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Abstract #3547

Deep Learning-based Perfusion Parameter Mapping (DL-PPM) with Simulated Microvascular Network Data

Liangdong Zhou1, Jinwei Zhang1,2, Qihao Zhang1,2, Pascal Spincemaille1, Thanh D Nguyen1, Yi Wang1,2, and Liangdong Zhou3
1Weill Medical School of Cornell University, New York, NY, United States, 2Cornell University, Ithaca, NY, United States, 3Radiology, Weill Medical School of Cornell University, New York, NY, United States

Perfusion parameters, including blood flow (BF), apparent blood velocity (V), blood volume (BV) and arterial transit time (ATT) are useful for the disgnosis of many dieases. Typically, perfusion quantification methods utilize the tracer concentration (ASL, DEC, DSC, etc.) as input and blood flow map as output. We proposed a deep learning-based perfusion parameters mapping (DL-PPM), which uses 4D time-revolved tracer concentration as input and perfusion parameters (BF, V, BV, ATT) as output. We tested the propose method using simulated data and in vivo data in kidney.

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