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Abstract #2653

End-to-end Motion Corrected Reconstruction using Deep Learning for Accelerated Free-breathing Cardiac MRI

Haikun Qi1, Gastao Cruz1, Thomas Kuestner1, Karl Kunze2, Radhouene Neji2, René Botnar1, and Claudia Prieto1
1School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 2MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom

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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