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Abstract #0078

Accelerated 2D PC-MRI using a Deep Learning-Based Reconstruction with Complex Difference Estimation: A Prospective Feasibility Study

Matthew J. Middione1, Julio A. Oscanoa1,2, Ali B. Syed1, Shreyas S. Vasanawala1, and Daniel B. Ennis1,3
1Department of Radiology, Stanford University, Stanford, CA, United States, 2Department of Bioengineering, Stanford University, Stanford, CA, United States, 3Cardiovascular Institute, Stanford University, Stanford, CA, United States


We have previously demonstrated a Deep Learning (DL) reconstruction of highly accelerated 2D PC-MRI datasets. We trained a DL reconstruction by retrospectively undersampling fully-sampled 2D PC-MRI datasets to enable up to 9x accelerated images with <5% error in accuracy and precision. Herein, we compare the accuracy and precision of 2D PC-MRI measurements for our prospectively deployed sequence and DL reconstruction. In this initial feasibility study, we show that our DL reconstruction provides <5% error in the accuracy of peak velocity and total flow relative to 2x parallel imaging and better accuracy and precision compared to 8x compressed sensing.

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