Two stage residual CNN for texture denoising and structure enhancement on low dose CT image
Two stage residual CNN for texture denoising and structure enhancement on low dose CT image
复制标题
用于低剂量 CT 图像纹理去噪和结构增强的两级残差 CNN
DOI：
10.1016/j.cmpb.2019.105115
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发表时间：
2020-02-01
影响因子：
6.1
通讯作者：
Zhang, Jitong
中科院分区：
文献类型：
--
作者：
Huang, Liangliang;Jiang, Huiyan;Zhang, Jitong
背景与目的：X线计算机体层摄影术（CT）在现代医学中占有重要地位。CT辐射引起的人体健康问题已引起学术界的广泛关注。减少辐射剂量会导致图像质量恶化，并进一步影响医生的诊断。因此，本文提出了一种新的低剂量CT（LDCT）图像去噪方法--两级残差卷积神经网络（TS-RCNN）。1)第一阶段的RCNN提出了通过平稳小波变换（SWT）和感知损失的纹理去噪。具体地，对每个正常剂量CT（NDCT）图像执行SWT，并且将生成的四个小波图像视为标签。2)第二级RCNN是在第一级网络的基础上，通过平均NDCT模型建立的结构增强网络。最后，通过逆SWT.Results获得去噪的CT图像：我们提出的TS-RCNN在三组模拟LDCT图像上进行训练，每组1123张图像，并在每组129张模拟LDCT图像上进行评估。此外，为了证明TS-RCNN的临床应用，我们还在2016年低剂量CT Grand Challenge数据集上测试了我们的方法。定量实验结果表明，TS-RCNN在MSE、SSIM和PSNR方面都达到了最佳效果。结论：实验结果和比较表明，TS-RCNN不仅保留了LDCT图像的更多纹理信息，而且增强了图像的结构信息。(C)2019爱思唯尔B. V.保留所有权利。
Background and objective: X-ray computed tomography (CT) plays an important role in modern medical science. Human health problems caused by CT radiation have attracted the attention of the academic community widely. Reducing radiation dose results in a deterioration in image quality and further affects doctor's diagnosis. Therefore, this paper introduces a new denoise method for low dose CT (LDCT) images, called two stage residual convolutional neural network (TS-RCNN).Methods: There are two important parts with respect to our network. 1) The first stage RCNN is proposed for texture denoising via the stationary wavelet transform (SWT) and the perceptual loss. Specifically, SWT is performed on each normal dose CT (NDCT) image and generated four wavelet images are considered as the labels. 2) The second stage RCNN is established for structure enhancement via the average NDCT model on the basis of the first network's result. Finally, the denoised CT image is obtained via inverse SWT.Results: Our proposed TS-RCNN is trained on three groups of simulated LDCT images in 1123 images per group and evaluated on 129 simulated LDCT images for each group. Besides, to demonstrate the clinical application of TS-RCNN, we also test our method on the 2016 Low Dose CT Grand Challenge dataset. Quantitative results show that TS-RCNN almost achieves the best results in terms of MSE, SSIM and PSNR compared to other methods.Conclusions: The experimental results and comparisons demonstrate that TS-RCNN not only preserves more texture information, but also enhances structural information on LDCT images. (C) 2019 Elsevier B.V. All rights reserved.