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.
Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning.
Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms.
Assessment of Lesion Detectability in Dynamic Whole-Body PET Imaging Using Compartmental and Patlak Parametric Mapping.
Spatially guided nonlocal mean approach for denoising of PET images.
Département de radiologie et informatique médicale