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. Yijun Lou, Mathematics and Statistics, York University
Lead faculty member: Dr. Jane Heffernan, Mathematics and Statistics, York University
Genital herpes (GH), caused by Herpes simplex virus type 1 or 2 (HSV-1 or -2), is one of the most prevalent sexually transmitted diseases in the world. Currently, there is no effective treatment for GH, but a new vaccine Simplirix (by GSK), is currently in clinical trials. Simplirix has had some success in preventing disease, but only in females that are HSV-1 and -2 negative. Since oral herpes (OH, also caused by HSV-1 and -2) infection can occur at very early ages, vaccination against GH may be most effective in a childhood vaccination program.
Postdoctoral fellow: Dr. Raluca Eftimie, Pathology and Molecular Medicine, McMaster University
Lead faculty member: Dr. Jonathan Bramson, Pathology and Molecular Medicine, McMaster University
Cancer emergence and progression are highly complex processes characterized by interactions among a large variety of cells and signalling molecules. It is very difficult to explain these complex interactions through linear thinking and molecular reductionist approaches. Mathematical modeling is a powerful tool that can substantially enhance our capacity for interpreting the data and generate new hypotheses.
Postdoctoral fellow: Dr. Paul Nguyen, Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto
Lead faculty member: Dr. Patrick Brown, Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto
This project aims to develop methods and software for performing spatio-temporal analysis of cancer incidence and smoking data in Ontario over long time periods with high spatial resolution. This will allow changes of cancer incidence over time to be better understood, and accommodate rare cancers that require long study periods in order to accumulate data. Because of small counts common to small area analysis, computationally intensive Bayesian inference methods will be needed.
Postdoctoral fellow: Dr. Majid Jaberi-Douraki, Mathematics and Statistics, York University
Lead faculty member: Dr. Seyed Moghadas, Mathematics and Statistics, York University
A major pharmaceutical intervention for management of many infectious diseases is the use of antiviral drugs. However, the rise of drug resistance poses significant threats to the effectiveness of drugs. This research proposes to determine optimal treatment strategies, through the development of population dynamical models for disease transmission and control, which can minimize the effect of resistance emergence in the population. This work will primarily focus on influenza infection, which still inflicts substantial morbidity, mortality, and socioeconomic costs worldwide.
Postdoctoral fellow: Dr. Babak Taati, Department of Occupational Science and Occupational Therapy, University of Toronto
Lead faculty member: Dr. Alex Mihailidis, Department of Occupational Science and Occupational Therapy, University of Toronto
Each year, about 50,000 Canadians suffer from a stroke and 75% of them are left with a post-stroke disability or impairment. The economic costs of strokes are $3.6 billion a year. Our proposal involves developing an advanced rehabilitation device that helps post-stroke patients regain their mobility. Such patients often suffer from partial paralysis due to brain tissue damage during the stroke. While the physical brain damage could somewhat recover over time, the mobility problems often persist as a “learned paralysis” that settles during recovery.
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. Yildiz Yilmaz, Dalla Lana School of Public Health, University of Toronto
Lead faculty member: Dr. Shelley Bull, Dalla Lana School of Public Health, University of Toronto
The objective of the project is to develop, evaluate and apply informative statistical methods to the task of identifying novel genes/pathways involved in breast cancer recurrence. A model for time to cancer recurrence using clinical, pathological, and molecular measures in the setting of high-dimensional genome-wide genetic scans will be developed that allows for a proportion of the patients to be long-term survivors.
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