A functional approach to deconvolve dynamic neuroimaging data

TL;DR

This paper introduces a nonparametric, functional principal component analysis-based deconvolution method for dynamic PET data, allowing more flexible analysis without assuming linear kinetics, and demonstrates its effectiveness through simulations and neuroimaging application.

Contribution

It proposes a novel nonparametric deconvolution approach for PET data that avoids traditional compartmental assumptions and improves robustness and efficiency.

Findings

Method performs well in simulations with 1-D and 2-D data.

Applied successfully to neuroimaging study quantifying opioid receptors.

Robust to noise and computationally efficient.

Abstract

Positron Emission Tomography (PET) is an imaging technique which can be used to investigate chemical changes in human biological processes such as cancer development or neurochemical reactions. Most dynamic PET scans are currently analyzed based on the assumption that linear first order kinetics can be used to adequately describe the system under observation. However, there has recently been strong evidence that this is not the case. In order to provide an analysis of PET data which is free from this compartmental assumption, we propose a nonparametric deconvolution and analysis model for dynamic PET data based on functional principal component analysis. This yields flexibility in the possible deconvolved functions while still performing well when a linear compartmental model setup is the true data generating mechanism. As the deconvolution needs to be performed on only a relative small number of basis functions rather than voxel by voxel in the entire 3-D volume, the methodology is both robust to typical brain imaging noise levels while also being computationally efficient. The new methodology is investigated through simulations in both 1-D functions and 2-D images and also applied to a neuroimaging study whose goal is the quantification of opioid receptor concentration in the brain.