减少计算机断层扫描 (CT) 中的辐射剂量对于降低继发性癌症风险至关重要。然而，低剂量 CT (LDCT) 图像的使用伴随着噪声的增加，可能会对诊断产生负面影响。尽管已经开发了许多用于 LDCT 去噪的深度学习算法，但仍然存在一些挑战，包括放射科医生遇到的视觉不一致、各种指标的性能不令人满意，以及对网络在其他 CT 领域的鲁棒性探索不足。为了解决这些问题，本研究提出了三个新颖的增长点。首先，我们通过多任务学习提出了一种具有鲁棒判别器的生成对抗网络（GAN），该网络同时执行三个视觉任务：恢复、图像级和像素级决策。执行的多任务越多，生成器的去噪性能就越好，这意味着多任务学习使鉴别器能够为生成器提供更有意义的反馈。其次，引入恢复一致性（RC）和无差异抑制（NDS）两种调节机制来提高判别器的表示能力。这些机制消除了不相关的区域，并比较了判别器的输入和恢复结果，从而促进有效的 GAN 训练。最后，我们将带有卷积 (Res-FFT-Conv) 块的残差快速傅里叶变换合并到生成器中，以利用频率和空间表示。这种方法通过使用空间（或局部）、光谱（或全局）和残差连接来提供混合感受野。我们的模型在两个去噪任务中使用各种像素和特征空间指标进行了评估。此外，我们还与放射科医生一起进行了视觉评分。结果表明，与最先进的降噪技术相比，在定量和定性测量方面都具有优越的性能。

Reducing the dose of radiation in computed tomography (CT) is vital to decreasing secondary cancer risk. However, the use of low-dose CT (LDCT) images is accompanied by increased noise that can negatively impact diagnoses. Although numerous deep learning algorithms have been developed for LDCT denoising, several challenges persist, including the visual incongruence experienced by radiologists, unsatisfactory performances across various metrics, and insufficient exploration of the networks' robustness in other CT domains. To address such issues, this study proposes three novel accretions. First, we propose a generative adversarial network (GAN) with a robust discriminator through multi-task learning that simultaneously performs three vision tasks: restoration, image-level, and pixel-level decisions. The more multi-tasks that are performed, the better the denoising performance of the generator, which means multi-task learning enables the discriminator to provide more meaningful feedback to the generator. Second, two regulatory mechanisms, restoration consistency (RC) and non-difference suppression (NDS), are introduced to improve the discriminator's representation capabilities. These mechanisms eliminate irrelevant regions and compare the discriminator's results from the input and restoration, thus facilitating effective GAN training. Lastly, we incorporate residual fast Fourier transforms with convolution (Res-FFT-Conv) blocks into the generator to utilize both frequency and spatial representations. This approach provides mixed receptive fields by using spatial (or local), spectral (or global), and residual connections. Our model was evaluated using various pixel- and feature-space metrics in two denoising tasks. Additionally, we conducted visual scoring with radiologists. The results indicate superior performance in both quantitative and qualitative measures compared to state-of-the-art denoising techniques.