Scale-equivariant deep model-based optoacoustic image reconstruction
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 differen...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Photoacoustics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213597925000503 |
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| _version_ | 1849419612165242880 |
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| author | Christoph Dehner Ledia Lilaj Vasilis Ntziachristos Guillaume Zahnd Dominik Jüstel |
| author_facet | Christoph Dehner Ledia Lilaj Vasilis Ntziachristos Guillaume Zahnd Dominik Jüstel |
| author_sort | Christoph Dehner |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e1867903d308445fbd5d45b6c4ba3a17 |
| institution | Kabale University |
| issn | 2213-5979 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Photoacoustics |
| spelling | doaj-art-e1867903d308445fbd5d45b6c4ba3a172025-08-20T03:32:01ZengElsevierPhotoacoustics2213-59792025-08-014410072710.1016/j.pacs.2025.100727Scale-equivariant deep model-based optoacoustic image reconstructionChristoph Dehner0Ledia Lilaj1Vasilis Ntziachristos2Guillaume Zahnd3Dominik Jüstel4iThera Medical GmbH, Munich, Germany; Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, GermanyiThera Medical GmbH, Munich, GermanyInstitute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany; Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), School of Medicine and Health, Technical University of Munich, Munich, Germany; Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich, Munich, GermanyiThera Medical GmbH, Munich, Germany; Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, GermanyInstitute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany; Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), School of Medicine and Health, Technical University of Munich, Munich, Germany; Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany; Corresponding author at: Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), School of Medicine and Health, Technical University of Munich, Munich, Germany.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.http://www.sciencedirect.com/science/article/pii/S2213597925000503Optoacoustic imagingModel-based reconstructionRegularizationScale-equivariance |
| spellingShingle | Christoph Dehner Ledia Lilaj Vasilis Ntziachristos Guillaume Zahnd Dominik Jüstel Scale-equivariant deep model-based optoacoustic image reconstruction Photoacoustics Optoacoustic imaging Model-based reconstruction Regularization Scale-equivariance |
| title | Scale-equivariant deep model-based optoacoustic image reconstruction |
| title_full | Scale-equivariant deep model-based optoacoustic image reconstruction |
| title_fullStr | Scale-equivariant deep model-based optoacoustic image reconstruction |
| title_full_unstemmed | Scale-equivariant deep model-based optoacoustic image reconstruction |
| title_short | Scale-equivariant deep model-based optoacoustic image reconstruction |
| title_sort | scale equivariant deep model based optoacoustic image reconstruction |
| topic | Optoacoustic imaging Model-based reconstruction Regularization Scale-equivariance |
| url | http://www.sciencedirect.com/science/article/pii/S2213597925000503 |
| work_keys_str_mv | AT christophdehner scaleequivariantdeepmodelbasedoptoacousticimagereconstruction AT ledialilaj scaleequivariantdeepmodelbasedoptoacousticimagereconstruction AT vasilisntziachristos scaleequivariantdeepmodelbasedoptoacousticimagereconstruction AT guillaumezahnd scaleequivariantdeepmodelbasedoptoacousticimagereconstruction AT dominikjustel scaleequivariantdeepmodelbasedoptoacousticimagereconstruction |