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Recent advances in parametric neuroreceptor mapping with dynamic PET: basic concepts and graphical analyses

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Abstract

Tracer kinetic modeling in dynamic positron emission tomography (PET) has been widely used to investigate the characteristic distribution patterns or dysfunctions of neuroreceptors in brain diseases. Its practical goal has progressed from regional data quantification to parametric mapping that produces images of kinetic-model parameters by fully exploiting the spatiotemporal information in dynamic PET data. Graphical analysis (GA) is a major parametric mapping technique that is independent on any compartmental model configuration, robust to noise, and computationally efficient. In this paper, we provide an overview of recent advances in the parametric mapping of neuroreceptor binding based on GA methods. The associated basic concepts in tracer kinetic modeling are presented, including commonly-used compartment models and major parameters of interest. Technical details of GA approaches for reversible and irreversible radioligands are described, considering both plasma input and reference tissue input models. Their statistical properties are discussed in view of parametric imaging.

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Authors and Affiliations

  1. Department of Nuclear Medicine, College of Medicine, Seoul, Korea

    Seongho Seo, Dong Soo Lee & Jae Sung Lee

  2. Department of Brain and Cognitive Sciences, College of Natural Sciences, Seoul, Korea

    Seongho Seo & Jae Sung Lee

  3. Institute of Radiation Medicine, Medical Research Center, Seoul, Korea

    Seongho Seo, Su Jin Kim, Dong Soo Lee & Jae Sung Lee

  4. Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul, Korea

    Dong Soo Lee

  5. Department of Biomedical Sciences, College of Medicine, Seoul National University, Seoul, Korea

    Jae Sung Lee

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Seo, S., Kim, S.J., Lee, D.S. et al. Recent advances in parametric neuroreceptor mapping with dynamic PET: basic concepts and graphical analyses. Neurosci. Bull. 30, 733–754 (2014). https://doi.org/10.1007/s12264-014-1465-9

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