Multiple-Frequency DBIM-TwIST Algorithm for Microwave Breast Imaging
Multiple-Frequency DBIM-TwIST Algorithm for Microwave Breast Imaging
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DOI：
10.1109/tap.2017.2679067
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发表时间：
2017-05-01
影响因子：
5.7
通讯作者：
Kosmas, Panagiotis
中科院分区：
文献类型：
--
作者：
Miao, Zhenzhuang;Kosmas, Panagiotis
提出了一种新的基于两步迭代收缩/阈值的失真玻恩迭代法(DBIM)微波乳腺成像算法。我们表明，这种实现比使用传统的Krylov子空间方法(如CGLS)作为不适定线性问题的求解器更灵活和健壮。本文提出了几种增强算法稳健性的策略：一种在成像精度和重建稳定性之间实现最佳折衷的混合多频方法；一种新的估计乳腺组织平均属性的方法，该方法基于沿着其可能值范围进行采样并运行多次DBIM迭代以寻找最小误差；以及一种新的基于L-1范数和帕累托曲线的DBIM方法的正则化策略。我们给出的重建例子说明了这些优化策略的好处，这些优化策略已经导致了DBIM算法，其性能优于我们之前的微波乳房成像实现。
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 L-1 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.