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Dr. Stephen Pistorius

University of Manitoba
Email: stephen.pistorius@umanitoba.ca
Date of Live Presentation: tba
Location: tba



Title

Turning Noise into Numbers: Innovative approaches to cancer diagnosis and optimized radiation therapy using microwaves, x-rays and gamma-rays.

Abstract

Electromagnetic radiation (which includes microwaves, gamma rays and x-rays) deposit energy and scatter when they interact with human tissue. Scattered radiation has historically been considered to be noise, contributing dose to the patient, but providing little by way of useful information. Traditional imaging systems such as Computed Tomography (CT) and Positron Emission Tomography (PET) provide valuable diagnostic information but are a significant contributor to an increasing population dose. X-ray mammography, while the standard for breast cancer screening, results in numerous false positives (increasing stress and cost), is not suitable for younger women, and can be uncomfortable. In addition, x-ray mammography requires significant human and capital infrastructure, which means that access by women in remote communities is limited. In radiation therapy, the motion of a tumour during treatment restricts our ability to optimize care by increasing the dose to the tumour while limiting the dose to normal (often radiation sensitive) organs. These are just some of the many problems encountered in cancer detection and treatment. In this presentation, I will present some novel approaches and algorithms that we are using to solve these problems. After providing a background to tomographic scatter imaging, I will summarise the results of our CT, and PET scatter-imaging research. I will show how the scatter-imaging techniques improve our ability to diagnose cancer without the dose or cost penalties associated with multi-modality scanning, can provide attenuation corrections and give an anatomical context to PET, which is otherwise a functional imaging modality. I will talk about how we track and predict tumour motion, using Optical Flow algorithms and Machine Learning (ML) to analyze in real time the tissue motion that is visualized using Electronic Portal Imaging devices. I will conclude with an introduction to microwave imaging and will describe how while using microwaves with less intensity than


Short bio

Prof. Stephen Pistorius trained and practiced as a Medical Physicist in South Africa where he obtained a B.Sc. (Physics & Geography), a Hons. B.Sc. (Radiation Physics), an M.Sc. (Medical Physics) and a Ph.D. (Physics) before moving to Winnipeg. He has experience in the military, in industry, healthcare, and academia. He is a Professor and Associate Head: Medical Physics in the Department of Physics and Astronomy at the University of Manitoba where he is responsible for the CAMPEP accredited Medical Physics graduate program. He is a certified Medical Physicist, a licensed Professional Physicist, a senior member of the IEEE and a fellow of the Canadian Organization of Medical Physics (COMP). He currently serves as the vice-director of the UM Biomedical Engineering Graduate program and as a Senior Scientist at the Research Institute for Oncology and Hematology. Dr. Pistorius is the Past President of the CAP spent and many years on the COMP Board, including a term as President. His current research interests are in novel imaging and machine learning applications for early and improved disease detection and optimized radiation therapy. He has held numerous National Grants, has over 200 publications and presentations and is currently supervising six students and is looking


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