Grant: Discovery Grants Program - Individual

The objectives of this Discovery Grants Program proposal are to advance the scientific and technological knowledge that forms the foundation of quantitative wavefield imaging, both electromagnetic (EM) and ultrasonic (US). The Electromagnetic Imaging Laboratory (EIL), led by Dr. LoVetri, has been developing microwave and ultrasound imaging (MW/USI) techniques primarily for the detection of breast cancer and lower frequency electromagnetic wave imaging (EWI) for the monitoring of stored grain. The latter application aims at detecting spoilage and optimizing stored-grain quality. Advanced EM modelling features will be incorporated into inversion algorithms, including novel sub-grid methods that accurately model thin-wires without the need to refine the mesh. This will allow researchers to accurately and efficiently model the transmitting and receiving antennas within the imaging domain, thus reducing modelling error and resulting in more accurate imaging. Accurate antenna-port models will also be developed that will make the simulated output data from the EM inversion models more closely resemble the data that is actually measured using data-acquisition systems. Techniques for imaging higher-level properties using whole-domain basis functions superimposed on the computational grid will also be investigated. These techniques are used, for example, to segment the breast into coarse fibroglandular, fat, and skin tissue regions. This preliminary segmentation will then be used as prior information in high-resolution algorithms for tumour detection. For stored-grain imaging, the basis functions are first used to image the inherent moisture layers that result from the method in which the grain bin is loaded and unloaded. Machine learning techniques will be introduced into the imaging workflow. This includes a broad range of methods, from the calibration of the measured scattered-field data all the way to post-processing the constructed images. This work will be advanced by creating new deep-learning architectures and trained using synthetic and experimental data sets. The recently constructed prototype US-MW breast imaging system, currently comprised of 60 piezo-electric transducers, forms ultrasonic wave-speed images of the breast's interior that are then used as prior information for MWI. The MW measurements are collected in the quasi-resonant flat-faceted MWI chamber that surrounds the US sensors. This platform will be used as a basis on which to perform phantom-based imaging experiments to evaluate and advance the imaging performance. This research constitutes a first-of-its-kind effort to unite these two imaging modalities into a single system. The dual-physics data will be valuable to researchers in the EIL as well as to other researchers worldwide who have relied only on synthetic data to investigate combined US-MW imaging. A total of ten highly qualified personnel will obtain their training from their involvement in this proposed research.