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Abstract #4650

Deep learning aided compressed sensing for accelerated cardiac cine MRI

Tobias Wech1, Thorsten A. Bley1, and Herbert Köstler1

1Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany

A reconstruction technique for accelerated functional cardiac MRI is presented that exploits a convolutional neural network trained for semantic segmentation of undersampled data. The idea is inspired by the experience that the human eye is capable of distinguishing between typical undersampling artifacts and cardiac shape and/or motion, even for high acceleration factors. The temporal courses of the segmentations determined by the network are used for an efficient sparsification within a compressed sensing algorithm.

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