Nuklearmedizin 2005; 44(S 01): S46-S50
DOI: 10.1055/s-0038-1625215
Original Articles
Schattauer GmbH

Motion correction in PET/CT

Bewegungskorrektur in der PET/CT
K. P. Schäfers
1   Department of Nuclear Medicine, University Hospital of Münster, Germany
,
M. Dawood
1   Department of Nuclear Medicine, University Hospital of Münster, Germany
,
N. Lang
1   Department of Nuclear Medicine, University Hospital of Münster, Germany
,
F. Büther
1   Department of Nuclear Medicine, University Hospital of Münster, Germany
,
M. Schäfers
1   Department of Nuclear Medicine, University Hospital of Münster, Germany
,
O. Schober
1   Department of Nuclear Medicine, University Hospital of Münster, Germany
› Author Affiliations
Further Information

Publication History

Received: 08 July 2005

in revised form: 08 August 2005

Publication Date:
11 January 2018 (online)

Summary:

Motion in PET/CT leads to artifacts in the reconstructed PET images due to the different acquisition times of positron emission tomography and computed tomography. The effect of motion on cardiac PET/CT images is evaluated in this study and a novel approach for motion correction based on optical flow methods is outlined. The Lukas-Kanade optical flow algorithm is used to calculate the motion vector field on both simulated phantom data as well as measured human PET data. The motion of the myocardium is corrected by non-linear registration techniques and results are compared to uncorrected images.

Zusammenfassung:

Bewegung in der PET/CT führt aufgrund der unterschiedlichen Akquisitionszeiten der Positronenemissionstomographie (PET) und Computertomographie zu Artefakten in den rekonstruierten PET-Bildern. In dieser Studie wurde die Auswirkung der Bewegung auf kardiale PET-Bilder untersucht und ein neuer Ansatz für eine Bewegungskorrektur basierend auf der Methode des „optical flow“ vorgestellt. Der von Lukas und Kanade entwickelte Optical-flow-Algorithmus wurde dazu benutzt, das Bewegungsvektorfeld anhand von simulierten Phantomdaten und gemessenen PET/ CT-Daten zu bestimmen. Die Bewegung des Myokards wurde mithilfe nicht-linearer Registrierung korrigiert und die Ergebnisse mit den unkorrigierten Bilddaten verglichen.

 
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