Fast formation of statistically reliable FDG parametric images based on clustering and principal components

Formation of parametric images requires voxel-by-voxel estimation of rate constants, a process sensitive to noise and computationally demanding. A model-based clustering method for a two-parameter model (CAKS) was extended to the FDG three-parameter model. The concept was to average voxels with similar kinetic signatures to reduce noise. Voxel kinetics were categorized by the first two principal components of the tissue time-activity curves for all voxels. k2 and k3 were estimated cluster-by-cluster, and K1 was estimated voxel-by-voxel within clusters. When CAKS was applied to simulated images with noise levels similar to brain FDG scans, estimation bias was well suppressed, and estimation errors were substantially smaller--1.3 times for Ki and 1.5 times for k3-than those of conventional voxel-based estimation. The statistical reliability of voxel-level estimation by CAKS was comparable with ROI analysis including 100 voxels. CAKS was applied to clinical cases with Alzheimer's disease (ALZ) and cortico basal degeneration (CBD). In ALZ, the affected regions had low Ki (K1k3/(k2 +k3)) and k3. In CBD, Ki was low, but k3 was preserved. These results were consistent with ROI-based kinetic analysis. Because CAKS decreased the number of invoked estimations, the calculation time was reduced substantially. In conclusion, CAKS has been extended to allow parametric imaging of a three-compartment model. The method is computationally efficient. with low bias and excellent noise properties.