A non-rigid respiratory motion-corrected reconstruction technique (non-rigid PROST) has achieved high-quality coronary MRA (CMRA). However, non-rigid PROST requires respiratory-resolved (bin) image reconstruction, bin-to-bin non-rigid registration and regularized reconstruction, leading to long computation time. In this study, we propose an end-to-end deep learning non-rigid motion-corrected reconstruction technique for highly undersampled free-breathing CMRA. It consists of a diffeomorphic motion estimation network and a motion-informed model-based deep learning reconstruction network that were trained jointly for motion-corrected undersampled reconstruction. Compared with non-rigid PROST, the proposed technique achieved better reconstruction performance in both retrospectively and prospectively 9x-accelerated CMRA, while operating orders of magnitude faster.
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