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

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4 Author(s)
Yun Zhou ; Dept. of Radiol., Johns Hopkins Univ., Baltimore, MD, USA ; Huang, S.-C. ; Shanglian Bao ; Wong, D.F.

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: Cpet=((K1+k2)Vp)∫Cpds-k2∫Cpetds+VpCp and ∫Cpet=(DV+Vp)∫Cpds-(1/k2)Cpet+(Vp/k2)Cp, were used to estimate K1 (permeability) and DV(=K1/k2), respectively. A reliable and robust linear regression with spatial constraint parametric imaging algorithm was developed to generate the K1 and DV images. Pixel-wise F statistics with 2 and k-2 degree of freedom was calculated as: F=(((k-2)k/(2(k2-1)))D2 with D2=(x-μ)'S-1(x-μ), where the sample is from two dimensional sample space {(K1, DV)} of reference regions in normal brain tissue pixels, the sample size k is the number of pixels within the normal reference ROIs, μ and S are, respectively, the sample mean vector (K1, 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 F0.2, 1 if F0.2=0.05, 2 if F0.05=0.01, and 3 if F0.01<=F. The methods were applied to eleven brain tumor Ga-68 EDAT dynamic PET studies. Results shown that the DV, K1, and F images are of good image quality. A highly correlated linear relationship (R2>0.92) was found between the values of K1 and DV estimated by model fitting to ROI time activity curve and ones calculated from parametric images. The method for generating K1, 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-phy- siological images may improve the sensitivity and specificity of brain tumor detection and evaluation of treatment.

Published in:

Nuclear Science Symposium Conference Record, 2001 IEEE  (Volume:2 )

Date of Conference:

4-10 Nov. 2001