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arXiv · 1807.03343

Complex Fully Convolutional Neural Networks for MR Image Reconstruction

Muneer Ahmad Dedmari·Sailesh Conjeti·Santiago Estrada·Phillip Ehses·Tony Stöcker·Martin 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 ($\mathbb{C}$DFNet) 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. $\mathbb{C}$DFNet 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 $\mathbb{C}$DFNet in contrast to its real-valued counterparts.

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Muneer Ahmad Dedmari, Sailesh Conjeti, Santiago Estrada, Phillip Ehses, Tony Stöcker, Martin Reuter. 2018-07-09. Complex Fully Convolutional Neural Networks for MR Image Reconstruction. https://arxiv.org/abs/1807.03343

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