Auto-fluorescence tomography re-establishing method based on multiplier method

Abstract

The invention discloses an auto-fluorescence tomography re-establishing method based on a multiplier method. The auto-fluorescence tomography re-establishing method based on the multiplier method comprises the following steps of: carrying out discretization by using a finite element method diffusion equation, and establishing an optimization problem model without constraint conditions based on a penalty term of an L1 norm; obtaining a dual model of the optimization problem model without constraint conditions; establishing an augmentation lagrange function of the dual model; simplifying the maximum function of the augmentation lagrange function; solving the maximum value of the augmentation lagrange function by using a truncated-Newton algorithm; upgrading the target vector by using the gradient of the augmentation lagrange function as the steepest descent direction of a target vector; upgrading a penalty vector; and calculating an objective function value J(w), calculating k=k+1 if the ratio of the norm of J(w)k-J(w)(k-1) to the norm of Pai m being not smaller than t0l is real, and jumping to the step S4, otherwise, ending the calculation, wherein t0l is the convergence efficiency threshold value of the target function. The auto-fluorescence tomography re-establishing method provided by the invention can quickly obtain accurate and reliable light source distribution information within a large image-forming region, so that other parameters except from the regularization parameter can realize self-adaptive adjustment for improving the image-forming robustness.

Description

technical field [0001] The invention belongs to the field of optical molecular imaging, and relates to an autofluorescence tomography reconstruction method, in particular to an autofluorescence tomography reconstruction method based on a multiplier method. Background technique [0002] Optical molecular imaging technology is to realize real-time, non-invasive, in vivo imaging of biological physiological and pathological changes at the molecular and cellular level. As an important optical molecular imaging modality, fluorescence tomography has many advantages such as high sensitivity, low cost, and no radioactivity. Wide range of applications. Due to the complex scattering of photons in the near-infrared and visible light wavelength ranges when propagating in biological tissues, two-dimensional planar autofluorescence imaging often cannot provide the accurate position of the autofluorescence light source, and has the disadvantage of not being able to obtain the depth informa...

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Problems solved by technology

Therefore, the regularization method based on the L1 norm can well reflect the sparsity characteristics of the light source, but this will cause the objective function of the optimization problem to be non-differentiable, which makes many existing light source reconstructions for the L2 norm regularization method The method cannot be directly used to solve the light source reconstruction problem based on the L1 norm

In addition, although there are already some mathematical methods that can be used to solve the regularization problem based on the L1 norm, such as iterative shrinkage method, interior point method, etc., the problem of autofluorescence tomography is highly ill-conditioned. And the system matrix is ​​a dense matrix of real numbers, which makes the above methods have low imaging efficiency, difficult parameter selection, and even can only obtain local optimal solutions but cannot obtain accurate reconstruction results

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