Abstract
.Quantitative photoacoustic tomography aims to recover maps of the local concentrations of tissue chromophores from multispectral images. While model-based inversion schemes are promising approaches, major challenges to their practical implementation include the unknown fluence distribution and the scale of the inverse problem. We describe an inversion scheme based on a radiance Monte Carlo model and an adjoint-assisted gradient optimization that incorporates fluence-dependent step sizes and adaptive moment estimation. The inversion is shown to recover absolute chromophore concentrations, blood oxygen saturation, and the Grüneisen parameter from in silico three-dimensional phantom images for different radiance approximations. The scattering coefficient is assumed to be homogeneous and known a priori.
Highlights
Biomedical photoacoustic (PA) tomography is a hybrid soft-tissue imaging modality that combines the high spatial resolution of ultrasound with the high contrast and specificity of optical imaging techniques.[1,2,3] It relies on the generation of acoustic waves inside the tissue, which result from the absorption of intensity-modulated light, such as laser pulses or frequency chirps, by the tissue chromophores
Quantitative PA tomography (QPAT) aims to exploit the wavelength dependence of the image intensity to recover the local concentrations of endogenous tissue chromophores and exogenous contrast agents from which functional parameters, such as blood oxygen saturation, can be derived
The accuracy of the recovered chromophore concentration and sO2 maps are reported in Secs. 3.1 and 3.2
Summary
Biomedical photoacoustic (PA) tomography is a hybrid soft-tissue imaging modality that combines the high spatial resolution of ultrasound with the high contrast and specificity of optical imaging techniques.[1,2,3] It relies on the generation of acoustic waves inside the tissue, which result from the absorption of intensity-modulated light, such as laser pulses or frequency chirps, by the tissue chromophores. Quantitative PA tomography (QPAT) aims to exploit the wavelength dependence of the image intensity to recover the local concentrations of endogenous tissue chromophores and exogenous contrast agents from which functional parameters, such as blood oxygen saturation, can be derived. To relate the PA image intensity to local chromophore concentrations, computational models of the physical processes during the image generation in conjunction with inversion schemes represent one approach to QPAT.[4,5] A major challenge in QPAT is the unknown light fluence in the tissue,[5,6,7] which is a nonlinear function of the concentrations and the scattering coefficient. Its effects on PA images have been described as spectral coloring and structural distortion.[5] For an accurate quantification of concentrations and their ratios (e.g., blood oxygenation), the wavelength-dependent fluence distribution has to be accounted for
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