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


Journal

Physics in medicine and biology
ISSN: 1361-6560
Titre abrégé: Phys Med Biol
Pays: England
ID NLM: 0401220

Informations de publication

Date de publication:
30 Jun 2023
Historique:
received: 18 10 2022
accepted: 07 06 2023
medline: 3 7 2023
pubmed: 8 6 2023
entrez: 7 6 2023
Statut: epublish

Résumé

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 maximum

Identifiants

pubmed: 37285850
doi: 10.1088/1361-6560/acdc7f
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Creative Commons Attribution license.

Auteurs

Zhuopin Sun (Z)

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

Georgios Angelis (G)

Faculty of Engineering, School of Biomedical Engineering, The University of Sydney, Sydney, Australia.
Australian National Imaging Facility, The University of Sydney, Sydney, Australia.

Steven Meikle (S)

Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.
Brain and Mind Centre, The University of Sydney, Sydney, Australia.
Sydney Imaging, The University of Sydney, Sydney, Australia.

Fernando Calamante (F)

Faculty of Engineering, School of Biomedical Engineering, The University of Sydney, Sydney, Australia.
Brain and Mind Centre, The University of Sydney, Sydney, Australia.
Sydney Imaging, The University of Sydney, Sydney, Australia.

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Classifications MeSH