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Research ArticleTranslational Investigation
Open Access

Quantitative Total-Body Imaging of Blood Flow with High-Temporal-Resolution Early Dynamic 18F-FDG PET Kinetic Modeling

Kevin J. Chung, Abhijit J. Chaudhari, Lorenzo Nardo, Terry Jones, Moon S. Chen, Ramsey D. Badawi, Simon R. Cherry and Guobao Wang
Journal of Nuclear Medicine May 2025, jnumed.124.268706; DOI: https://doi.org/10.2967/jnumed.124.268706
Kevin J. Chung
1Department of Radiology, University of California Davis Health, Sacramento, California;
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Abhijit J. Chaudhari
1Department of Radiology, University of California Davis Health, Sacramento, California;
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Lorenzo Nardo
1Department of Radiology, University of California Davis Health, Sacramento, California;
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Terry Jones
1Department of Radiology, University of California Davis Health, Sacramento, California;
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Moon S. Chen Jr.
2Department of Internal Medicine, University of California Davis Health, Sacramento, California; and
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Ramsey D. Badawi
1Department of Radiology, University of California Davis Health, Sacramento, California;
3Department of Biomedical Engineering, University of California at Davis, Davis, California
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Simon R. Cherry
1Department of Radiology, University of California Davis Health, Sacramento, California;
3Department of Biomedical Engineering, University of California at Davis, Davis, California
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Guobao Wang
1Department of Radiology, University of California Davis Health, Sacramento, California;
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  • FIGURE 1.
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    FIGURE 1.

    Modeling intravascular delivery of PET tracers. (A) PET voxel partly comprises arteries, arterioles, capillaries, venules, and veins. PET tracers are initially delivered to and circulate through these vascular volumes via blood flow (F). Tracer transport from blood into (K1) and out of tissue (k2) occurs almost exclusively at capillary level. S1TC model (B) assumes that tracer instantaneously mixes in vascular volume, and effectively mean vascular transit time (Tc) is zero. AATH model (C) accounts for plug flow with single transit time (Tc) for tracer to traverse total vascular volume via blood flow.

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    FIGURE 2.

    Time–activity curve fits in cortical gray matter using S1TC model (A) and AATH model (B) at HTR (60 × 1 s/frame, 30 × 2 s/frame). Dashed red and green lines represent intravascular and extravascular components of fitted curve, respectively. Black arrows indicate areas where S1TC fitting was poor.

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    FIGURE 3.

    Difference in AIC between AATH and S1TC models using original HTR data (60 × 1 s/frame, 30 × 2 s/frame) (A) and at different simulated frame intervals (B). Negative AIC indicates preference toward AATH model. GM = gray matter.

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    FIGURE 4.

    Correlation (left) and Bland–Altman (right) plots comparing 11C-butanol blood flow against 18F-FDG blood flow with AATH model (A) and S1TC model (B) K1 in same participants. GM = gray matter; MD = mean difference.

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    FIGURE 5.

    Total-body parametric imaging of blood flow with early dynamic 18F-FDG method compared with 11C-butanol flow-tracer PET reference in same participant. White arrows indicate sagittal and transverse sinuses in brain.

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    FIGURE 6.

    Regional blood flow in 34 healthy participants estimated with early dynamic 18F-FDG method. Plots are separated by range of blood-flow values. Average estimates mostly fell within range of average blood-flow values reported in literature (Supplemental Table 1), indicated by green boxes. GM = gray matter.

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    TABLE 1

    18F-FDG Early Kinetics Across Healthy Participants with AATH Model (n = 34)

    Tissue regionBlood flow (mL/min/cm3)K1 (mL/min/cm3)Extraction fractionvb (mL/cm3)Tc (s)
    Cortical GM0.507 ± 0.1220.136 ± 0.0180.278 ± 0.0460.036 ± 0.0064.4 ± 0.9
    White matter0.165 ± 0.0410.066 ± 0.0090.416 ± 0.0610.018 ± 0.0036.9 ± 1.4
    Subcortical GM0.461 ± 0.1280.143 ± 0.0190.327 ± 0.0690.033 ± 0.0054.6 ± 1.5
    Brain stem0.339 ± 0.0870.125 ± 0.0150.386 ± 0.0820.030 ± 0.0055.6 ± 1.8
    Cerebellum0.447 ± 0.1040.145 ± 0.0160.336 ± 0.0520.037 ± 0.0055.1 ± 1.0
    Spleen1.676 ± 0.4841.204 ± 0.4040.728 ± 0.1510.166 ± 0.0646.5 ± 3.3
    Renal cortex1.938 ± 0.4020.657 ± 0.0910.348 ± 0.0650.318 ± 0.03910.1 ± 1.7
    Skeletal muscle0.039 ± 0.0130.034 ± 0.0120.890 ± 0.0480.017 ± 0.00429.1 ± 8.3
    Bone marrow0.136 ± 0.0460.130 ± 0.0460.954 ± 0.0510.053 ± 0.01824.6 ± 9.0
    Lungs2.031 ± 0.6390.072 (0.059–0.134)*0.041 (0.033–0.067)*0.143 ± 0.0314.4 ± 0.8
    • ↵* Values are expressed as median with interquartile range in parentheses as a result of 2 measurements shifting distribution (Supplemental Fig. 7).

    • AATH = adiabatic approximation to tissue homogeneity; GM = gray matter.

    • Data are expressed as mean ± SD unless otherwise noted.

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Journal of Nuclear Medicine: 66 (5)
Journal of Nuclear Medicine
Vol. 66, Issue 5
May 1, 2025
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Quantitative Total-Body Imaging of Blood Flow with High-Temporal-Resolution Early Dynamic 18F-FDG PET Kinetic Modeling
Kevin J. Chung, Abhijit J. Chaudhari, Lorenzo Nardo, Terry Jones, Moon S. Chen, Ramsey D. Badawi, Simon R. Cherry, Guobao Wang
Journal of Nuclear Medicine May 2025, jnumed.124.268706; DOI: 10.2967/jnumed.124.268706

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Quantitative Total-Body Imaging of Blood Flow with High-Temporal-Resolution Early Dynamic 18F-FDG PET Kinetic Modeling
Kevin J. Chung, Abhijit J. Chaudhari, Lorenzo Nardo, Terry Jones, Moon S. Chen, Ramsey D. Badawi, Simon R. Cherry, Guobao Wang
Journal of Nuclear Medicine May 2025, jnumed.124.268706; DOI: 10.2967/jnumed.124.268706
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Keywords

  • total-body PET
  • blood-flow/perfusion imaging
  • high-temporal resolution dynamic imaging
  • tracer kinetic modeling
  • distributed kinetic modeling
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