A nonlocal averaging technique for kinetic parameter estimation from dynamic PET data

Due to the high noise levels in frame-by-frame reconstructed dynamic PET images, voxel-wise estimation of kinetic parameters remains a challenge. Most traditional denoising schemes compute the time activity curve for a given voxel by averaging voxels in its immediate local neighborhood. We recognize the fact that similarities between time activity curves are not necessarily local due to the distributed nature of the tracer uptake properties of similar tissue types. We, therefore, present a denoising scheme based on the nonlocal mean (NLM) that allows us to compute voxel-wise estimates of kinetic parameters. We validate our method using a simulation study and compare the results of our approach with that of the traditional Gaussian filtering technique. The results demonstrate that nonlocal averaging generates lower bias as well as lower variance in the denoised time series for different regions of interest. We show that this method can thereby be used to generate better estimates of kinetic parameters than those obtained using a Gaussian filter. To enable application to real data sets, we have accelerated the developed NLM denoising framework using a prior clustering step. The compound approach was applied to preclinical [¹⁸F] FDG PET data from a mouse study. Significant qualitative improvement was observed in Patlak parametric images computed after NLM denoising.