  A novel neural network architecture, known as DL-ESPIRiT, is proposed to
reconstruct rapidly acquired cardiac MRI data without field-of-view limitations
which are present in previously proposed deep learning-based reconstruction
frameworks. Additionally, a novel convolutional neural network based on
separable 3D convolutions is integrated into DL-ESPIRiT to more efficiently
learn spatiotemporal priors for dynamic image reconstruction. The network is
trained on fully-sampled 2D cardiac cine datasets collected from eleven healthy
volunteers with IRB approval. DL-ESPIRiT is compared against a state-of-the-art
parallel imaging and compressed sensing method known as $l_1$-ESPIRiT. The
reconstruction accuracy of both methods is evaluated on retrospectively
undersampled datasets (R=12) with respect to standard image quality metrics as
well as automatic deep learning-based segmentations of left ventricular
volumes. Feasibility of this approach is demonstrated in reconstructions of
prospectively undersampled data which were acquired in a single heartbeat per
slice.
