Speaker
João M. Sousa
(Uppsala University)
Description
The performance of the Bayesian likelihood reconstruction algorithm in PET/MR brain imaging using receptor or transporter ligands has not previously been investigated. Ten patients underwent PET/MR brain scanning with the dopamine transporter ligand 11C-PE2I and images were reconstructed using a Bayesian penalized likelihood algorithm, with a regularization term of 25, 50, and 100, in addition to TOF-OSEM. The Bayesian algorithm produced images with a higher signal-to-noise ratio and decreasing noise as the regularization term was increased. Data reconstructed using a regularization term of 50 and 100 presented a higher signal-to-noise ration that that from TOF-OSEM.
Primary author
João M. Sousa
(Uppsala University)
Co-authors
Dr
Håkan Ahlström
(Uppsala University)
Mr
Lieuwe Appel
(Uppsala University)
Mr
Mark Lubberink
(Uppsala University)
Mr
Martin Bolin
(Uppsala University)
Mr
Mathias Engström
(GE Healthcare)
Dr
Sangtae Ahn
(GE Global Research)
