Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network
Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network
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DOI：
10.1109/tmi.2018.2823756
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发表时间：
2018-06-01
影响因子：
10.6
通讯作者：
Ye, Jong Chul
中科院分区：
文献类型：
--
作者：
Kang, Eunhee;Chang, Won;Ye, Jong Chul
基于模型的迭代重建算法用于低剂量X射线计算机断层扫描（CT）是计算昂贵的。为了解决这个问题，我们最近提出了一种用于低剂量X射线CT的深度卷积神经网络（CNN），并在2016年AAPM低剂量CT挑战赛中获得第二名。然而，一些纹理没有完全恢复。为了解决这个问题，我们提出了一种新的基于小框架的去噪算法，该算法使用小波残差网络，协同结合了深度学习的表达能力和基于小框架的去噪算法的性能保证。新算法的灵感来自于最近将深度CNN解释为级联卷积小框架信号表示。大量的实验结果证实，所提出的网络有显着提高的性能，并保持原始图像的细节纹理。
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.