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HALO: High-frequency enhanced dose-aware diffusion model for arbitrary low-dose PET reconstruction.

Zixin Tang1, Caiwen Jiang1, Kaicong Sun1

  • 1School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials & Devices, ShanghaiTech University, Shanghai, 201210, China.

Medical Image Analysis
|November 20, 2025
PubMed
Summary

We developed HALO, a novel framework for low-dose PET reconstruction. It reconstructs standard-dose PET images from low-dose scans, reducing radiation exposure while preserving image details.

Keywords:
Arbitrary low-dose PET reconstructionDose-aware diffusion modelHigh-frequency residual

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Area of Science:

  • Medical Imaging
  • Radiological Physics
  • Computational Imaging

Background:

  • Positron Emission Tomography (PET) imaging often requires standard doses, leading to radiation exposure concerns.
  • Reconstructing high-quality PET images from low-dose scans is challenging due to inherent noise and loss of high-frequency details.
  • Individual effective dose in PET varies due to factors like body composition and scanner protocols, complicating dose standardization.

Purpose of the Study:

  • To develop a novel framework, HALO (High-frequency enhanced dose-Aware framework for LOw-dose PET reconstruction), for reconstructing standard-dose PET images from arbitrary low-dose inputs.
  • To address challenges of radiation dose reduction and preservation of high-frequency details in PET imaging.
  • To create a dose-aware reconstruction method that adapts to varying effective doses in PET scans.

Main Methods:

  • Proposed a High-frequency enhanced dose-Aware framework for LOw-dose PET reconstruction (HALO).
  • Introduced a Dose Adaptation module to estimate and integrate effective dose information into the reconstruction process.
  • Employed a high-frequency residual generation strategy using a pre-trained CNN and a diffusion model, incorporating a Frequency Information Separator (FIS) and High-Frequency Modulator (HFM).

Main Results:

  • HALO successfully reconstructs standard-dose PET images from low-dose inputs.
  • The Dose Adaptation module effectively captures and utilizes effective dose information.
  • The high-frequency residual generation strategy, with FIS and HFM, significantly enhances image details.
  • Quantitative and qualitative experiments on a public dataset show HALO outperforms existing state-of-the-art methods.

Conclusions:

  • HALO offers a promising solution for low-dose PET reconstruction, balancing radiation dose reduction with image quality preservation.
  • The framework's dose-aware and high-frequency enhancement capabilities make it suitable for clinical applications.
  • This approach has the potential to improve patient safety and diagnostic accuracy in PET imaging.