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| Home > Publications Database > Complex Fully Convolutional Neural Networks for MR Image Reconstruction |
| Home > Publications Database > Complex Fully Convolutional Neural Networks for MR Image Reconstruction |
| Book/Conference Presentation (Other) | DZNE-2020-00874 |
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2018
ISBN: 978-3-030-00128-5 (print), 978-3-030-00129-2 (electronic)
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Please use a persistent id in citations: doi:10.1007/978-3-030-00129-2_4
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 (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. 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 CDFNet in contrast to its real-valued counterparts.
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