Adaptive truncated total least squares on distorted Born iterative method in ultrasound inverse scattering problem

摘要

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 theinhomogeneous Greens function, as the kernel function, to alternatively calculate the total eld for the forwardproblem and the scattering function for the inverse problem. One principal computational problem involved isthat inverse problem is ill-posed, which will result in divergence of the DBI method if inappropriate regularizationis used. This paper presents the regularization with truncated total least square (TTLS) where the adaptivealgorithm is used to choose the regularization parameter in each iteration of DBI instead of using a xed truncatedvalue in all the iterations. In order to prevent the solution from being contaminated by noise, adaptive algorithmtruncates the smallest singular values while minimizing the loss of signal obtained from transducers. Numericalsimulations demonstrate that the proposed adaptive algorithm in conjunction with TTLS outperform TTLS with xed truncation parameter by e ectively reducing the noise and minimizing the relative error.