PENTAGON: Physics-enhanced neural network for volumetric flame chemiluminescence tomography

This study proposes a physics-enhanced neural network, PENTAGON, as an inference framework for volumetric tomography applications. By leveraging the synergistic combination of data-prior and forward-imaging model, we can accurately predict 3D optical fields, even when the number of projection views...

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Bibliographic Details
Published inOptics express Vol. 32; no. 19; p. 32732
Main Authors Jin, Ying, Zhu, Sunyong, Wang, Shouyu, Wang, Fei, Wu, Quanying, Situ, Guohai
Format Journal Article
LanguageEnglish
Published 09.09.2024
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Summary:This study proposes a physics-enhanced neural network, PENTAGON, as an inference framework for volumetric tomography applications. By leveraging the synergistic combination of data-prior and forward-imaging model, we can accurately predict 3D optical fields, even when the number of projection views decreases to three. PENTAGON is proven to overcome the generalization limitation of data-driven deep learning methods due to data distribution shift, and eliminate distortions introduced by conventional iteration algorithms with limited projections. We evaluated PENTAGON using numerical and experimental results of a flame chemiluminescence tomography example. Results showed that PENTAGON can potentially be generalized for inverse tomography reconstruction problems in many fields.
ISSN:1094-4087
1094-4087
DOI:10.1364/OE.536550