MGTP: Multi-Granularity Textual Prompts for Low-Dose Brain PET Image Denoising via Adversarial Diffusion Model

Positron emission tomography (PET) is an advanced nuclear imaging technique and has been widely applied in clinic. However, radiation risks associated with standard-dose PET imaging raise health concerns, whereas the quality of low-dose PET images fails to meet clinical requirements. To reduce the tracer dose while maintaining image quality, it is of great interest to estimate high-quality PET images from low-dose images. However, existing low-dose PET image denoising methods primarily focus on image data, overlooking crucial information in non-image textual data such as patients' clinical tabular and textual descriptions of general image quality. This neglect can lead to subpar denoising quality with inaccurate contexts and poor details. To address these problems, in this paper, we propose Multi-Granularity Textual Prompts, namely MGTP, to denoise low-dose PET images via an adversarial diffusion model. Different from prior methods that rely solely on image conditioning, our MGTP innovatively introduces textual prompts spanning diverse granularities to capture both high-level semantic-related contexts and low-level degradation-related details. To harmonize multi-granularity textual prompts with low-dose PET images, we design a Cross-Modality Selective Conditioning (CMSC) module, which prioritizes semantic- and detail-relevant information while eliminating irrelevant components. The resulting features are fed into diffusion model as conditions, enforcing a more controlled diffusion process. In addition, we develop a Masked Prompt Reconstruction Network (MPR-Net) to enhance the preservation of semantics and details in denoised images, mitigating distortions brought by the random noise in the diffusion process. Experiments on clinical PET data show that our method achieves the state-of-the-art performance.