Physical phantom validation of clustering-initiated factorization in dynamic PET

Abstract

Background

Dynamic positron emission tomography (PET) enables the quantification of physiological parameters of radiotracers employed in the investigation of neuropsychiatric disorders. We previously introduced a factor analysis-based algorithm, Cluster-Initialized Factor Analysis (CIFA), designed to overcome the problem of specifying reference regions. CIFA is capable of automatically extracting distinct radiotracer binding distributions across many modalities based on the differences in tracer dynamics, and thus can distinguish regions of specific- and non-specific binding without requiring prior segmentation.

Purpose

Our goal is to quantitatively validate the ability of CIFA to resolve different dynamic biological processes by comparing the output of the algorithm to an independent benchmark. As an intermediate goal, we aim to create a physical phantom capable of modeling unique aspects of dynamic imaging and to use this phantom as the benchmark in evaluating CIFA.

Methods

CIFA was used to reconstruct ¹⁸F-flortaucipir dynamic brain PET datasets acquired at Lawrence Berkeley National Lab. The resulting factor curves served as the foundation for creating dynamic input time-activity curve (TAC) combinations in a physical brain phantom specifically constructed for this purpose. The phantom represented three components: two overlapping tissue types and free radiotracer, constructed with a combination of small hydraulic elements. The physical components were scanned separately to generate a library of images, allowing us to reproduce scans of any duration with prescribed dynamics and realistic partial volume effects. The phantom was designed to produce noisy instances with compartment mixing of dynamic scans with desired activity TACs for free, non-specifically bound, and specifically bound radiotracers. Ten distinct dynamic simulations with varying levels of TAC similarity were estimated with CIFA.

Results

We directly evaluated CIFA's performance in analyzing each of the 10 dynamic datasets by computing the Pearson correlation coefficient between the estimated outputs and the ground truth tissue TACs and corresponding tissue distributions. For seven out of 10 modeled dynamics, which captured the full spectrum of realistically expected tissue TAC shapes, the curve correlation of the specific binding tissue was above 95%.

Conclusions

This work formulated an innovative process by combining a physical phantom design with PET images for evaluating the application of CIFA in the extraction of dynamic TACs from dynamic PET image data. In most cases the CIFA algorithm accurately reproduced the dynamics of the phantom simulated data.