Chen Hu, Zhang Yi, Zhang Weihua, Liao Peixi, Li Ke, Zhou Jiliu, Wang Ge
College of Computer Science, Sichuan University, Chengdu 610065, China; National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu 610065, China.
College of Computer Science, Sichuan University, Chengdu 610065, China.
Biomed Opt Express. 2017 Jan 9;8(2):679-694. doi: 10.1364/BOE.8.000679. eCollection 2017 Feb 1.
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.
为了降低潜在的辐射风险，低剂量CT已引起越来越多的关注。然而，简单地降低辐射剂量会显著降低图像质量。在本文中，我们提出了一种新的通过深度学习进行低剂量CT降噪的方法，无需访问原始投影数据。这里使用一个深度卷积神经网络以逐块的方式将低剂量CT图像映射到其对应的正常剂量图像。定性结果表明该方法在减少伪影和保留结构方面具有巨大潜力。在定量指标方面，与现有的竞争方法相比，该方法在峰值信噪比（PSNR）、均方根误差（RMSE）和结构相似性指数（SSIM）上有显著提高。此外，我们的方法速度比迭代重建和基于块的图像去噪方法快一个数量级。
