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Unpaired Low-Dose CT Denoising Network Based on Cycle-Consistent Generative Adversarial Network with Prior Image Information

机译:基于循环一致的生成对冲网络的未成对低剂量CT去噪网络,具有先前的图像信息

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摘要

The widespread application of X-ray computed tomography (CT) in clinical diagnosis has led to increasing public concern regarding excessive radiation dose administered to patients. However, reducing the radiation dose will inevitably cause server noise and affect radiologists’ judgment and confidence. Hence, progressive low-dose CT (LDCT) image reconstruction methods must be developed to improve image quality. Over the past two years, deep learning-based approaches have shown impressive performance in noise reduction for LDCT images. Most existing deep learning-based approaches usually require the paired training dataset which the LDCT images correspond to the normal-dose CT (NDCT) images one-to-one, but the acquisition of well-paired datasets requires multiple scans, resulting the increase of radiation dose. Therefore, well-paired datasets are not readily available. To resolve this problem, this paper proposes an unpaired LDCT image denoising network based on cycle generative adversarial networks (CycleGAN) with prior image information which does not require a one-to-one training dataset. In this method, cyclic loss, an important trick in unpaired image-to-image translation, promises to map the distribution from LDCT to NDCT by using unpaired training data. Furthermore, to guarantee the accurate correspondence of the image content between the output and NDCT, the prior information obtained from the result preprocessed using the LDCT image is integrated into the network to supervise the generation of content. Given the map of distribution through the cyclic loss and the supervision of content through the prior image loss, our proposed method can not only reduce the image noise but also retain critical information. Real-data experiments were carried out to test the performance of the proposed method. The peak signal-to-noise ratio (PSNR) improves by more than 3 dB, and the structural similarity (SSIM) increases when compared with the original CycleGAN without prior information. The real LDCT data experiment demonstrates the superiority of the proposed method according to both visual inspection and quantitative evaluation.
机译:X射线计算机断层扫描(CT)在临床诊断中的广泛应用导致对患者施用过量的辐射剂量的公众关注。然而,降低辐射剂量将不可避免地引起服务器噪声并影响放射科医师的判断和信心。因此,必须开发渐进低剂量CT(LDCT)图像重建方法以提高图像质量。在过去两年中,基于深度学习的方法对LDCT图像的降噪表现令人印象深刻。基于最现有的深度学习的方法通常需要LDCT图像对应于正常剂量CT(NDCT)图像的配对训练数据集,但是对良好的配对数据集采集需要多次扫描,从而增加辐射剂量。因此,配对良好的数据集不容易获得。为了解决这个问题,本文提出了一种基于周期生成的对冲网络(Cyclegan)的未配对的LDCT图像去噪网络,其具有不需要一对一训练数据集的先前图像信息。在这种方法中,循环损失,是未配对的图像到图像转换中的重要技巧,有望通过使用未配对的训练数据将分布从LDCT映射到NDCT。此外,为了保证输出和NDCT之间的图像内容的准确对应关系,从使用LDCT图像预处理的结果获得的先前信息被集成到网络中以监督内容的产生。鉴于通过循环丢失和通过先前的图像丢失的分布的地图,我们提出的方法不仅可以降低图像噪声,还可以保留关键信息。进行真实数据实验以测试所提出的方法的性能。峰值信噪比(PSNR)提高了3dB以上,并且与未经事先信息的原始Conscargan相比,结构相似性(SSIM)增加。真实的LDCT数据实验证明了根据目视检查和定量评估的提出方法的优越性。

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