A coarse-to-fine multi-scale feature hybrid low-dose CT denoising network

With the growing development and wide clinical application of CT technology, the potential radiation damage to patients has sparked public concern. However, reducing the radiation dose may cause large amounts of noise and artifacts in the reconstructed images, which may affect the accuracy of the clinical diagnosis. Therefore, improving the quality of low-dose CT scans has become a popular research topic. Generative adversarial networks (GAN) have provided new research ideas for low-dose CT (LDCT) denoising. However, utilizing only image decomposition or adding new functional subnetworks cannot effectively fuse the same type of features with different scales (or different types of features). Thus, most current GAN-based denoising networks often suffer from low feature utilization and increased network complexity. To address these problems, we propose a coarse-to-fine multiscale feature hybrid low-dose CT denoising network (CMFHGAN). The generator consists of a global denoising module, local texture feature enhancement module, and self-calibration feature fusion module. The three modules complement each other and guarantee overall denoising performance. In addition, to further improve the denoising performance, we propose a multi-resolution inception discriminator with multiscale feature extraction ability. Experiments were performed on the Mayo and Piglet datasets, and the results showed that the proposed method outperformed the state-of-the-art denoising algorithms.

Automated summary

With the development of CT technology, reducing radiation dose in CT scans is a concern due to potential damage. A new approach, called CMFHGAN, is proposed to improve the quality of low-dose CT scans by effectively fusing features of different scales. The experiments show that CMFHGAN outperforms existing denoising algorithms.