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Dynamic PET image reconstruction incorporating a median nonlocal means kernel method.

Shuangliang Cao1, Yuru He1, Hao Sun1

  • 1Guangdong Provincial Key Laboratory of Medical Image Processing and School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, Guangdong, 510515, China.

Computers in Biology and Medicine
|November 12, 2021
PubMed
Summary

This study introduces a median nonlocal means (MNLM)-based kernel method to reduce noise in dynamic positron emission tomography (PET) imaging. The MNLM method significantly improves image accuracy and clarity, especially in low-count frames.

Keywords:
Kernel methodMedianNonlocal meansPositron emission tomographyReconstruction

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Area of Science:

  • Medical Imaging
  • Nuclear Medicine
  • Image Reconstruction

Background:

  • Dynamic positron emission tomography (PET) imaging often suffers from high noise in single frames due to limited projection data statistics.
  • This noise degrades image quality and complicates accurate quantitative analysis.
  • Existing reconstruction methods struggle to effectively balance noise reduction and preservation of image details in low-count scenarios.

Purpose of the Study:

  • To develop and evaluate a novel median nonlocal means (MNLM)-based kernel method for dynamic PET image reconstruction.
  • To assess the performance of the MNLM kernel method against established techniques using simulated and real low-count PET data.
  • To demonstrate improvements in quantitative accuracy and visual quality for dynamic PET imaging.

Main Methods:

  • A kernel matrix was derived using median nonlocal means (MNLM) on pre-reconstructed composite images.
  • PET image intensities were modeled using this kernel matrix within the forward model of PET projection data.
  • Coefficients were estimated via maximum likelihood, and performance was compared against MLEM, conventional kernel methods, and NLM kernel methods using simulated Zubal head phantom data and real 18F-FDG rat data.

Main Results:

  • The MNLM kernel method demonstrated superior visual and quantitative accuracy compared to other methods.
  • It achieved a lower ensemble mean squared error (10.43%) in low-count frames (frame 2) than NLM kernel (13.68%), conventional kernel methods (11.88%, 23.50%), and MLEM (24.77%).
  • Analysis of real low-dose 18F-FDG rat data confirmed significant improvements in noise versus intensity mean performance.

Conclusions:

  • The proposed MNLM-based kernel method is effective in reducing noise and enhancing image quality in dynamic PET imaging.
  • It offers significant advantages over conventional and NLM kernel methods, particularly under low counting statistics.
  • This method holds promise for improving the diagnostic utility of dynamic PET scans in clinical and research settings.