Abstract
LDCT has drawn major attention in the medical imaging field due to the potential health risks of CT-associated X-ray radiation to patients. Reducing the radiation dose, however, decreases the quality of the reconstructed images, which consequently compromises the diagnostic performance. Various deep learning techniques have been introduced to improve the image quality of LDCT images through denoising. GANs-based denoising methods usually leverage an additional classification network, i.e. discriminator, to learn the most discriminate difference between the denoised and normal-dose images and, hence, regularize the denoising model accordingly; it often focuses either on the global structure or local details. To better regularize the LDCT denoising model, this paper proposes a novel method, termed DU-GAN, which leverages U-Net based discriminators in the GANs framework to learn both global and local difference between the denoised and normal-dose images in both image and gradient domains. The merit of such a U-Net based discriminator is that it can not only provide the per-pixel feedback to the denoising network through the outputs of the U-Net but also focus on the global structure in a semantic level through the middle layer of the U-Net. In addition to the adversarial training in the image domain, we also apply another U-Net based discriminator in the image gradient domain to alleviate the artifacts caused by photon starvation and enhance the edge of the denoised CT images. Furthermore, the CutMix technique enables the per-pixel outputs of the U-Net based discriminator to provide radiologists with a confidence map to visualize the uncertainty of the denoised results, facilitating the LDCT-based screening and diagnosis. Extensive experiments on the simulated and real-world datasets demonstrate superior performance over recently published methods both qualitatively and quantitatively.
Highlights
Computed tomography (CT) can provide the cross-sectional images of the internal body by the x-ray radiation, which is one of the most important imaging modalities in clinical diagnosis
Different network architectures such as 2D convolutional neural networks (CNNs) [5], 3D CNNs [7], [10], and residual encoder-decoder CNNs (REDCNN) [12] have been explored for Low-dose computed tomography (LDCT) denoising, literature has shown that the loss function is relatively more important than the network architecture as it has a direct impact on the image quality [7], [13]
2) In addition to adversarial training in the image domain, the proposed DU-generative adversarial networks (GANs) performs adversarial training in the image gradient domains, which can alleviate the streak artifacts caused by photon starvation and enhance the edge of the denoised images
Summary
Computed tomography (CT) can provide the cross-sectional images of the internal body by the x-ray radiation, which is one of the most important imaging modalities in clinical diagnosis. The computation of the adversarial loss is based on the discriminator, which is a classification network to learn a representation differentiating the denoised images from the normal-dose images; it can measure the most discriminant difference either in a global or local level, depending on that one unit of the output of discriminator corresponds to the whole image or a local region Such a discriminator is prone to forgetting previous difference because the distribution of synthetics samples shifts as the generator constantly changes through training, failing to maintain a powerful data representation to characterize the global and local image difference [16].
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