Louis ArchambaultPh.D.

Dr. Archambault is a FRQ-S Junior 2 Research Scholar and a radiation physicist with expertise in medical physics, radiation oncology, and medical imaging. He joined the CHU de Québec in 2010 after a postdoctoral fellowship in the division of radiation oncology at the MD Anderson Cancer Center and became a professor in the department of physics, engineering physics, and optics at Laval University in 2013. Hi work focuses on developing new instruments and novel algorithms to make radiation treatments more efficient. His work has been recognized on multiple occasions by the scientific community. Since 2012, he has received the Sylvia Fedoruk prize twice from the Canadian Organization of Medical Physicists (COMP) annually acknowledging the best scientific publication on medical physics by Canadian authors.

Improving the efficiency of radiation treatments

The success of a radiation treatment depends on our ability to focus a high dose of radiation on a tumor target while sparing surrounding tissues. To this end, the complexity of radiation treatment delivery has tremendously increased in recent years, and new tools are required to rapidly and accurately monitor radiation dose delivery. Using materials that emit visible light when irradiated, Dr. Archambault’s team has developed new types radiation dosimeters such as one of the first time-resolved 3D radiation dose detectors. These innovative tools are uniquely positioned to address the challenges of modern radiation treatments (e.g. delivery in the presence of strong magnetic fields) and offer a new way of studying the factors that limit the efficiency of radiation treatments such as anatomical changes.

A second aspect of the research is the development of smart algorithms that automatically analyze data and images produced during radiation treatments to guarantee accurate delivery. Thus, building such a virtual safety net can complement and support the expertise of healthcare professionals to guarantee that every cancer patient treated with radiotherapy receives the best possible treatment. Using machine learning, these algorithms can even predict which patients are likely to require an adaptation of their treatment plan, thus opening new possibilities in personalized radiotherapy.