551-01-006 / 551-01-006 ISMRM Abstract

Demonstration of automated cardiac MRI at 0.55T with high performance gradients

Primary: Cardiovascular - Myocardium

Secondary: Physics & Engineering - Low-Field MRI

551-01-006 · Frontiers in Cardiovascular MR · Wednesday, 13 May, 8:20 AM–9:56 AM · Power Pitch Theatre 1

Keywords: 0.55T Cardiac magnetic resonance Acceptability Automated

Accepted

Ziwei Zhao ¹, Ye Tian², Eric Peterson¹, Hadas Shiran³, Bob S Hu¹, Krishna S Nayak^4,5, Juan M Santos¹, William Overall¹

¹Vista AI, Inc., Palo Alto, United States of America

²University of Southern California, Los Angeles, United States of America

³Palo Alto Medical Foundation, Palo Alto, United States of America

⁴Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, United States of America

⁵Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, United States of America

Presenting Author: Ziwei Zhao

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. Pennell DJ. Cardiovascular magnetic resonance. Circulation. 2010;121(5):692-705. doi:10.1161/CIRCULATIONAHA.108.811547 [doi]

2. Addy NO, Jiang W, Overall WR, Santos JM, Hu BS. Autonomous CMR: Prescription to Ejection Fraction in less than 3 Minutes. In: International Society for Magnetic Resonance in Medicine Machine Learning Workshop 2018. Pacific Grove; 2018. http://arxiv.org/abs/1603.04467.

3. Peterson E, He JJ, Overall W. Automatic Inversion Time Tracking for Late Gadolinium Enhancement Cardiac MRI. In: Proceedings of the 33rd Annual Meeting of ISMRM. Honolulu; 2025:4340.

4. Goyeneche A De, Tang S, Addy ON, Hu B, Overall W, Santos J. Deep-Learning-Based Motion Correction For Quantitative Cardiac MRI. In: Proceedings of the 29th Annual Meeting of ISMRM. Online; 2021:0387.

5. Jiang W, Addy O, Overall W, Hu B, Santos J. Automatic Motion Artifact Detection as Scan-aided Tool in an Autonomous MRI Environment. In: Proceedings of the 26th Annual Meeting of ISMRM. Paris; 2018:4114. https://arxiv.org/abs/1512.00567.

6. Varghese J, Jin N, Giese D, et al. "Building a comprehensive cardiovascular magnetic resonance exam on a commercial 0.55 T system: a pictorial essay on potential applications." Frontiers in Cardiovascular Medicine. 2023;10:1120982.

7. Campbell-Washburn AE, Varghese J, Nayak KS, Ramasawmy R, Simonetti OP. Cardiac MRI at Low Field Strengths. Journal of Magnetic Resonance Imaging. 2024;59(2):412-430. doi:10.1002/jmri.28890 [doi]

8. Santos JM, Wright GA, Pauly JM. Flexible Real-Time Magnetic Resonance Imaging Framework. In The 26th annual international conference of the IEEE Engineering in Medicine and Biology Society. 2004;01:1048-1051.

9. Campbell-Washburn AE, Ramasawmy R, Restivo MC, et al. Opportunities in interventional and diagnostic imaging by using high-performance low-field-strength MRI. Radiology. 2019;293(2):384-393. doi:10.1148/radiol.2019190452 [doi]

10. C. Chefd’ hotel, G. Hermosillo, O. Faugeras. Flows of diffeomorphisms for multimodal image registration. In: Proceedings IEEE International Symposium on Biomedical Imaging, Washington, DC. IEEE; 2002:753-756. 10.1109/ISBI.2002.1029367. Accessed October 27, 2025. [doi]

11. Xue H, Hooper SM, Pierce I, et al. SNRAware: Improved Deep Learning MRI Denoising with SNR Unit Training and G-factor Map Augmentation. arXiv preprint arXiv:2503.18162. 2025.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/551-01-006