KEYWORDS: Luminescence, Monte Carlo methods, 3D modeling, Data modeling, Fluorescence tomography, Tomography, Optimization (mathematics), Optical tomography, Photons, Performance modeling
Fluorescence Molecular Tomography is an optical imaging technique which aims at reconstructing the 3D distribution
of fluorescent markers in bio-tissues based on surface measurements of emitted photons and a model of light
propagation. The gold standard of accuracy in creating this light propagation model is the Monte Carlo method (MC),
which simulates the path of photon packets through a discretized model of the tissue. One drawback of MC is the
computational burden associated with its stochastic nature. Mesh based MC are computational implementations of
MC techniques with favorable computational costs. Herein, we investigate the effects of locally refining a mesh
discretization on reconstruction accuracy in mesh based Fluorescence Molecular Tomography.
Using a mouse model created from μCT data and average murine optical properties, we are investigating the
performances of mesh refinement strategies in reconstructing an 48.9 mm3 fluorescence inclusion in the center of the
model. Iterative mesh optimization is applied to the inverse problem in which after each reconstruction, the mesh is
refined in the area of interest. Performance of the method is evaluated in terms of in volume and center of mass
position of the inclusion compared to the ground truth. Our preliminary results indicate that accuracy improves with
each refinement until convergence. Moreover, a method for rescaling analytically the forward model to fit each new
mesh is also proposed in order to reduce the computational expense of the procedure while maintaining the
improvements in accuracy
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