Liao Peixi, Zhang Xucan, Wu Yaoyao, Chen Hu, Du Wenchao, Liu Hong, Yang Hongyu, Zhang Yi
Department of Stomatology, The Sixth People's Hospital of Chengdu, Chengdu, China.
The National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu, China.
Quant Imaging Med Surg. 2024 Aug 1;14(8):5571-5590. doi: 10.21037/qims-24-68. Epub 2024 Jul 26.
Low-dose computed tomography (LDCT) is a diagnostic imaging technique designed to minimize radiation exposure to the patient. However, this reduction in radiation may compromise computed tomography (CT) image quality, adversely impacting clinical diagnoses. Various advanced LDCT methods have emerged to mitigate this challenge, relying on well-matched LDCT and normal-dose CT (NDCT) image pairs for training. Nevertheless, these methods often face difficulties in distinguishing image details from nonuniformly distributed noise, limiting their denoising efficacy. Additionally, acquiring suitably paired datasets in the medical domain poses challenges, further constraining their applicability. Hence, the objective of this study was to develop an innovative denoising framework for LDCT images employing unpaired data.
In this paper, we propose a LDCT denoising network (DNCNN) that alleviates the need for aligning LDCT and NDCT images. Our approach employs generative adversarial networks (GANs) to learn and model the noise present in LDCT images, establishing a mapping from the pseudo-LDCT to the actual NDCT domain without the need for paired CT images.
Within the domain of weakly supervised methods, our proposed model exhibited superior objective metrics on the simulated dataset when compared to CycleGAN and selective kernel-based cycle-consistent GAN (SKFCycleGAN): the peak signal-to-noise ratio (PSNR) was 43.9441, the structural similarity index measure (SSIM) was 0.9660, and the visual information fidelity (VIF) was 0.7707. In the clinical dataset, we conducted a visual effect analysis by observing various tissues through different observation windows. Our proposed method achieved a no-reference structural sharpness (NRSS) value of 0.6171, which was closest to that of the NDCT images (NRSS =0.6049), demonstrating its superiority over other denoising techniques in preserving details, maintaining structural integrity, and enhancing edge contrast.
Through extensive experiments on both simulated and clinical datasets, we demonstrated the superior efficacy of our proposed method in terms of denoising quality and quantity. Our method exhibits superiority over both supervised techniques, including block-matching and 3D filtering (BM3D), residual encoder-decoder convolutional neural network (RED-CNN), and Wasserstein generative adversarial network-VGG (WGAN-VGG), and over weakly supervised approaches, including CycleGAN and SKFCycleGAN.
低剂量计算机断层扫描（LDCT）是一种旨在将患者辐射暴露降至最低的诊断成像技术。然而，这种辐射剂量的降低可能会损害计算机断层扫描（CT）图像质量，对临床诊断产生不利影响。为应对这一挑战，出现了各种先进的LDCT方法，这些方法依靠匹配良好的LDCT和正常剂量CT（NDCT）图像对进行训练。然而，这些方法在从分布不均匀的噪声中区分图像细节时往往面临困难，限制了它们的去噪效果。此外，在医学领域获取合适的配对数据集也存在挑战，进一步限制了它们的适用性。因此，本研究的目的是开发一种使用未配对数据的创新型LDCT图像去噪框架。
在本文中，我们提出了一种LDCT去噪网络（DNCNN），该网络无需对齐LDCT和NDCT图像。我们的方法采用生成对抗网络（GAN）来学习和建模LDCT图像中存在的噪声，在无需配对CT图像的情况下建立从伪LDCT到实际NDCT域的映射。
在弱监督方法领域，与CycleGAN和基于选择性内核的循环一致GAN（SKFCycleGAN）相比，我们提出的模型在模拟数据集上表现出更好的客观指标：峰值信噪比（PSNR）为43.9441，结构相似性指数测量（SSIM）为0.9660，视觉信息保真度（VIF）为0.7707。在临床数据集中，我们通过不同观察窗口观察各种组织进行了视觉效果分析。我们提出的方法实现了无参考结构清晰度（NRSS）值为0.6171，最接近NDCT图像的NRSS值（NRSS = 0.6049），表明其在保留细节、保持结构完整性和增强边缘对比度方面优于其他去噪技术。
通过在模拟和临床数据集上进行的大量实验，我们证明了我们提出的方法在去噪质量和数量方面具有卓越的效果。我们的方法在包括块匹配和3D滤波（BM3D）、残差编码器 - 解码器卷积神经网络（RED - CNN）以及瓦瑟斯坦生成对抗网络 - VGG（WGAN - VGG）等监督技术，以及包括CycleGAN和SKFCycleGAN等弱监督方法方面均表现出优越性。
