Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
631-02-010 / 631-02-010 ISMRM Abstract

Validation of QTMnet based kinetic parameter mapping using a whole brain perfusion simulation

Primary: Contrast Mechanisms - Perfusion
Secondary: Acquisition & Reconstruction - AI methods
631-02-010 · Everything About Contrast Mechanism · Thursday, 14 May, 1:40 PM–3:16 PM · Roof Terrace
631-02-010 · Everything About Contrast Mechanism · Thursday, 14 May, 1:40 PM–3:16 PM · Roof Terrace
Keywords: Perfusion Deep learning Quantitative Transport Mapping Simulation Blood Flow Quantification
Accepted
Benjamin Weppner1,2, Qihao Zhang2,3, Dominick Romano1,2,3, Renjiu Hu2,4, Ilhami Kovanlikaya2, Pascal Spincemaille 2,5,6, Yi Wang 1,2,6,7
1Biomedical Engineering, Cornell University, Ithaca, United States of America
2Radiology, Weill Cornell Medicine, New York, United States of America
3Weill Cornell Medical College, New York, United States of America
4Mechanical & Aerospace Engineering, Cornell University, Ithaca, United States of America
5Weill Cornell Medicine, New York, United States of America
6Department of Radiology, Weill Cornell Medicine, New York, United States of America
7Department 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. Calamante, F. (2013). Arterial input function in perfusion MRI: a comprehensive review. Progress in nuclear magnetic resonance spectroscopy, 74, 1–32. https://doi.org/10.1016/j.pnmrs.2013.04.002 [doi]
2. Romano, D., Zhang, Q., Roberts, A.G., Weppner, B., et al. (2025) Validation of quantitative transport mapping (QTM) with an ex vivo perfused liver model. Magn Reason Med. 94(4): 1779-1792. doi:10.1002/mrm.20581 [doi]
3. Hu, R., Zhang, Q., Romano, D.J., & Wang, Y. (2023). Quantitative transport mapping (QTM) of the brain with simulated microvasculature model. ISMRM, Abstract #1181.
4. Karch, R., Neumann, F., Neumann, M., & Schreiner, W. (1999). A three-dimensional model for arterial tree representation, generated by constrained constructive optimization. Computers in biology and medicine, 29(1), 19–38. https://doi.org/10.1016/s0010-4825(98)00045-6 [doi]
5. Weppner, B., Zhang, Q., Romano, D.J., Hu, R., Kovanlikaya, I., Spincemaille, P., & Wang, Y. (2025). Validating Kinetic Perfusion Parameter Mapping Using a High-Fidelity Whole Brain Simulation. ISMRM, Abstract #2311.
6. Bumgarner, J.R., & Nelson, R.J. (2022). Open-source analysis and visualization of segmented vasculature datasets with VesselVio. Cell Rep Methods 2, 100189. https://doi.org/10.1016/j.crmeth.2022.100189 [doi]
7. Schirmer, M.D., et al. (2019). Spatial signature of white matter hyperintensities in stroke patients. Frontiers in neurology 10:208. https://doi.org/10.3389/fneur.2019.00208 [doi]
8. Patil, V., & Johnson, G. (2011). An improved model for describing the contrast bolus in perfusion MRI. Medical physics, 38(12), 6380–6383. https://doi.org/10.1118/1.3658570 [doi]
9. Oktay, O., et al. (2018). Attention u-net: Learning where to look for the pancreas. arXiv preprint. https://doi.org/10.48550/arXiv.1804.03999 [doi]

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http://echo.ismrm.org/p/ISMRM2026/631-02-010