Model-based respiratory motion compensation for emission tomography image reconstruction - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Article Dans Une Revue Physics in Medicine and Biology Année : 2007

Model-based respiratory motion compensation for emission tomography image reconstruction

Résumé

In emission tomography imaging, respiratory motion causes artifacts in lungs and cardiac reconstructed images, which lead to misinterpretations, imprecise diagnosis, impairing of fusion with other modalities, etc. Solutions like respiratory gating, correlated dynamic PET techniques, list-mode data based techniques and others have been tested, which lead to improvements over the spatial activity distribution in lungs lesions, but which have the disadvantages of requiring additional instrumentation or the need of discarding part of the projection data used for reconstruction. The objective of this study is to incorporate respiratory motion compensation directly into the image reconstruction process, without any additional acquisition protocol consideration. To this end, we propose an extension to the maximum likelihood expectation maximization (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 respiratory motion as well as from phantom and patient data.
Fichier non déposé

Dates et versions

inria-00616030 , version 1 (19-08-2011)

Identifiants

  • HAL Id : inria-00616030 , version 1

Citer

Mauricio Antonio Reyes Aguirre, Grégoire Malandain, Pierre Malick Koulibaly, Miguel Ángel González Ballester, Jacques Darcourt. Model-based respiratory motion compensation for emission tomography image reconstruction. Physics in Medicine and Biology, 2007, 52 (12), pp.3579-600. ⟨inria-00616030⟩
72 Consultations
0 Téléchargements

Partager

Gmail Facebook X LinkedIn More