The main focus of this work is to introduce an unsupervised deep generative manifold model for the alignment and joint recovery of the slices in free-breathing and ungated cardiac cine MRI. The main highlights are
(1) the ability to align multi-slice data and capitalize on the redundancy between the slices.
(2) The ability to estimate the gating information directly from the k-t space data.
(3) The unsupervised learning strategy that eliminates the need for extensive training data.
The joint recovery facilitates the acquisition of data from the whole heart in around 2 minutes of acquisition time.
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