VLM- and LLM-Driven Multi-Agent System for PET Image Denoising
Positron emission tomography (PET) imaging suffers from limited spatial resolution and low signal-to-noise ratio, which can compromise quantitative accuracy and lesion detectability. Deep learning-based denoising methods have demonstrated strong potential for improving PET image quality. However, their practical deployment in real-world settings remains challenging, often requiring multiple specialized models and expert interventions, such as identifying motion-induced misregistration artifacts, estimating noise levels to select an appropriate denoiser, and performing lesion-focused quantitative assessment after denoising. Recent advances in vision-language models (VLMs) for image quality...
| Authors | Boxiao Yu, Savas Ozdemir, Yang Xing et al. |
|---|---|
| Primary category | eess.IV |
| Published | 13 August 2026 |
| Last updated at source | 24 August 2026 |
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