Key result
A method based on the maximum entropy principle successfully recovered physiological component time-activity curves and corresponding images from simulated dynamic radionuclide sequences.
A novel maximum entropy-based method demonstrates feasibility in recovering physiological components from dynamic radionuclide images using simulated data.
Hypothesis-generating for component separation in dynamic radionuclide imaging; validation in clinical data required before any use.
A method based on the maximum entropy principle is described for recovering physiological components from a sequence of dynamic radionuclide images. The method does not require any assumption about the shapes of the component curves. It is, however, assumed that the components are nonnegative. The method requires normalization of component curves. The sum of squares of each component curve to be estimated is normalized so as to be unity. A first-pass cardiac study and a hepatic study are selected for investigation of applicability of the method. The method is investigated numerically using computer-generated time-activity curves with six components for the cardiac study and seven components for the hepatic study. Within the regions studied, the results have demonstrated that the method may recover the component time-activity curves and the corresponding images of physiological components from a sequence of dynamic radionuclide images. The ability and limitations of the method to recover physiological components are discussed and potential applications are suggested.
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Nakamura et al. (1989) studied this question. Maximum entropy principle method was evaluated on Recovery of component time-activity curves and corresponding images. A method based on the maximum entropy principle successfully recovered physiological component time-activity curves and corresponding images from simulated dynamic radionuclide sequences.
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