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.
Fast Feature Extraction and Non Iterative Multi Modal Image Registration for Orthopaedic Trauma using Local Phase Features
Postdoctoral fellow: Dr. Ilker Hacihaliloglu, Department of Orthopaedics, University of British Columbia
Lead faculty member: Dr. David Wilson, Department of Orthopaedics, University of British Columbia
The Canadian National Trauma Registry have recorded that out of 109,738 major injuries occurring in 1999, 4531 had a pelvis fracture. Traditional intraoperative imaging modality in orthopaedic surgery is 2D fluoroscopy which makes identification of 3D bone surfaces very difficult and exposes the patient and the surgical team to harmful ionizing radiation. Ultrasound has traditionally been used to image the body's soft tissue, organs, and blood flow 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.