scholarly journals Dimensionality Reduction Based Optimization Algorithm for Sparse 3-D Image Reconstruction in Diffuse Optical Tomography

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Tanmoy Bhowmik ◽  
Hanli Liu ◽  
Zhou Ye ◽  
Soontorn Oraintara
Keyword(s):  
Image Reconstruction ◽  
Dimensionality Reduction ◽  
Optimization Algorithm ◽  
Optical Tomography ◽  
Diffuse Optical Tomography

scholarly journals Image Reconstruction in Diffuse Optical Tomography Using Adaptive Moment Gradient Based Optimizers: A Statistical Study

2020 ◽  
Vol 10 (24) ◽  
pp. 9117
Author(s):  
Nada Chakhim ◽  
Mohamed Louzar ◽  
Abdellah Lamnii ◽  
Mohammed Alaoui
Keyword(s):  
Inverse Problem ◽  
Image Reconstruction ◽  
Convergence Rate ◽  
Optimization Algorithm ◽  
Optical Tomography ◽  
Diffuse Optical Tomography ◽  
High Convergence Rate ◽  
Significant Difference ◽  
The Right

Diffuse optical tomography (DOT) is an emerging modality that reconstructs the optical properties in a highly scattering medium from measured boundary data. One way to solve DOT and recover the quantities of interest is by an inverse problem approach, which requires the choice of an optimization algorithm for the iterative approximation of the solution. However, the well-established and proven fact of the no free lunch principle holds in general. This paper aims to compare the behavior of three gradient descent-based optimizers on solving the DOT inverse problem by running randomized simulation and analyzing the generated data in order to shade light on any significant difference—if existing at all—in performance among these optimizers in our specific context of DOT. The major practical problems when selecting or using an optimization algorithm in a production context for a DOT system is to be confident that the algorithm will have a high convergence rate to the true solution, reasonably fast speed and high quality of the reconstructed image in terms of good localization of the inclusions and good agreement with the true image. In this work, we harnessed carefully designed randomized simulations to tackle the practical problem of choosing the right optimizer with the right parameters in the context of practical DOT applications, and derived statistical results concerning rate of convergence, speed, and quality of image reconstruction. The statistical analysis performed on the generated data and the main results for convergence rate, reconstruction speed, and quality between three optimization algorithms are presented in the paper at hand.

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 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.

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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