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Fluorescent diffuse optical tomography (FDOT) seeks to estimate the three-dimensional distribution of the concentrations or lifetime of fluorophores (exogenous and endogenous) in live tissue using optical measurements. This method has wide range of applications in cancer research as well as photodynamic therapy. The acquisition scheme consists of a scanning laser system that excites the specimen and a sensitive CCD array that collects the scattered light. The reconstruction of the fluorophore concentrations from the scattered light is often ill-posed due to the diffuse nature of light scattering in the tissue and autofluorescence. Standard approaches such as conjugate gradients and Tikhonov regularized reconstruction schemes often have limited capability in resolving the fluorophore distribution. The reconstructions are also computationally challenging due to the large volumes of data and the huge system matrix. The central goal of the project is to develop an robust and computationally efficient constrained reconstruction algorithm to address these problems.
This is a collaborative project between University of Rochester and Duke University.
Master project to be performed at the Department of Biomedical Engineering, University of Rochester, USA
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