Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
467-03-001 ISMRM Abstract

Physiology-driven, Patient specific Parameter Optimization for Coronary MR Angiography

Primary: Cardiovascular - Vascular and Vessel Wall
Secondary: Acquisition & Reconstruction - Subsampling: Parallel imaging, compressed sensing and low-rank modeling
467-03-001 · Perfusion and Coronary · Tuesday, 12 May, 1:40 PM–2:35 PM · Digital Posters Row H
Accepted
yue jiang1, da wei zhou 2, Junye Yao3
1radiology, jiangsu province official hospital, Nan jing, China
2jiangsu province official hospital — radiology, Nan jing, China
3Clinical & Technical Support, Philips Healthcare, Guangzhou, China
Presenting Author: da wei zhou

Synopsis

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References

1. 1.Xi W, Lu T ,WanJL,, et al. Feasibility of accelerated non‑contrast‑enhanced whole‑heart bSSFP coronary MR angiography by deep learning–constrained compressed sensing. European Radiology.doi.org/10.1007/s00330-023-09740-8 [doi]
2. 2.Yuan Y, Jiang Y, Lu GM, et al. Performance of 3-T Nonenhanced Whole-Heart bSSFP Coronary MR Angiography: A Comparison with 3-T Modified Dixon Water-Fat Separation Sequence. Radiol Cardiothorac Imaging. 2025;7(2):1-10. doi:10.1148/ryct.240162 [doi]
3. 3.Tian D, Zhao SH, Wang Y, Lu HF, Chen YY, Guo JJ, et al. Unenhanced Whole-Heart Coronary MRA: Prospective Intraindividual Comparison of 1.5-T SSFP and 3-T Dixon Water-Fat Separation GRE Methods Using Coronary Angiography as Reference. Ajr Am J Roentgenol 2022;219:199-211. doi: 10.2214/AJR.21.27292. [doi]
4. 4.Zhang Z. Predictive analytics with gradient boosting in clinical medicine. Ann Transl Med. 2019;7(7):152. doi: 10.21037/atm.2019.03.2. [doi]
5. 5.Iosipoi L, Vakhrushev A. SketchBoost: Fast Gradient Boosted Decision Tree for Multi-output Problems. In: Advances in Neural Information Processing Systems (NeurIPS); 2022.
6. 6.Jiang J, Zhang Y, Wang J, et al. Boosting tree-assisted multitask deep learning for small scientific datasets. J Chem Inf Model. 2020;60(6):2820–2831.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/467-03-001