scholarly journals Diffuse Optical Tomography by Inverse Problem Technique.

2002 ◽  
Vol 30 (11) ◽  
pp. 625-629
Author(s):  
Yukio YAMADA ◽  
Feng GAO
Keyword(s):  
Inverse Problem ◽  
Optical Tomography ◽  
Diffuse Optical Tomography

State space regularization in the nonstationary inverse problem for diffuse optical tomography

2011 ◽  
Vol 27 (2) ◽  
pp. 025009 ◽  
Author(s):  
P Hiltunen ◽  
S Särkkä ◽  
I Nissilä ◽  
A Lajunen ◽  
J Lampinen
Keyword(s):  
Inverse Problem ◽  
State Space ◽  
Optical Tomography ◽  
Diffuse Optical Tomography

scholarly journals Frequency Shifting Model for Diffuse Optical Tomography

2021 ◽  
Author(s):  
Huseyin Ozgur Kazanci
Keyword(s):  
Inverse Problem ◽  
Frequency Domain ◽  
Continuous Wave ◽  
Model Problem ◽  
Optical Tomography ◽  
Diffuse Optical Tomography ◽  
Solution Space ◽  
Forward Model ◽  
Problem Solution ◽  
Frequency Shifting

Abstract Diffuse Optical Tomography (DOT) imaging technique has been interesting research field for researchers since it has uncertainties in the solution space. DOT modality is unsolved scientific problem. Inverse problem solution and image reconstruction has never been in its best quality. Reconstructed images have low spatial resolution. Scattering nature of diffusive light is the obscuring effect for DOT modality. DOT has 3 functional sub-branches which of these are Continuous Wave (CW), Time-Resolved (TR), and Frequency-Domain (FD). In this work, one new approach to Frequency Domain Diffuse Optical Tomography (FDDOT) biomedical optic imaging modality is presented to the readers. Frequency Shifting data were added to the forward model problem which basically has source-detector couplings and number of imaging voxels. 100 MHz center core light modulation frequency was selected. 169 source-detector matches were used on back-reflected imaging geometry. Absorption coefficient ma was selected 0.1 cm− 1. Scattering coefficient µs was selected 100 cm− 1. 1 micrometer x, y, z cartesian grid coordinates were used in each direction for imaging tissue-like simulation media. The total of 100 frequency shift was added to the forward model problem which has 5 Hz frequency step. 2 inclusion objects were embedded inside the imaging simulation phantom. 2 inclusion images were successfully reconstructed with the low contrast to noise ratio (CNR) error and position error (PE). Frequency shifting technique is first applied for FDDOT here. This technique has increased the total number of equations in the forward model problem; hence it is helping to solve the inverse problem. In this work, the positive effect of using multi frequency methodology was observed. Differentiation of 2 embedded inclusions was successfully completed and illustrated in this work.

scholarly journals A wavelet multi-scale method for the inverse problem of diffuse optical tomography

2015 ◽  
Vol 289 ◽  
pp. 267-281 ◽  
Author(s):  
Fabien Dubot ◽  
Yann Favennec ◽  
Benoit Rousseau ◽  
Daniel R. Rousse
Keyword(s):  
Inverse Problem ◽  
Optical Tomography ◽  
Diffuse Optical Tomography ◽  
Multi Scale ◽  
Scale Method

scholarly journals Graph- and finite element-based total variation models for the inverse problem in diffuse optical tomography

2019 ◽  
Vol 10 (6) ◽  
pp. 2684 ◽  
Author(s):  
Wenqi Lu ◽  
Jinming Duan ◽  
David Orive-Miguel ◽  
Lionel Herve ◽  
Iain B. Styles
Keyword(s):  
Inverse Problem ◽  
Finite Element ◽  
Total Variation ◽  
Optical Tomography ◽  
Diffuse Optical Tomography

scholarly journals Time-resolved diffuse optical tomography system using an accelerated inverse problem solver

2018 ◽  
Vol 26 (2) ◽  
pp. 963 ◽  
Author(s):  
Mrwan Alayed ◽  
Mohamed A. Naser ◽  
Ishaq Aden-Ali ◽  
M. Jamal Deen
Keyword(s):  
Inverse Problem ◽  
Optical Tomography ◽  
Diffuse Optical Tomography ◽  
Problem Solver ◽  
Time Resolved ◽  
Tomography System

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.

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