Gradient regularized convolutional neural networks for low-dose CT image enhancement
Gradient regularized convolutional neural networks for low-dose CT image enhancement
复制标题
用于低剂量 CT 图像增强的梯度正则化卷积神经网络
DOI：
10.1088/1361-6560/ab325e
复制
发表时间：
2019-08-01
影响因子：
3.5
通讯作者：
Jiao, Licheng
中科院分区：
文献类型：
--
作者：
Gou, Shuiping;Liu, Wei;Jiao, Licheng
X射线对患者的潜在风险已将公众的注意力从正常剂量CT（NDCT）转移到低剂量CT（LDCT）。然而，简单地降低CT系统的辐射剂量会显着降低CT图像的质量，例如噪声和伪影，从而影响诊断性能。因此，在过去的几十年里，人们提出了各种方法来解决这个问题。尽管这些方法取得了令人印象深刻的效果，但它们也存在去噪后图像细节平滑的缺点，这给临床诊断和治疗带来了困难。为了解决这个问题，本文引入了一种新的 LDCT 增强梯度正则化方法。与重建过程中仅考虑像素级灰度值损失的常见方法不同，我们还考虑了图像梯度损失以保留图像细节。通过将梯度正则化方法和卷积神经网络（CNN）框架相结合，提出了一种梯度正则化卷积神经网络（GRCNN）来增强LDCT图像，该图像在我们的实验中在视觉和定量方面都取得了可喜的性能。
The potential risks of x-ray to patients have transferred the public's attention from normal dose CT (NDCT) to low-dose CT (LDCT). However, simply lowering the radiation dose of the CT system will significantly degrade the quality of CT images such as noise and artifacts, which compromises the diagnostic performance. Hence, various methods have been proposed to solve this problem over the past decades. Although these methods have achieved impressive results, they also suffer from a drawback of smoothing image details after denoising, which makes it difficult for clinical diagnosis and treatment. To address this issue, this paper introduces a novel gradient regularization method for LDCT enhancement. Rather than common methods which only consider the pixel-wise gray value loss in the reconstruction procedure, we also take the image gradient loss into consideration to preserve image details. By combining the gradient regularization method and the convolutional neural network (CNN) framework, a gradient regularized convolutional neural network (GRCNN) is proposed to enhance LDCT images which has achieved promising performance in our experiments both visually and quantitatively.