Abstract #2645

Deep Learning Based ESPIRiT Reconstruction for Highly Accelerated 2D Phase Contrast MRI

Julio A. Oscanoa^1,2, Matthew J. Middione², Christopher M. Sandino³, Shreyas S. Vasanawala², and Daniel B. Ennis^2,4

¹Department of Bioengineering, Stanford University, Stanford, CA, United States, ²Department of Radiology, Stanford University, Stanford, CA, United States, ³Department of Electrical Engineering, Stanford University, Stanford, CA, United States, ⁴Cardiovascular Institute, Stanford University, Stanford, CA, United States

We propose a novel Deep Learning (DL) based reconstruction framework for accelerated 2D Phase Contrast MRI (PC-MRI) datasets. We extend a previously developed DL method based on ESPIRiT reconstruction for cardiac cine and combine it with a direct Complex Difference estimation approach. We tested the DL methods using retrospectively undersampled 2D PC-MRI data and compared it with conventional Compressed Sensing (CS) reconstruction. Our method outperformed CS and enabled higher acceleration factors up to 8x while maintaining error metrics within a targeted accuracy of ±5%.

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