Cross-Domain Low-Dose CT Image Denoising With Semantic Preservation and Noise Alignment

Jiaxin Huang, Kecheng Chen, Yazhou Ren, Jiayu Sun, Xiaorong Pu, Ce Zhu

Abstract: Deep learning (DL)-based Low-dose CT (LDCT) image denoising methods may face domain shift problem, where data from different domains (i.e., hospitals) may have similar anatomical regions but exhibit different intrinsic noise characteristics. Therefore, we propose a plug-and-play model called Low- and High-frequency Alignment (LHFA) to address this issue by leveraging semantic features and aligning noise distributions of different CT datasets, while maintaining diagnostic image quality and suppressing noise. Specifically, the LHFA model consists of a Low-frequency Alignment (LFA) module that preserves semantic features (i.e., low-frequency components) with fewer perturbations from both domains for reconstruction. Notably, a High-frequency Alignment (HFA) module is proposed to quantify the discrepancy between noise representations (i.e., high-frequency components) in a latent space mapped by an auto-encoder. Experimental results demonstrate that the LHFA model effectively alleviates the domain shift problem and significantly improves the performance of DL-based methods on cross-domain LDCT image denoising task, outperforming other domain adaptation-based methods.