Multiple-Frequency DBIM-TwIST Algorithm for Microwave Breast Imaging

摘要

A novel distorted Born iterative method (DBIM) algorithm is proposed for microwave breast imaging based on the two-step iterative shrinkage/thresholding method. We show that this implementation is more flexible and robust than using traditional Krylov subspace methods such as the CGLS as solvers of the ill-posed linear problem. This paper presents several strategies to increase the algorithm's robustness: a hybrid multifrequency approach to achieve an optimal tradeoff between imaging accuracy and reconstruction stability; a new approach to estimate the average breast tissues properties, based on sampling along their range of possible values and running a few DBIM iterations to find the minimum error; and finally, a new regularization strategy for the DBIM method based on the L1 norm and the Pareto curve. We present reconstruction examples which illustrate the benefits of these optimization strategies, which have resulted in a DBIM algorithm that outperforms our previous implementations for microwave breast imaging.