Reconstruction of ultrasound tomography for cancer detection using total least squares and conjugate gradient method

The distorted Born iterative (DBI) method is a powerful approach for solving the inverse scattering problem for ultrasound tomographic imaging. This method iteratively solves the inverse problem for the scattering function and the forward problem for the inhomogeneous Green's function and the total field. Because of the ill-posed system from the inverse problem, regularization methods are needed to obtain a smooth solution. The three methods compared are truncated total least squares (TTLS), conjugate gradient for least squares (CGLS), and Tikhonov regularization. This paper uses numerical simulations to compare these three approaches to regularization in terms of both quality of image reconstruction and speed. Noise from both transmitters and receivers is very common in real applications, and is considered in stimulation as well. The solutions are evaluated by residual error of scattering function of region of interest(ROI), convergence of total field solutions in all iteration steps, and accuracy of estimated Green's functions. By comparing the result of reconstruction quality as well as the computational cost of the three methods under different ultrasound frequency, we prove that TTLS method has the lowest error in solving strongly ill-posed problems. CGLS consumes the shortest computational time but its error is higher than TTLS, but lower than Tikhonov regularization.