Postdoctoral fellow: Dr. Ian Jeffrey, Electrical and Computer Engineering, University of Manitoba
Lead faculty member: Dr. Joe LoVetri, Electrical and Computer Engineering, University of Manitoba
Among the core components of Magnetic Resonance Imaging (MRI) systems are the radio frequency (RF) transmitter and receiver coils responsible for acquiring the signals used to create images. Specialized imaging techniques typically include the use of custom RF coils to maximize signal-to-noise ratio and localize the area within the body being imaged. The design of such RF coils requires sophisticated electromagnetic (EM) algorithms that include, for example, the modeling of interface circuitry and cabling used to drive the coils.
Real Time 3D Reconstruction of Breast Microwave Multistatic Radar Images using Adaptive Holographic Technique
Postdoctoral Fellow: Dr. Daniel Flores-Tapia, Department of the Mathematics, University of Manitoba
Lead faculty member: Dr. Kirill Kopotun, Department of the Mathematics, University of Manitoba
Breast Microwave Radar is a promising new technology for breast cancer detection. Nevertheless, current image formation methods face issues that limit the use of this technology in clinical scenarios. The goal of this project is to use mathematical modeling and analysis to develop a novel image formation method for breast microwave radar suitable for use in realistic breast imaging settings. This technique will be capable of generating accurate and high contrast images for a specific patient in real time.
Postdoctoral fellow: Dr. Xiteng Liu, Mathematics and Statistics, York University
Lead faculty member: Dr. Hongmei Zhu, Mathematics and Statistics, York University
Magnetic Resonance Imaging (MRI) is an important medical imaging technology for clinical diagnostics. However, its slowness in data acquisition poses major problems in practice. In recent years, many research efforts to accelerate MRI data acquisition were based on the compressed sensing (CS) theory. CS is effective for signals that have highly sparse representations. However, it suffers from high computational complexity and lack of performance stability.
Dr. Adrian Nachman , University of Toronto
Dr. Jiri Patera, Université de Montréal
The development of new biomedical imaging techniques has resulted in significantly better tools for doctors and scientists to image humans and animals in-vivo. Technological developments and new types of imagers with more capabilities are revolutionizing the field. Currently, available technologies for brain imaging include Magnetic Resonance Imaging (MRI), functional MRI, Diffuse Optical Tomography (DOT), Electro-Encephalography (EEG) and Magneto-Encephalography.