Ongoing projects

The primary objective of this research project is to detect cancer pain at an early stage by analyzing patients’ medical images. 
Development of an algorithm to do this can be achieved by combining two computer science techniques: one that allows us to gather information about pain from medical notes, and one that extracts information from medical images. We will use the first technique in a computer program that will extract and quantify pain intensity recorded in patients' medical notes. 
The second technique will be employed in another program that will analyze radiographic images of cancer patients’ to extract information about their bone metastases (such as tumor volume, and shape). Then, we will implement advanced statistical and mathematical techniques to model the relationship between identified tumor features and extracted pain intensities. 
Finally, to validate our model, we will use pain scores that are directly collected from thousands of future cancer patients via a mobile app that has been developed in our group (opalmedapps.com).
 

This project is focused on determining if patient-reported outcomes are better correlated with actual dose delivered than with planned dose in prostate cancer patients receiving radiotherapy.

The project will use daily cone-beam CT images to calculate the daily and total radiation dose delivered to patients, and the Opal app to collect their patient-reported outcomes. 

This project consists of establishing good practices in health data management and building a software infrastructure in order to apply them.

We have developed pipelines that allow daily recovery of brachytherapy treatment data in order to calculate and store their dosimetric indices in a database dedicated to research. These indices are essential for planning radiotherapy treatments and for estimating their quality.

The aggregation of these indices allows different researchers such as bio-statisticians and radiation oncologists to carry out studies on larger data sets.

Duel-energy Computed Tomography (CT) imaging has the potential to better characterize materials. DE CT images would allow for a more accurate identification of tissues present in the human anatomy. The presence of highdensity elements (e.g. region of the shoulder, posterior fossa, metallic inserts, etc.) in the scanned subject causes deterioration of the CT image quality (e.g. beam-hardening artifacts). The polychromatic nature of the X-ray beam used in CT scanners is the origin of some image artifacts. In this work, we propose a physics-rich polychromatic projection model that uses the spectrum information, the detector response, the filter geometry and a calibration curve. This model is embedded in an iterative reconstruction algorithm, and inherently reduces beam-hardening artifacts. With dual-energy acquisitions, one can reconstruct quantitative images, with effective atomic number, and electron density information. Besides that, various reconstructions techniques are explored, so high-quality images can be obtained with less artifacts, ultimately, improving the characterization and identification of elements in the image.

The first objective of the project is to design efficient convolutional network classification models (CNNs) using mass spectrometry data (1D and 2D) for clinical diagnosis (cancer and infection).

Once finalized, the second objective is the interpretation of these classification models in order to identify spectral regions of interest that may correspond to new diagnosis or therapeutic biomarkers.

This project focuses on the use of machine learning techniques in external radiotherapy for cancer treatment planning.
Stochastic frontier analysis is a parametric approach used in econometrics and appropriated for medical physics. Using a retrospective bank of treated patients it will be possible to predict the optimal dose of radiation for tumor and healthy organs.
This method is applied to multiple cancer treatment sites which emerge new challenge in the context of prediction, and data processing.