Confocal Microwave Imaging (CMI) for the early detection of breast cancer has been under development for over two decades and is currently going through early-phase clinical evaluation. The image reconstruction algorithm is a key signal processing component of any CMI-based breast imaging system and impacts the efficacy of CMI in detecting breast cancer. Several image reconstruction algorithms for CMI have been developed since its inception. These image reconstruction algorithms have been previously evaluated and compared, using both numerical and physical breast models, and healthy volunteer data. However, no study has been performed to evaluate the performance of image reconstruction algorithms using clinical patient data. In this study, a variety of imaging algorithms, including both data-independent and data-adaptive algorithms, were evaluated using data obtained from a small-scale patient study conducted at the University of Calgary. Six imaging algorithms were applied to reconstruct 3D images of five clinical patients. Reconstructed images for each algorithm and each patient were compared to the available clinical reports, in terms of abnormality detection and localisation. The imaging quality of each algorithm was evaluated using appropriate quality metrics. The results of the conventional Delay-and-Sum algorithm and the Delay-Multiply-and-Sum (DMAS) algorithm were found to be consistent with the clinical information, with DMAS producing better quality images compared to all other algorithms.

Global statistics have demonstrated that breast cancer is the most frequently diagnosed invasive cancer and the leading cause of cancer death among female patients. Survival following a diagnosis of breast cancer is grossly determined by the stage of the disease at the time of initial diagnosis, highlighting the importance of early detection. Improving early diagnosis will require a multi-faceted approach to optimizing the use of currently available imaging modalities and investigating new methods of detection. The application of microwave technologies in medical diagnostics is an emerging field of research, with breast cancer detection seeing the most significant progress in the last twenty years. In this review, the application of current conventional imaging modalities is discussed, and recurrent shortcomings highlighted. Microwave imaging is rapid and inexpensive. If the preliminary results of its diagnostic capacity are substantiated, microwave technology may offer a non-ionizing, non-invasive, and painless adjunct or stand-alone modality that could possibly be implemented in routine diagnostic breast care. Author Contributions: Conceptualization, B.M.M., D.O.'L, S.A.E., and M.J.K.; writing-original draft preparation, B.M.M., D.O.'L and S.A.E., writing-review and editing, B.M.M., D.O.'L, S.A.E., and M.J.K.; supervision, M.J.K.; funding acquisition, M.J.K.; All authors have read and agreed to the published version of the manuscript.

Many new clinical investigations of microwave breast imaging have been published in recent years. Trials with over one hundred participants have indicated the potential of microwave imaging to detect breast cancer, with particularly encouraging sensitivity results reported from women with dense breasts. The next phase of clinical trials will involve larger and more diverse populations, including women with no breast abnormalities or benign breast diseases. These trials will need to address clinical efficacy in terms of sensitivity and specificity. A number of challenges exist when using microwave imaging with broad populations: 1) addressing the substantial variance in breast composition observed in the population; and 2) achieving high specificity given differences between individuals. This work analyses these challenges using a diverse phantom set which models the variance in breast composition and tumour shape and size seen in the population. The data show that the sensitivity of microwave breast imaging in breasts of differing density can suffer if patient-specific beamforming is not used. Moreover, the results suggest that achieving high specificity in dense breasts may be difficult, but that patient-specific beamforming does not adversely affect the expected specificity. In summary, this work finds that patientspecific beamforming has a tangible impact on expected sensitivity in experimental cases and that achieving high specificity in dense breasts may be challenging.

In this paper, a new set of tumour phantoms for the experimental evaluation of Microwave Breast Imaging (MBI) as a method to diagnose breast cancer is presented. The phantoms were based on previously developed numerical models that had been clinically validated, supporting the appropriateness of the phantoms for the development of experimental systems. The proposed tumour phantom set was developed using polyurethane rubber with graphite and carbon-black powders and is the first to incorporate a large number of different shapes and levels of spiculation to emulate different levels of tumour malignancy. A series of spherical, non-spiculated targets was fabricated to model benign tumours, and a series of targets with irregular shapes and increasing spiculation was fabricated to model malignant tumours. The tumour phantoms can be combined with a variety of breast phantoms fabricated with the same method, which are unique in their diversity of glandular tissue content. The modular design of the phantom set allows for tumour and breast phantoms to be dynamically combined, creating an experimental test platform for MBI with a total of 154 cases. Moreover, the dielectric properties of the phantoms display good agreement with the literature, and the phantoms are constructed using materials that have demonstrated stable properties over time. Results also demonstrate how the shape and level of spiculation of a tumour can influence microwave reflections, and therefore impact the performance of imaging and diagnostic systems.

Inaccurate estimation of average dielectric properties can have a tangible impact on microwave radar-based breast images. Despite this, recent patient imaging studies have used a fixed estimate although this is known to vary from patient to patient. Parameter search algorithms are a promising technique for estimating the average dielectric properties from the reconstructed microwave images themselves without additional hardware. In this work, qualities of accurately reconstructed images are identified from point spread functions. As the qualities of accurately reconstructed microwave images are similar to the qualities of focused microscopic and photographic images, this work proposes the use of focal quality metrics for average dielectric property estimation. The robustness of the parameter search is evaluated using experimental dielectrically heterogeneous phantoms on the three-dimensional volumetric image. Based on a very broad initial estimate of the average dielectric properties, this paper shows how these metrics can be used as suitable fitness functions in parameter search algorithms to reconstruct clear and focused microwave radar images.