We present the headlines of a maximum-likelihood image reconstruction and motion estimation method in positron emission tomography (PET). The basics of PET imaging, Poisson spatial processes and expectation maximisation (EM) reconstruction are revisited. We then introduce our algorithm and the quasi-Newton approach we adopted for motion estimation prom projection data. Finally, we present results on patient PET/CT and PET/MRI data.

This presentation is part of Minisymposium “MS73 - Mathematical Methods for Spatiotemporal Imaging (2 parts)”
organized by: Chong Chen (LSEC, ICMSEC, Academy of Mathematics and Systems Science, Chinese Academy of Sciences) , Barbara Gris (Laboratoire Jacques-Louis Lions) , Ozan Öktem (KTH - Royal Institute of Technology) .

Authors:

Alexandre Bousse (Institute of Nuclear Medicine, University of College London)

Elise Emond (University College London)

Keywords:

emission tomography, image reconstruction, motion compensation