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Research ArticlePhysics and Instrumentation

Clinical Evaluation of a Data-Driven Respiratory Gating Algorithm for Whole-Body PET with Continuous Bed Motion

Florian Büther, Judson Jones, Robert Seifert, Lars Stegger, Paul Schleyer and Michael Schäfers
Journal of Nuclear Medicine October 2020, 61 (10) 1520-1527; DOI: https://doi.org/10.2967/jnumed.119.235770
Florian Büther
1Department of Nuclear Medicine, University Hospital Münster, Münster, Germany
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Judson Jones
2Siemens Healthcare, Knoxville, Tennessee; and
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Robert Seifert
1Department of Nuclear Medicine, University Hospital Münster, Münster, Germany
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Lars Stegger
1Department of Nuclear Medicine, University Hospital Münster, Münster, Germany
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Paul Schleyer
2Siemens Healthcare, Knoxville, Tennessee; and
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Michael Schäfers
1Department of Nuclear Medicine, University Hospital Münster, Münster, Germany
3European Institute for Molecular Imaging, University of Münster, Münster, Germany
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  • FIGURE 1.
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    FIGURE 1.

    Comparison of respiratory signals (A) and local correlation coefficients (B) for typical case (landmarks passing scanner center are indicated by arrows). a.u. = arbitrary units.

  • FIGURE 2.
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    FIGURE 2.

    (A) Superposition of local correlation coefficients (gray) for all scans in normalized axial position. Average is indicated by black line. (B) Box plot of regional correlation coefficients for all scans. Asterisks denote outliers.

  • FIGURE 3.
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    FIGURE 3.

    Scatterplots of SUVmax (A), SUVmean (B), and V50% (C) for static, BG-EMOCO, and DDG-EMOCO reconstructions. Subgroup of 1 lesion per scan is denoted by big dots; black line is line of identity.

  • FIGURE 4.
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    FIGURE 4.

    Scatterplots of SUVmax (A), SUVmean (B), and V50% (C) for BG-OG, and DDG-OG reconstructions. Subgroup of 1 lesion per scan is denoted by big dots; black line is line of identity.

  • FIGURE 5.
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    FIGURE 5.

    Typical outcome of OG and EMOCO using BG and DDG signals, compared with static reconstruction. Visual improvements of liver lesions (arrows) are apparent.

  • FIGURE 6.
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    FIGURE 6.

    Pairwise visual quality comparison between EMOCO and static reconstructions (left) and between DDG and BG (right) (n = 45). White areas denote cases in which method 1 was superior to method 2; black areas denote cases in which method 1 was inferior; and gray areas denote cases in which quality was equal.

  • FIGURE 7.
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    FIGURE 7.

    Maximum-intensity projection of scan with elevated colon uptake (arrows; R1 and R2) demonstrating better delineation with BG and DDG.

Tables

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    TABLE 1

    Definition of Regions and Correlation Coefficients Between BG and DDG Signals

    ParameterR0R1R2R3R4R5Global
    StartProximal femurBladderRight kidneyLiver domeAortic archLung apexProximal femur
    EndBladderRight kidneyLiver domeAortic archLung apexHead/neckHead/neck
    Correlation coefficient0.12 ± 0.17 (−0.12–0.61)0.72 ± 0.15 (0.35–0.96)0.89 ± 0.07 (0.68–0.97)0.77 ± 0.16 (0.26–0.94)0.41 ± 0.32 (−0.71–0.86)0.05 ± 0.08 (−0.15–0.21)0.48 ± 0.11 (0.30–0.75)
    • Data are mean ± SD, followed by range in parentheses.

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    TABLE 2

    Lesion SUVs and V50% Determined from Static Reconstructions

    RegionLesions (n)Patients (n)SUVmaxSUVmeanV50% (cm3)
    Upper lungs261412.5 ± 16.1 (5.1–84.1)8.1 ± 10.3 (3.4–52.1)0.2 ± 0.7 (0.0–2.6)
    Upper mediastinum131018.3 ± 10.9 (5.7–38.9)12.4 ± 7.4 (3.4–25.9)0.8 ± 1.5 (0.1–5.4)
    Lower lungs823212.9 ± 15.8 (3.0–88.0)8.5 ± 10.9 (2.0–58.8)0.4 ± 1.7 (0.1–11.5)
    Lower mediastinum201614.1 ± 12.5 (5.5–57.4)9.2 ± 9.0 (3.6–41.9)0.4 ± 1.1 (0.1–3.6)
    Liver311114.5 ± 6.0 (7.1–26.9)9.0 ± 3.8 (4.4–17.6)1.5 ± 3.8 (0.1–14.4)
    Other infradiaphragmatic regions241020.4 ± 9.3 (7.8–43.0)13.6 ± 6.1 (4.9–28.0)0.8 ± 3.0 (0.1–12.0)
    Total1964514.3 ± 13.4 (3.0–88.0)9.3 ± 9.1 (2.0–58.8)0.5 ± 2.4 (0.0–14.4)
    • Data are median ± SD, followed by range in parentheses.

    • View popup
    TABLE 3

    Characteristics of All 196 Lesions for All Correction Strategies

    ParameterStaticBG-EMOCODDG-EMOCOBG-OGDDG-OG
    SUVmax14.3 ± 13.419.8 ± 15.720.5 ± 15.619.6 ± 17.118.9 ± 16.6
     P value to static<0.001<0.001
     P value to BG0.600.19
    SUVmean9.3 ± 9.113.5 ± 11.613.7 ± 11.412.6 ± 12.512.4 ± 12.2
     P value to static<0.001<0.001
     P value to BG0.650.11
    V50% (cm3)0.5 ± 2.40.3 ± 1.40.3 ± 1.30.3 ± 1.20.3 ± 1.3
     P value to static<0.001<0.001
     P value to BG0.870.50
    • Data are median ± SD.

    • View popup
    TABLE 4

    Characteristics of 45 Randomly Chosen Lesions for All Correction Strategies

    ParameteStaticBG-EMOCODDG-EMOCOBG-OGDDG-OG
    SUVmax9.5 ± 10.513.4 ± 13.513.5 ± 13.715.2 ± 14.614.6 ± 12.5
     P value to static<0.001<0.001
     P value to BG0.210.28
    SUVmean6.8 ± 7.18.9 ± 9.48.9 ± 9.29.5 ± 10.29.0 ± 8.3
     P value to static<0.001<0.001
     P value to BG0.440.20
    V50% (cm3)0.6 ± 1.80.4 ± 0.60.3 ± 0.60.3 ± 0.80.3 ± 0.7
     P value to static<0.001<0.001
     P value to BG0.710.33
    • Data are median ± SD.

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Journal of Nuclear Medicine: 61 (10)
Journal of Nuclear Medicine
Vol. 61, Issue 10
October 1, 2020
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Clinical Evaluation of a Data-Driven Respiratory Gating Algorithm for Whole-Body PET with Continuous Bed Motion
Florian Büther, Judson Jones, Robert Seifert, Lars Stegger, Paul Schleyer, Michael Schäfers
Journal of Nuclear Medicine Oct 2020, 61 (10) 1520-1527; DOI: 10.2967/jnumed.119.235770

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Clinical Evaluation of a Data-Driven Respiratory Gating Algorithm for Whole-Body PET with Continuous Bed Motion
Florian Büther, Judson Jones, Robert Seifert, Lars Stegger, Paul Schleyer, Michael Schäfers
Journal of Nuclear Medicine Oct 2020, 61 (10) 1520-1527; DOI: 10.2967/jnumed.119.235770
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