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MRI tractography-guided PET image reconstruction regularisation using connectome-based nonlocal means filtering.

Zhuopin Sun1, Georgios Angelis1,2, Steven Meikle3,4,5

  • 1Faculty of Engineering, School of Biomedical Engineering, The University of Sydney, Sydney, Australia.

Physics in Medicine and Biology
|June 7, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new method integrating diffusion MRI connectivity data into PET image reconstruction for improved neurodegenerative disease imaging. The technique enhances noise reduction and lesion contrast, offering more targeted regularization for PET scans.

Keywords:
PETPET-MRIanatomical priorsconnectomediffusion MRIfibre-trackingimage reconstruction

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Area of Science:

  • Neuroimaging
  • Medical Physics
  • Biomedical Engineering

Background:

  • Positron Emission Tomography (PET) and Diffusion MRI (dMRI) offer complementary insights into neurodegenerative diseases like Alzheimer's.
  • dMRI provides crucial data on brain microstructure and structural connectivity (SC).
  • Integrating dMRI information into PET reconstruction could enhance image quality but remains underexplored.

Purpose of the Study:

  • To develop and evaluate a novel method (CONN-NLM-OSLMAP) for incorporating dMRI-derived SC information into PET image reconstruction.
  • To regularize PET images using complementary connectivity data from dMRI.
  • To assess the method's effectiveness in noise reduction and contrast enhancement.

Main Methods:

  • Proposed a CONNectome-based non-local means one-step late maximum a posteriori (CONN-NLM-OSLMAP) method.
  • Integrated dMRI-derived SC information directly into the PET iterative image reconstruction process.
  • Evaluated the method using a realistic tau-PET/MRI simulated phantom.

Main Results:

  • The CONN-NLM-OSLMAP method demonstrated superior noise reduction and lesion contrast improvement compared to alternative methods.
  • Achieved the lowest overall bias in PET image reconstruction.
  • Showed more effective and targeted denoising and regularization by incorporating SC information.

Conclusions:

  • The proposed method effectively integrates dMRI structural connectivity into PET image reconstruction.
  • This integration significantly enhances PET image quality, offering improved diagnostic potential for neurodegenerative conditions.
  • Demonstrated the feasibility and effectiveness of using complementary SC information for PET image regularization.