Cardiovascular magnetic resonance cine imaging is utilised to give comprehensive information on ventricular function. Arrhythmia obstructs acquisition of these images, where the use of faster acquisition protocols with deep learning reconstruction methods may aid in solving the problem. We evaluated cine images of three patients in atrial arrhythmia, acquired using standard method; fast, variable density spatiotemporal sampling acquisition (VD kt) of one(1rr) or three(3rr) heart beats; and deep learning reconstruction of the same (DL-1rr,DL-3rr). Our results showed that undersampled techniques combined with deep-learning algorithms result in image quality improvements with no significant difference in quantitative values between all acquisition techniques.
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