A Penalized Linear and Nonlinear Combined Conjugate Gradient Method for the Reconstruction of Fluorescence Molecular Tomography

Shang Shang,Xiaolei Song,Jaclyn Lau,Hongkai Wang,Jing Bai

doi:10.1155/2007/84724

Abstract

Conjugate gradient method is verified to be efficient for nonlinear optimization problems of large-dimension data. In this paper, a penalized linear and nonlinear combined conjugate gradient method for the reconstruction of fluorescence molecular tomography (FMT) is presented. The algorithm combines the linear conjugate gradient method and the nonlinear conjugate gradient method together based on a restart strategy, in order to take advantage of the two kinds of conjugate gradient methods and compensate for the disadvantages. A quadratic penalty method is adopted to gain a nonnegative constraint and reduce the illposedness of the problem. Simulation studies show that the presented algorithm is accurate, stable, and fast. It has a better performance than the conventional conjugate gradient-based reconstruction algorithms. It offers an effective approach to reconstruct fluorochrome information for FMT.

Highlights

Light with wavelength in the near-infrared range can propagate a few centimeters through the tissue because of low tissue absorption in the spectral of “near-infrared window.” This finding has encouraged the development of fluorescence techniques to visualize specific biochemical events inside living subjects [1, 2]
A numerical model was set up to test the validity of the PLN-conjugate gradient (CG) algorithm
Simulation studies have indicated that this PLN-CG method can exhibit very favorable performance and produce relatively stable behavior

Summary

Introduction

Light with wavelength in the near-infrared range can propagate a few centimeters through the tissue because of low tissue absorption in the spectral of “near-infrared window.” This finding has encouraged the development of fluorescence techniques to visualize specific biochemical events inside living subjects [1, 2]. Light with wavelength in the near-infrared range can propagate a few centimeters through the tissue because of low tissue absorption in the spectral of “near-infrared window.” This finding has encouraged the development of fluorescence techniques to visualize specific biochemical events inside living subjects [1, 2]. The inverse reconstruction problem of FMT is to find the fluorescent source distribution in the target tissue based on the precalculated weighting matrix and the measured data. The conjugate gradient (CG) methods, which need less storage and computation, are favorable for the problems with large-dimension data They have been reported to be adopted successfully in the reconstruction algorithms for imaging modalities such as the positron emission tomography (PET) [15,16,17] and diffusion optical tomography (DOT) [18]. Combining them together may generate an improved algorithm, which has the advantages of both of them

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Conclusion