Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network.
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| Title: | Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network. |
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| Authors: | Kang, Eunhee1 eunheekang@kaist.ac.kr, Chang, Won2 sword1981@hanmail.net, Yoo, Jaejun1 jaejun2004@kaist.ac.kr, Ye, Jong Chul1 jong.ye@kaist.ac.kr |
| Source: | IEEE Transactions on Medical Imaging. Jun2018, Vol. 37 Issue 6, p1358-1369. 12p. |
| Subjects: | Computed tomography, Artificial neural networks, Algorithms, Wavelet transforms, Deep learning |
| Abstract: | Model-based iterative reconstruction algorithms for low-dose X-ray computed tomography (CT) are computationally expensive. To address this problem, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the textures were not fully recovered. To address this problem, here we propose a novel framelet-based denoising algorithm using wavelet residual network which synergistically combines the expressive power of deep learning and the performance guarantee from the framelet-based denoising algorithms. The new algorithms were inspired by the recent interpretation of the deep CNN as a cascaded convolution framelet signal representation. Extensive experimental results confirm that the proposed networks have significantly improved performance and preserve the detail texture of the original images. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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