Respiratory Motion Correction in Emission Tomography Image Reconstruction Mauricio Reyes 1 , Gr´ egoire Malandain 1 , Pierre Malick Koulibaly 2 , Miguel A. Gonz´ alez Ballester 3 , and Jacques Darcourt 2 1 Epidaure team, INRIA Sophia Antipolis, France {Mauricio.Reyes Aguirre,Gregoire.Malandain}@sophia.inria.fr 2 Nuclear medicine department, Centre Antoine Lacassagne, Nice, France 3 MEM center, University of Bern, Switzerland Abstract. In Emission Tomography imaging, respiratory motion causes artifacts in lungs and cardiac reconstructed images, which lead to misin- terpretations and imprecise diagnosis. Solutions like respiratory gating, correlated dynamic PET techniques, list-mode data based techniques and others have been tested with improvements over the spatial activity distribution in lungs lesions, but with the disadvantages of requiring ad- ditional instrumentation or discarding part of the projection data used for reconstruction. The objective of this study is to incorporate respi- ratory motion correction directly into the image reconstruction process, without any additional acquisition protocol consideration. To this end, we propose an extension to the Maximum Likelihood Expectation Max- imization (MLEM) algorithm that includes a respiratory motion model, which takes into account the displacements and volume deformations produced by the respiratory motion during the data acquisition process. We present results from synthetic simulations incorporating real respira- tory motion as well as from phantom and patient data. 1 Introduction Respiratory motion during the data acquisition process leads to blurred images, making difficult an accurate diagnosis, planning and following. For instance, mis- localizations of lesions in the fusion of positron emission tomography (PET) and computerized tomography (CT) have been found [1]. Similarly, significant tumor motion has been reported in others studies (e.g. [2,3]) as well as significant vol- ume increase of lung lesions in images reconstructed without respiratory motion compensation [4]. To our knowledge, motion correction in Emission Tomography (ET) has been seldom investigated in the literature. Current methods can be classified in four main categories: post-processing, Multiple Acquisition Frame (MAF), sinogram data selection based on detected motion, and sinogram correction. – Post-processing methods are based on transformations performed either in projection-space (e.g [5]) or in image-space (e.g. [6]). However, the motion models used in projection-space are too simplistic (e.g. global scaling), and J. Duncan and G. Gerig (Eds.): MICCAI 2005, LNCS 3750, pp. 369–376, 2005. c  Springer-Verlag Berlin Heidelberg 2005