Generative Adversarial Network With Robust Discriminator Through Multi-Task Learning for Low-Dose CT Denoising

被引:1
|
作者
Kyung, Sunggu [1 ]
Won, Jongjun [1 ]
Pak, Seongyong [1 ]
Kim, Sunwoo [2 ]
Lee, Sangyoon [2 ]
Park, Kanggil [1 ]
Hong, Gil-Sun [3 ,4 ]
Kim, Namkug [2 ]
机构
[1] Univ Ulsan, Asan Med Inst Convergence Sci & Technol, Asan Med Ctr, Dept Biomed Engn,Coll Med, Seoul 05505, South Korea
[2] Univ Ulsan, Coll Med, Asan Med Ctr, Dept Convergence Med, Seoul 05505, South Korea
[3] Univ Ulsan, Coll Med, Asan Med Ctr, Dept Radiol, Seoul 05505, South Korea
[4] Univ Ulsan, Res Inst Radiol, Coll Med, Asan Med Ctr, Seoul 05505, South Korea
关键词
Noise reduction; Computed tomography; Biomedical imaging; Generators; Task analysis; Image restoration; Multitasking; Fourier transform; generative adversarial network; low-dose CT denoising; multi-task learning; robust discriminator; COMPUTED-TOMOGRAPHY; ALGORITHMS;
D O I
10.1109/TMI.2024.3449647
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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.
引用
收藏
页码:499 / 518
页数:20
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