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Abstract #4128

Removing Large Vessels is Essential for the Accurate Estimation of Tissue Flow

Dominick Romano1,2, Qihao Zhang2, Alexandra Roberts2,3, Benjamin Weppner1,2, Renjiu Hu2,4, Thanh Nguyen2, Pascal Spincemaille2, and Yi Wang1,2
1Biomedical Engineering, Cornell University, Ithaca, NY, United States, 2Radiology, Weill Cornell Medical College, New York, NY, United States, 3Electrical and Computer Engineering, Cornell University, Ithaca, NY, United States, 4Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY, United States

Synopsis

Keywords: AI/ML Software, Flow, Perfusion; Augmentation; Modeling

Motivation: To investigate the contribution of large vessels to the accuracy of tissue flow in kinetic parameter mapping.

Goal(s): Compare the accuracy of quantitative transport mapping (QTM) deep learning method (QTMnet) with and without large vessel augmentations in its training data.

Approach: The simulated tissue data for QTMnet training is further augmented by adding simulated large vessels. We then evaluated model performance on ex vivo liver flow data with the ground truth total tissue flow.

Results: Augmented QTMnet performs the best over the whole liver ROI (4.16% ± 44.33%) when compared to QTMnet (299.6% ± 91.6%) and conventional tracer kinetics estimations (211.3%±126.3%) respectively.

Impact: We show that large vessel flow must be removed from tissue perfusion maps. In QTMnet, which trains a deep learning model on synthetic data to obtain blood flow, this can be achieved with large vessel augmentations of the training data.

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Keywords