Tissue sensing adaptive radar for breast cancer detection: investigations of reflections from the skin

Alternative methods of breast cancer detection are of interest due to the limitations of the current gold standard imaging method, mammography (S.J. Nass et al., eds., Mammography and Beyond: Developing Technologies for the Early Detection of Breast Cancer, National Research Council, 2001.). One method of radar-based breast imaging is tissue sensing adaptive radar (TSAR), which senses all tissues in the volume of interest and adapts the algorithm accordingly (E.C. Fear and M. Okoniewski, Microwave and RF Appl., American Ceramic Soc., pp. 487-494, 2003). The dominant reflection in the initial TSAR signal is due to the skin surrounding the breast, which is electrically different from normal breast tissues. This reflection contains information such as the location of the object of interest, and depends on the skin thickness and electrical properties. By analysing the skin reflection, we may extract more information to use in the TSAR algorithm. For example, the location information is used by the TSAR algorithms to create an outline of the region of interest. The thickness information and electrical properties are used in image formation. In this paper, we investigate the application of deconvolution techniques to the initial TSAR signals. The aim of deconvolution in this case is to provide improved estimates of both skin location and thickness. Improved estimates of both of these quantities are expected to result in improved TSAR images.