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In dynamic cardiac SPECT estimates of kinetic parameters ofa one-compartment perfusion model are usually obtained in a two stepprocess: 1) first a MAP iterative algorithm, which properly models thePoisson statistics and the physics of the data acquisition, reconstructsa sequence of dynamic reconstructions, 2) then kinetic parameters areestimated from time activity curves generated from the dynamicreconstructions. This paper provides a method for calculating thecovariance matrix of the kinetic parameters, which are determined usingweighted least squares fitting that incorporates the estimated varianceand covariance of the dynamic reconstructions. For each transaxial slicesets of sequential tomographic projections are reconstructed into asequence of transaxial reconstructions usingfor each reconstruction inthe time sequence an iterative MAP reconstruction to calculate themaximum a priori reconstructed estimate. Time-activity curves for a sumof activity in a blood region inside the left ventricle and a sum in acardiac tissue region are generated. Also, curves for the variance of thetwo estimates of the sum and for the covariance between the two ROIestimates are generated as a function of time at convergence using anexpression obtained from the fixed-point solution of the statisticalerror of the reconstruction. A one-compartment model is fit to the tissueactivity curves assuming a noisy blood input function to give weightedleast squares estimates of blood volume fraction, wash-in and wash-outrate constants specifying the kinetics of 99mTc-teboroxime for theleftventricular myocardium. Numerical methods are used to calculate thesecond derivative of the chi-square criterion to obtain estimates of thecovariance matrix for the weighted least square parameter estimates. Eventhough the method requires one matrix inverse for each time interval oftomographic acquisition, efficient estimates of the tissue kineticparameters in a dynamic cardiac SPECT study can be obtained with presentday desk-top computers

osti oai (u.s. department of energy office of scientific and technical information)

Estimation of the parameter covariance matrix for aone-compartment cardiac perfusion model estimated from a dynamic sequencereconstructed using map iterative reconstruction algorithms