Simplified spherical harmonics approximation of the time-dependent equation of radiative transfer for the forward problem in time-domain diffuse optical tomography

2009 ◽  
Author(s):  
Yves Bérubé-Lauzière ◽  
Vivian Issa ◽  
Jorge Bouza Dominguez
Keyword(s):  
Radiative Transfer ◽  
Spherical Harmonics ◽  
Time Domain ◽  
Optical Tomography ◽  
Diffuse Optical Tomography ◽  
Forward Problem ◽  
Time Dependent

Multi-Level Wavelet Domain Decomposition Based Time-Domain Diffuse Optical Tomography

2008 ◽  
Vol 28 (8) ◽  
pp. 1571-1578
Author(s):  
杨芳 Yang Fang ◽  
马艺闻 Ma Yiwen ◽  
高峰 Gao Feng ◽  
赵会娟 Zhao Huijuan
Keyword(s):  
Domain Decomposition ◽  
Time Domain ◽  
Optical Tomography ◽  
Diffuse Optical Tomography ◽  
Wavelet Domain ◽  
Multi Level

A Methodology on Time-Domain Fluorescence Diffuse Optical Tomography Based on GPU-Accelerated Monte-Carlo Modeling

2013 ◽  
Vol 40 (5) ◽  
pp. 0504002
Author(s):  
易茜 Yi Xi ◽  
武林会 Wu Linhui ◽  
王欣 Wang Xin ◽  
陈玮婷 Chen Weiting ◽  
张丽敏 Zhang Limin ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Time Domain ◽  
Optical Tomography ◽  
Diffuse Optical Tomography ◽  
Monte Carlo Modeling ◽  
Fluorescence Diffuse Optical Tomography

scholarly journals Image Reconstruction for Diffuse Optical Tomography Based on Radiative Transfer Equation

2015 ◽  
Vol 2015 ◽  
pp. 1-23 ◽  
Author(s):  
Bo Bi ◽  
Bo Han ◽  
Weimin Han ◽  
Jinping Tang ◽  
Li Li
Keyword(s):  
Image Reconstruction ◽  
Radiative Transfer ◽  
Transfer Equation ◽  
Radiative Transfer Equation ◽  
Optical Tomography ◽  
Measurement Data ◽  
Diffuse Optical Tomography ◽  
Forward Model ◽  
Sparsity Regularization ◽  
Reconstruction Methods

Diffuse optical tomography is a novel molecular imaging technology for small animal studies. Most known reconstruction methods use the diffusion equation (DA) as forward model, although the validation of DA breaks down in certain situations. In this work, we use the radiative transfer equation as forward model which provides an accurate description of the light propagation within biological media and investigate the potential of sparsity constraints in solving the diffuse optical tomography inverse problem. The feasibility of the sparsity reconstruction approach is evaluated by boundary angular-averaged measurement data and internal angular-averaged measurement data. Simulation results demonstrate that in most of the test cases the reconstructions with sparsity regularization are both qualitatively and quantitatively more reliable than those with standardL2regularization. Results also show the competitive performance of the split Bregman algorithm for the DOT image reconstruction with sparsity regularization compared with other existingL1algorithms.

Sign in / Sign up

Export Citation Format

Share Document