Parametric imaging and statistical mapping of brain tumor in Ga-68 EDTA dynamic PET studies

To improve the reliability and sensitivity of quantitative analysis in the study and evaluation of brain tumor using Ga-68 EDTA dynamic PET, a linear parametric imaging algorithm was developed in this study for estimation of both distribution volume (DV) and blood brain barrier permeability. F statistics was used for separating tumor from normal tissue. A two-compartmental model was used to describe the tracer kinetics. The operational equations: C/sub pet/=((K/sub 1/+k/sub 2/)V/sub p/)/spl int/C/sub p/ds-k/sub 2//spl int/C/sub pet/ds+V/sub p/C/sub p/ and /spl int/C/sub pet/=(DV+V/sub p/)/spl int/C/sub p/ds-(1/k/sub 2/)C/sub pet/+(V/sub p//k/sub 2/)C/sub p/, were used to estimate K/sub 1/ (permeability) and DV(=K/sub 1//k/sub 2/), respectively. A reliable and robust linear regression with spatial constraint parametric imaging algorithm was developed to generate the K/sub 1/ and DV images. Pixel-wise F statistics with 2 and k-2 degree of freedom was calculated as: F=(((k-2)k/(2(k/sup 2/-1)))D/sup 2/ with D/sup 2/=(x-/spl mu/)'S/sup -1/(x-/spl mu/), where the sample is from two dimensional sample space {(K/sub 1/, DV)} of reference regions in normal brain tissue pixels, the sample size k is the number of pixels within the normal reference ROIs, /spl mu/ and S are, respectively, the sample mean vector (K/sub 1/, DV) and covariance matrix. By setting critical a values at 0.2, 0.05, and 0.001, statistical significance level images were generated, and its pixel values can be, 0 if F0.92) was found between the values of K/sub 1/ and DV estimated by model fitting to ROI time activity curve and ones calculated from parametric images. The method for generating K/sub 1/, DV, F, and significance level images is of high computation efficiency and is easy to be implemented. The statistical model developed in the current study provided a tool to integrate the multi-dimensional physiological information. The normal reference region method and the integration of multi-physiological images may improve the sensitivity and specificity of brain tumor detection and evaluation of treatment.