468-06-011 ISMRM Abstract

Model-Based 4D CMR Reconstructions using Neural Fields and Tensor Product Expansions

Primary: Acquisition & Reconstruction - Image Reconstruction: Non-AI

Secondary: Cardiovascular - Myocardium

468-06-011 · Acquisition and Reconstruction Strategies for Dynamic and Cardiac Imaging · Tuesday, 12 May, 4:55 PM–5:50 PM · Digital Posters Row I

Keywords: Cardiac MRI 4D CINE Parallel imaging acceleration Coil sensitivity estimation Neural Fields

Accepted

Ray Sheombarsing ¹, Max H.C. van Riel¹, David G.J. Heesterbeek¹, Cornelis A van den Berg¹, Alessandro A Sbrizzi¹

¹Computational Imaging Group for MRI Therapy & Diagnostics, Department of Radiotherapy, UMC Utrecht, Utrecht, Netherlands

Presenting Author: Ray Sheombarsing

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References

1. Yao J, Xu Z, Huang X, Huang J. An efficient algorithm for dynamic MRI using low-rank and total variation regularizations. Med Image Anal. 2018;44:14–27. https://doi.org/10.1016/j.media.2017.11.003 [doi]

2. Schloegl M, Holler M, Schwarzl A, Bredies K, Stollberger R. Infimal convolution of total generalized variation functionals for dynamic MRI. Magn Reson Med. 2017;78(1):142–55. https://doi.org/10.1002/mrm.26352 [doi]

3. Royuela-del-Val J, Cordero-Grande L, Simmross-Wattenberg F, Martín-Fernández M, Alberola-López C. Jacobian weighted temporal total variation for motion compensated compressed sensing reconstruction of dynamic MRI. Magn Reson Med. 2017;77(3):1208–15. https://doi.org/10.1002/mrm.26198 [doi]

4. Arshad SM, Potter LC, Chen C, Liu Y, Chandrasekaran P, Crabtree C, et al. Motion-robust free-running volumetric cardiovascular MRI. Magn Reson Med. 2024;92(3):1248–62. https://doi.org/10.1002/mrm.30123 [doi]

5. Küstner T, Fuin N, Hammernik K, Bustin A, Qi H, Hajhosseiny R, et al. CINENet: deep learning-based 3D cardiac CINE MRI reconstruction with multi-coil complex-valued 4D spatio-temporal convolutions. Sci Rep. 2020;10:13710. https://doi.org/10.1038/s41598-020-70551-8 [doi]

6. Giannakopoulos II, Muckley M, Kim J, Breen M, Johnson PM, Lui YW, et al. Accelerated MRI reconstructions via variational network and feature domain learning. Sci Rep. 2024;14:12345. https://doi.org/10.1038/s41598-024-59705-0 [doi]

7. Lyu J, Qin C, Wang S, Wang F, Li Y, Wang Z, et al. The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023. 2024. https://doi.org/10.1016/j.media.2025.103485 [doi]

8. Sitzmann V, Martel J, Bergman A, Lindell D, Wetzstein G. Implicit Neural Representations with Periodic Activation Functions. Adv Neural Inf Process Syst. 2020;33:7462–73.

9. Chen C, Liu Y, Schniter P, Tong M, Zareba K, Simonetti O, et al. OCMR (v1.0)--Open-Access Multi-Coil k-Space Dataset for Cardiovascular Magnetic Resonance Imaging. https://doi.org/10.48550/arXiv.2008.03410 [doi]

10. Uecker M, Ong F, Tamir J, Bahri D, Virtue P, Cheng JY, et al. Berkeley Advanced Reconstruction Toolbox. Proc Intl Soc Mag Reson Med. 2015;23:2486.

11. Uecker M, Lai P, Murphy M, Virtue P, Elad M, Pauly JM, et al. ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: Where SENSE meets GRAPPA. Magn Reson Med. 2014;71(3):990–1001. https://doi.org/10.1002/mrm.24751 [doi]

12. Akiba T, Sano S, Yanase T, Ohta T, Koyama M. Optuna: A Next-Generation Hyperparameter Optimization Framework. Proc 25th ACM SIGKDD Int Conf Knowl Discov Data Min. 2019;2623–31. https://doi.org/10.1145/3292500.3330701 [doi]

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http://echo.ismrm.org/p/ISMRM2026/468-06-011