468-06-005 ISMRM Abstract

An End-to-End Neural Network for Establishing Left-Ventricular Volume from Sparse Cine Slices

Primary: Cardiovascular - Myocardium

Secondary: Acquisition & Reconstruction - Image Reconstruction: AI

468-06-005 · Acquisition and Reconstruction Strategies for Dynamic and Cardiac Imaging · Tuesday, 12 May, 4:55 PM–5:50 PM · Digital Posters Row I

Keywords: Deep learning Left ventricle volume modeling Sparse cine MRI Cardiac function quantification

Accepted

Yu Lian¹, Bowei Liu², Ancong Wang¹, Yingwei Fan¹, Peng Wu ³, Haiying Ding², Xiaoying Tang¹, Rui Guo¹

¹School of Medical Technology, Beijing Institute of Technology, Beijing, China

²Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China

³Philips Healthcare (Shanghai), Shang Hai, China

Presenting Author: Peng Wu

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. Kramer C M, Barkhausen J, Bucciarelli-Ducci C, et al. Standardized cardiovascular magnetic resonance imaging (CMR) protocols: 2020 update. Journal of Cardiovascular Magnetic Resonance, 2020;22(1): 17. https://doi.org/10.1186/s12968-020-00607-1 [doi]

2. Schulz-Menger J, Bluemke D A, Bremerich J, et al. Standardized image interpretation and post processing in cardiovascular magnetic resonance: Society for Cardiovascular Magnetic Resonance (SCMR) board of trustees task force on standardized post processing. Journal of cardiovascular magnetic resonance, 2013;15(1): 35. https://doi.org/10.1186/1532-429X-15-35 [doi]

3. Tan L K, McLaughlin R A, Lim E, et al. Fully automated segmentation of the left ventricle in cine cardiac MRI using neural network regression. Journal of Magnetic Resonance Imaging, 2018;48(1): 140-152. https://doi.org/10.1002/jmri.25932 [doi]

4. Puntmann V O, Valbuena S, Hinojar R, et al. Society for Cardiovascular Magnetic Resonance (SCMR) expert consensus for CMR imaging endpoints in clinical research: part I-analytical validation and clinical qualification. Journal of Cardiovascular Magnetic Resonance, 2018;20(1): 67. https://doi.org/10.1186/s12968-018-0484-5 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/468-06-005