Accelerating cardiac cine MRI using a deep learning-based ESPIRiT reconstruction
This work addresses faster and more efficient cardiac MRI reconstruction for medical imaging applications, representing an incremental improvement over existing methods.
The authors tackled the problem of accelerating cardiac cine MRI by proposing DL-ESPIRiT, a deep learning-based reconstruction method that eliminates field-of-view limitations and uses separable 3D convolutions to learn spatiotemporal priors, achieving reconstruction on data undersampled at R=12 and demonstrating feasibility in single-heartbeat acquisitions.
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.