CBCT dose planning for adaptive radiotherapy solutions

Adaptive radiotherapy (ART) consists of adjusting irradiation at each treatment phase in response to changes in the patient’s body (such as weight loss) or from the patient’s change in position. Indeed, since initial dose plans are determined from standard CT, the initial dose distribution may vary and yield sub-optimal dose delivery. Intra-procedural imaging such as cone-beam CT (CBCT) can be used to adapt plans on a daily basis, but requires real-time performance with the patient on the table. In this project, we propose a set of software tools based on deep learning to simplify the clinical workflow by automatically fusing the initial plan determined from CT, with the latest CBCT acquired right before treatment. We also plan to improve the delineation of the patient tumours using multimodal imaging. As a result of this project, we plan to streamline the radiotreatment process and improve patient outcomes.

Faculty Supervisor:

Samuel Kadoury

Student:

Partner:

Elekta

Discipline:

Engineering

Sector:

Health and Related Sciences & Technology; Manufacturing

University:

Polytechnique Montréal

Program: