Medial Physics Projects

Multiscale Monte Carlo Simulations for Radiation Therapy

Radiation transport simulations are broadly used to study many aspects of physics related to radiotherapy treatments for cancer on different length scales from patients to cells to subcellular components.

Different projects involving computational and theoretical studies of the interactions of radiation with matter are possible and may be tailored to the student's background and interests.  Some projects involve use of egs_brachy, a fast Monte Carlo dose calculation for brachytherapy (developed in the Carleton Laboratory for Radiotherapy Physics), to investigate questions in brachytherapy physics (brachytherapy is a type of radiation treatment in which radioactive sources are placed next to or inside a tumour) as well as coupling advanced dose calculations with models of biological effect.  Other projects involve simulation of radiation interactions at cellular or subcellular levels, including comparison of quantum and classical approaches for modelling electron transport at low energies.


Prof. Rowan Thomson:
rthomson atphysics [dot] carleton [dot] ca

Volume-of-interest cone beam CT imaging

Project description: In radiation therapy of cancer, cone beam CT images are used to position the patient immediately before each treatment session. Cone beam CT images are acquired by directing a cone of kilovoltage x rays at the patient from different angles during a full 360-degree rotation. At each angle, the x-rays transmitted through the patient are detected using a flat panel detector past the patient. The data from all angles are fed into image reconstruction software to reconstruct the 3D volume image of the patient. The "cone" nature in cone beam CT imaging makes the images suffer from a large scatter component and a higher patient imaging dose. This project aims to  develop a prototype that explores the relatively novel concept of "volume of interest" cone beam CT imaging where the x-ray beam is dynamically collimated to the patient areas of interest, and the image reconstruction is modified to handle missing information due to the collimation during acquisition. The project has a strong experimental component to develop the prototype and to interface it with the state-of-the-art radiation treatment facilities at The Ottawa Hospital Cancer, as well as programming component using Python.

Supervisor: Ali Elsayed, elali attoh [dot] ca 

4D Monte Carlo simulation of anatomical changes during radiation therapy

Radiotherapy treatments seek to deliver a tightly conformal dose of radiation to a tumour, while sparing nearby healthy organs. Tumour and organ motion due to physiological processes such as respiration can cause a blurring of the delivered dose which decreases the dose to the tumour and increases dose to healthy tissues. Furthermore, radiotherapy treatments are typically not delivered in a single session but rather in multiple treatment ‘fractions’ delivered over several weeks. Anatomical changes due to treatment response or disease progression may cause deviations of the radiation dose delivered to the tumour from the intended dose.

My research group has developed simulation tools for estimating the impact of anatomical changes on radiotherapy treatments. Potential summer student projects would involve applying or extending these tools to different scenarios, for example looking at the dosimetric impact of prostate swelling following implant of radioactive seeds (prostate brachytherapy) or developing organ motion models from different imaging modalities such as cone-beam CT. Some programming experience is required.  


Quantitative analysis of cancer margins in fluorescence microscopy images of patient tissue. 

The Laboratory for Laser-Assisted Medical Physics and Engineering (LLAMPE) team aims to develop non-invasive optical imaging techniques for rapid diagnostics, and to enable fundamental biophysics discovery. 

Fluorescence-guided surgery is a powerful intraoperative tool used in brain cancer surgery to achieve maximal resection of cancer tissue while preserving essential brain functions and patient performance. However, the surgeons currently rely on visual inspection of fluorescence microscopy images that involves qualitative and subjective analyses. This project aims to develop quantitative analysis of cancer margins in fluorescence microscopy images of patient tissue. The student will analyze fluorescence microscopy images taken during surgery before and after the tumor resection with white and blue light. The image analysis will involve multiple approaches including machine learning to discriminate between cancer and background normal tissue based on the fluorescence intensity. There is also an opportunity to collaborate on developing a design for a prototype hand-held fluorescence microscopy probe to complement the existing fluorescence microscope in the clinic.  

Supervisor: Prof. Sangeeta Murugkar,  smurugkar atphysics [dot] carleton [dot] ca 

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