End-to-End Deep Learning of Non-rigid Groupwise Registration and Reconstruction of Dynamic MRI.

ABSTRACT: Temporal correlation has been exploited for accelerated dynamic MRI reconstruction. Some methods have modeled inter-frame motion into the reconstruction process to produce temporally aligned image series and higher reconstruction quality. However, traditional motion-compensated approaches requiring iterative optimization of registration and reconstruction are time-consuming, while most deep learning-based methods neglect motion in the reconstruction process. We propose an unrolled deep learning framework with each iteration consisting of a groupwise diffeomorphic registration network (GRN) and a motion-augmented reconstruction network. Specifically, the whole dynamic sequence is registered at once to an implicit template which is used to generate a new set of dynamic images to efficiently

SUBMITTER: Yang J 

PROVIDER: S-EPMC9095964 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature