Cancer is a leading cause of death globally, and based on North American statistics, approximately half of the population will develop cancer during their lifetime. Here at the Quantitative Radiomolecular Imaging and Therapy (Qurit) lab and provincial program (pronounced Cure-It!), we aim to improve diagnosis and prognosis, and offer new radiotherapy alternatives that help save lives. We do this by quantifying how radiomolecules (radiopharmaceuticals), a combination of drugs and radioactive isotopes, precisely target cancer cells, in both imaging and therapy applications.
We’re a multicultural team of scientists, researchers and programmers from disciplines such as physics, engineering and computer science. We are located at the BC Cancer Research Institute, in close connection with the clinical environment and having collaborations with researchers from other disciplines.
If you’re interested in applications of physics and technology to help cure cancer, and to familiarize yourself with techniques such as artificial intelligence (AI) and machine learning algorithms, we invite you to talk to us. We are continuously and actively recruiting students, postdocs, volunteers, interns, and visiting students from all around the world. If you are proactive and like to stay updated with the exciting pace of developments and applications in medical imaging and AI, you are probably a great fit for our team and we are for you!
We have collaborations with the industry, and some of our close partners include General Electric, Siemens, Microsoft and MIM. This gives our team members the opportunity to also gain skills that are valuable for translation of our methods to routine clinical practice.
In Partnership With
- 3 minute video summary of our research!3 minute summary (pitch) of our research to graduate students at the UBC Department of Physics & Astronomy (Sept 24, 2020):
- DICOM Networking in PythonSixth post in a series of posts written by members of the Qurit Team. Written by Adam Watkins. At Qurit, our research relies heavily on analyzing medical images from PET and SPECT scanners. These images, and the majority of all clinical images, come in the DICOM file format. A somewhat underrated yet needed part of the […]
- Identification of Parkinson’s Disease Subtypes using Radiomics and Machine LearningFourth post in a series of posts written by members of the Qurit Team. Written by Mohammad Salmanpour. At Qurit lab, my research aim is to subdivide Parkinson’s disease (PD) into specific subtypes. This is important since homogeneous groups of patients are more likely to share genetic and pathological features, enabling potentially earlier disease recognition and […]