Mapping the architecture of the temporal artery with photoacoustic imaging for diagnosing giant cell arteritis.

Giant cell arteritis Noninvasive, clinical diagnosis Photoacoustic imaging Unsupervised spectral analysis

Journal

Photoacoustics
ISSN: 2213-5979
Titre abrégé: Photoacoustics
Pays: Germany
ID NLM: 101622604

Informations de publication

Date de publication:
Sep 2022
Historique:
received: 15 01 2022
revised: 02 05 2022
accepted: 01 07 2022
entrez: 7 9 2022
pubmed: 8 9 2022
medline: 8 9 2022
Statut: epublish

Résumé

Photoacoustic (PA) imaging is rapidly emerging as a promising clinical diagnostic tool. One of the main applications of PA imaging is to image vascular networks in humans. This relies on the signal obtained from oxygenated and deoxygenated hemoglobin, which limits imaging of the vessel wall itself. Giant cell arteritis (GCA) is a treatable, but potentially sight- and life-threatening disease, in which the artery wall is infiltrated by leukocytes. Early intervention can prevent complications making prompt diagnosis of importance. Temporal artery biopsy is the gold standard for diagnosing GCA. We present an approach to imaging the temporal artery using multispectral PA imaging. Employing minimally supervised spectral analysis, we produce histology-like images where the artery wall is clearly discernible from the lumen and further differentiate between PA spectra from biopsies diagnosed as GCA- and GCA+ in 77 patients.

Identifiants

pubmed: 36068803
doi: 10.1016/j.pacs.2022.100384
pii: S2213-5979(22)00049-0
pmc: PMC9441260
doi:

Types de publication

Journal Article

Langues

eng

Pagination

100384

Informations de copyright

© 2022 The Authors.

Déclaration de conflit d'intérêts

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Auteurs

Magdalena Naumovska (M)

Department of Clinical Sciences Lund, Ophthalmology, Lund University, Skåne University Hospital, Lund, Sweden.

Aboma Merdasa (A)

Department of Clinical Sciences Lund, Ophthalmology, Lund University, Skåne University Hospital, Lund, Sweden.

Björn Hammar (B)

Department of Clinical Sciences Lund, Ophthalmology, Lund University, Skåne University Hospital, Lund, Sweden.

John Albinsson (J)

Department of Clinical Sciences Lund, Ophthalmology, Lund University, Skåne University Hospital, Lund, Sweden.

Ulf Dahlstrand (U)

Department of Clinical Sciences Lund, Ophthalmology, Lund University, Skåne University Hospital, Lund, Sweden.

Magnus Cinthio (M)

Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden.

Rafi Sheikh (R)

Department of Clinical Sciences Lund, Ophthalmology, Lund University, Skåne University Hospital, Lund, Sweden.

Malin Malmsjö (M)

Department of Clinical Sciences Lund, Ophthalmology, Lund University, Skåne University Hospital, Lund, Sweden.

Classifications MeSH