Abstract
Bioluminescence tomography (BLT) is used to localize and quantify bioluminescent sources in a small living animal. By advancing bioluminescent imaging to a tomographic framework, it helps to diagnose diseases, monitor therapies and facilitate drug development. In this paper, we establish a direct linear relationship between measured surface photon density and an unknown bioluminescence source distribution by using a finite-element method based on the diffusion approximation to the photon propagation in biological tissue. We develop a novel reconstruction algorithm to recover the source distribution. This algorithm incorporates a priori knowledge to define the permissible source region in order to enhance numerical stability and efficiency. Simulations with a numerical mouse chest phantom demonstrate the feasibility of the proposed BLT algorithm and reveal its performance in terms of source location, density, and robustness against noise. Lastly, BLT experiments are performed to identify the location and power of two light sources in a physical mouse chest phantom.
Highlights
Small animal imaging has become an important tool for biomedical research at the anatomical, functional, cellular and molecular levels
Is the light source distribution to be reconstructed. This Bayesian framework should be subject to the previously mentioned constraints, such as that the source distribution must stay in the permissible region Ωs, which can be determined in reference to the bioluminescent signals and a priori knowledge available from a specific biomedical application
We have developed a reconstruction algorithm to identify a 3D bioluminescent source distribution by incorporating a priori knowledge
Summary
Small animal imaging has become an important tool for biomedical research at the anatomical, functional, cellular and molecular levels. To study a small animal using molecular imaging techniques, the animal organ/tissue is typically transfected with a reporter gene in a viral promoter. Diffuse optical tomography (DOT) can be used to reconstruct the spatially variable optical parameters with a priori information [13] In this feasibility study, published optical parameters (absorption, reduced scattering) for the major anatomical components were used to build a geometrical model of the mouse. We develop a novel reconstruction algorithm to identify the bioluminescent source distribution from the measured external photon density This algorithm incorporates a priori knowledge to define the permissible source region to enhance numerical stability and efficiency. Relevant issues are discussed and conclusions drawn
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