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. Oscanoa^2,3, Christopher M. Sandino⁴, Shreyas S. Vasanawala², and Daniel B. Ennis^2,5

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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