Keywords: Machine Learning/Artificial Intelligence, CardiovascularCardiac CINE MRI plays an essential role in cardiac diagnosis, but not all patients are eligible for 3D imaging, which is associated with long acquisition times. We propose a deep-learning super-resolution model to generate 3D CINE from multi-planar 2D real-time MRI using external signals for cardiac and respiratory motion estimation. The proposed neural field model is trained on a single subject and performs non-rigid motion compensation and implicit representation learning in an end-to-end manner. A preliminary study with three healthy volunteers demonstrates promising reconstruction performance and computation times compared to traditional registration-based approaches.
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