Convergence optimization of parametric MLEM reconstruction for estimation of Patlak plot parameters

Georgios I. Angelis, Kris Thielemans, Andri C. Tziortzi, Federico E. Turkheimer, Charalampos Tsoumpas*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

14 Citations (Scopus)

Abstract

In dynamic positron emission tomography data many researchers have attempted to exploit kinetic models within reconstruction such that parametric images are estimated directly from measurements. This work studies a direct parametric maximum likelihood expectation maximization algorithm applied to [F-18]DOPA data using reference-tissue input function. We use a modified version for direct reconstruction with a gradually descending scheme of subsets (i.e. 18-6-1) initialized with the FBP parametric image for faster convergence and higher accuracy. The results compared with analytic reconstructions show quantitative robustness (i.e. minimal bias) and clinical reproducibility within six human acquisitions in the region of clinical interest. Bland-Altman plots for all the studies showed sufficient quantitative agreement between the direct reconstructed parametric maps and the indirect FBP (-0.035x + 0.48E-5). (C) 2011 Elsevier Ltd. All rights reserved.

Original languageEnglish
Pages (from-to)407-416
Number of pages10
JournalComputerized medical imaging and graphics
Volume35
Issue number5
DOIs
Publication statusPublished - Jul-2011
Externally publishedYes

Keywords

  • FDOPA
  • Kinetic modelling
  • Parametric reconstruction
  • Physiological parameters
  • Patlak plot
  • STRIATAL DOPAMINERGIC SYSTEM
  • BRAIN TRANSFER CONSTANTS
  • IMAGE-RECONSTRUCTION
  • DYNAMIC PET
  • GRAPHICAL EVALUATION
  • PARKINSONS-DISEASE
  • NOISE PROPERTIES
  • ORDERED-SUBSETS
  • LINEAR-MODELS
  • EM ALGORITHM

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