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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

In this study, we propose an end-to-end deep learning non-rigid motion-corrected reconstruction technique for fast reconstruction of highly undersampled free-breathing CMRA.

Fig. 5 Reformatted coronary arteries from 9x accelerated CMRA reconstructed using non-rigid PROST and the proposed MoCo-MoDL. First row: the test patient shown in Fig. 4; second row: one of the test healthy subjects.

Fig. 4 Whole-heart 9x accelerated CMRA from one representative test patient, reconstructed using non-rigid PROST (left) and the proposed MoCo-MoDL (right).