Patent ID: 9672639
Date: 2017-06-06
CPC Classifications: A61B,G06T,G16H

Claim:
1. A method for bioluminescence tomography reconstruction using multitask Bayesian compressed sensing, the method comprising: performing three-dimensional reconstruction of a bioluminescent source based on a high order approximation model associated with light propagation in a biological tissue, inner-correlation among multispectral measurements associated with a multitask learning method and incorporation of a reconstruction algorithm as prior information to reduce ill-posedness of BLT reconstruction by: (Step one) defining problems and performing initialization by acquiring P wavelengths' boundary data at M measurement points, and setting gamma prior distribution α given effect of spectrum, SP wherein S represents light function, μ based on the SP based on the boundary conditions of SP wherein {right arrow over (n)} represents the outward unit normal vector which is perpendicular to the boundary, A wherein, J (Step two) constructing the linear relationship based on finite element method to discretize SP incorporating two SP3 equations should be written in corresponding weak solution forms to solve the SP wherein Ψ(r, τ Incorporating two boundary conditions for the formula (8) and (9) in corresponding weak solution forms: incorporating the weak solution forms of the boundary conditions into that for the SP wherein e wherein in the above formulas, the form for W is as follow: wherein, for mesh generation, the distribution of light source S(r, τ discretizing the SP wherein B is the matrix of size N×N, φ wherein the linear relationship between the surface emission light Φ(τ wherein the above equation constructs the relationship between the distribution of unknown bioluminescent light source and the boundary emission light, which is: wherein, in multispectral conditions, the energy ratio ω(τ setting S to represent the total energy of all spectral bands of light sources, which is S=Σ wherein all spectral bands of light sources and boundary measurements are incorporated, the relationship between the distribution of unknown bioluminescent light source and the boundary emission light in multispectral conditions is formulated as follow: Now, (Step three) estimating the shared prior based on empirical Bayesian maximum likelihood function, infer parameter α which represents the correlation among multi-wavelength; wherein, the emission light measurement Φ(τ introducing a multitask idea is introduced to describe the correlation among spectral bands, wherein the whole light source is regarded as a whole task T, S(τ wherein α=(α wherein B (Step four) reconstructing an unknown light source based on the estimated {circumflex over (α)} of the known hyper-parameter and boundary measurements Φ after obtaining the estimated {circumflex over (α)}, incorporating all spectral bands of light sources and boundary measurements, wherein the maximum likelihood function of the unknown light source S is represented as: wherein, wherein, in the formula, Ξ=diag({circumflex over (α)}