Simultaneous iterative reconstruction technique for diffuse optical tomography imaging: iteration criterion and image recognition

Yu, Zong Han; Wu, Chun Ming; Lin, Yo Wei; Chuang, Ming Lung; Tsai, Jui-che; Sun, Chia‐Wei

doi:10.1117/12.764559

Cited by 4 publications

(4 citation statements)

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“…With moving mesh method, we can get more accurate solution without increasing computation. On the other hand, we have to find stopping rules such as the well-known discrepancy principle of Morozov for the reconstruction method [31,32].…”

Section: Discussionmentioning

confidence: 99%

An EM‐like reconstruction method for diffuse optical tomography

Wang

2010

Numer Methods Biomed Eng

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SUMMARYDiffuse optical tomography (DOT) is an optical imaging modality which provides the spatial distributions of optical parameters inside an object. The forward model of DOT is described by the diffusion approximation of radiative transfer equation, while the DOT is to reconstruct the optical parameters from boundary measurements. In this paper, an EM-like iterative reconstruction method specifically for the steady state DOT problem is developed. Previous iterative reconstruction methods are mostly based on the assumption that the measurement noise is Gaussian, and are of least-squares type. In this paper, with the assumption that the boundary measurements have independent and identical Poisson distributions, the inverse problem of DOT is solved by maximizing a log-likelihood functional with inequality constraints, and then an EM-like reconstruction algorithm is developed according to the Kuhn-Tucker condition. The proposed algorithm is a variant of the well-known EM algorithm. The performance of the proposed algorithm is tested with three-dimensional numerical simulation.

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Section: Discussionmentioning

confidence: 99%

An EM‐like reconstruction method for diffuse optical tomography

Wang

2010

Numer Methods Biomed Eng

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“…> δ (δ = 0.001) then: Update g k+1 by solving (18). Update f k+1 by solving (19). Update b k+1 by solving (17).…”

Section: The Sbi Algorithmmentioning

confidence: 99%

“…The ART only uses a set of projection data for each iteration, which has a lower computational cost [18]. The SIRT is an improvement of the ART algorithm, but these two algorithms require multiple iterations before obtaining satisfactory results [19]. The Landweber algorithm is a method that can effectively deal with ill-conditioned inverse problems [20].…”

Section: Introductionmentioning

confidence: 99%

A novel computational imaging algorithm based on split Bregman iterative for electrical capacitance tomography

Qing

Shi

Chai

et al. 2021

Meas. Sci. Technol.

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As an advanced detection technology in the industrial field, electrical capacitance tomography (ECT) can better reconstruct the material distribution state in the measured area by selecting the appropriate algorithm. In order to improve the reconstruction quality, this paper devises a novel objective function to model the ECT image reconstruction problem, in which L 1 -norm is deployed as data fidelity with the focus on weakening the influence of capacitance outliers on the reconstruction quality, L P regularization reinforces the sparseness of the phantom objects, and L 1 regularization applies to model deviation variables to increase the robustness of the system. Based on the fast-iterative shrinkage thresholding technique and the soft thresholding method as sub-solvers, the split Bregman iterative method is designed as an effective solver for the proposed objective function. Numerical simulation and experimental validate that the proposed algorithm has excellent imaging ability compared with other imaging algorithms, which provides a good choice for the practical application of ECT in the future.

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“…The semi-convergence phenomenon [24,25] occurs in both numerical experiments. However, this phenomenon is not contrary to the convergence of the reconstruction algorithm due to the initial guess of optical parameters and the non-uniqueness of the problem.…”

Section: Numerical Examplementioning

confidence: 99%