Abstract
Parametric images generated from dynamic positron emission tomography (PET) studies are useful for presenting functional/biological information in the 3-dimensional space, but usually suffer from their high sensitivity to image noise. To improve the quality of these images, we proposed in this study a modified linear least square (LLS) fitting method named cLLS that incorporates a clustering-based spatial constraint for generation of parametric images from dynamic PET data of high noise levels. In this method, the combination of K-means and hierarchical cluster analysis was used to classify dynamic PET data. Compared with conventional LLS, cLLS can achieve high statistical reliability in the generated parametric images without incurring a high computational burden. The effectiveness of the method was demonstrated both with computer simulation and with a human brain dynamic FDG PET study. The cLLS method is expected to be useful for generation of parametric images from dynamic FDG PET study.
Highlights
Positron emission tomography (PET) is a powerful quantitative tool for in vivo imaging compounds labeled with positron emitting radioisotopes that trace biological processes in the body
To improve the quality of these images, we proposed in this study a modified linear least square (LLS) fitting method named cLLS that incorporates a clustering-based spatial constraint for generation of parametric images from dynamic positron emission tomography (PET) data of high noise levels
A combination of Kmeans and hierarchical cluster analysis is used for clustering dynamic FDG PET data, and the kinetics of clusters of high signal-to-noise ratio are applied to regression matrix for LLS to produce parametric images
Summary
Positron emission tomography (PET) is a powerful quantitative tool for in vivo imaging compounds labeled with positron emitting radioisotopes that trace biological processes in the body. In many occasions, the biological parameters on the image voxel level as determined by conventional statistical estimation methods suffer from large statistical uncertainty. This paper addressed this problem to make the quantitative estimation of physiological parameters more reliable. The clustering-based analyses developed recently [12,13,14,15,16,17] reduced the noise effectively because these methods averaged the data over a large volume that included many tissues with similar tracer kinetics or physiological characteristics. A combination of Kmeans and hierarchical cluster analysis is used for clustering dynamic FDG PET data, and the kinetics of clusters of high signal-to-noise ratio are applied to regression matrix for LLS to produce parametric images. The method was verified using computer simulated data and a human FDG PET data set to show its superior performance compared to the conventional LLS or Patlak graphic analysis
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