Deep Image Prior for photoacoustic tomography can mitigate limited-view artifacts
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908983135043584 |
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| author | Pulkkinen, Hanna Poimala, Jenni Kunyansky, Leonid Gröhl, Janek Hauptmann, Andreas |
| author_facet | Pulkkinen, Hanna Poimala, Jenni Kunyansky, Leonid Gröhl, Janek Hauptmann, Andreas |
| contents | We study the deep image prior (DIP) framework applied to photoacoustic tomography (PAT) as an unsupervised reconstruction approach to mitigate limited-view artifacts and noise commonly encountered in experimental settings. Efficient implementation is achieved by employing recently published fast forward and adjoint algorithms for circular measurement geometries. Initialization via a fast inverse and total variation (TV) regularization are applied to further suppress noise and mitigate overfitting. For comparison, we compute a classical TV reconstruction. Our experiments comprise simulated PAT measurements under limited-view geometries and varying levels of added noise as well as experimental measurements together with using a digital twin for quality assessment. Our findings suggest that DIP framework provides an effective unsupervised strategy for robust PAT reconstruction even in the challenging case of a limited view geometry providing improvement in several quantitative measures over total variation reconstructions. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_19176 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Deep Image Prior for photoacoustic tomography can mitigate limited-view artifacts Pulkkinen, Hanna Poimala, Jenni Kunyansky, Leonid Gröhl, Janek Hauptmann, Andreas Image and Video Processing Machine Learning Optimization and Control 65N21, 65M32, 65F22, 68T07, 92C55 We study the deep image prior (DIP) framework applied to photoacoustic tomography (PAT) as an unsupervised reconstruction approach to mitigate limited-view artifacts and noise commonly encountered in experimental settings. Efficient implementation is achieved by employing recently published fast forward and adjoint algorithms for circular measurement geometries. Initialization via a fast inverse and total variation (TV) regularization are applied to further suppress noise and mitigate overfitting. For comparison, we compute a classical TV reconstruction. Our experiments comprise simulated PAT measurements under limited-view geometries and varying levels of added noise as well as experimental measurements together with using a digital twin for quality assessment. Our findings suggest that DIP framework provides an effective unsupervised strategy for robust PAT reconstruction even in the challenging case of a limited view geometry providing improvement in several quantitative measures over total variation reconstructions. |
| title | Deep Image Prior for photoacoustic tomography can mitigate limited-view artifacts |
| topic | Image and Video Processing Machine Learning Optimization and Control 65N21, 65M32, 65F22, 68T07, 92C55 |
| url | https://arxiv.org/abs/2604.19176 |