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Proceedings Paper

Imagewise model fitting for generating parametric images in dynamic PET studies
Author(s): Sung-Cheng Huang; Yun Zhou; David Stout; Jorge R. Barrio
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Paper 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.

Paper Details

Date Published: 3 July 1998
PDF: 5 pages
Proc. SPIE 3337, Medical Imaging 1998: Physiology and Function from Multidimensional Images, (3 July 1998); doi: 10.1117/12.312564
Show Author Affiliations
Sung-Cheng Huang, UCLA School of Medicine (United States)
Yun Zhou, UCLA School of Medicine (United States)
David Stout, UCLA School of Medicine (United States)
Jorge R. Barrio, UCLA School of Medicine (United States)


Published in SPIE Proceedings Vol. 3337:
Medical Imaging 1998: Physiology and Function from Multidimensional Images
Eric A. Hoffman, Editor(s)

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