Deep Learning-Based Reconstruction of Accelerated Cardiac Cine MRI at 0.55T

Acceleration of cardiac cine MRI is highly desirable in order to decrease the required breath-hold duration. On low-field MRI systems in particular, this could help make cardiac MRI more widely available. We present a method based on the Variational Network for reconstruction of cardiac cine MRI, trained on data from 1.5 and 3T systems. Reconstructions of retrospectively and prospectively undersampled acquisitions at 0.55T with an acceleration rate of eight are shown and compared to Compressed Sensing reconstructions. Despite the domain shift to low-field data, the neural network achieved an SSIM of 94.6%, which is comparable to the Compressed Sensing results.