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Abstract #3636

Locally and Globally Concatenated Network for MR Image Reconstruction

Zechen Zhou1, Christophe Schülke2, Chun Yuan3, and Peter Börnert2
1Philips Research North America, Cambridge, MA, United States, 2Philips Research Hamburg, Hamburg, Germany, 3Department of Radiology, University of Washington, Seattle, WA, United States

Recently, the convolutional neural network (CNN) based reconstruction concept has emerged as a promising implementation of compressed sensing tailored for specific fast imaging applications. The reconstruction performance of such data-driven models may depend on the CNN structure which determines the feature extraction process for sparse representation. In this study, a locally and globally concatenated network is proposed and compared with the residual network as well as the traditional L1-wavelet ESPIRiT. Preliminary experiments on a public knee imaging database showed that the proposed approach provided improved fine structure (e.g. vessel wall) restoration and background noise reduction.

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