Fluorescent diffusion optical cross-sectional image reestablishing method based on dfMC model

Abstract

The invention discloses a fluorescent diffusion optical cross-sectional image reestablishing method based on a decoupling fluorescence Monte Carlo (dfMC) model, and belongs to the technical field of biomedical engineering. The method includes the steps of firstly, determining a detecting area, selecting a plurality of scanning points in the detecting area, and obtaining fluorescent intensity distribution on a detector; secondly, establishing a three-dimensional digital model for depicting a tissue optical parameter space structure, conducting forward-direction white Monte Carlo simulation of stimulating photons according to the scanning positions and directions of a light source, tracking the stimulating photons, and recording the corresponding physical quantities of the photons reaching the detector on a path; thirdly, calculating the weight of fluorescent photons through a dfMC method, and calculating a fluorescent Jacobi matrix; fourthly, calculating the positions and absorbing coefficients of fluorophores in tissue through iterative reconstruction of GPU clusters. The method has the advantage of providing an accurate and rapid reestablishing method for a three-dimensional fluorescence tomography system through the high-precision dfMC model on the basis of the accelerated iterative reconstruction process of the GPU clusters.

Description

technical field [0001] The invention belongs to the technical field of biomedical engineering, in particular to a method for reconstructing a fluorescence diffusion optical tomographic image based on a decoupled fluorescence Monte Carlo dfMC model. Background technique [0002] In recent years, fluorescence diffusion optical tomography has developed into an important imaging tool [1] , and are widely used in cancer diagnosis, drug development, and gene expression visualization research. Accurate reconstruction of fluorescent targets depends on establishing an accurate photon transport model (the so-called forward problem) and an inversion model of the photon transport model (the inverse problem). The forward problem is mainly based on the diffusion approximation model under the Boltzmann radiative transfer framework of energy conservation [2] and Monte Carlo (MC) model [3] . The diffusion approximation model is widely used due to its low time cost for image reconstructio...

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Problems solved by technology

However, the perturbation method ignores the influence of the fluorophore on the excitation light, and ignores the difference in optical parameters between the excitation light and the fluorescence in the tissue. These many assumptions make the perturbation method a biased MC simulation, and its fluorescence statistics are consistent with the actual There will be deviations in the fluorescence value, and this deviation will change with the optical parameters and the number of photons. In most cases, the error will be large, and due to the huge information of the photon path, the reconstruction efficiency will be reduced in the case of the voxel model. Low, the whole process takes a lot of time

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