PuSH - Publication Server of Helmholtz Zentrum München

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

Scale-equivariant deep model-based optoacoustic image reconstruction.

Photoacoustics 44:100727 (2025)
Publ. Version/Full Text Research data 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.
Altmetric
Additional Metrics?
[➜Log in]
Edit extra informations Login
Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Model-based Reconstruction ; Optoacoustic Imaging ; Regularization ; Scale-equivariance; Response Characterization Method
ISSN (print) / ISBN 2213-5979
Journal Photoacoustics
Quellenangaben Volume: 44, Issue: , Pages: , Article Number: 100727 Supplement: ,
Publisher Elsevier
Publishing Place Hackerbrucke 6, 80335 Munich, Germany
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Biological and Medical Imaging (IBMI)
Institute of Computational Biology (ICB)
Grants Bavarian Ministry of Economic Affairs, Energy and Technology (StMWi)