scholarly journals Non-iterative model based image reconstruction of diffuse optical tomography based on DE and RTE in quality control of agricultural product studies

2018 ◽  
Vol 1057 ◽  
pp. 012006
Author(s):  
Vebi Nadhira ◽  
E. Juliastuti ◽  
Agah D. Garnadi ◽  
Deddy Kurniadi ◽  
Yoko Hoshi
Keyword(s):  
Quality Control ◽  
Image Reconstruction ◽  
Optical Tomography ◽  
Diffuse Optical Tomography ◽  
Agricultural Product ◽  
Model Based ◽  
Iterative Model

scholarly journals Non-Iterative Model Based Image Reconstruction of Diffuse and Radiative Transfer Optical Tomography in Quality Control on Agricultural Produce Studies

2017 ◽  
Author(s):  
Agah D. Garnadi
Keyword(s):  
Quality Control ◽  
Image Reconstruction ◽  
Radiative Transfer ◽  
Near Infrared ◽  
Continuous Wave ◽  
Optical Tomography ◽  
Residual Value ◽  
Iterative Image Reconstruction ◽  
Model Based ◽  
Agricultural Produce

In the previous study, we developed the non-iterative image reconstruction based on diffusionequation.Within this research, we applied the same non-iterative algorithm scheme using radiative transfer equation. Basically, the non-iterative image reconstruction was a development of model based image reconstruction that implemented truncated singular value decomposition and L-curve analysis to solve the ill-posed problem.These algorithm reduces the computation time to reconstruct the cross sectional area.As part of the continuing development of agricultural produce quality control based on optical tomography, potato experiment was conducted to evaluate these two non-iterative algorithms. The object was illuminated by the near infrared source from 8 positions on object’s boundary.In this experiment, we vary the position and amount of epoxy as targets on the object then we analyze the residual value between measurement and reconstructed boundary data. The reconstructions were performed with continuous-wave domain.Furthermore, we compare the residual value fromdiffuse optical tomography and radiative transfer optical tomography. The result of this study indicated that these algorithmshave shown promising to detect the presence of epoxy on potato which is significant for agricultural produce quality control.

scholarly journals Numerical modelling and image reconstruction in diffuse optical tomography

2009 ◽  
Vol 367 (1900) ◽  
pp. 3073-3093 ◽  
Author(s):  
Hamid Dehghani ◽  
Subhadra Srinivasan ◽  
Brian W. Pogue ◽  
Adam Gibson
Keyword(s):  
Image Reconstruction ◽  
Numerical Modelling ◽  
Spatial Resolution ◽  
Light Propagation ◽  
Light Transmission ◽  
Imaging Modality ◽  
Optical Tomography ◽  
Diffuse Optical Tomography ◽  
Optical Parameters ◽  
Model Based

The development of diffuse optical tomography as a functional imaging modality has relied largely on the use of model-based image reconstruction. The recovery of optical parameters from boundary measurements of light propagation within tissue is inherently a difficult one, because the problem is nonlinear, ill-posed and ill-conditioned. Additionally, although the measured near-infrared signals of light transmission through tissue provide high imaging contrast, the reconstructed images suffer from poor spatial resolution due to the diffuse propagation of light in biological tissue. The application of model-based image reconstruction is reviewed in this paper, together with a numerical modelling approach to light propagation in tissue as well as generalized image reconstruction using boundary data. A comprehensive review and details of the basis for using spatial and structural prior information are also discussed, whereby the use of spectral and dual-modality systems can improve contrast and spatial resolution.

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.

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