Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University. Xi'an, Shaanxi, People's Republic of China.
Phys Med Biol. 2019 Aug 21;64(16):165017. doi: 10.1088/1361-6560/ab325e.
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.
X 射线对患者的潜在风险已经将公众的注意力从常规剂量 CT（NDCT）转移到低剂量 CT（LDCT）。然而，仅仅降低 CT 系统的辐射剂量会显著降低 CT 图像的质量，如噪声和伪影，从而影响诊断性能。因此，在过去的几十年中，已经提出了各种方法来解决这个问题。尽管这些方法已经取得了令人印象深刻的结果，但它们也存在一个缺点，即在去噪后平滑图像细节，这使得临床诊断和治疗变得困难。为了解决这个问题，本文提出了一种用于 LDCT 增强的新型梯度正则化方法。与仅在重建过程中考虑像素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。通过结合梯度正则化方法和卷积神经网络（CNN）框架，提出了一种梯度正则化卷积神经网络（GRCNN），该方法在我们的实验中无论是在视觉上还是在定量上都取得了很有前景的性能。
