Abstract
Diffusion equations (DEs) or simplified spherical harmonic equations are commonly used forward models in bioluminescence tomography (BLT), which are usually numerically calculated by the finite element method to construct the system matrix for reconstruction. However, the numerical solver is not accurate enough. The Monte Carlo (MC) method is regarded as the golden standard for modeling light propagation in biological tissue. In this paper, we proposed a GPU-accelerated inverse MC method for BLT reconstruction. The main feature is that the system matrix for BLT reconstruction is calculated by the MC method instead of the model-based numerical approximation. We evaluated the performance of the proposed method with both phantom-based simulation and animal-based in vivo experiment. The results show that, compared with the DE-based method, the proposed GPU-accelerated inverse MC method is more accurate and effective in BLT reconstruction.
Highlights
Bioluminescence imaging (BLI) is a non-invasive imaging technique and plays an important role in biomedical research and clinical applications for many years.1,2 BLI has the advantages of low cost, high specificity, and high sensitivity compared with other imaging modalities.3 As the extension technique of BLI, bioluminescence tomography (BLT) is able to localize and quantify probe distributions more accurately in three dimensions in vivo.4The reconstruction of BLT attempts to recover the true distribution of the bioluminescence source from the measured light on the surface based on a photon propagation model
The Central Processing Units (CPUs) code of Monte Carlo (MC) simulation is executed on a 3.20 GHz Intel i7 processor, while the Graphics Processing Unit (GPU) code is run on the Nvidia Titan XP device
The GPU-accelerated MC inverse solver was proposed for BLT reconstruction
Summary
Bioluminescence imaging (BLI) is a non-invasive imaging technique and plays an important role in biomedical research and clinical applications for many years. BLI has the advantages of low cost, high specificity, and high sensitivity compared with other imaging modalities. As the extension technique of BLI, bioluminescence tomography (BLT) is able to localize and quantify probe distributions more accurately in three dimensions in vivo.4The reconstruction of BLT attempts to recover the true distribution of the bioluminescence source from the measured light on the surface based on a photon propagation model. As the extension technique of BLI, bioluminescence tomography (BLT) is able to localize and quantify probe distributions more accurately in three dimensions in vivo.. The key point of the BLT problem is the accurate modeling of light propagation. The simplified harmonic approximation (SPN) method is used to solve the RTE.. The simplified harmonic approximation (SPN) method is used to solve the RTE.7 This method is not able to model the light propagation in the void region accurately. Hybrid approximation methods of RTE are proposed to solve the forward problem of light propagation.. Hybrid approximation methods of RTE are proposed to solve the forward problem of light propagation.8,9 These numerical methods for BLT are not accurate enough for the case of light propagation in a complex heterogeneous medium. In addition to the model-based approach, the Monte Carlo (MC) method is regarded as the gold standard for modeling the light propagation in turbid media. The MC method can get the result of light propagation in heterogeneous medium with desirable accuracy
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