Creation and validation of a GAN-based approach to minimize the cost and radiation burden of PET radiopharmaceuticals

This project will facilitate the development and validation of a novel AI-based model that can improve the quality of PET images such that diagnostic-quality images can be reconstructed from administering a smaller radiopharmaceutical dose to the patient. The two main advantages of this are decreased radiation dose to the patient which decreases the chance of developing a radiation-induced cancer, and also decreased cost to the healthcare system because less radiopharmaceutical is required to produce a diagnostic-quality study. This will have the downstream effect of expanding the PET radiopharmaceutical arsenal of Canadian physicians, which is often limited due to the significant cost of new radiopharmaceuticals.

Faculty Supervisor:

Alexander Bilbily

Student:

Partner:

Siemens Healthcare Limited

Discipline:

Life Sciences

Sector:

Manufacturing; Professional, scientific and technical services

University:

University of Toronto

Program: