Diffusion optical imaging method and system based on controlled Monte Carlo method

Abstract

The invention discloses a diffusion optical imaging method and system based on a controlled Monte Carlo method. The method comprises: determining an attracting area according to the position relationship between the light source and the detection area and the extrapolated boundary; determining an attracting vector according to a spatial position vector, a attracting point position vector and an optical characteristic parameter of a current photon; determining the selection probability of a photon scattering direction according to a attracting vector, a attracting factor, and a photon scattering direction; and finally, determining whether the photon performs the next scattering and whether to update the photon weight according to the size relationship between the selection probability and the random number. According to the diffusion optical imaging method and system based on the controlled Monte Carlo method, photons scattered by the next time are selected through the attracting area,and the weight of the selected photons is updated in real time. On the premise of emitting the same number of photon, the number of photons and the accuracy of light distribution in the detection areacan be effectively improved, and the imaging quality and the imaging efficiency are guaranteed. Meanwhile, the problem that the light distribution precision is unstable caused by different scatteringcoefficients of the target biological tissue or the distance between the light source and the detection area can be solved.

Description

technical field [0001] The invention relates to the field of optical imaging, in particular to a diffuse optical imaging method and system based on a control Monte Carlo method. Background technique [0002] The Monte Carlo (MC) model has high universality and has been widely used in the field of biological tissue optics. In the MC model, by solving the integral form of the radiative transport equation (RTE), the corresponding joint probability sampling function is established to simulate the transmission of diffuse photons in tissue. Currently, both MC models for layered tissue simulation and voxel-based MC models for complex anatomical structures are capable of accurately simulating photon propagation in tissue. The MC model inherits the accuracy of the RTE equation and is applicable to various complex boundaries and complex heterogeneous tissues. Therefore, the MC model is the best choice to improve the precision and quantitative performance of diffuse photon imaging te...

AI Technical Summary

Problems solved by technology

However, when the detection area is far away from the light source, or the detection area is small, the number of photons reaching the detection area will decrease, seriously affecting the final image quality

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