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Abstract #1958

Alignment & joint recovery of multi-slice cine MRI data using deep generative manifold model

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

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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