Enhanced parameter estimation from noisy PET data: Part I--methodology

The reliability of positron emission tomographic (PET) images depends on the number of annihilation events that are detected. Short image durations are required to capture rapid tracer dynamics, and the resultant images are noisy. Consequently, direct parameter estimation from time-activity curves at high resolution often is unreliable. If adjacent voxels are combined into larger regions of interest the reliability of parameter estimation may be improved, but at the expense of decreased spatial resolution. In this report, a method is presented that provides an alternative to degrading image resolution.

Following the approach of Kimura et al, voxels are grouped not by spatial proximity, but by the similarity of their kinetics. Parameter estimation is performed on these groups, and derived parameters are assigned to all members of the group. Spatial information thus is preserved, but at the expense of parametric discretization. An improvement to the method of Kimura et al is described, in which data are grouped using principal components derived from artificial data.

The application of the method is demonstrated by analysis of PET images of human lungs obtained by the nitrogen-13 infusion-washout technique. In a comparison of the accuracy of parameter estimates, the enhanced method is shown to outperform the original method at all noise levels, with the difference increasing as the amount of noise increases. The robustness of this parameter estimation method in the presence of noise is described in part II of this report in this issue of Academic Radiology.

A method is described that provides demonstrably robust parameter estimates from noisy PET data, while not compromising image resolution.