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Abstract #3362

A Recurrent Inference Machine for accelerated MRI reconstruction at 7T

Kai L√łnning1, Patrick Putzky1, Max Welling1, and Matthan W.A. Caan2,3

1Institute for Informatics, University of Amsterdam, Amsterdam, Netherlands, 2Radiology, Academic Medical Center, Amsterdam, Netherlands, 3Spinoza Centre for Neuroimaging, Amsterdam, Netherlands

Accelerating high resolution brain imaging at 7T is needed to reach clinically feasible scanning times. Deep learning applies multi-layered neural networks as universal function approximators and is able to find its own compression implicitly. We propose a Recurrent Inference Machine (RIM) that is designed to be a general inverse problem solver. Its recurrent architecture can acquire great network depth, while still retaining a low number of parameters. The RIM outperforms compressed sensing in reconstructing 0.7mm brain data. On the reconstructed phase images, Quantitative Susceptibility Mapping can be performed.

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