Abstract
Bioluminescence tomography (BLT) is an important noninvasive optical molecular imaging modality in preclinical research. To improve the image quality, reconstruction algorithms have to deal with the inherent ill-posedness of BLT inverse problem. The sparse characteristic of bioluminescent sources in spatial distribution has been widely explored in BLT and many L1-regularized methods have been investigated due to the sparsity-inducing properties of L1 norm. In this paper, we present a reconstruction method based on L[Formula: see text] regularization to enhance sparsity of BLT solution and solve the nonconvex L[Formula: see text] norm problem by converting it to a series of weighted L1 homotopy minimization problems with iteratively updated weights. To assess the performance of the proposed reconstruction algorithm, simulations on a heterogeneous mouse model are designed to compare it with three representative sparse reconstruction algorithms, including the weighted interior-point, L1 homotopy, and the Stagewise Orthogonal Matching Pursuit algorithm. Simulation results show that the proposed method yield stable reconstruction results under different noise levels. Quantitative comparison results demonstrate that the proposed algorithm outperforms the competitor algorithms in location accuracy, multiple-source resolving and image quality.
Highlights
Bioluminescence tomography (BLT) is a powerful preclinical imaging modality that localizes and quanties internal bioluminescent sources with images of the light emitted through the animal surface
This paper presents an e±cient source reconstruction method for BLT based on L1=2 regularization
Due to the nonconvexity of the objective, we convert the L1=2-regularized problem into a series of L1 minimization problems by iterative reweighting approach, and perform a homotopy-based algorithm to obtain fast and accurate reconstruction
Summary
Bioluminescence tomography (BLT) is a powerful preclinical imaging modality that localizes and quanties internal bioluminescent sources with images of the light emitted through the animal surface. It provides a method of quantitative measuring and visualizing a range of molecular and cellular-level biological processes that occur in vivo. Nonconvex Lp regularizernd its place in °uorescence molecular tomography (FMT) for sparsity enhancement.[15,16,17,18,19,20,21] Chen et al introduced Lp regularizer into BLT and proposed the weighted interior-point algorithm (WIPA) for solving the nonconvex optimization problem.[22]. To assess the performance of the proposed algorithm, we compared our L1=2 norm method with three representative sparse reconstruction algorithm, including the WIPA22 for L1=2 regularization, the homotopy method for L1 minimization (L1 homotopy),[23] and the Stagewise Orthogonal Matching Pursuit (StOMP).[24]
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