A factor-image framework to quantification of brain receptor dynamic PET studies

Z. Jane Wang, Zsolt Szabo, Peng Lei, József Varga, K. J.Ray Liu

Research output: Contribution to journalArticlepeer-review

Abstract

The positron emission tomography (PET) imaging technique enables the measurement of receptor distribution or neurotransmitter release in the living brain and the changes of the distribution with time and thus allows quantification of binding sites as well as the affinity of a radioligand. However, quantification of receptor binding studies obtained with PET is complicated by tissue heterogeneity in the sampling image elements (i.e., voxels, pixels). This effect is caused by a limited spatial resolution of the PET scanner. Spatial heterogeneity is often essential in understanding the underlying receptor binding process. Tracer kinetic modeling also often requires an intrusive collection of arterial blood samples. In this paper, we propose a likelihood-based framework in the voxel domain for quantitative imaging with or without the blood sampling of the input function. Radioligand kinetic parameters are estimated together with the input function. The parameters are initialized by a subspace-based algorithm and further refined by an iterative likelihood-based estimation procedure. The performance of the proposed scheme is examined by simulations. The results show that the proposed scheme provides reliable estimation of factor time-activity curves (TACs) and the underlying parametric images. A good match is noted between the result of the proposed approach and that of the Logan plot. Real brain PET data are also examined, and good performance is obs ed in determining the TACs and the underlying factor images.

Original languageEnglish (US)
Pages (from-to)3473-3487
Number of pages15
JournalIEEE Transactions on Signal Processing
Volume53
Issue number9
DOIs
StatePublished - Sep 2005

Keywords

  • Brain receptor study
  • Compartmental model
  • Distribution volume
  • Dynamic imaging
  • Likelihood
  • PET
  • Tracer kinetic modeling
  • Voxel-domain quantitative imaging

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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