Image reconstruction in quantitative photoacoustic tomography using the radiative transfer equation and the diffusion approximation

2013 ◽  
Author(s):  
Tanja Tarvainen ◽  
Aki Pulkkinen ◽  
Ben T. Cox ◽  
Jari P. Kaipio ◽  
Simon R. Arridge
Keyword(s):  
Image Reconstruction ◽  
Radiative Transfer ◽  
Diffusion Approximation ◽  
Transfer Equation ◽  
Radiative Transfer Equation ◽  
Photoacoustic 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.

Optical tomography of turbid media containing diffusive and non-diffusive inclusions: An error modeling approach

2019 ◽  
Vol 27 (6) ◽  
pp. 402-415
Author(s):  
P Pardini ◽  
G Baez ◽  
D Iriarte ◽  
J Pomarico
Keyword(s):  
Optical Properties ◽  
Radiative Transfer ◽  
Diffusion Approximation ◽  
Transfer Equation ◽  
Radiative Transfer Equation ◽  
Optical Tomography ◽  
Computation Time ◽  
Error Modeling ◽  
Modeling Approach ◽  
Optical Properties Of Tissue

Diffuse optical techniques have been extensively employed during the last years to retrieve the optical properties of tissue with and without inclusions. The usual approach is to use the diffusion approximation to the radiative transfer equation. However, if low- or non-diffusive regions are inside the studied volume, the diffusion approximation does not hold and the radiative transfer equation needs to be solved, which is computationally much more demanding. In this contribution, the problem of determining the optical properties of the whole volume of a turbid host medium containing both diffusive and non-diffusive inhomegeneities is examined. The situation reproduces clinical cases in which tumors and cysts can be present inside a studied tissue. To achieve this, an extended Kalman filter with compensation by Bayesian error modeling approach was adopted. Applying this technique, the diffusion approximation is used for calculations, reducing computation time, and discrepancies in the non-diffusive regions are compensated by the radiative transfer equation, thus keeping accuracy over the whole volume. The proposal is validated by phantom experiments showing very good results.

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