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Updated: Sep 8, 2025

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DUAL PROMPTING FOR DIVERSE COUNT-LEVEL PET DENOISING.

Xiaofeng Liu1,2, Yongsong Huang1, Thibault Marin1,2

  • 1Dept. of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|August 20, 2025
PubMed
Summary

This study introduces dual prompts for positron emission tomography (PET) denoising, improving performance across various noise levels. The novel approach enables a unified model for diverse PET imaging cases.

Keywords:
Count LevelImage DenoisingPositron Emission TomographyPrompt Learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Signal Processing

Background:

  • Positron emission tomography (PET) imaging is challenged by diverse count levels in acquired volumes, impacting denoising model generalization.
  • Existing denoising methods struggle to effectively handle the wide range of noise variations inherent in PET data.
  • Unified models for PET denoising are needed to address varying count statistics without compromising image quality.

Purpose of the Study:

  • To develop a generalizable PET denoising method using prompt learning that accommodates diverse count levels.
  • To propose a novel dual-prompt strategy for guiding PET denoising in a unified model.
  • To enhance the adaptability and performance of PET denoising models for varied clinical applications.

Main Methods:

  • Employed prompt learning with explicitly count-level and implicitly general denoising prompts.
  • Developed a prompt fusion module to integrate heterogeneous prompts.
  • Implemented a prompt-feature interaction module to dynamically guide noise-conditioned denoising.
  • Utilized a dataset of 1940 low-count 3D PET volumes from 97 18F-MK6240 tau PET studies.

Main Results:

  • The dual-prompting approach significantly improved PET denoising performance compared to count-conditional models.
  • The unified model demonstrated effective generalization across different PET count levels.
  • Personalized prompts allowed for tailored denoising based on specific PET study characteristics.

Conclusions:

  • Dual prompting offers an effective strategy for generalizable PET denoising across varied count levels.
  • The proposed prompt learning framework enables efficient training and deployment of unified PET denoising models.
  • This method advances the application of AI in medical imaging by improving PET image quality and consistency.