The analysis of positron emission tomography (PET) images at the pixel level may yield unreliable parameter estimates due to the low signal-to-noise ratio of pixel time activity curves (TAC). To address this issue it can be helpful to use techniques developed in the pharmacokinetic/pharmacodynamic area and referred to as 'population approaches.' In this paper, we describe a new estimation algorithm, the Global-Two-Stage (GTS), and assess its performances through Monte Carlo simulations. GTS was compared to the basis function method on synthetic [11C](R)-PK11195 data, and to weighted nonlinear least squares on synthetic [11C]WAY100,635 data. In both cases, GTS produced parameter estimates with lower root mean square error and lower bias than the well-established estimation methods used for comparison, with a negligible increase of computational time. GTS was applied first to all the pixels of the simulated slices. Then, after a preliminary segmentation of pixels into more homogeneous populations, GTS was applied to each subpopulation separately: this last approach provided the best results. In conclusion, GTS is a powerful and fast technique that can be applied to improve parametric maps, as long as preliminary estimates of parameters and of their covariance are available.

PET parametric imaging improved by global-two-stage method.

TOMASI, GIAMPAOLO;BERTOLDO, ALESSANDRA;COBELLI, CLAUDIO
2009

Abstract

The analysis of positron emission tomography (PET) images at the pixel level may yield unreliable parameter estimates due to the low signal-to-noise ratio of pixel time activity curves (TAC). To address this issue it can be helpful to use techniques developed in the pharmacokinetic/pharmacodynamic area and referred to as 'population approaches.' In this paper, we describe a new estimation algorithm, the Global-Two-Stage (GTS), and assess its performances through Monte Carlo simulations. GTS was compared to the basis function method on synthetic [11C](R)-PK11195 data, and to weighted nonlinear least squares on synthetic [11C]WAY100,635 data. In both cases, GTS produced parameter estimates with lower root mean square error and lower bias than the well-established estimation methods used for comparison, with a negligible increase of computational time. GTS was applied first to all the pixels of the simulated slices. Then, after a preliminary segmentation of pixels into more homogeneous populations, GTS was applied to each subpopulation separately: this last approach provided the best results. In conclusion, GTS is a powerful and fast technique that can be applied to improve parametric maps, as long as preliminary estimates of parameters and of their covariance are available.
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/2467166
Citazioni
  • ???jsp.display-item.citation.pmc??? 2
  • Scopus 8
  • ???jsp.display-item.citation.isi??? 7
  • OpenAlex ND
social impact