![[2602.16357] Optical Inversion and Spectral Unmixing of Spectroscopic Photoacoustic Images with Physics-Informed Neural Networks](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.16357] Optical Inversion and Spectral Unmixing of Spectroscopic Photoacoustic Images with Physics-Informed Neural Networks
Summary
This article presents the Spectroscopic Photoacoustic Optical Inversion Autoencoder (SPOI-AE), a novel approach using physics-informed neural networks to accurately estimate chromophore concentrations in spectroscopic photoacoustic images, addressing challenges in optical inve...
Why It Matters
The ability to accurately estimate chromophore concentrations in biological tissues can significantly enhance diagnostic imaging and therapeutic monitoring. This research addresses the limitations of conventional methods, potentially improving outcomes in medical imaging and related fields.
Key Takeaways
- SPOI-AE improves reconstruction of spectroscopic photoacoustic images compared to traditional algorithms.
- The method provides biologically coherent estimates for optical parameters and tissue oxygen saturation.
- Validation of SPOI-AE's accuracy was achieved using simulated data, enhancing its reliability for real-world applications.
Computer Science > Machine Learning arXiv:2602.16357 (cs) [Submitted on 18 Feb 2026] Title:Optical Inversion and Spectral Unmixing of Spectroscopic Photoacoustic Images with Physics-Informed Neural Networks Authors:Sarkis Ter Martirosyan, Xinyue Huang, David Qin, Anthony Yu, Stanislav Emelianov View a PDF of the paper titled Optical Inversion and Spectral Unmixing of Spectroscopic Photoacoustic Images with Physics-Informed Neural Networks, by Sarkis Ter Martirosyan and 4 other authors View PDF HTML (experimental) Abstract:Accurate estimation of the relative concentrations of chromophores in a spectroscopic photoacoustic (sPA) image can reveal immense structural, functional, and molecular information about physiological processes. However, due to nonlinearities and ill-posedness inherent to sPA imaging, concentration estimation is intractable. The Spectroscopic Photoacoustic Optical Inversion Autoencoder (SPOI-AE) aims to address the sPA optical inversion and spectral unmixing problems without assuming linearity. Herein, SPOI-AE was trained and tested on \textit{in vivo} mouse lymph node sPA images with unknown ground truth chromophore concentrations. SPOI-AE better reconstructs input sPA pixels than conventional algorithms while providing biologically coherent estimates for optical parameters, chromophore concentrations, and the percent oxygen saturation of tissue. SPOI-AE's unmixing accuracy was validated using a simulated mouse lymph node phantom ground truth. Subjects: ...
