Unmixing multispectral photoacoustic (PA) images is difficult because the excitation spectra in deep tissue are contaminated by absorption and scattering of the surrounding tissue in a highly unpredictable manner. In this work, we found a close relationship between the covariance matrix of a multispectral photoacoustic image and its average tissue oxygenation level. Based on the photon diffusion process, a spectral-domain model of multispectral photoacoustic imaging is established. Combined with the above two findings, accurate estimation of blood oxygen saturation (median error 2.7%) and accurate probe identification (detection rate 86%, false alarm rate 0.035%) were realized in realistic simulation test.
KEYWORDS: Photoacoustic imaging, Blood, Tissue optics, Blood oxygen saturation, In vivo imaging, Data modeling, Multispectral imaging, Diffusion, Biological research, Monte Carlo methods
In multispectral photoacoustic imaging (PAI), the illumination spectrum inside biological tissue varies spatially, leading to poor quantification accuracy of blood oxygen saturation (SO2). The key to solving this problem is to invert light diffusion, which is extremely complicated and inaccurate due to the limited information available in PAI. Despite the great effort devoted, to date, the few available methods are all limited in terms of in vivo performance and physical insights. Here, we introduce an analytical Monte Carlo method, with which we prove that the light spectrum in biological tissue mathematically lies in a high dimensional convex cone set. The model offers new insights into the origin of the spectral deterioration, and we find it possible to calculate blood oxygen saturation (SO2) accurately by using only the photoacoustic data at a single spatial location when signal to noise ratio is sufficient. The method was demonstrated numerically, and our preliminary phantom experiment results also confirmed its effectiveness.
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