Optical tomography reconstruction method based on iterative measurement

Abstract

The invention discloses an optical tomography reconstruction method based on iterative measurement, which divides the boundary measurement data into two groups. In the iterative reconstruction process, the two groups of boundary measurement data are alternately used for iterative reconstruction until the value of the objective function is smaller than a preset threshold value, and the final absorption coefficient to be reconstructed is obtained. The forward model error between radiative transfer equation and diffusion approximation is incorporated into the imaging process, so that the forwardmodel error caused by diffusion approximation can be compensated, and the modeling accuracy and imaging speed of the optical transmission process can be improved.

Description

technical field [0001] The invention belongs to the technical field of biological optical imaging, in particular to an optical tomography reconstruction method based on iterative measurement. Background technique [0002] Optical tomography is a very important imaging method in biomedical research, and it is a highly sensitive non-destructive detection technology. This technology can be used to image the optical parameters such as absorption coefficient and scattering coefficient of various parts of biological tissues, so as to provide important functional information of biological tissues, which has broad development prospects and research value. Its notable features include: non-invasive, portability, continuity, functional measurement. At present, this technology has been successfully applied in blood oxygen detection, early diagnosis and location of cerebral hemorrhage, detection of breast cancer and other fields. [0003] Optical tomographic image reconstruction inclu...

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Problems solved by technology

But the diffusion approximation has inherent limitations: first, for regions where the medium is low scattering or non-scattering, the diffusion equation does not apply

However, this leads to an increase in the size of the matrix, which increases the computational cost of the reconstruction

Using the method of simplifying the model, the dimensionality reduction subspace is used to represent the unknown. This method needs to use the basis vector to construct the transformation matrix, which increases the computational burden of the reconstruction process.

In addition, using the method of data subsets can improve the imaging speed, but the imaging error in the target area is large

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