scholarly journals Image reconstruction for diffuse optical tomography using sparsity regularization and expectation-maximization algorithm

2007 ◽  
Vol 15 (21) ◽  
pp. 13695 ◽  
Author(s):  
Nannan Cao ◽  
Arye Nehorai ◽  
Mathews Jacobs
Keyword(s):  
Image Reconstruction ◽  
Expectation Maximization ◽  
Optical Tomography ◽  
Expectation Maximization Algorithm ◽  
Diffuse Optical Tomography ◽  
Sparsity Regularization

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.

Ordered subset expectation maximization algorithm for image reconstruction applied to zero-flow or low-flow mimicking brain phantom

2005 ◽  
Vol 25 (1_suppl) ◽  
pp. S678-S678
Author(s):  
Yasuhiro Akazawa ◽  
Yasuhiro Katsura ◽  
Ryohei Matsuura ◽  
Piao Rishu ◽  
Ansar M D Ashik ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Expectation Maximization ◽  
Expectation Maximization Algorithm ◽  
Low Flow ◽  
Brain Phantom ◽  
Zero Flow

Effective optode configuration for the image reconstruction in diffuse optical tomography

2010 ◽  
Vol 25 (3) ◽  
pp. 154-160
Author(s):  
Kazuhiro Uchida ◽  
Shinpei Okawa ◽  
Shoko Matsuhashi ◽  
Yoko Hoshi ◽  
Yukio Yamada
Keyword(s):  
Image Reconstruction ◽  
Optical Tomography ◽  
Diffuse Optical Tomography

scholarly journals Evaluating real-time image reconstruction in diffuse optical tomography using physiologically realistic test data

2015 ◽  
Vol 6 (12) ◽  
pp. 4719 ◽  
Author(s):  
Sabrina Brigadoi ◽  
Samuel Powell ◽  
Robert J. Cooper ◽  
Laura A. Dempsey ◽  
Simon Arridge ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Real Time ◽  
Test Data ◽  
Optical Tomography ◽  
Diffuse Optical Tomography ◽  
Real Time Image ◽  
Time Image

Dynamic image reconstruction in time-resolved diffuse optical tomography

2015 ◽  
Author(s):  
Samuel Powell ◽  
Robert J. Cooper ◽  
Jeremy C. Hebden ◽  
Simon R. Arridge
Keyword(s):  
Image Reconstruction ◽  
Optical Tomography ◽  
Diffuse Optical Tomography ◽  
Time Resolved ◽  
Dynamic Image

scholarly journals Depth sensitivity and image reconstruction analysis of dense imaging arrays for mapping brain function with diffuse optical tomography

2009 ◽  
Vol 48 (10) ◽  
pp. D137 ◽  
Author(s):  
Hamid Dehghani ◽  
Brian R. White ◽  
Benjamin W. Zeff ◽  
Andrew Tizzard ◽  
Joseph P. Culver
Keyword(s):  
Image Reconstruction ◽  
Brain Function ◽  
Optical Tomography ◽  
Diffuse Optical Tomography ◽  
Imaging Arrays ◽  
Depth Sensitivity
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