Our objective is to develop image reconstruction techniques, modeling/simulation tools and accurate attenuation and scatter correction techniques for Positron Emission Tomography (PET) and Single-Photon Emission Computed Tomography (SPECT), as well as the assessment of new possible designs of PET detection modules using sophisticated Monte Carlo techniques. We are particularly interested in improving the quality and quantitative accuracy of nuclear medicine images, and statistical analysis of different reconstruction algorithms and attenuation and scatter correction techniques. We are particularly interested in developing brain imaging protocols using nuclear medicine technology to study brain function and to apply multivariate statistics to brain images to study the neurofunctional networks underlying cognitive performance. More recently, the lab has been involved in the development of detector modules and novel designs for dedicated high-resolution PET cameras in collaboration with CERN and other research institutions.
Impact of feature harmonization on radiogenomics analysis: Prediction of EGFR and KRAS mutations from non-small cell lung cancer PET/CT images.
Non-contrast Cine Cardiac Magnetic Resonance image radiomics features and machine learning algorithms for myocardial infarction detection.
COLI-Net: Deep learning-assisted fully automated COVID-19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images.
Overall Survival Prognostic Modelling of Non-small Cell Lung Cancer Patients Using Positron Emission Tomography/Computed Tomography Harmonised Radiomics Features: The Quest for the Optimal Machine Learning Algorithm.
Département de radiologie et informatique médicale