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.
Quantitative Molecular Positron Emission Tomography Imaging Using Advanced Deep Learning Techniques.
The promise of artificial intelligence and deep learning in PET and SPECT imaging.
Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients.
Feasibility of Deep Learning-Guided Attenuation and Scatter Correction of Whole-Body 68Ga-PSMA PET Studies in the Image Domain.
Département de radiologie et informatique médicale