Improving cardiac cine MRI using a deep learning-based ESPIRiT reconstruction with self attention

A deep learning based ESPIRiT (DL-ESPIRiT) was recently proposed to reconstruct dynamic MRI data with higher reconstruction accuracy. However, the method still has difficulty resolving fine anatomic structures. We propose incorporating self-attention to the network using a computationally lightweight squeeze-excitation block (DL-ESPIRiT SE), which uses global information to select more important features while suppressing less important ones. We demonstrate improved reconstruction with DL-ESPIRiT SE, which is most pronounced during faster cardiac motion such as in ventricular ejection.