Ph.D. candidate Ph.D. candidate Faculté des sciences et de génie Université Laval

The aim of this doctoral thesis is to develop a tool able to automatically provide organs of interest segmentations on computed tomography images using machine learning techniques.

This tool will then be used to calculate organ doses in order to establish personalized dosimetric records in medical imaging. Doses will be calculated using informations obtained from images, radiographic technique and GPU-based Monte Carlo dose calculation algorithm (GPUMCD). Automated pipelines will be implemented to process large amounts of data.

This project permits to better evaluate the exposure of the population to ionizing radiation caused by medical imaging procedures

Personalized dosimetry in computed tomography imaging


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Radiotherapy treatments currently used in the clinical field are rarely modified. They generally consist of a global therapy of 50 grays, fractionated in five treatments of two grays every week for five weeks.
Thus, it could be worthwhile to develop a numeric tool, based on mathematical models found in the literature, in order to compare different types of treatment without having to test them on real tissues. Several parameters are known to alter the tissue response after irradiation including oxygen

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