Synopsis
Motivation:
Goals:
Approach:
Results:
368-01-015
ISMRM Abstract
Hybrid AI–Physics Framework for 3D Heart Motion Reconstruction Using Differential Scattering Parameters
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. D. Buikman, P. Helzel T Fau - Röschmann, and P. Röschmann, "The rf coil as a sensitive motion detector for magnetic resonance imaging," (in eng), no. 0730-725X (Print).
2. J. Ludwig, P. Speier, F. Seifert, T. Schaeffter, and C. Kolbitsch, "Pilot tone–based motion correction for prospective respiratory compensated cardiac cine MRI," Magnetic Resonance in Medicine, vol. 85, no. 5, pp. 2403-2416, 2021/05/01 2021, doi: https://doi.org/10.1002/mrm.28580. [doi]
3. R. J. M. Navest, S. Mandija, S. E. Zijlema, B. Stemkens, A. Andreychenko, J. J. W. Lagendijk , C. A. T. van den Berg, "The noise navigator for MRI-guided radiotherapy: an independent method to detect physiological motion," Phys Med Biol, vol. 65, no. 12, p. 12NT01, Jun 18 2020, doi: 10.1088/1361-6560/ab8cd8. [doi]
4. B. R. Steensma, C. A. Louka, A. J. E. Raaijmakers, and C. A. T. van den Berg, "Measuring stroke volume with wearable RF antennas: a validation study with EM simulations and MRI," in Conference of the International Society of Magnetic Resonance in Medicine, London, 2022, 31 ed., p. 3952.
5. A. S. Beaverstone, D. S. Shumakov, and N. K. Nikolova, "Frequency-Domain Integral Equations of Scattering for Complex Scalar Responses," IEEE Transactions on Microwave Theory and Techniques, vol. 65, no. 4, pp. 1120-1132, 2017, doi: 10.1109/TMTT.2016.2638428. [doi]
6. E.F. Meliado, C.A. Louka, C.A.T. van den Berg, B.R. Steensma, “Dynamic imaging of the heart from scattering parameters using deep learning – an MR based feasibility study”. Proceedings of the ISMRM & ISMRT Annual Meeting & Exhibition, 4-9 May 2024. p. 1869.
7. E.F. Meliado, V. Koloskov, C.A.T. van den Berg, B.R. Steensma. “Imaging Heart Motion Using Differential Scattering Parameters: An AI-Based Approach”. Proceedings of the ISMRM & ISMRT Annual Meeting & Exhibition, 10-15 May 2025. p. 3301.
8. A. Jaus et al., Towards Unifying Anatomy Segmentation: Automated Generation of a Full-body CT Dataset via Knowledge Aggregation and Anatomical Guidelines.
9. V. M. Campello et al., "Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge," in IEEE Transactions on Medical Imaging, vol. 40, no. 12, pp. 3543-3554, Dec. 2021.
10. B. Zhu, J. Z. Liu, S. F. Cauley, B. R. Rosen, and M. S. Rosen, "Image reconstruction by domain-transform manifold learning," Nature, vol. 555, no. 7697, pp. 487-492, 2018/03/01 2018, doi: 10.1038/nature25988. [doi]
11. O. Ronneberger, P. Fischer, and T. Brox, "U-Net: Convolutional Networks for Biomedical Image Segmentation," in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, Cham, N. Navab, J. Hornegger, W. M. Wells, and A. F. Frangi, Eds., 2015// 2015: Springer International Publishing, pp. 234-241
12. D.P. Kingma, M, Welling. “Auto-Encoding Variational Bayes”. In Proceedings of the 2nd International Conference on Learning Representations (ICLR) 2014. arXiv:1312.6114
13. C. Mauger, K. Gilbert, A.M. Lee, M.M. Sanghvi, N. Aung, K. Fung, et al. Right ventricular shape and function: cardiovascular magnetic resonance reference morphology and biventricular risk factor morphometrics in UK Biobank. J Cardiovasc Magn Reson. 2019 Jul 18;21(1):41. doi: 10.1186/s12968-019-0551-6. PMID: 31315625; PMCID: PMC6637624. [doi] [pmid]
Cite this abstract
http://echo.ismrm.org/p/ISMRM2026/368-01-015