A novel DBIM algorithm is proposed for microwave breast imaging based on the TwIST 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 illposed linear problem. The paper presents several strategies to increase the algorithm’s robustness: a hybrid multi-frequency approach to achieve an optimal trade-off 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.

We present a first prototype of a wideband microwave tomography system with potential application to medical imaging. The system relies on a compact and robust printed monopole antenna which can operate in the 1.0–3.0 GHz range when fully immersed in commonly used coupling liquids, such as glycerine–water solutions. By simulating the proposed imaging setup in CST Microwave Studio, we study the signal transmission levels and array sensitivity for different target and coupling liquid media. We then present the experimental prototype design and data acquisition process, and show good agreement between experimentally measured data and results from the CST simulations. We assess imaging performance by applying our previously proposed two-dimensional (2-D) DBIM TwIST-algorithm to both simulated and experimental datasets, and demonstrate that the system can reconstruct simple cylindrical targets at multiple frequencies.

This paper studies how limited information in data acquired by a wideband microwave tomography (MWT) system can affect the quality of reconstructed images. Limitations can arise from experimental errors, mismatch between the system and its model in the imaging algorithm, or losses in the immersion and coupling medium which are required to moderate this mismatch. We also present a strategy for improving reconstruction performance by discarding data that is dominated by experimental errors. The approach relies on recording transmitted signals in a wide frequency range, and then correlating the data in different frequencies. We apply this method to our wideband MWT prototype, which has been developed in our previous work. Using this system, we present results from simulated and experimental data which demonstrate the practical value of the frequency selection approach. We also propose a K-neighbour method to identify low quality data in a robust manner. The resulting enhancement in imaging quality suggests that this approach can be useful for various medical imaging scenarios, provided that data from multiple frequencies can be acquired and used in the reconstruction process.