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Proceedings Paper

Estimation of physiological parameters using knowledge-based factor analysis of dynamic nuclear medicine image sequences
Author(s): Jeffrey T. Yap; Chin-Tu Chen; Malcolm Cooper
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Paper Abstract

We have previously developed a knowledge-based method of factor analysis to analyze dynamic nuclear medicine image sequences. In this paper, we analyze dynamic PET cerebral glucose metabolism and neuroreceptor binding studies. These methods have shown the ability to reduce the dimensionality of the data, enhance the image quality of the sequence, and generate meaningful functional images and their corresponding physiological time functions. The new information produced by the factor analysis has now been used to improve the estimation of various physiological parameters. A principal component analysis (PCA) is first performed to identify statistically significant temporal variations and remove the uncorrelated variations (noise) due to Poisson counting statistics. The statistically significant principal components are then used to reconstruct a noise-reduced image sequence as well as provide an initial solution for the factor analysis. Prior knowledge such as the compartmental models or the requirement of positivity and simple structure can be used to constrain the analysis. These constraints are used to rotate the factors to the most physically and physiologically realistic solution. The final result is a small number of time functions (factors) representing the underlying physiologic processes and their associated weighting images representing the spatial localization of these functions. Estimation of physiological parameters can then be performed using the noise-reduced image sequence generated from the statistically significant PCs and/or the final factor images and time functions. These results are compared to the parameter estimation using standard methods and the original raw image sequences. Graphical analysis was performed at the pixel level to generate comparable parametric images of the slope and intercept (influx constant and distribution volume).

Paper Details

Date Published: 24 May 1995
PDF: 5 pages
Proc. SPIE 2433, Medical Imaging 1995: Physiology and Function from Multidimensional Images, (24 May 1995); doi: 10.1117/12.209693
Show Author Affiliations
Jeffrey T. Yap, Univ. of Chicago (United States)
Chin-Tu Chen, Univ. of Chicago (United States)
Malcolm Cooper, Univ. of Chicago (United States)


Published in SPIE Proceedings Vol. 2433:
Medical Imaging 1995: Physiology and Function from Multidimensional Images
Eric A. Hoffman, Editor(s)

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