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. Oscanoa^1,2, Ali B. Syed¹, Shreyas S. Vasanawala¹, and Daniel B. Ennis^1,3

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

Synopsis

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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