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A novel neural network architecture, known as DL-ESPIRiT, is proposed toreconstruct rapidly acquired cardiac MRI data without field-of-view limitationswhich are present in previously proposed deep learning-based reconstructionframeworks. Additionally, a novel convolutional neural network based onseparable 3D convolutions is integrated into DL-ESPIRiT to more efficientlylearn spatiotemporal priors for dynamic image reconstruction. The network istrained on fully-sampled 2D cardiac cine datasets collected from eleven healthyvolunteers with IRB approval. DL-ESPIRiT is compared against a state-of-the-artparallel imaging and compressed sensing method known as $l_1$-ESPIRiT. Thereconstruction accuracy of both methods is evaluated on retrospectivelyundersampled datasets (R=12) with respect to standard image quality metrics aswell as automatic deep learning-based segmentations of left ventricularvolumes. Feasibility of this approach is demonstrated in reconstructions ofprospectively undersampled data which were acquired in a single heartbeat perslice.

Accelerating cardiac cine MRI using a deep learning-based ESPIRiT reconstruction