X-ray CT image denoising with MINF: A modularized iterative network framework for data from multiple dose levels
X-ray CT image denoising with MINF: A modularized iterative network framework for data from multiple dose levels
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DOI：
10.1016/j.compbiomed.2022.106419
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发表时间：
2022-12
影响因子：
7.7
通讯作者：
Qiang Du;Yufei Tang;Jiping Wang;Xiaowen Hou;Zhongyi Wu;Ming Li;Xiaodong Yang;Jian Zheng
中科院分区：
文献类型：
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作者：
Qiang Du;Yufei Tang;Jiping Wang;Xiaowen Hou;Zhongyi Wu;Ming Li;Xiaodong Yang;Jian Zheng
在临床应用中，多剂量扫描协议将导致计算机断层扫描（CT）图像的噪声水平大幅波动。流行的低剂量CT（LDCT）去噪网络通过LDCT图像与其对应的地面真值之间的端到端映射来输出去噪图像。这种方法的局限性在于，降低的图像噪声水平可能无法满足医生的诊断需求。为了建立一种适应多噪声水平鲁棒性的去噪模型，提出了一种新颖高效的模块化迭代网络框架（MINF），用于学习原始LDCT的特征和前几个模块的输出，这些输出可以在后续模块中重用。所提出的网络可以实现渐进去噪的目标，输出具有不同去噪水平的临床图像，并为审查医生提供更高的诊断信心。此外，设计了多尺度卷积神经网络（MCNN）模块，以在网络训练期间提取尽可能多的特征信息。在公共和私人临床数据集上进行了广泛的实验，并与几种最先进的方法进行了比较，结果表明，该方法对LDCT图像的噪声抑制可以取得令人满意的效果。与模块化自适应处理神经网络（MAP-NN）相比，该网络具有上级逐步或渐进的去噪性能。考虑到高质量的渐进去噪结果，所提出的方法可以获得令人满意的性能，在图像对比度和细节保护方面的去噪水平的增加，这表明它的潜力，适合于多剂量水平的去噪任务。
In clinical applications, multi-dose scan protocols will cause the noise levels of computed tomography (CT) images to fluctuate widely. The popular low-dose CT (LDCT) denoising network outputs denoised images through an end-to-end mapping between an LDCT image and its corresponding ground truth. The limitation of this method is that the reduced noise level of the image may not meet the diagnostic needs of doctors. To establish a denoising model adapted to the multi-noise levels robustness, we proposed a novel and efficient modularized iterative network framework (MINF) to learn the feature of the original LDCT and the outputs of the previous modules, which can be reused in each following module. The proposed network can achieve the goal of gradual denoising, outputting clinical images with different denoising levels, and providing the reviewing physicians with increased confidence in their diagnosis. Moreover, a multi-scale convolutional neural network (MCNN) module is designed to extract as much feature information as possible during the network's training. Extensive experiments on public and private clinical datasets were carried out, and comparisons with several state-of-the-art methods show that the proposed method can achieve satisfactory results for noise suppression of LDCT images. In further comparisons with modularized adaptive processing neural network (MAP-NN), the proposed network shows superior step-by-step or gradual denoising performance. Considering the high quality of gradual denoising results, the proposed method can obtain satisfactory performance in terms of image contrast and detail protection as the level of denoising increases, which shows its potential to be suitable for a multi-dose levels denoising task.