Related Experiment Video
Updated: Jan 18, 2026
Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping
Published on: June 13, 2025
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.
Abstract:
Positron emission tomography (PET) molecular biomarkers and diffusion magnetic resonance imaging (dMRI) derived information show associations and highly complementary information in a number of neurodegenerative conditions, including Alzheimer's disease. Diffusion MRI provides valuable information about the microstructure and structural connectivity (SC) of the brain which could guide and improve the PET image reconstruction when such associations exist. However, this potental has not been previously explored. In the present study, we propose a CONNectome-based non-local means one-atep late maximuma posteriori(CONN-NLM-OSLMAP) method, which allows diffusion MRI-derived connectivity information to be incorporated into the PET iterative image reconstruction process, thus regularising the estimated PET images. The proposed method was evaluated using a realistic tau-PET/MRI simulated phantom, demonstrating more effective noise reduction and lesion contrast improvement, as well as the lowest overall bias compared with both a median filter applied as an alternative regulariser and CONNectome-based non-local means as a post-reconstruction filter. By adding complementary SC information from diffusion MRI, the proposed regularisation method offers more useful and targeted denoising and regularisation, demonstrating the feasibility and effectiveness of integrating connectivity information into PET image reconstruction.
Insights
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.
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.
