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Abstract #2644

Cross Validation of a Deep Learning-Based ESPIRiT Reconstruction for Accelerated 2D Phase Contrast MRI

Jack R. Warren1, Matthew J. Middione2, Julio A. Oscanoa2,3, Christopher M. Sandino4, Shreyas S. Vasanawala2, and Daniel B. Ennis2,5
1Department of Computing + Mathematical Sciences, California Institute of Technology, Pasadena, CA, United States, 2Department of Radiology, Stanford University, Stanford, CA, United States, 3Department of Bioengineering, Stanford University, Stanford, CA, United States, 4Department of Electrical Engineering, Stanford University, Stanford, CA, United States, 5Cardiovascular Institute, Stanford University, Stanford, CA, United States

Phase Contrast MRI (PC-MRI) measures the flow of blood. In order to obtain high-quality measurements, patients must hold their breath for ~20 seconds, which oftentimes can be difficult. Advances in deep learning (DL) have allowed for the reconstruction of highly undersampled MRI data. A 2D PC-MRI DL-ESPIRiT network was recently proposed to undersample the data acquisition by up to 8x without compromising clinically relevant measures of flow accuracy within ±5%. This work uses k-fold cross validation to evaluate the DL-ESPIRiT network on 2D PC-MRI data in terms of accuracy and variability for pixel velocity, peak velocity, and net flow.

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