Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
667-04-009 ISMRM Abstract

Efficient Diffusion-based Reconstruction for 3D Non-Cartesian UTE Imaging

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Musculoskeletal - Bone
667-04-009 · Novel Visualization and Assessment of Osseous Tissues · Thursday, 14 May, 2:35 PM–3:30 PM · Digital Posters Row H
Keywords: AI/ML Image Reconstruction Diffusion Reconstruction Machine Learning/Artificial Intelligence MSK
Accepted
Jonas Petersen 1,2, Thomas Yu3,4,5, Marcel Dominik Nickel1, Thomas Küstner2, Stefan Sommer3,6
1Research & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany
2Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany
3Swiss Innovation Hub, Siemens Healthineers International AG, Switzerland
4Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
5Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
6Swiss Center for Musculoskeletal Imaging (SCMI), Zurich, Switzerland
Presenting Author: Jonas Petersen

Synopsis

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References

1. Heckel, Reinhard, et al. "Deep learning for accelerated and robust MRI reconstruction: a review." arXiv preprint arXiv:2404.15692 (2024).
2. Fessler, Jeffrey A. “On NUFFT-Based Gridding for Non-Cartesian MRI.” Journal of Magnetic Resonance 188, no. 2 (2007): 191–195.
3. Chung, Hyungjin, and Jong Chul Ye. "Score-based diffusion models for accelerated MRI." Medical image analysis 80 (2022): 102479.
4. Petersen, J., Grodzki, D., Küstner, T., & Sommer, S. “Memory-Efficient Image Reconstruction using Diffusion Models for Accelerated 3D Non-Cartesian UTE imaging.” Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM) (2025)
5. Hammernik, Kerstin, and Thomas Küstner. "Machine Enhanced Reconstruction Learning and Interpretation Networks (MERLIN)." Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM) (2022)
6. Vannesjo S.J. et. al. “Image Reconstruction Using a Gradient Impulse Response Model for Trajectory Prediction.” Magnetic Resonance in Medicine 76 (2016): 45-58
7. Chung, Hyungjin, Suhyeon Lee, and Jong Chul Ye. "Decomposed diffusion sampler for accelerating large-scale inverse problems." arXiv preprint arXiv:2303.05754 (2023).
8. Fujita, Shohei, et al. "Physics-informed deep learning reconstruction for ultrafast clinical 3D fluid-attenuated inversion recovery brain MRI." Radiology Advances 2.3 (2025)
9. Ronneberger, O., Fischer, P., & Brox, T. “U-net: Convolutional networks for biomedical image segmentation.” International Conference on Medical image computing and computer-assisted intervention (2015): 234-241.
10. Liu Z, et al. “Deep Learning Reconstruction for 7T MP2RAGE and SPACE MRI: Improving Image Quality at High Acceleration Factors.” AJNR Am J Neuroradiol (2025). doi: 10.3174/ajnr.A8841. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/667-04-009