530-03-004 ISMRM Abstract

Low-latency real-time 3D MRI for guiding cardiovascular interventions

Primary: Interventional - MR-Guided Interventions

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

530-03-004 · Interventional MRI: Tracking, Temperature Mapping, and Image-Guided Therapy · Wednesday, 13 May, 1:40 PM–3:30 PM · Meeting Room 2.60

Keywords: Acquisition Methods Image Reconstruction MR-Guided Interventions Real-time MRI MRI simulation

Accepted

Prakash Kumar ^1,2, Rajiv Ramasawmy², Ahsan Javed², Duc H Le¹, Kendall J O'Brien², Andrea E Jaimes², Dominique Franson³, Kelvin Chow⁴, Fang Dong⁵, Florian Maier⁵, Robert J Lederman², Krishna S Nayak^6,7, Adrienne E Campbell-Washburn²

¹Electrical and Computer Engineering, University of Southern California, Los Angeles, United States of America

²Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, United States of America

³Siemens Medical Solutions USA, Inc., Malvern, United States of America

⁴Siemens Healthcare Limited, Montreal, Canada

⁵Siemens Healthineers AG, Erlangen, Germany

⁶Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, United States of America

⁷Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, United States of America

Presenting Author: Prakash Kumar

Synopsis

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References

1. 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]

2. Campbell-Washburn AE, Tavallaei MA, Pop M, et al. Real-time MRI guidance of cardiac interventions. J Magn Reson Imaging. 2017;46(4):935-950. doi:10.1002/jmri.25749 [doi]

3. Kumar P, Ramasawmy R, Javed A, et al. Low-latency real-time 3D imaging for interventional cardiovascular MRI at 0.55T. In: Proceedings of the 33rd Annual Meeting of ISMRM. Honolulu, Hawaii, USA. ; 2025:0645.

4. Hansen MS, Sørensen TS. Gadgetron: An open source framework for medical image reconstruction. Magn Reson Med. 2013;69(6):1768-1776. doi:10.1002/mrm.24389 [doi]

5. Ong F, Lustig M. SigPy: A Python Package for High Performance Iterative Reconstruction. In: Proceedings of the ISMRM 27th Annual Meeting, Montreal, Quebec, Canada. ; 2019:4819.

6. D. H. Le, P. Kumar, E. Yagiz, Y. Tian, and K. S. Nayak, “ Online Spatiotemporally Constrained Reconstruction for Real-Time Interactive MRI,” Magnetic Resonance in Medicine (2025): 1–9, doi:10.1002/mrm.70131. [doi]

7. 3D Slicer image computing platform. 3D Slicer. https://slicer.org/.

8. Kikinis R, Pieper SD, Vosburgh KG. 3D Slicer: A Platform for Subject-Specific Image Analysis, Visualization, and Clinical Support. In: Jolesz FA, ed. Intraoperative Imaging and Image-Guided Therapy. New York, NY: Springer; 2014:277-289. doi:10.1007/978-1-4614-7657-3_19 [doi]

9. Tokuda J, Fischer GS, Papademetris X, et al. OpenIGTLink: an open network protocol for image-guided therapy environment. Int J Med Robot Comput Assist Surg MRCAS. 2009;5(4):423-434. doi:10.1002/rcs.274 [doi]

10. Segars WP, Sturgeon G, Mendonca S, Grimes J, Tsui BMW. 4D XCAT phantom for multimodality imaging research. Med Phys. 2010;37(9):4902-4915. doi:10.1118/1.3480985 [doi]

11. Tian Y, Lee NG, Nayak KS. Realistic Simulation of Real-Time Cardiac Cine and First Pass Perfusion on High Performance 0.55T system. In: Proceedings of the ISMRM 29th Annual Meeting, Online. ; :2888.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/530-03-004