Eigenspectra optoacoustic tomography achieves quantitative blood oxygenation imaging deep in tissues
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Abstract (arXiv)
Light propagating in tissue attains a spectrum that varies with location due to wavelength-dependent fluence attenuation by tissue optical properties, an effect that causes spectral corruption. Predictions of the spectral variations of light fluence in tissue are challenging since the spatial distribution of optical properties in tissue cannot be resolved in high resolution or with high accuracy by current methods. Spectral corruption has fundamentally limited the quantification accuracy of optical and optoacoustic methods and impeded the long sought-after goal of imaging blood oxygen saturation (sO2) deep in tissues; a critical but still unattainable target for the assessment of oxygenation in physiological processes and disease. We discover a new principle underlying light fluence in tissues, which describes the wavelength dependence of light fluence as an affine function of a few reference base spectra, independently of the specific distribution of tissue optical properties. This finding enables the introduction of a previously undocumented concept termed eigenspectra Multispectral Optoacoustic Tomography (eMSOT) that can effectively account for wavelength dependent light attenuation without explicit knowledge of the tissue optical properties. We validate eMSOT in more than 2000 simulations and with phantom and animal measurements. We find that eMSOT can quantitatively image tissue sO2 reaching in many occasions a better than 10-fold improved accuracy over conventional spectral optoacoustic methods. Then, we show that eMSOT can spatially resolve sO2 in muscle and tumor; revealing so far unattainable tissue physiology patterns. Last, we related eMSOT readings to cancer hypoxia and found congruence between eMSOT tumor sO2 images and tissue perfusion and hypoxia maps obtained by correlative histological analysis.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Nov 2015.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 2; 2025: 1; 2026: 1.
6 papers.
- 2021Semantic segmentation of multispectral photoacoustic images using deep learningcited by 4
- 2024Distribution-informed and wavelength-flexible data-driven photoacoustic oximetrycited by 1
- 2021Quantitative photoacoustic oximetry imaging by multiple illumination learned spectral decoloringcited by 0
- 2024Advances in Photoacoustic Imaging Reconstruction and Quantitative Analysis for Biomedical Applicationscited by 0
- 2025Application of a Virtual Imaging Framework for Investigating a Deep Learning-Based Reconstruction Method for 3D Quantitative Photoacoustic Computed Tomographycited by 0
- 2026Optical Inversion and Spectral Unmixing of Spectroscopic Photoacoustic Images with Physics-Informed Neural Networkscited by 0
- 2026Optical Inversion and Spectral Unmixing of Spectroscopic Photoacoustic Images with Physics-Informed Neural Networks18 Feb 2026
- 2025Application of a Virtual Imaging Framework for Investigating a Deep Learning-Based Reconstruction Method for 3D Quantitative Photoacoustic Computed Tomography3 Oct 2025
- 2024Advances in Photoacoustic Imaging Reconstruction and Quantitative Analysis for Biomedical Applications5 Nov 2024
- 2024Distribution-informed and wavelength-flexible data-driven photoacoustic oximetry21 Mar 2024
- 2021Semantic segmentation of multispectral photoacoustic images using deep learning20 May 2021
- 2021Quantitative photoacoustic oximetry imaging by multiple illumination learned spectral decoloring22 Feb 2021