%0 Conference Paper %F Oral %T Dynamic and clinical PET data reconstruction: A nonparametric Bayesian approach %+ Université Paris Descartes - Paris 5 (UPD5) %+ Mathématiques Appliquées Paris 5 (MAP5 - UMR 8145) %+ Université Sorbonne Paris Cité (USPC) %+ Laboratoire Modélisation et Simulation de Systèmes (LM2S) %+ Service Hospitalier Frédéric Joliot (SHFJ) %A Fall, Marne Diarra %A Barat, Eric %A Comtat, Claude %A Dautremer, Thomas %A Montagu, Thierry %A Stute, Simon %< avec comité de lecture %B 2013 20th IEEE International Conference on Image Processing (ICIP) %C Melbourne, Australia %I IEEE %P 6738071 %8 2013-09-15 %D 2013 %R 10.1109/ICIP.2013.6738071 %K Positron emission tomography (PET) %K clinical data %K Nonparametric Bayes methods %K Hierarchical model %K MCMC algorithms %K Image reconstruction %K Data models %K Photonics %K Bismuth %K image representation %K medical image processing %K nonparametric statistics %K clinical PET data reconstruction %K dynamic PET data reconstruction %K nuclear medicine imaging modality %K positron emitting radionuclide %K in vivo molecular interactions %K biological processes %K probability density %K reconstruction error %K independent time-frame reconstruction %K maximum likelihood algorithm %K ML-EM %K dynamic PET image reconstruction %Z Computer Science [cs]/Signal and Image Processing %Z Life Sciences [q-bio]/Bioengineering/Imaging %Z Life Sciences [q-bio]/Bioengineering/Nuclear medicine %Z Computer Science [cs]/Numerical Analysis [cs.NA] %Z Statistics [stat]/Applications [stat.AP]Conference papers %X We propose a nonparametric and Bayesian method for reconstructing dynamic Positron Emission Tomography (PET) images from clinical data. PET is a nuclear medicine imaging modality that uses molecules labeled with a positron emitting radionuclide. It is then possible to image in vivo molecular interactions of biological processes. Our approach is non-parametric in the sense that the image representing the 4D (3D+t) activity distribution is viewed as a probability density on R 3 × R + and inferred directly from the data, without any prior space or time discretization. Being nonparametric, we do not pre-assume any particular functional form for this space-time distribution. Formulating the nonparametric problem in the Bayesian framework allows to characterize the entire 4D distribution of the unknown. Furthermore, this framework allows to access directly to the reconstruction error. The ability of the proposed model is assessed using data from clinical studies and we evaluate its performance against the conventional independent time-frame reconstruction approach using the maximum likelihood algorithm (ML-EM). %G English %L cea-01886295 %U https://cea.hal.science/cea-01886295 %~ CEA %~ UNIV-PARIS5 %~ CNRS %~ MAP5 %~ DRT %~ JOLIOT %~ CEA-DRF %~ LIST %~ SHFJ %~ DM2I %~ UNIV-PARIS %~ UP-SCIENCES %~ DIN