2016: John S. Laughlin Young Scientist Award, American Association of Physicists in Medicine (AAPM)
Molecular imaging of cancer, Quantitative Imaging, Image reconstruction, Machine learning and radiomics, Theranostics
Quantitative PET for enhanced diagnosis, prognosis and treatment response assessment of cancer patients, Quantitative SPECT-based dosimetry in molecular radiotherapy, Dynamic whole-body PET/CT imaging, Statistical image recon algorithms, etc.
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. At our Quantitative Radiomolecular Imaging and Therapy (Qurit) lab, 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.
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.
In our team, we pursue interdisciplinary research towards enhanced quantitative image generation and analysis for medical imaging devices (PET, SPECT). Our lab has extensive experience and expertise with mathematical, computational and engineering tools to achieve state-of-the-art medical imaging, and we continue to lead collaborations with researchers in the areas of imaging physics, engineering, robotics, mathematics, computer science, radiochemistry, material science, drug delivery and biological modeling. We aim to develop and validate novel solutions for medical device data acquisition, data correction, image reconstruction and quantification.