Near-field Microwave Imaging of Complex Objects

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THE OBJECTIVE of this proposal is the development of methods for the imaging of objects which are optically opaque but are penetrable by microwaves. There is a great variety of such materials including living tissues, wood, ceramics, plastics, clothing, concrete, soil, etc. The focus of this proposal is on the near-field microwave imaging of objects with complex internal structure. This line of research has important applications in biomedical imaging, concealed-weapon detection and underground detection. In theory, near-field measurements, as opposed to far-field measurements, offer the potential for higher image quality due to the possibility of capturing much finer shape details. In practice, this potential has remained largely untapped. Recently, our team has proposed two novel image reconstruction algorithms: model-based holography and sensitivity-based imaging. One important advantage of both techniques is that they perform in quasi-real time; in other words, producing the images from the measured data takes negligible time compared to the measurement itself. More importantly, they can take full advantage of the wealth of information contained in the near-field scattered microwave signals and achieve fine-resolution images where shape details as small as one-hundredth of a wavelength can be distinguished. Since the two techniques exploit conceptually different reconstruction principles, they are complementary in the sense that they provide independent diagnostic or detection results. Thus, they can be combined synergistically in a methodology the outcome of which is based on the results of the two underlying techniques but is superior to any one of them taken separately. The current proposal aims at the development of the synergistic methodology described above and its integration with an experimental setup for near-field microwave data acquisition. The software and hardware development will target tools for tissue sensing with applications in early stage breast-cancer detection, which is of great societal importance. At the same time, the developed approaches will have a much wider range of applications with possible extensions into concealed-weapon and buried-object detection.