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Abstract #0574

ReconNet: A Deep Learning Framework for Transforming Image Reconstruction into Pixel Classification

Kamlesh Pawar1,2, Zhaolin Chen1,3, N Jon Shah1,4, and Gary F Egan1,2

1Monash Biomedical Imaging, Monash University, Melbourne, Australia, 2School of Psychological Sciences, Monash University, Melbourne, Australia, 3Department of Electrical and Computer System Engineering, Monash University, Melbourne, Australia, 4Institute of Medicine, Research Centre Juelich, Juelich, Germany

A deep learning framework is presented that transforms the image reconstruction problem from under-sampled k-space data into pixel classification. The underlying target image is represented by a quantized image, which makes it possible to design a network that classifies each pixel to a quantized level. We have compared two deep learning encoder-decoder networks with the same complexity: one is a classification network and the other is a regression network. Even though the complexity of both the networks is the same, the images reconstructed using the classifier network have resulted in a six times improvement in the mean squared error compared to the regression network.

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