A hybrid deconvolution approach for estimation of in vivo non-displaceable binding for brain PET targets without a reference region

• Published in: PloS one Vol. 12; no. 5; p. e0176636
• Main Authors: Zanderigo, Francesca, Mann, J. John, Ogden, R. Todd
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.05.2017; Public Library of Science (PLoS)
• Subjects: Algorithms; Animals; Benzylamines - pharmacokinetics; Binding; Biology and Life Sciences; Blood levels; Blood plasma; Brain; Brain - diagnostic imaging; Brain - metabolism; Brain research; Carbon Radioisotopes - pharmacokinetics; Computer and Information Sciences; Computer Simulation; Datasets as Topic; Deconvolution; Displacement; Estimates; Humans; Impulse response; In vivo methods and tests; Kinetics; Medicine and Health Sciences; Metabolites; Methods; Models, Neurological; Neuroimaging; Nuclear medicine; Papio; Parameter estimation; Positron emission tomography; Positron-Emission Tomography - methods; Radiation; Radiation effects; Radiopharmaceuticals - pharmacokinetics; Reproducibility; Reproducibility of Results; Singular value decomposition; Statistics, Nonparametric; Target recognition; Tomography; Tracers; Tuning; New York; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0176636

Estimation of a PET tracer's non-displaceable distribution volume (VND) is required for quantification of specific binding to its target of interest. VND is generally assumed to be comparable brain-wide and is determined either from a reference region devoid of the target, often not available for many tracers and targets, or by imaging each subject before and after blocking the target with another molecule that has high affinity for the target, which is cumbersome and involves additional radiation exposure. Here we propose, and validate for the tracers [11C]DASB and [11C]CUMI-101, a new data-driven hybrid deconvolution approach (HYDECA) that determines VND at the individual level without requiring either a reference region or a blocking study. HYDECA requires the tracer metabolite-corrected concentration curve in blood plasma and uses a singular value decomposition to estimate the impulse response function across several brain regions from measured time activity curves. HYDECA decomposes each region's impulse response function into the sum of a parametric non-displaceable component, which is a function of VND, assumed common across regions, and a nonparametric specific component. These two components differentially contribute to each impulse response function. Different regions show different contributions of the two components, and HYDECA examines data across regions to find a suitable common VND. HYDECA implementation requires determination of two tuning parameters, and we propose two strategies for objectively selecting these parameters for a given tracer: using data from blocking studies, and realistic simulations of the tracer. Using available test-retest data, we compare HYDECA estimates of VND and binding potentials to those obtained based on VND estimated using a purported reference region. For [11C]DASB and [11C]CUMI-101, we find that regardless of the strategy used to optimize the tuning parameters, HYDECA provides considerably less biased estimates of VND than those obtained, as is commonly done, using a non-ideal reference region. HYDECA test-retest reproducibility is comparable to that obtained using a VND determined from a non-ideal reference region, when considering the binding potentials BPP and BPND. HYDECA can provide subject-specific estimates of VND without requiring a blocking study for tracers and targets for which a valid reference region does not exist.