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A penalized linear and nonlinear combined conjugate gradient method for the reconstruction of fluorescence molecular tomography.

Shang S, Bai J, Song X, Wang H, Lau J - Int J Biomed Imaging (2007)

Bottom Line: A quadratic penalty method is adopted to gain a nonnegative constraint and reduce the illposedness of the problem.It has a better performance than the conventional conjugate gradient-based reconstruction algorithms.It offers an effective approach to reconstruct fluorochrome information for FMT.

View Article: PubMed Central - PubMed

Affiliation: Medical Engineering and Health Technology Research Group, Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China.

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.

No MeSH data available.


(a) Configuration of the simulation experiment usingtwo excitation sources. The object is homogeneous, with a fluorophore(designated with •) imbedded init. Two excitation sources (designated with ∘) are placedaround the inner surface of the object. (b) Mesh in the forward FEM model.
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fig2: (a) Configuration of the simulation experiment usingtwo excitation sources. The object is homogeneous, with a fluorophore(designated with •) imbedded init. Two excitation sources (designated with ∘) are placedaround the inner surface of the object. (b) Mesh in the forward FEM model.

Mentions: In this experiment, a numerical model was set up totest the validity of the PLN-CG algorithm. A circular object was simulated withan outer diameter of 25 mm, which had a fluorophore with a diameter of 4 mmembedded in it. We supposed the optical property to be homogeneous, with μa= 0.005mm −1 and μs= 1 mm−1 . In order to show theefficiency of PLN-CG better, only two excitation sources were used this time.They were placed around the inner surface of the circular object (as shown inFigure 2(a)), and were turned on in turn. For each source, 32 detector readingswere available through the detector fibers, which were distributed uniformly onthe surface of the circular object.


A penalized linear and nonlinear combined conjugate gradient method for the reconstruction of fluorescence molecular tomography.

Shang S, Bai J, Song X, Wang H, Lau J - Int J Biomed Imaging (2007)

(a) Configuration of the simulation experiment usingtwo excitation sources. The object is homogeneous, with a fluorophore(designated with •) imbedded init. Two excitation sources (designated with ∘) are placedaround the inner surface of the object. (b) Mesh in the forward FEM model.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2267888&req=5

fig2: (a) Configuration of the simulation experiment usingtwo excitation sources. The object is homogeneous, with a fluorophore(designated with •) imbedded init. Two excitation sources (designated with ∘) are placedaround the inner surface of the object. (b) Mesh in the forward FEM model.
Mentions: In this experiment, a numerical model was set up totest the validity of the PLN-CG algorithm. A circular object was simulated withan outer diameter of 25 mm, which had a fluorophore with a diameter of 4 mmembedded in it. We supposed the optical property to be homogeneous, with μa= 0.005mm −1 and μs= 1 mm−1 . In order to show theefficiency of PLN-CG better, only two excitation sources were used this time.They were placed around the inner surface of the circular object (as shown inFigure 2(a)), and were turned on in turn. For each source, 32 detector readingswere available through the detector fibers, which were distributed uniformly onthe surface of the circular object.

Bottom Line: A quadratic penalty method is adopted to gain a nonnegative constraint and reduce the illposedness of the problem.It has a better performance than the conventional conjugate gradient-based reconstruction algorithms.It offers an effective approach to reconstruct fluorochrome information for FMT.

View Article: PubMed Central - PubMed

Affiliation: Medical Engineering and Health Technology Research Group, Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China.

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.

No MeSH data available.