4D Cardiac MR Image Reconstruction by Deep Learning with Wavelet Transform

We present a CNN-based deep learning model to reconstruct the cardiac cine images from undersampled single-channel 4D MR data. The wavelet transform and spatial-temporal attention mechanisms are introduced in the model. The proposed model could reconstruct the cardiac images and recover the cardiac wall motion more robustly than the low-rank plus sparsity (i.e., L+S) reconstruction method. This approach presents one promising solution for accelerated cardiac dynamic imaging with one single channel through deep learning from the sparsity in spatial and temporal domains.