Cardiac disease is a frequent comorbidity and cause of death in subjects with compromised pulmonary function (e.g. COPD). High-resolution and free breathing cine imaging sequences are necessary for the evaluation of cardiac function in such subjects. Several self-gated and manifold algorithms were introduced for free-breathing MRI with good success. The main focus of this work is to further reduce the scan time of current manifold methods. We introduce a novel unsupervised deep generative manifold framework to recover free-breathing cine data from around 7seconds/slice, which will facilitate the acquisition of the whole volume in under two minutes.
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