Unsupervised Denoising of Real Clinical Low Dose Liver CT with Perceptual Attention Networks
It addresses the practical bottleneck of denoising real clinical low-dose CT images where supervised learning is infeasible due to lack of paired clean/noisy data.
This paper proposes an unsupervised denoising framework for low-dose liver CT using a Cycle-GAN-inspired approach combining U-Net, attention, and residual networks with perceptual loss, achieving clinically acceptable denoising performance on real clinical data without paired training samples.
With the development of deep learning, medical image processing has been widely used to assist clinical research. This paper focuses on the denoising problem of low-dose computed tomography using deep learning. Although low-dose computed tomography reduces radiation exposure to patients, it also introduces more noise, which may interfere with visual interpretation by physicians and affect diagnostic results. To address this problem, inspired by Cycle-GAN for unsupervised learning, this paper proposes an end-to-end unsupervised low-dose computed tomography denoising framework. The proposed framework combines a U-Net structure for multi-scale feature extraction, an attention mechanism for feature fusion, and a residual network for feature transformation. It also introduces perceptual loss to improve the network for the characteristics of medical images. In addition, we construct a real low-dose computed tomography dataset and design a large number of comparative experiments to validate the proposed method, using both image-based evaluation metrics and medical evaluation criteria. Compared with classical methods, the main advantage of this paper is that it addresses the limitation that real clinical data cannot be directly used for supervised learning, while still achieving excellent performance. The experimental results are also professionally evaluated by imaging physicians and meet clinical needs.