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2018 | OriginalPaper | Buchkapitel

Complex Fully Convolutional Neural Networks for MR Image Reconstruction

verfasst von : Muneer Ahmad Dedmari, Sailesh Conjeti, Santiago Estrada, Phillip Ehses, Tony Stöcker, Martin Reuter

Erschienen in: Machine Learning for Medical Image Reconstruction

Verlag: Springer International Publishing

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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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Metadaten
Titel
Complex Fully Convolutional Neural Networks for MR Image Reconstruction
verfasst von
Muneer Ahmad Dedmari
Sailesh Conjeti
Santiago Estrada
Phillip Ehses
Tony Stöcker
Martin Reuter
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-030-00129-2_4

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