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

Shang Shang | Zendy; Jing Bai | Zendy; Xiaolei Song | Zendy; Hongkai Wang | Zendy; Jaclyn Lau | Zendy

Open Access

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

Author(s) -

Shang Shang,

Jing Bai,

Xiaolei Song,

Hongkai Wang,

Jaclyn Lau

Publication year - 2007

Publication title -

international journal of biomedical imaging

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.626

H-Index - 41

eISSN - 1687-4196

pISSN - 1687-4188

DOI - 10.1155/2007/84724

Subject(s) - conjugate gradient method , nonlinear conjugate gradient method , derivation of the conjugate gradient method , conjugate , computer science , nonlinear system , gradient method , conjugate residual method , algorithm , constraint (computer aided design) , dimension (graph theory) , tomography , mathematical optimization , mathematics , gradient descent , artificial intelligence , physics , mathematical analysis , artificial neural network , optics , geometry , quantum mechanics , pure mathematics

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.

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