Abstract
Low-dose CT (LDCT) images have been widely applied in the medical imaging field due to the potential risk of exposing patients to X-ray radiations. Given the fact that reducing the radiation dose may result in increased noise and artifacts, methods that can eliminate the noise and artifacts in the LDCT image have drawn increasing attentions and produced impressive results over the past decades. However, recent proposed methods mostly suffer from noise remaining, over-smoothing structures or false lesions derived from noise. In this paper, we propose a generative adversarial network (GAN) with novel architecture and loss function for restoring the LDCT image. Firstly, the inception-residual block and residual mapping are incorporated in the U-Net structure. The modified U-Net is applied as the generator of the GAN network so that the noise feature can be eliminated during the forward propagation. Secondly, a novel multi-level joint discriminator is designed by concatenating multiple convolutional neural networks (CNNs) where the output of each deconvolutional layer in the generator is compared with the corresponding down-sampled ground truth image. The adversarial training can be sensitive to noise and artifacts in different scales with this discriminator. Thirdly, we novely define a loss function consisting of the least square adversarial loss, VGG based perceptual loss, MSE based pixel loss and the noise loss, so that the differences in pixel, visual perception and noise distribution are comprehensively considered to optimize the network. Experimental results on both simulated and official simulated clinical images have demonstrated that the proposed method can provide superior performance to the state-of-the-art methods in noise removal, structure preservation and false lesions elimination.
Highlights
X-ray computed tomography (CT) plays an important role in medical imaging and has been widely used in modern clinical institutions recently
Improve the quality of low-dose CT (LDCT) images have been proposed in the past decades, which can be generally divided into three categories: 1) sinogram domain filtering [3]–[6], 2) iterative reconstruction [7]–[10], and 3) image processing [11]–[15]
Du et al [48] attempted to inject visual attention knowledge into the learning process of generative adversarial network (GAN) to provide powerful prior of the noise distribution, so the network would pay special attention to noisy regions and surrounding structures and explicitly assess the local consistency of the recovered regions. These GAN-based denoising methods can provide convincing performance, they suffer from the following drawbacks: 1) noise was transferred into the decoding blocks along with the shortcut connection from the corresponding encoding blocks, resulting in a large amount of noise remaining in the generated image, even some false lesions generated from the noise; 2) only the result from final decoding blocks of generator was sent to the discriminator, ignoring the impact of results from pervious decoding blocks on the final result, and 3) Wasserstein distance was useful for stabilizing the GAN training but not good enough for improving the image quality [49]
Summary
X-ray computed tomography (CT) plays an important role in medical imaging and has been widely used in modern clinical institutions recently. Statistical noise models [18] and prior information in the image domain, such as total variation (TV) and its variants [9], [19], as well as dictionary learning [10], [20], [21], were incorporated with the system model to optimize objective functions as the reconstructed image These algorithms improved the image quality a lot but they still suffered from losing details and remaining artifacts. Du et al [48] attempted to inject visual attention knowledge into the learning process of GAN to provide powerful prior of the noise distribution, so the network would pay special attention to noisy regions and surrounding structures and explicitly assess the local consistency of the recovered regions These GAN-based denoising methods can provide convincing performance, they suffer from the following drawbacks: 1) noise was transferred into the decoding blocks along with the shortcut connection from the corresponding encoding blocks, resulting in a large amount of noise remaining in the generated image, even some false lesions generated.
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