LINEAR LEAST-SQUARES COMPARTMENTAL-MODEL-INDEPENDENT PARAMETER-IDENTIFICATION IN PET

A simplified approach involving linear-regression straight-line parame ter fitting of dynamic scan data is developed for both specific and no nspecific models, Where compartmental-model topologies apply, the meas ured activity may be expressed in terms of: its integrals, plasma acti vity and plasma integrals-all in a linear expression with macroparamet ers as coefficients, Multiple linear regression, as in spreadsheet sof tware, determines parameters for best data fits, Positron emission tom ography (PET)-acquired gray-matter images in a dynamic scan are analyz ed: both by this method and by traditional iterative nonlinear least s quares, Both patient and simulated data were used, Regression and trad itional methods are in expected agreement, Monte-Carlo simulations eva luate parameter standard deviations, due to data noise, and much small er noise-induced biases, Unique straight-line graphical displays permi t visualizing data influences on various macroparameters as changes in slopes, Advantages of regression fitting are: simplicity, speed, ease of implementation in spreadsheet software, avoiding risks of converge nce failures or false solutions in iterative least squares, and provid ing various visualizations of the uptake process by straight line grap hical displays, Multiparameter model-independent analyses on lesser un derstood systems is also made possible.