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Abstract #2872

Deep generative manifold model: a novel approach for free breathing dynamic MRI

Qing Zou1, Abdul Haseeb Ahmed1, Prashant Nagpal1, Rolf Schulte2, and Mathews Jacob1
1University of Iowa, Iowa City, IA, United States, 2GE Global Research, Munich, Germany

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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