Low-cost, smartphone-based instant three-dimensional registration system for infant functional near-infrared spectroscopy applications.

diffuse optical tomography functional near-infrared spectroscopy infant spatial registration three-dimensional scan

Journal

Neurophotonics
ISSN: 2329-423X
Titre abrégé: Neurophotonics
Pays: United States
ID NLM: 101632875

Informations de publication

Date de publication:
Oct 2023
Historique:
received: 06 03 2023
revised: 05 10 2023
accepted: 06 10 2023
medline: 25 10 2023
pubmed: 25 10 2023
entrez: 25 10 2023
Statut: ppublish

Résumé

To effectively apply functional near-infrared spectroscopy (fNIRS)/diffuse optical tomography (DOT) devices, a three-dimensional (3D) model of the position of each optode on a subject's scalp and the positions of that subject's cranial landmarks are critical. Obtaining this information accurately in infants, who rarely stop moving, is an ongoing challenge. We propose a smartphone-based registration system that can potentially achieve a full-head 3D scan of a 6-month-old infant instantly. The proposed system is remotely controlled by a custom-designed Bluetooth controller. The scanned images can either be manually or automatically aligned to generate a 3D head surface model. A full-head 3D scan of a 6-month-old infant can be achieved within 2 s via this system. In testing on a realistic but static infant head model, the average Euclidean error of optode position using this device was 1.8 mm. This low-cost 3D registration system therefore has the potential to permit accurate and near-instant fNIRS/DOT spatial registration.

Identifiants

pubmed: 37876984
doi: 10.1117/1.NPh.10.4.046601
pii: 23013TNRR
pmc: PMC10593123
doi:

Types de publication

Journal Article

Langues

eng

Pagination

046601

Informations de copyright

© 2023 The Authors.

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Auteurs

Yunjia Xia (Y)

University College London, HUB of Intelligent Neuro-Engineering, CREATe, Division of Surgery and Interventional Science, Stanmore, United Kingdom.
University College London, DOT-HUB, Department of Medical Physics and Biomedical Engineering, London, United Kingdom.

Kui Wang (K)

University College London, HUB of Intelligent Neuro-Engineering, CREATe, Division of Surgery and Interventional Science, Stanmore, United Kingdom.
University College London, DOT-HUB, Department of Medical Physics and Biomedical Engineering, London, United Kingdom.

Addison Billing (A)

University College London, DOT-HUB, Department of Medical Physics and Biomedical Engineering, London, United Kingdom.
University of Cambridge, Prediction and Learning Laboratory, Department of Psychology, Cambridge, United Kingdom.

Matthew Billing (M)

London South Bank University, School of Engineering, London, United Kingdom.

Robert J Cooper (RJ)

University College London, DOT-HUB, Department of Medical Physics and Biomedical Engineering, London, United Kingdom.

Hubin Zhao (H)

University College London, HUB of Intelligent Neuro-Engineering, CREATe, Division of Surgery and Interventional Science, Stanmore, United Kingdom.
University College London, DOT-HUB, Department of Medical Physics and Biomedical Engineering, London, United Kingdom.

Classifications MeSH