Optical Inversion and Spectral Unmixing of Spectroscopic Photoacoustic Images with Physics-Informed Neural Networks
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Abstract (arXiv)
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 textitin 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.
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References · 6
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2017Solving ill-posed inverse problems using iterative deep neural networkscited by 145
- 2017EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixingcited by 29
- 2019Hyperspectral Unmixing via Deep Autoencoder Networks for a Generalized Linear-Mixture/Nonlinear-Fluctuation Modelcited by 16
- 2023Moving beyond simulation: data-driven quantitative photoacoustic imaging using tissue-mimicking phantomscited by 10
- 2015Eigenspectra optoacoustic tomography achieves quantitative blood oxygenation imaging deep in tissuescited by 6
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