ArticleJournal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism2015
Model-free quantification of dynamic PET data using nonparametric deconvolution.
Article in Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Parametric imaging of dynamic long-axial-field-of-view PET scans: Technical challenges, statistical insights and clinical applications.Zeitschrift fur medizinische Physik · 2026Review
- Quantitation of dynamic total-body PET imaging: recent developments and future perspectives.European journal of nuclear medicine and molecular imaging · 2023Review
- Determination of hepatic extraction fraction with gadoxetate low-temporal resolution DCE-MRI-based deconvolution analysis: validation with ALBI score and Child-Pugh class.Journal of medical radiation sciences · 2023Article
- Brain serotonin transporter binding, plasma arachidonic acid and depression severity: A positron emission tomography study of major depression.Journal of affective disorders · 2019Article
- Nonlinear Mixed-Effects Models for PET Data.IEEE transactions on bio-medical engineering · 2019Article
- Functional Data Analysis of Dynamic PET Data.Journal of the American Statistical Association · 2019Article
- A hybrid deconvolution approach for estimation of in vivo non-displaceable binding for brain PET targets without a reference region.PloS one · 2017Article
- A Functional Approach to Deconvolve Dynamic Neuroimaging Data.Journal of the American Statistical Association · 2016Article
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3 authors.
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Abstract
Dynamic positron emission tomography (PET) data are usually quantified using compartment models (CMs) or derived graphical approaches. Often, however, CMs either do not properly describe the tracer kinetics, or are not identifiable, leading to nonphysiologic estimates of the tracer binding. The PET data are modeled as the convolution of the metabolite-corrected input function and the tracer impulse response function (IRF) in the tissue. Using nonparametric deconvolution methods, it is possible to obtain model-free estimates of the IRF, from which functionals related to tracer volume of distribution and binding may be computed, but this approach has rarely been applied in PET. Here, we apply nonparametric deconvolution using singular value decomposition to simulated and test-retest clinical PET data with four reversible tracers well characterized by CMs ([(11)C]CUMI-101, [(11)C]DASB, [(11)C]PE2I, and [(11)C]WAY-100635), and systematically compare reproducibility, reliability, and identifiability of various IRF-derived functionals with that of traditional CMs outcomes. Results show that nonparametric deconvolution, completely free of any model assumptions, allows for estimates of tracer volume of distribution and binding that are very close to the estimates obtained with CMs and, in some cases, show better test-retest performance than CMs outcomes.
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