Fast Acquisition and Low-delay Reconstruction of Stack-of-stars Trajectory Using Temporal Multiresolution Images and a Convolutional Neural Network

For fast data acquisition and low-delay reconstruction in applications using stack-of-stars trajectory, the authors propose a new reconstruction method using a CNN with temporal multiresolution inputs. Conventionally, stack-of-stars images reconstructed from a few spokes contain streaking artifacts. By utilizing view sharing technique for suppressing the artifacts, reconstructed images are often blurred. For low-delay reconstruction, it is not straightforward to use well-studied methods based on compressed sensing with temporal priors. The proposed method aims to adjust spatio-temporal resolution to a suitable one. Experimental results show that the proposed method could reconstruct highly under-sampled radial dynamic images with reduced artifacts.