Abstract #2658

Deep Learning-Based ESPIRiT Reconstruction for Accelerated 2D Phase Contrast MRI: Analysis of the Impact of Reconstruction Induced Phase Errors

Matthew J. Middione¹, Julio A. Oscanoa^1,2, Michael Loecher¹, Christopher M. Sandino³, Shreyas S. Vasanawala¹, and Daniel B. Ennis^1,4

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

2D PC-MRI Compressed sensing (CS) and deep learning (DL) reconstruction techniques may introduce a reconstruction induced phase bias, distinct from eddy current-induced background phase offsets, which may impact the accuracy of flow measurements if not corrected. Herein, we analyzed this reconstruction induced phase bias to determine the maximum acceleration factor that could be used with CS and DL reconstruction frameworks for 2D PC-MRI while minimizing errors in peak velocity and total flow within ±5%.

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