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.
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.
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.
