Pixel-wise reconstruction of tissue absorption coefficients in photoacoustic tomography using a non-segmentation iterative method

In Photoacoustic Tomography (PAT), the acquired image represents a light energy deposition map of the imaging object. For quantitative imaging, the PAT image is converted into an absorption coefficient ( μ a ) map by dividing the light fluence (LF). Previous methods usually assume a uniform tissue μ a distribution, and consequently degrade the LF correction results. Here, we propose a simple method to reconstruct the pixel-wise μ a map. Our method is based on a non-segmentation-based iterative algorithm, which alternately optimizes the LF distribution and the μ a map. Using simulation data, as well as phantom and animal data, we implemented our algorithm and compared it to segmentation-based correction methods. The results show that our method can obtain accurate estimation of the LF distribution and therefore improve the image quality and feature visibility of the μ a map. Our method may facilitate efficient calculation of the concentration distributions of endogenous and exogenous agents in vivo.