Novel l 2,1-norm optimization method for fluorescence molecular tomography reconstruction.

Shixin Jiang,Kunshan He,Guanglei Zhang,Jie Tian,Yu An,Jie Liu,Jinzuo Ye,Chongwei Chi,Yamin Mao

doi:10.1364/boe.7.002342

Abstract

Fluorescence molecular tomography (FMT) is a promising tomographic method in preclinical research, which enables noninvasive real-time three-dimensional (3-D) visualization for in vivo studies. The ill-posedness of the FMT reconstruction problem is one of the many challenges in the studies of FMT. In this paper, we propose a l 2,1-norm optimization method using a priori information, mainly the structured sparsity of the fluorescent regions for FMT reconstruction. Compared to standard sparsity methods, the structured sparsity methods are often superior in reconstruction accuracy since the structured sparsity utilizes correlations or structures of the reconstructed image. To solve the problem effectively, the Nesterov's method was used to accelerate the computation. To evaluate the performance of the proposed l 2,1-norm method, numerical phantom experiments and in vivo mouse experiments are conducted. The results show that the proposed method not only achieves accurate and desirable fluorescent source reconstruction, but also demonstrates enhanced robustness to noise.

Highlights

In recent years, fluorescence molecular imaging (FMI) has attracted great attention due to the increasing availability of fluorescent proteins, dyes, and probes, which enable the noninvasive study of gene expression, protein function, interactions, and a large number of cellular processes [1, 2]
In order to quantitatively evaluate the performance of the reconstruction results, the position error (PE) and contrast-to-noise ratio (CNR) are introduced in this paper
In this study, a novel method based on the l2,1-norm method has been proposed to reconstruct the internal fluorescent sources for fluorescence molecular tomography (FMT)

Summary

Introduction

Fluorescence molecular imaging (FMI) has attracted great attention due to the increasing availability of fluorescent proteins, dyes, and probes, which enable the noninvasive study of gene expression, protein function, interactions, and a large number of cellular processes [1, 2]. FMT reconstruction is an inverse problem, where the fluorescent source is obtained by the system matrix and measurement data sets. The primary benefit of using Tikhonov regularization is the simplicity of the optimization problem involved, which can be efficiently solved by standard minimization tools, such as Newton’s method and conjugate gradient method [13,14,15]. The image obtained by Tikhonov regularization turns out to be over-smoothed when we penalize large values in this method. Sharp boundaries of the reconstructed images in general are difficult to obtain via Tikhonov regularization [16]

Methods

Results

Conclusion