Undergraduate Student Research Awards

Description:
A machine learning classification technique will be developed and applied to the in-vivo near-infrared spectroscopy data acquired on humans and animals to improve the assessment of the tissue and blood conditions.  The ideal candidate would be a student in electrical engineering, biomedical engineering, biomedical physics, or biomedical science program with excellent written and oral communication skills with some experience with Matlab. Also understanding of electromagnetism and optics basics and ability to work with animals are required.

Supervisor:
Dr. Vlad Toronov
 Email

Description:
A real-time data acquisition user interface will be developed for the real-time measurements of the biological tissue microvascular and metabolic parameters by the Avantes near-infrared spectrometer. The interface will be used for the dynamic tracking of the physiological parameters in animal models and human patients in various dynamic clinical scenarios.   The ideal candidate would be a student in electrical engineering, computer science, biomedical engineering, biomedical physics, or biomedical science program with excellent written and oral communication skills and with some Matlab experience and knowledge of Python and/or other programming language. Also understanding of electromagnetism and optics basics is required.

Supervisor:
Dr. Vlad Toronov
 Email

Description:
The specifics of the project will depend on the student's skills and interests. Applicants should be self-motivated, very excited about computer programming, and should want to develop computational models of real systems (virus infection in vitro) in python. This work will be primarily programming-based and math-based, with no experimental work. Programming skills, especially in python, would be a big plus. Having taken or intending to take PCS 350 this Winter would also be great. To see the type of past projects by this group, see our publications or check out some videos of public lectures (Video 1 (external link) , Video 2 (external link) ) I gave. You are also welcome to schedule (via email) an appointment with me to discuss what projects might be available before applying. Please include your CV and unofficial transcript in PDF format.

Supervisor:
Dr. Catherine Beauchemin
 Email

Description:
Microbubbles, a shell-encapsulated gas-core agent, are being invested as a drug and nanoparticle delivery vehicle in combination with therapeutic ultrasound.  Ultrasonically-stimulated microbubbles have been shown to enhance delivery of gold nanoparticles through the transient pores/disruptions on the plasma membrane allowing macromolecules to enter a cell which otherwise would be excluded, and enhanced endocytosis. This project aims at investigating the long-term uptake kinetics of nanoparticles following ultrasound-microbubbles using an in vitro cell model. The student will grow biological cells using tissue culture and treat them using ultrasound and microbubbles, and assess uptake using flow cytometry techniques.

Supervisor:
Dr. Raffi Karshafian
 Email

Description:
Chronic Obstructive Pulmonary Disease (COPD) is a lung disease that causes progressive structural changes. In addition to changes in lung structure, recent literature has shows sarcopenic changes, the involuntary loss of muscle mass, also exist in individuals with COPD. The Pectoralis Muscle Area (PMA) can be measured directly from computed tomography (CT) images, and has been shown to be associated with COPD outcomes. However, most PMA extracting algorithms are semi-automated, and are therefore time consuming and introduce inter- and intra-observer variability. Fully automated approaches are required for analyzing large, population-based COPD cohorts, as well as for translation into clinical management of COPD patients. The objective of this study is to develop a pectoralis muscle segmentation deep learning network using a U-Net based architecture.

Supervisor:
Dr. Miranda Kirby
 Email

Description:
The epidermal growth factor (EGF) receptor is a central cell physiology regulator and can drive cancer tumour progression, with receptor activation from extracellular ligand binding. EGF receptor activation leads these receptors to form dimers and higher-order multimers in quantities that vary with stimulus strength. Experiments suggest that the various multimers provide distinct signaling modes. The student completing this project will build on existing models and simulation code to develop stochastic simulations and differential equation descriptions of EGF receptor multimerization and downstream signaling. This project will aim to explain experimental data for how multimer concentrations change with EGF receptor stimulation and downstream signaling behaviour. This project will develop skills for building simulations and designing quantitative models of biological systems.

Supervisor:
Dr. Aidan Brown
 Email

Description:
This project aims to image the local sound speed of biological tissues with synthetic aperture ultrasound imaging.  Local sound speed can be used as a biomarker for disease diagnosis and to correct phase aberration in ultrasound imaging. Various inverse problem techniques can be used to estimate the local speeds. The proposed method is expected to provide better spatial resolution and accuracy than other available methods to estimate the local speed.  The student will work with a postdoc fellow and have the opportunity to work on both numerical simulations and experimental studies.

Supervisor:
Dr. Yuan Xu
 Email

Description:
Medical imaging techniques such as X-ray, CT scans, and MRI play a crucial role in diagnosing diseases and planning surgical procedures. However, the quality of these images heavily depends on the accuracy and realism of the phantoms used for calibration and testing. Biomimetic bone phantoms, which mimic the properties of real bone tissues, have emerged as promising alternatives to traditional phantoms due to their ability to provide more realistic imaging results. This research project aims to develop advanced biomimetic bone phantoms for improved medical imaging and surgical training applications. This project will focus on the design and fabricate biomimetic bone phantoms with anatomically accurate structures and mechanical properties resembling real bone tissues; the characterization of the physical, mechanical, and radiological properties of the developed phantoms through comprehensive testing and analysis; the validation of the performance of the biomimetic bone phantoms in various medical imaging modalities; and, the evaluation of the suitability of the phantoms for medical imaging calibration, image quality assessment, and surgical training applications. The long term outcomes of this project include the development of biomimetic bone phantoms with anatomically accurate structures and realistic mechanical properties; comprehensive characterization and validation of the phantoms for use in various medical imaging modalities; the demonstration of the potential applications of the phantoms in medical imaging calibration, image quality assessment, and surgical training; and, contribution to the advancement of medical imaging technology and surgical education through the use of more realistic and reliable phantoms. The development of biomimetic bone phantoms holds great promise for enhancing medical imaging accuracy and improving surgical training outcomes. By closely mimicking the properties of real bone tissues, these phantoms can serve as valuable tools for medical device calibration, image quality assessment, and training simulations, ultimately leading to improved patient care and surgical outcomes.

Supervisor:
Dr. Eric Da Silva
 Email