Abstract
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 standard L 2 regularization. Results also show the competitive performance of the split Bregman algorithm for the DOT image reconstruction with sparsity regularization compared with other existing L 1 algorithms.
Highlights
Diffuse optical tomography (DOT) is an emerging imaging modality that has attracted much attention in clinical diagnosis, for example, in breast cancer detection, monitoring of infant brain tissue oxygenation level, and functional brain activation studies, cerebral hemodynamic, and so forth; compare [1,2,3]
A justification of this phenomenon is that the energy of the incident current is decayed much due to the large and relatively frequently change of the scattering coefficient of the inclusion. One can alleviate this phenomenon by increasing the number of detectors or measurement data
0.5 (d) internal measurement to test the validity of the proposed method; results show that the proposed method is practicable and feasible; it performs steadily with various measurement data; the internal measurement can better-pose the inverse problem and achieve more accurate results
Summary
Diffuse optical tomography (DOT) is an emerging imaging modality that has attracted much attention in clinical diagnosis, for example, in breast cancer detection, monitoring of infant brain tissue oxygenation level, and functional brain activation studies, cerebral hemodynamic, and so forth; compare [1,2,3]. The forward problem in DOT describes the photon propagation in tissues and the inverse problem involves estimating the absorption and scattering coefficients of tissues from light measurements on the surface. Because the sparsity regularization is nonsmooth, it is still a challenge to find efficient methods to solve this convex basis pursuit optimization problem, and the choice of techniques for solving it becomes crucial. Motivated by the inverse problem in imaging [22,23,24,25], the authors used and developed the Bregman iteration technique for the L1 regularization problems and proved that the Bregman iteration method is an effective way to solve the L1 norm minimization problems. We adopt the split Bregman method for solving sparsity regularization problems.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Computational and mathematical methods in medicine
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
