Volume 9, Issue 2 (lasers in medicine 2012)
lmj 2012, 9(2): 22-27 |
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Solve the Forward Problems by Green Function in New Fluorescence Molecular Tomography Imaging System. lmj 2012; 9 (2) :22-27
URL: http://icml.ir/article-1-253-en.html
URL: http://icml.ir/article-1-253-en.html
Solve the Forward Problems by Green Function in New Fluorescence Molecular Tomography Imaging System
Abstract: (9802 Views)
Background: Molecular imaging can be used to visualization of molecular events in the cellular or sub-cellular level. This method is widely used to diagnose and treat skin and vascular diseases. Fluorescent molecular tomography imaging is established as a molecular imaging modality in which the fluorescent is injected into the lesion. The sample is irradiated by a laser beam and fluorescence emitted from it. By rotating the CCD camera around the sample, intensity on the surface of the sample is recorded. The main goal of optical imaging is giving fluorescent concentration in target tissue according to fluorescence intensity on the surface. Therefore, the intensity at surface of the tissue is determined by mathematical formulas as Forward problem. The purpose of this article is development of a Forward algorithm for fluorescence molecular tomography.
Material and Methods: In this study a new optical set up was implemented in the cylindrical geometry. The phantom like tissue was constructed and imaging was done and fluorescent intensity at surface of the sample is recorded. The intensity was considered as reference data. In the next step forward algorithm was written based on the diffusion approximation in Matlab. In order to verify performance of the algorithm, the signal to noise ratios were compared with given images. Furthermore, the intensities were compared of the correlations.Results: The results showed that there is no significant difference between signal to noise ratio in written algorithm and given images. Also correlation between written algorithm and reference data was more than 0.95.
Material and Methods: In this study a new optical set up was implemented in the cylindrical geometry. The phantom like tissue was constructed and imaging was done and fluorescent intensity at surface of the sample is recorded. The intensity was considered as reference data. In the next step forward algorithm was written based on the diffusion approximation in Matlab. In order to verify performance of the algorithm, the signal to noise ratios were compared with given images. Furthermore, the intensities were compared of the correlations.
Results: The results showed that there is no significant difference between signal to noise ratio in written algorithm and given images. Also correlation between written algorithm and reference data was more than 0.95.
Discussion and conclusion: In this article an algorithm that solves the 3D diffusion equation in homogeneous turbid phantom like tissue was presented. This algorithm as an appropriate method with correlation more than 0.95 can be used for determining the location of the lesion in the soft tissues
Keywords: Forward problem, Diffusion approximation, Green's functions, Kirchhoff approximation, Born approximation
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