Convergence properties of algorithms for direct parametric estimation of linear models in dynamic PET

Charalampos Tsoumpas*, Federico Turkheimer, Kris Thielemans

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

16 Citaten (Scopus)


In dynamic PET studies, the time changing activity of the radiotracer is measured through multiple consecutive frames. Subsequently, voxel-wise application of the kinetic model is expected to estimate parametric images. In this work we investigate the convergence properties of direct reconstruction algorithms of parametric images in 3D PET for the case where the kinetic model is linear in its parameters. As direct reconstruction algorithms we use a modification of the PIR algorithm [1], corresponding to the MLEM formula for parametric images, and a transformed version of the Separable Paraboloid Surrogate (SPS) algorithm formula [2]. The directly reconstructed images are compared with indirectly generated parametric maps using filtered back projection where the kinetic parameters are estimated using the Patlak plot, a standard linear regression method for the estimation of irreversibly bound tracers. Results show that direct MLEM and SPS parametric reconstruction algorithms have remarkably slow convergence. This is explained by the high correlation of the kinetic parameters. The method has been implemented in STIR library (Software for Tomographic Image Reconstruction) [3].

Originele taal-2English
Titel2007 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS-MIC
Aantal pagina's4
StatusPublished - 2007
Extern gepubliceerdJa
Evenement2007 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS-MIC - Honolulu, HI, United States
Duur: 27-okt.-20073-nov.-2007

Publicatie series

NaamIEEE Nuclear Science Symposium Conference Record
ISSN van geprinte versie1095-7863


Conference2007 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS-MIC
Land/RegioUnited States
StadHonolulu, HI

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