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EJNMMI Physics
EJNMMI Physics Cover Image

Volume 1 Supplement 1

Proceedings of the 3rd PSMR Conference on PET/MR and SPECT/MR

  • Meeting abstract
  • Open Access

Optimisation of PET framing sequences

  • 1 and
  • 2
EJNMMI Physics20141 (Suppl 1) :A60

https://doi.org/10.1186/2197-7364-1-S1-A60

©  Patrício and Caldeira; licensee Springer 2014

  • Published:

Keywords

  • Positron Emission Tomography
  • Positron Emission Tomography Scanner
  • Time Activity Curve
  • Order Subset Expectation Maximisation
  • Positron Emission Tomography Data

Our goal is to propose and evaluate a method for choosing framing sequences for Positron Emission Tomography (PET) acquisition protocols. Data detected by PET scanners is translated into concentration of radiotracer by using image reconstruction methods. Most of these methods require a sequence of frame durations to be defined a priori. After reconstruction, the PET data needs to be quantified and combined with MR images to allow statistical intersubject comparisons. The resulting kinetic parameters then depend on the framing sequence adopted. However, though the literature offers a set of such sequences to choose from for common radiotracers, there are no methods for optimising this choice.

We generated perfect reference and tissue data with the simplified reference tissue method (SRTM) [1] model, using data from the literature for [11C]-Raclopride and by choosing parameter values for regions of high binding and low binding (R1=1.15, k2=6.3E-3, BP=3.3 and R1=1.15, k2=6.3E-3, BP=4.3E-1, respectively), [2]. A list-mode data file simulating dynamic PET data for the Siemens 3T MR-BrainPET was further generated, using the Utah phantom. It was such that the time activity curves obtained after reconstruction correspond to the perfect curves. The total number of true counts was set to 100 million.

Statistical error was then added to the list-mode file data, which was subsequently reconstructed using the Ordinary Poisson Ordered Subset Expectation Maximisation (OP-OSEM) method in PRESTO and adopting different framing sequences. After reconstruction and quantification using a basis function implementation of SRTM [3], the resulting kinetic parameters were compared to the exact solution, cf. [4]. By comparing the errors obtained for the binding potential, at each voxel, using the framing sequences proposed in [1] and [3], we show that clear differences arise when using different framing sequences to compute kinetic parameters.

Authors’ Affiliations

(1)
Laboratory of Biostatistics and Medical Informatics, IBILI – Faculty of Medicine, University of Coimbra, Portugal
(2)
Institute of Neurosciences and Medicine at the Forschungszentrum Jülich, Germany

References

  1. Lammertsma AA, Hume SP: Simplified Reference Tissue Model for PET Receptor Studies. Neuroimage 1996,4(3):153–158. 10.1006/nimg.1996.0066PubMedView ArticleGoogle Scholar
  2. Farde L, et al.: Kinetic Analysis of Central [11C]-Raclopride Binding to D2-Dopamine Receptors Studied by PET-A Comparison to the Equilibrium Analysis. Journal of Cerebral Blood Flow and Metabolism 1989, 9: 696–708. 10.1038/jcbfm.1989.98PubMedView ArticleGoogle Scholar
  3. Gunn RN, et al.: Parametric imaging of ligand-receptor binding in PET using a simplified reference region model. Neuroimage 1997,6(4):279–287. 10.1006/nimg.1997.0303PubMedView ArticleGoogle Scholar
  4. Reilhac A, et al.: Simulation-based evaluation of OSEM iterative reconstruction methods in dynamic brain PET studies. Neuroimage 2008,39(1):359–368. 10.1016/j.neuroimage.2007.07.038PubMedView ArticleGoogle Scholar

Copyright

© Patrício and Caldeira; licensee Springer 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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