Abstract

Optical molecular imaging is a promising technique and has been widely used in physiology, and pathology at cellular and molecular levels, which includes different modalities such as bioluminescence tomography, fluorescence molecular tomography and Cerenkov luminescence tomography. The inverse problem is ill-posed for the above modalities, which cause a nonunique solution. In this paper, we propose an effective reconstruction method based on the linearized Bregman iterative algorithm with sparse regularization (LBSR) for reconstruction. Considering the sparsity characteristics of the reconstructed sources, the sparsity can be regarded as a kind of a priori information and sparse regularization is incorporated, which can accurately locate the position of the source. The linearized Bregman iteration method is exploited to minimize the sparse regularization problem so as to further achieve fast and accurate reconstruction results. Experimental results in a numerical simulation and in vivo mouse demonstrate the effectiveness and potential of the proposed method.

Highlights

  • Optical molecular imaging provides the promising tools to monitor physiological and pathological activities at cellular and molecular levels and has become an important technique for biomedical research

  • To overcome the limitation of planar imaging, bioluminescence tomography (BLT) [5], fluorescence molecular tomography (FMT) [6], and Cerenkov luminescence tomography (CLT) [7] were developed to determine the 3D distribution inside a phantom or a small animal with the reconstruction algorithm from the signal detected on the external surface associated with anatomical structure and optical properties [8]

  • Numerical simulation and in vivo experiments were conducted to evaluate the proposed method (LBSR) compared with the l2-norm regularization method based on the conjugate gradient method (l2-CG) and l1-norm regularization method based on the Split Bregman iterative method (l1-SB) [27] for source reconstruction, respectively

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Summary

Introduction

Optical molecular imaging provides the promising tools to monitor physiological and pathological activities at cellular and molecular levels and has become an important technique for biomedical research. In order to obtain a unique solution, many reconstruction algorithms have been developed and applied to linear inverse problem such as multispectral measurement [11, 12] and permissible source region (PSR) [13, 14]. These methods improve reconstruction results to a certain degree and in turn impose a variety of limitations on practical applications. The multispectral methods have some limitations by increasing measurable information [15], and both the size and position of the PSR have significant impact on the reconstruction results [16]

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