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Complex Fully Convolutional Neural Networks for MR Image Reconstruction

9 July 2018
M. A. Dedmari
Sailesh Conjeti
Santiago Estrada
Phillip Ehses
T. Stöcker
M. Reuter
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Abstract

Undersampling the k-space data is widely adopted for acceleration of Magnetic Resonance Imaging (MRI). Current deep learning based approaches for supervised learning of MRI image reconstruction employ real-valued operations and representations by treating complex valued k-space/spatial-space as real values. In this paper, we propose complex dense fully convolutional neural network (C\mathbb{C}CDFNet) for learning to de-alias the reconstruction artifacts within undersampled MRI images. We fashioned a densely-connected fully convolutional block tailored for complex-valued inputs by introducing dedicated layers such as complex convolution, batch normalization, non-linearities etc. C\mathbb{C}CDFNet leverages the inherently complex-valued nature of input k-space and learns richer representations. We demonstrate improved perceptual quality and recovery of anatomical structures through C\mathbb{C}CDFNet in contrast to its real-valued counterparts.

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