Scale Time-Equivariant Convolutional Neural Networks For Dynamic Magnetic Resonance Imaging

Motivation: The scale symmetry of anatomical structures commonly exists in dynamic magnetic resonance imaging (MRI) data but have rarely been explored. Goal(s): Our goal is to effectively leverage the scale symmetry of local structures in both spatial and temporal dimensions to improve the reconstrcution quality in dynamic MRI. Approach: We present a novel method that incorporates the scale equivariant convolution modules into an unrolled deep neural network. Results: The proposed method was test on the cardiac cine MRI data reconstruction tasks and achieved the improved performance with a PSNR of 43.6967 and a SSIM of 0.9834. Impact: Our method improved the data-efficiency for deep dynamic MRI reconstructions and robustness to drifts in scale of the images that might stem from the variability of patient anatomies or change in field-of-view across different MRI scanners.