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Enabling the autofocus approach for parameter optimization in planar measurement geometry clinical optoacoustic imaging.

J. Biophotonics 15:e202200032 (2022)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
In optoacoustic (photoacoustic) tomography, several parameters related to tissue and detector features are needed for image formation, but they may not be known a priori. An autofocus (AF) algorithm is generally used to estimate these parameters. However, the algorithm works iteratively and is therefore impractical for clinical imaging with planar geometry systems due to the long reconstruction times. We have developed a fast autofocus (FAF) algorithm for 3D optoacoustic systems with planar geometry. Such an algorithm exploits the symmetries of the planar geometry and a virtual source concept to reduce the dimensionality of the parameter estimation problem. The dimensionality reduction makes FAF much simpler computationally than the conventional AF algorithm. We show that the FAF algorithm required about 5 sec to provide accurate estimates of the speed of sound in simulated data and experimental data obtained using an imaging system that is poised to enter the clinic. The applicability of FAF for estimating other image formation parameters is discussed. We expect the FAF algorithm to contribute decisively to the clinical use of optoacoustic tomography systems with planar geometry.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Autofocus ; Image Formation Algorithm ; Optoacoustics ; Photoacoustics ; Speed Of Sound
Sprache englisch
Veröffentlichungsjahr 2022
HGF-Berichtsjahr 2022
ISSN (print) / ISBN 1864-063X
e-ISSN 1864-0648
Quellenangaben Band: 15, Heft: 9, Seiten: , Artikelnummer: e202200032 Supplement: ,
Verlag Wiley
Begutachtungsstatus Peer reviewed
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-505500-001
Förderungen Agency European Commission
German Society of Dermatology
PubMed ID 35599314
Erfassungsdatum 2022-09-21