Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
564-01-005 ISMRM Abstract

Quantitative transport mapping network for predicting survival time of nasopharyngeal carcinoma using compartment model

Primary: Acquisition & Reconstruction - Quantitative Imaging: head and neck
Secondary: Contrast Mechanisms - Perfusion
564-01-005 · Perfusion and ASL Imaging: Recent Advances · Wednesday, 13 May, 8:20 AM–9:15 AM · Digital Posters Row E
Keywords: Permeability Nasopharyngeal carcinoma Perfusion quantification Artificial Intelligence (AI) Deep Learning Dynamic contrast-enhanced MRI (DCE-MRI)
Accepted
Qihao Zhang1, Renjiu Hu1, Dominick Romano1,2,3, Benjamin Weppner1,2,3,4,5, Thanh D Nguyen3,4,6, Pascal Spincemaille 3,4,7, Yi Wang 2,3,4,8
1Weill Cornell Medical College, New York, United States of America
2Biomedical Engineering, Cornell University, Ithaca, United States of America
3Radiology, Weill Cornell Medicine, New York, United States of America
4Department of Radiology, Weill Cornell Medicine, New York, United States of America
5Cornell University, Ithaca, United States of America
6Radiology, Weill Cornell Medical College, New York, United States of America
7Weill Cornell Medicine, New York, United States of America
8Department Radiology & Biomedical Engineering, Weill Cornell Medicine, Cornell University, New York, United States of America
Presenting Author: Yi Wang

Synopsis

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References

1. ) Zhang, Qihao, et al. "Fluid mechanics approach to perfusion quantification: vasculature computational fluid dynamics simulation, quantitative transport mapping (QTM) analysis of dynamics contrast enhanced MRI, and application in nonalcoholic fatty liver disease classification." IEEE Transactions on Biomedical Engineering 70.3 (2022): 980-990.
2. Romano D, Zhang Q, Roberts A G, et al. Validation of quantitative transport mapping (QTM) with an ex vivo perfused liver model[J]. Magnetic Resonance in Medicine, 2025. doi:10.1002 doi:10.1002
3. Oktay O, Schlemper J, Folgoc L L, et al. Attention u-net: Learning where to look for the pancreas[J]. arXiv preprint arXiv:1804.03999, 2018.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/564-01-005