Y. Zhou et al., Linear ridge regression with spatial constraint for generation of parametric images in dynamic positron emission tomography studies, IEEE NUCL S, 48(1), 2001, pp. 125-130
Due to its simplicity, computational efficiency, and reliability, weighted
linear regression (WLR) is widely used for generation of parametric imaging
in positron emission tomography (PET) studies, but parametric images estim
ated by WLR usually have high image noise level. To improve the stability a
nd signal-to-noise ratio of the estimated parametric images, we have added
ridge regression, a statistical technique that reduces estimation variabili
ty at the expense of a small bias. To minimize the bias, spatially smoothed
images obtained with WLR are used as a; constraint for ridge regression. T
his new algorithm consists of two steps. First, parametric images are gener
ated by WLR and are spatially smoothed. Ridge regression is then applied us
ing the smoothed parametric images obtained in the first step as the constr
aint. Since both "generalized" ridge regression. and "simple" ridge regress
ion are used in statistical applications, we evaluated specifically in this
study the relative advantages of the two when incorporated for generating
parametric images fi om dynamic O-15 water PET studies. Computer simulation
s of a dynamic PET study with the spatial configuration of Hoffman's brain
phantom and a real human PET study were used as the data for the evaluation
. Results reveal ridge regressions improve image quality of parametric imag
es for studies with high or middle noise level. as compared to WLR. Use of
generalized ridge regression offers little advantage over that of simple ri
dge regression.