560-03-002 ISMRM Abstract

Fast free breathing cine imaging with deep learning super-resolution reconstruction in congenital heart diseases

Primary: Pediatrics - Cardiovascular

Secondary: Acquisition & Reconstruction - Image Reconstruction: AI

560-03-002 · Congenital Heart Disease, Valves, and Vessels · Wednesday, 13 May, 1:40 PM–2:35 PM · Digital Posters Row A

Keywords: Cardiovascular magnetic resonance Congenital Heart Disease Super-resolution reconstruction Free breathing cine

Accepted

Limin Zhou ¹, Kinsey Brassaw², Kathryn Dern², Kevin Moulin³, Andrew J Powell^2,4

¹Philips North America Clinical Science, Rochester, United States of America

²Department of Cardiology, Boston Children's Hospital and Harvard Medical School, Boston, United States of America

³Boston Children's Hospital and Harvard Medical School, Boston, United States of America

⁴Department of Pediatrics, Harvard Medical School, Boston, United States of America

Presenting Author: Limin Zhou

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.

References

1. G. W. Lenz, E. M. Haacke, and R. D. White, “Retrospective cardiac gating: a review of technical aspects and future directions,” Magn. Reson. Imaging, vol. 7, no. 5, pp. 445–455, 1989, doi: 10.1016/0730-725x(89)90399-8. [doi]

2. “Deep learning super-resolution reconstruction for fast and high-quality cine cardiovascular magnetic resonance | European Radiology.” Accessed: Jun. 09, 2025. [Online]. Available: https://link.springer.com/article/10.1007/s00330-024-11145-0

3. M. Vermersch et al., “Compressed sensing real-time cine imaging for assessment of ventricular function, volumes and mass in clinical practice,” Eur. Radiol., vol. 30, no. 1, pp. 609–619, Jan. 2020, doi: 10.1007/s00330-019-06341-2. [doi]

4. F. Ghanbari et al., “Free-breathing single-beat exercise cardiovascular magnetic resonance with generative artificial intelligence for evaluation of volumetric and functional cardiac indices: A reproducibility study,” J. Cardiovasc. Magn. Reson., vol. 27, no. 1, p. 101901, 2025, doi: 10.1016/j.jocmr.2025.101901. [doi]

5. L. Lin et al., “Free-breathing cardiac cine MRI with compressed sensing real-time imaging and retrospective motion correction: clinical feasibility and validation,” Eur. Radiol., vol. 33, no. 4, pp. 2289–2300, Apr. 2023, doi: 10.1007/s00330-022-09210-7. [doi]

6. A. Merlocco et al., “Single heart beat cine SSFP utilizing deep learning in a child with non-sustained ventricular tachycardia,” J. Cardiovasc. Magn. Reson., vol. 27, p. 101807, 2025, doi: 10.1016/j.jocmr.2024.101807. [doi]

7. C. Dong, C. C. Loy, K. He, and X. Tang, “Image Super-Resolution Using Deep Convolutional Networks,” Jul. 31, 2015, arXiv: arXiv:1501.00092. doi: 10.48550/arXiv.1501.00092. [doi]

8. Y. Li, B. Sixou, and F. Peyrin, “A Review of the Deep Learning Methods for Medical Images Super Resolution Problems,” IRBM, vol. 42, no. 2, pp. 120–133, Apr. 2021, doi: 10.1016/j.irbm.2020.08.004. [doi]

9. J. Wetzl et al., “Single-breath-hold 3-D CINE imaging of the left ventricle using Cartesian sampling,” Magn. Reson. Mater. Phys. Biol. Med., vol. 31, no. 1, pp. 19–31, Feb. 2018, doi: 10.1007/s10334-017-0624-1. [doi]

10. V. Klinke et al., “Quality assessment of cardiovascular magnetic resonance in the setting of the European CMR registry: description and validation of standardized criteria,” J. Cardiovasc. Magn. Reson., vol. 15, no. 1, p. 55, Jan. 2013, doi: 10.1186/1532-429X-15-55. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/560-03-002