Blind estimation of compartmental model parameters.

Computation of physiologically relevant kinetic parameters from dynamic PET or SPECT imaging requires knowledge of the blood input function. This work is concerned with developing methods to accurately estimate these kinetic parameters blindly; that is, without use of a directly measured blood input function. Instead, only measurements of the output functions--the tissue time-activity curves--are used. The blind estimation method employed here minimizes a set of cross-relation equations, from which the blood term has been factored out, to determine compartmental model parameters. The method was tested with simulated data appropriate for dynamic SPECT cardiac perfusion imaging with 99mTc-teboroxime and for dynamic PET cerebral blood flow imaging with 15O water. The simulations did not model the tomographic process. Noise levels typical of the respective modalities were employed. From three to eight different regions were simulated, each with different time-activity curves. The time-activity curve (24 or 70 time points) for each region was simulated with a compartment model. The simulation used a biexponential blood input function and washin rates between 0.2 and 1.3 min(-1) and washout rates between 0.2 and 1.0 min(-1). The system of equations was solved numerically and included constraints to bound the range of possible solutions. From the cardiac simulations, washin was determined to within a scale factor of the true washin parameters with less than 6% bias and 12% variability. 99mTc-teboroxime washout results had less than 5% bias, but variability ranged from 14% to 43%. The cerebral blood flow washin parameters were determined with less than 5% bias and 4% variability. The washout parameters were determined with less than 4% bias, but had 15-30% variability. Since washin is often the parameter of most use in clinical studies, the blind estimation approach may eliminate the current necessity of measuring the input function when performing certain dynamic studies.