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Dehner, C. ; Lilaj, L.* ; Ntziachristos, V. ; Zahnd, G. ; Jüstel, D.

Scale-equivariant deep model-based optoacoustic image reconstruction.

Photoacoustics 44:100727 (2025)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Model-based reconstruction provides state-of-the-art image quality for multispectral optoacoustic tomography. However, optimal regularization of in vivo data necessitates scan-specific adjustments of the regularization strength to compensate for fluctuations of the signal magnitudes between different sinograms. Magnitude fluctuations within in vivo data also pose a challenge for supervised deep learning of a model-based reconstruction operator, as training data must cover the complete range of expected signal magnitudes. In this work, we derive a scale-equivariant model-based reconstruction operator that i) automatically adjusts the regularization strength based on the L2 norm of the input sinogram, and ii) facilitates supervised deep learning of the operator using input singorams with a fixed norm. Scale-equivariant model-based reconstruction applies appropriate regularization to sinograms of arbitrary magnitude, achieves slightly better accuracy in quantifying blood oxygen saturation, and enables more accurate supervised deep learning of the operator.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Model-based Reconstruction ; Optoacoustic Imaging ; Regularization ; Scale-equivariance; Response Characterization Method
Sprache englisch
Veröffentlichungsjahr 2025
HGF-Berichtsjahr 2025
ISSN (print) / ISBN 2213-5979
Zeitschrift Photoacoustics
Quellenangaben Band: 44, Heft: , Seiten: , Artikelnummer: 100727 Supplement: ,
Verlag Elsevier
Verlagsort Hackerbrucke 6, 80335 Munich, Germany
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Biological and Medical Imaging (IBMI)
Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-505500-001
G-503800-001
Förderungen Bavarian Ministry of Economic Affairs, Energy and Technology (StMWi)
DOI 10.1016/j.pacs.2025.100727
WOS ID 001497539300001
Scopus ID 105005168781
PubMed ID 40487237
Erfassungsdatum 2025-05-27
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