Optimizing the precision of laser speckle contrast imaging.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
20 10 2023
Historique:
received: 10 07 2023
accepted: 18 10 2023
medline: 31 10 2023
pubmed: 21 10 2023
entrez: 20 10 2023
Statut: epublish

Résumé

Laser speckle contrast imaging (LSCI) is a rapidly developing technology broadly applied for the full-field characterization of tissue perfusion. Over the recent years, significant advancements have been made in interpreting LSCI measurements and improving the technique's accuracy. On the other hand, the method's precision has yet to be studied in detail, despite being as important as accuracy for many biomedical applications. Here we combine simulation, theory and animal experiments to systematically evaluate and re-analyze the role of key factors defining LSCI precision-speckle-to-pixel size ratio, polarisation, exposure time and camera-related noise. We show that contrary to the established assumptions, smaller speckle size and shorter exposure time can improve the precision, while the camera choice is less critical and does not affect the signal-to-noise ratio significantly.

Identifiants

pubmed: 37864006
doi: 10.1038/s41598-023-45303-z
pii: 10.1038/s41598-023-45303-z
pmc: PMC10589309
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

17970

Informations de copyright

© 2023. Springer Nature Limited.

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Auteurs

Alberto González Olmos (A)

Department of Clinical Medicine, Aarhus University, 8200, Aarhus, Denmark.

Sharvari Zilpelwar (S)

Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA.

Smrithi Sunil (S)

Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA.

David A Boas (DA)

Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA.

Dmitry D Postnov (DD)

Department of Clinical Medicine, Aarhus University, 8200, Aarhus, Denmark. dpostnov@cfin.au.dk.

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Classifications MeSH