Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
569-03-007 ISMRM Abstract

Fast undersampled dynamic MRI reconstruction using explicit representation learning with Gaussian splatting

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Acquisition & Reconstruction - Motion Correction: Non-Neuro
569-03-007 · Super-Resolving MRI: Methods and Applications · Wednesday, 13 May, 1:40 PM–2:35 PM · Digital Posters Row J
Keywords: AI/ML Image Reconstruction Image Reconstruction Machine Learning/Artificial Intelligence Motion Correction Super-resolution
Accepted
Maarten Terpstra 1, Cornelis A van den Berg1
1Computational Imaging Group for MRI Therapy & Diagnostics, Center of Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands
Presenting Author: Maarten Terpstra

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. Huang, Wenqi, et al. "Neural implicit k-space for binning-free non-cartesian cardiac MR imaging." International Conference on Information Processing in Medical Imaging. Cham: Springer Nature Switzerland, 2023.
2. Spieker, Veronika, et al. "Iconik: Generating respiratory-resolved abdominal mr reconstructions using neural implicit representations in k-space." International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2023.
3. Feng, Jie, et al. "Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstruction." IEEE Transactions on Medical Imaging (2025).
4. Kerbl, Bernhard, et al. "3D Gaussian splatting for real-time radiance field rendering." ACM Trans. Graph. 42.4 (2023): 139-1.
5. Zhang, Xinjie, et al. "Gaussianimage: 1000 fps image representation and compression by 2d gaussian splatting." European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2024.
6. Chen, Chong, et al. "OCMR (v1. 0)--open-access multi-coil k-space dataset for cardiovascular magnetic resonance imaging." arXiv preprint arXiv:2008.03410 (2020).
7. Joshi, Mihir, et al. "Technical Report (v1. 0)--Pseudo-random Cartesian Sampling for Dynamic MRI." arXiv preprint arXiv:2206.03630 (2022).
8. Uecker, Martin, et al. "ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA." Magnetic resonance in medicine 71.3 (2014): 990-1001.
9. Zhang, Richard, et al. "The unreasonable effectiveness of deep features as a perceptual metric." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
10. Otazo, Ricardo, Emmanuel Candes, and Daniel K. Sodickson. "Low‐rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components." Magnetic resonance in medicine 73.3 (2015): 1125-1136.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/569-03-007