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Evaluation of Partial Volume Effect Correction Methods for Brain Positron Emission Tomography: Quantification and Reproducibility

Harri Merisaari, Mika Teräs, Jussi Hirvonen, Olli Nevalainen, Jarmo Hietala, Evaluation of Partial Volume Effect Correction Methods for Brain Positron Emission Tomography: Quantification and Reproducibility. Journal of Medical Physics 32(3), 108-117, 2007.

Abstract:

Quantitative accuracy of positron emission tomography (PET) is decreased by
the partial volume effect (PVE). The PVE correction (PVC) methods proposed
by Alfano et al., Rousset et al., Müller-Gärtner et al. and Meltzer et
al. were evaluated in the present study to obtain guidelines for selecting
among them. For accuracy evaluation, the Hoffman brain phantom was scanned
with three PETs of differing spatial resolution in order to measure the
effect of PVC on radioactivity distribution. Test-retest data consisting of
duplicate dynamic emission recordings of the dopamine D2-receptor ligand
[11 C] raclopride obtained in eight healthy control subjects were used to
test the correction effect in different regions of interest. The PVC method
proposed by Alfano et al. gave the best quantification accuracy in the
brain gray matter region. When the effect of PVC on reliability was tested
with human data, the method of Meltzer et al. proved to be the most
reliable. The method by Alfano et al. may be better for group comparison
studies and the method by Meltzer et al. for intra-subject drug-effect
studies.

BibTeX entry:

@ARTICLE{jMeTeHiNeHi07a,
  title = {Evaluation of Partial Volume Effect Correction Methods for Brain Positron Emission Tomography: Quantification and Reproducibility},
  author = {Merisaari, Harri and Teräs, Mika and Hirvonen, Jussi and Nevalainen, Olli and Hietala, Jarmo},
  journal = {Journal of Medical Physics},
  volume = {32},
  number = {3},
  pages = {108-117},
  year = {2007},
  keywords = {Brain imaging, partial volume effects, positron emission tomography },
}

Belongs to TUCS Research Unit(s): Algorithmics and Computational Intelligence Group (ACI)

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