A Primal-Dual Reconstruction Algorithm for Fluorescence and Bioluminescence Tomography

We introduce a new primal-dual reconstruction algorithm for fluorescence and bioluminescence tomography. As often in optical tomography, image reconstruction is performed by optimizing a multi-term convex cost function. Current reconstruction methods employed in the field are usually limited to cost functions with a smooth data fidelity term; quadratic in general. In addition, the use of a composite regularization term (a sum of multiple terms) requires a substantial adaptation of these methods. Typically one would have to solve a subproblem via a primal-dual method at each iteration. The primal-dual scheme presented here is designed to handle directly cost functions composed of multiple, possibly non-smooth, terms. This allows more freedom for the design of tailored cost functions leading to enhanced reconstructions. We illustrate the method on two cases. First, we use a cost function composed of $ ℓ _{ 1 } $ fidelity and regularization terms. We compare to the reconstructions obtained with the quadratic fidelity counterpart. Second, we employ a cost function composed of three terms : $ ℓ _{ 1 } $ for data fidelity, total-variation plus (2,1)-mixed norms for regularization.

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Proceedings of the Eighth IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI'11), Chicago IL, USA, 960–963

 Record created 2015-09-18, last modified 2018-10-07

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