3. Griswold M a., Jakob PM, Heidemann RM, Nittka M, Jellus V, Wang J, et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn Reson Med 2002;47:1202–10.
4. Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE: Sensitivity encoding for fast MRI. Magn Reson Med 1999;42:952–62.
5. Breuer FA, Blaimer M, Heidemann RM, Mueller MF, Griswold MA, Jakob PM. Controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA) for multi-slice imaging. Magn Reson Med 2005;53:684–91.
6. Lustig M, Donoho D, Pauly JM. Sparse MRI: The application of compressed sensing for rapid MR imaging. Magn Reson Med 2007;58:1182–95. https://doi.org/10.1002/mrm.21391. [doi]
7. Pandit P, Rivoire J, King K, Li X. Accelerated T1ρ acquisition for knee cartilage quantification using Compressed Sensing and data-driven Parallel Imaging: A feasibility study. Magn Reson Med 2016;75:1256–61. https://doi.org/10.1002/mrm.25702.Accelerated. [doi]
8. Jaspan ON, Fleysher R, Lipton ML. Compressed sensing MRI: A review of the clinical literature. Br J Radiol 2015;88:1–12. https://doi.org/10.1259/bjr.20150487. [doi]
9. Sharafi A, Mickevicius NJ, Baboli M, Nencka AS, Koch KM. Variable Resolution Sampling and Deep Learning Image Recovery for Accelerated Multi-Spectral MRI Near Metal Implants 2024:1–10.
10. Bridson R. Fast Poisson disk sampling in arbitrary dimensions. ACM SIGGRAPH 2007;1.
11. Beck A, Teboulle M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2009;2:183–202.
12. Hammernik K, Klatzer T, Kobler E, Recht MP, Sodickson DK, Pock T, et al. Learning a Variational Network for Reconstruction of Accelerated MRI Data. Nat Prod as Platforms To Overcome Antibiot Resist Sean 2019;79:3055–3071. https://doi.org/10.1002/mrm.26977.Learning. [doi]
13. Bathla G, Messina S, Black D. Deep Learning–Based Reconstruction of 3D T1 SPACE Vessel Wall Imaging Provides Improved Image Quality with Reduced Scan Times: A Preliminary Study. Am J Neuroradiol 2004.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/351-02-010