## Abstract

Regularization methods have been broadly applied to bioluminescence tomography
(BLT) to obtain stable solutions, including ${l}_{2}$
and ${l}_{1}$
regularizations. However, ${l}_{2}$
regularization can oversmooth reconstructed images and ${l}_{1}$
regularization may sparsify the source distribution, which degrades image
quality. In this paper, the use of total variation (TV) regularization in BLT is
investigated. Since a nonnegativity
constraint can lead to improved
image quality, the nonnegative constraint should be considered in BLT. However,
TV regularization with a nonnegativity constraint is extremely difficult to
solve due to its nondifferentiability and nonlinearity. The aim of this work is
to validate the split Bregman method to minimize the TV regularization problem
with a nonnegativity constraint for BLT. The performance of split
Bregman-resolved TV (SBRTV) based BLT reconstruction algorithm was verified with
numerical and *in vivo* experiments. Experimental results
demonstrate that the SBRTV regularization can provide better regularization
quality over ${l}_{2}$
and ${l}_{1}$
regularizations.

©2012 Optical Society of America

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