Low-dose CT Denoising with Language-engaged Dual-space Alignment
Zhihao Chen Tao Chen Chenhui Wang Chuang Niu Ge Wang Hongming Shan
While various deep learning methods were proposed for low-dose computed
tomography (CT) denoising, they often suffer from over-smoothing, blurring, and
lack of explainability. To alleviate these issues, we propose a plug-and-play
Language-Engaged Dual-space Alignment loss (LEDA) to optimize low-dose CT
denoising models. Our idea is to leverage large language models (LLMs) to align
denoised CT and normal dose CT images in both the continuous perceptual space
and discrete semantic space, which is the first LLM-based scheme for low-dose
CT denoising. LEDA involves two steps: the first is to pretrain an LLM-guided
CT autoencoder, which can encode a CT image into continuous high-level features
and quantize them into a token space to produce semantic tokens derived from
the LLM's vocabulary; and the second is to minimize the discrepancy between the
denoised CT images and normal dose CT in terms of both encoded high-level
features and quantized token embeddings derived by the LLM-guided CT
autoencoder. Extensive experimental results on two public LDCT denoising
datasets demonstrate that our LEDA can enhance existing denoising models in
terms of quantitative metrics and qualitative evaluation, and also provide
explainability through language-level image understanding. Source code is
available at https://github.com/hao1635/LEDA.