# Quantitative Reconstruction of Absorption Coefficients for Photoacoustic Tomography
## 5 Discussion and Conclusions  
In this paper, we have successfully demonstrated the applicability of the novel method for quantitative reconstruction of the distribution of optical absorption coefficients, which has higher practicability compared with the traditional iterative recovery method. The 3D Monte Carlo simulation is carried out by utilizing this approach, and the parameters to initialize the Monte Carlo are obtained by pure background fluence compensation. The research weakens the dependence on the prior knowledge of the tissue model (including various optical properties and geometric information). However, studies have shown that the automatic segmentation process can be integrated with an iterative inversion algorithm to provide a reliable way to obtain a more accurate reconstruction of the actual distribution of absorption coefficients. In the research work of [PERSON] et al. [PERSON] et al. (2015), a joint reconstruction method is proposed in which the sound velocity and initial pressure distribution are simultaneously estimated accurately from the photoacoustic measurement results. [PERSON] et al. [PERSON] et al. (2015) investigated the applicability of active contour models for boundary segmentation in optoacoustic tomographic reconstructions and further used a reconstruction mask for quantitative mapping the optical absorption coefficient via light fluence normalization. Their research shows the prior knowledge obtained by the segmentation method. In the algorithm, we limit our current investigation to the optical inverses without considering the effects of acoustic inverse problems. In fact, photoacoustic images are constituted by integrating optical information and ultrasonic propagation. In the hybrid imaging process, the solution of the acoustic inverse problem (i.e., the absorbed energy distribution) is input into the optical inverse problem to quantify the absorption coefficient distribution or the chromophore concentration in the tissue. Obviously, the noise in the former results will directly affect the latter quantified results. The noise of optical data is modeled as Gaussian distributed in the study by [PERSON] et al. [PERSON] et al. (2016), Bayesian approximation error modeling is further applied to compensate for modeling errors in the optical data caused by the acoustic solver, thereby improving optical reconstruction. [PERSON] et al. [PERSON] et al. (2018) proposed an algorithm for photoacoustic tomography based on an entropy maximization algorithm, which uses logarithmic regularization for attaining nonnegative reconstructions and showed excellent reconstruction performance. In addition, this method updates the iterative absorption coefficient by making use of the difference of the absorption energy distribution between measurements and calculated values, while at the same time ensuring the convergence of the algorithm and effectively improving the efficiency. By minimizing the deviation in the absorbed energy, the recovery values converge to the true absorption distribution. The results of numerical simulation and phantom experiment theoretically and experimentally demonstrate that the proposed method can estimate the distribution of optical absorption coefficients accurately and quantitatively. This paper is promising for the application of vascular system and breast cancer detection photoacoustic imaging quantitative analysis, etc., which need to accurately estimate the absorption distribution. In the future work, we will continue to research a better optimization method for addressing the quantitative photoacoustic imaging problem. In addition, we will try to explore more complex phantom experiments, ex-vivo experiments or in-vivo experiments. This work expects to provide accurate quantitative information for absorbers within tissues or organs, thereby broadening the clinical applications of PAT. Conceptualization and methodology, Y.L. and M.S. ; Data processing, Y.L., T.L., Y.M. and C.L. ; Software and Formal Analysis, D.H., N.F. and Y.M. ; Writing, Y.L. and S.M.