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Abstract #0659

DL-ESPIRiT: Improving robustness to SENSE model errors in deep learning-based reconstruction

Christopher M. Sandino1, Peng Lai2, Shreyas S. Vasanawala3, and Joseph Y. Cheng3

1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Applied Sciences Laboratory, GE Healthcare, Menlo Park, CA, United States, 3Radiology, Stanford University, Stanford, CA, United States

Parallel imaging concepts, such as sensitivity encoding (SENSE), have been incorporated into DL reconstruction frameworks by augmenting the acquisition model with knowledge of the coil sensitivities. However, SENSE-based methods rely on accurate estimation of sensitivity maps; otherwise, residual aliasing may arise due to model errors such as in reduced field-of-view imaging. Here we propose DL-ESPIRiT, an ESPIRiT-based neural network architecture with improved robustness to model errors. We show that DL-ESPIRiT can reconstruct 10X accelerated 2D cardiac CINE data with higher fidelity and allow for more accurate automatic assessment of cardiovascular function than l1-ESPIRiT.

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