Paper
15 March 2019 Adaptive truncated total least square on distorted born iterative method in ultrasound inverse scattering problem
Anita Carević, Xingzhao Yun, Mohamed Almekkawy
Author Affiliations +
Abstract
One of the most powerful approach in ultrasound tomography (UT) is making use of distorted Born iterative (DBI) method to reconstruct high quality image in order to help locate and identify tumors more precisely. Due to its iterative nature, it begins with Born approximation as the initial guess. Then, it makes use of the inhomogeneous Greens function, as the kernel function, to alternatively calculate the total field for the forward problem and the scattering function for the inverse problem. One principal computational problem involved is that inverse problem is ill-posed, which will result in divergence of the DBI method if inappropriate regularization is used. This paper presents the regularization with truncated total least square (TTLS) where the adaptive algorithm is used to choose the regularization parameter in each iteration of DBI instead of using a fixed truncated value in all the iterations. In order to prevent the solution from being contaminated by noise, adaptive algorithm truncates the smallest singular values while minimizing the loss of signal obtained from transducers. Numerical simulations demonstrate that the proposed adaptive algorithm in conjunction with TTLS outperform TTLS with fixed truncation parameter by effectively reducing the noise and minimizing the relative error.
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Anita Carević, Xingzhao Yun, and Mohamed Almekkawy "Adaptive truncated total least square on distorted born iterative method in ultrasound inverse scattering problem", Proc. SPIE 10955, Medical Imaging 2019: Ultrasonic Imaging and Tomography, 1095515 (15 March 2019); https://doi.org/10.1117/12.2512416
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Cited by 2 scholarly publications.
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KEYWORDS
Scattering

Data transmission

Inverse scattering problem

Transducers

Ultrasonography

Inverse problems

Iterative methods

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