Speaker
Alexandre Bousse
(LaTIM U1101)
Description
Generative models provide powerful learned priors for solving challenging inverse problems in medical imaging. In this talk, I will present diffusion-based, physics-informed approaches for CT and PET reconstruction, illustrated through three applications: motion-compensated head cone-beam CT, material decomposition in photon-counting CT, and joint activity-attenuation reconstruction in CT-less PET. These examples demonstrate how learned image priors can be combined with measurement physics to achieve high-quality, data-consistent reconstruction without paired training data.
Primary author
Alexandre Bousse
(LaTIM U1101)
