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Abstract #2311

Validating Kinetic Perfusion Parameter Mapping Using a High-Fidelity Whole Brain Simulation

Benjamin Weppner1,2, Qihao Zhang2, Dominick Romano1,2, Renjiu Hu2,3, Ilhami Kovanlikaya2, Pascal Spincemaille2, and Yi Wang1,2
1Biomedical Engineering, Cornell University, Ithaca, NY, United States, 2Radiology, Weill Cornell Medical College, New York, NY, United States, 3Mechanical Engineering, Cornell University, Ithaca, NY, United States

Synopsis

Keywords: Perfusion, Quantitative Imaging, Whole brain blood flow simulation, perfusion modeling

Motivation: While experimental phantoms are useful for validating perfusion modeling, they lack the complexity of organs.

Goal(s): To construct an in-silico whole brain perfusion model and use it for assessing the estimation errors of a two-compartment perfusion model.

Approach: A large vessel segmentation obtained in a human subject is combined simulated arteries, veins and capillaries and their flow. Two-compartment modeling is then performed on the corresponding concentration maps computed and compared with the known ground truth.

Results: The data demonstrate an underestimation of flow (mean of -18.40%), likely due to the delay and dispersion effects related to the choice of a global AIF.

Impact: We demonstrate a detailed in-silico perfusion model that allows the investigate the accuracy of perfusion modeling methods.

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Keywords