Linearized Methods for Quantitative Analysis and Parametric Mapping of Brain PET.
- Author:
Su Jin KIM
1
;
Jae Sung LEE
Author Information
1. Department of Nuclear Medicine and Interdisciplinary Program in Radiation Applied Life Science, College of Medicine and Institute of Radiation Medicine, Medical Research Center, Seoul National University, Korea. jaes@snu.ac.kr
- Publication Type:Review
- Keywords:
linearized method;
linear regression;
multilinear regression;
parametric image;
PET;
graphical analysis;
kinetic modeling
- MeSH:
Bias (Epidemiology);
Brain Diseases;
Brain*;
Linear Models
- From:Nuclear Medicine and Molecular Imaging
2007;41(2):78-84
- CountryRepublic of Korea
- Language:Korean
-
Abstract:
Quantitative analysis of dynamic brain PET data using a tracer kinetic modeling has played important roles in the investigation of functional and molecular basis of various brain diseases. Parametric imaging of the kinetic parameters (voxel-wise representation of the estimated parameters) has several advantages over the conventional approaches using region of interest (ROI). Therefore, several strategies have been suggested to generate the parametric images with a minimal bias and variability in the parameter estimation. In this paper, we will review the several approaches for parametric imaging with linearized methods which include graphical analysis and mulilinear regression analysis.
