Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
607-01-003 ISMRM Abstract

Deep Learning-based Estimation of Myocardial Extracellular Volume Without Blood Sampling: Multicenter Study in 9,700 Patients

Primary: Analysis Methods - Classification and Prediction
Secondary: Cardiovascular - Myocardium
607-01-003 · Advanced Cardiac Quantification Methods · Thursday, 14 May, 8:30 AM–10:20 AM · Meeting Room 1.40
Keywords: Deep learning Extracellular volume Hematocrit
Accepted
Zhuoan Li 1,2, Khalid Youssef3, Venkateshwar Polsani4, Michael Elliott5, Rohan Dharmakumar6, Robert Judd7, Dipan Shah8, Orlando Simonetti9,10, Matthew Tong9,11, Behzad Sharif1,2
1Lab for Translational Imaging for Microcirculation, Purdue University, West Lafayette, United States of America
2Weldon School of Biomedical Engineering, Purdue University, West Lafayette, United States of America
3Radiology & Imaging Sciences, Indiana University School of Medicine, Indianapolis, United States of America
4Piedmont Healthcare, Atlanta, United States of America
5Atrium Health Wake forest Bapist Medical Center, Winston-Salem, United States of America
6Indiana University School of Medicine, Indianapolis, United States of America
7Duke University School of Medicine, Durham, United States of America
8Houston Methodist Hospital, Houston, United States of America
9The Ohio State University, Columbus, United States of America
10Department of Radiology, The Ohio State University, Columbus, United States of America
11Division of Cardiovascular Medicine, The Ohio State University, Columbus, United States of America
Presenting Author: Zhuoan Li

Synopsis

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References

1. Treibel TA, Fontana M, Maestrini V, et al. Automatic Measurement of the Myocardial Interstitium: Synthetic Extracellular Volume Quantification Without Hematocrit Sampling. JACC Cardiovasc Imaging. 2016;9(1):54-63. doi:10.1016/j.jcmg.2015.11.008 [doi]
2. Fent GJ, Garg P, Foley JRJ, et al. Synthetic Myocardial Extracellular Volume Fraction. JACC Cardiovasc Imaging. 2017;10(11):1402-1404. doi:10.1016/j.jcmg.2016.12.007 [doi]
3. Chen W, Doeblin P, Al-Tabatabaee S, et al. Synthetic Extracellular Volume in Cardiac Magnetic Resonance Without Blood Sampling: a Reliable Tool to Replace Conventional Extracellular Volume. Circ Cardiovasc Imaging. 2022;15(4):e013745. doi:10.1161/CIRCIMAGING.121.013745 [doi]
4. Reiter C, Puseljic M, Fuchsjäger M, Schmid J. Estimating synthetic hematocrit and extracellular volume from native blood pool T1 times at 3 Tesla CMR: Derivation of a conversion equation, accuracy and comparison with published formulas. Eur J Radiol. 2024;178:111659. doi:10.1016/j.ejrad.2024.111659 [doi]
5. Tong MS, Slivnick JA, Sharif B, et al. The Society for Cardiovascular Magnetic Resonance Registry at 150,000. J Cardiovasc Magn Reson. Published online July 4, 2024. doi:10.1016/j.jocmr.2024.101055 [doi]
6. Youssef, K., Shao, K., Moon, S. et al. Landslide susceptibility modeling by interpretable neural network. Commun Earth Environ 4, 162 (2023). https://doi.org/10.1038/s43247-023-00806-5 [doi]
7. K. Youssef, L. Bouchard, K. Haigh, J. Silovsky, B. Thapa and C. V. Valk, "Machine Learning Approach to RF Transmitter Identification," in IEEE Journal of Radio Frequency Identification, vol. 2, no. 4, pp. 197-205, Dec. 2018, doi: 10.1109/JRFID.2018.2880457 [doi]
8. Li Z, Youssef K, Amian M, et al. Multi-feature deep learning for deriving synthetic hematocrit without blood sampling: Initial results from the SCMR registry[J]. Journal of Cardiovascular Magnetic Resonance, 2025, 27. https://doi.org/10.1016/j.jocmr.2024.101299 [doi]
9. Gottbrecht M, Kramer CM, Salerno M. Native T1 and Extracellular Volume Measurements by Cardiac MRI in Healthy Adults: A Meta-Analysis. Radiology. 2019;290(2):317-326. doi:10.1148/radiol.2018180226 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/607-01-003