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PET Image Denoising via Text-Guided Diffusion: Integrating Anatomical Priors through Text Prompts by Yu, Boxiao; Ozdemir, Savas; Wu, Jiong; Chen, Yizhou; Fang, Ruogu; Shi, Kuangyu; Gong, Kuang is a scholarly article available to read on EtoBox.

What is PET Image Denoising via Text-Guided Diffusion: Integrating Anatomical Priors through Text Prompts about?

Low-dose Positron Emission Tomography (PET) imaging presents a significant challenge due to increased noise and reduced image quality, which can compromise its diagnostic accuracy and clinical utility. Denoising diffusion probabilistic models (DDPMs) have demonstrated promising performance for PET image denoising. However, existing DDPM-based methods typically overlook valuable metadata such as patient demographics, anatomical information, and scanning parameters, which should further enhance the denoising performance if considered. Recent advances in vision-language models (VLMs), particularly the pre-trained Contrastive Language-Image Pre-training (CLIP) model, have highlighted the potential of incorporating text-based information into visual tasks to improve downstream performance. In this preliminary study, we proposed a novel text-guided DDPM for PET image denoising that integrated anatomical priors through text prompts. Anatomical text descriptions were encoded using a pre-trained CLIP text encoder to extract semantic guidance, which was then incorporated into the diffusion process via the cross-attention mechanism. Evaluations based on paired 1/20 low-dose and normal-dose 18

Author

Yu, Boxiao; Ozdemir, Savas; Wu, Jiong; Chen, Yizhou; Fang, Ruogu; Shi, Kuangyu; Gong, Kuang

Published

2025

Language

EN