Image-wise model fitting for generating parametric images in dynamic PET studies

Sung Cheng Huang, Yun Zhou, David Stout, Jorge R. Barrio

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

In this study, we explore the use of non-linear regression for model fitting of PET measured kinetics on a pixel-by-pixel basis for generating parametric images of micro-parameters of kinetic models. We evaluate quantitatively the noise propagation of two regression methods using computer simulated data, and examine the feasibility of generating parametric images for two different real PET studies - a human FDG study and a monkey FDOPA study. The results demonstrated that general image-wise model fitting is practically feasible for dynamic PET studies.

Original languageEnglish
Pages (from-to)198-202
Number of pages5
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume3337
DOIs
StatePublished - Dec 1 1998
Externally publishedYes
EventMedical Imaging 1998: Physiology and Function from Multidimensional Images - San Diego, CA, United States
Duration: Feb 22 1998Feb 23 1998

Keywords

  • Biological information
  • Computer simulation
  • Dynamics
  • Model fitting
  • Non-linear regression
  • Nuclear medicine
  • Parametric image
  • Positron emission tomography (PET)
  • [F-18]-L-DOPA (FDOPA)
  • [F-18]Fluorodeoxyglucose (FDG)

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