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Abstract #0662

Undersampled MR Image Reconstruction Using an Enhanced Recursive Residual Network

Lijun Bao1 and Fuze Ye1

1Department of Electronic Science, Xiamen University, Xiamen, China

We propose an enhanced recursive residual network (ERRN) that improves the basic recursive residual network with both a high-frequency feature guidance and dense connections. The feature guidance is designed to predict the underlying anatomy based on image a priori learning from the label data, playing a complementary role to the residual learning. The ERRN is adapted to include super resolution MRI and compressed sensing MRI, while an application-specific error-correction unit is added into the framework, i.e. back projection for SR-MRI and data consistency for CS-MRI due to their different sampling schemes.

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