Low-dose CT via convolutional neural network
Low-dose CT via convolutional neural network
复制标题
通过卷积神经网络进行低剂量 CT
DOI：
10.1364/boe.8.000679
复制
发表时间：
2017-02-01
影响因子：
3.4
通讯作者：
Wang, Ge
中科院分区：
文献类型：
--
作者：
Chen, Hu;Zhang, Yi;Wang, Ge
为了减少潜在的辐射风险，低剂量CT越来越受到人们的重视。然而，简单地降低辐射剂量会显著降低图像质量。本文提出了一种新的基于深度学习的低剂量CT去噪方法，该方法不需要访问原始投影数据。这里使用深度卷积神经网络以逐块的方式将低剂量CT图像映射到对应的正常剂量的对应图像。定性结果表明，该方法在去伪影和结构保留方面具有很大的潜力。在量化指标方面，该方法在PSNR、RMSE和SSIM方面都比现有的同类方法有了很大的改善。此外，我们的方法的速度比迭代重建和基于块的图像去噪方法快一个数量级。(C)2017年美国光学学会
In order to reduce the potential radiation risk, low-dose CT has attracted an increasing attention. However, simply lowering the radiation dose will significantly degrade the image quality. In this paper, we propose a new noise reduction method for low-dose CT via deep learning without accessing original projection data. A deep convolutional neural network is here used to map low-dose CT images towards its corresponding normal-dose counterparts in a patch-by-patch fashion. Qualitative results demonstrate a great potential of the proposed method on artifact reduction and structure preservation. In terms of the quantitative metrics, the proposed method has showed a substantial improvement on PSNR, RMSE and SSIM than the competing state-of-art methods. Furthermore, the speed of our method is one order of magnitude faster than the iterative reconstruction and patch-based image denoising methods. (C) 2017 Optical Society of America