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Journal of Nuclear Medicine

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OtherBasic Science Investigations

Correction Methods for Random Coincidences in Fully 3D Whole-Body PET: Impact on Data and Image Quality

David Brasse, Paul E. Kinahan, Carole Lartizien, Claude Comtat, Mike Casey and Christian Michel
Journal of Nuclear Medicine May 2005, 46 (5) 859-867;
David Brasse
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Paul E. Kinahan
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Carole Lartizien
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Claude Comtat
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Mike Casey
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Christian Michel
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  • FIGURE 1.
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    FIGURE 1.

    Illustrative axial cross-section of cylindric scanner with and without slice-defining septa. Removing septa increases sensitivity by allowing detection of true coincidences that are not perpendicular to scanner axis but also increases FOV for activity that can generate random coincidences. 2D = 2-dimensional.

  • FIGURE 2.
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    FIGURE 2.

    Estimated sinogram NEC rates (7) for a fully 3D patient scan centered over liver for ECAT HR+ scanner show increase in NEC rates by using a noiseless random coincidence estimation method (k = 0) relative to subtraction of delayed coincidences (k = 1).

  • FIGURE 3.
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    FIGURE 3.

    Test phantom. (A) Coronal and 3 of 5 transverse planes of test phantom containing hot and cold test targets. (B) Representative total number of coincidences (T + S + R) used for 4 of the bed positions in simulations as a function of the activity level at scan start.

  • FIGURE 4.
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    FIGURE 4.

    SNRI measured from simulation study as a function of activity concentration.

  • FIGURE 5.
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    FIGURE 5.

    Improvement in SNR using a noiseless correction relative to an online correction. Improvement in SNR is measured in sinogram using global NEC rate (Eq. 1) and locally in image, as measured by detection of small targets.

  • FIGURE 6.
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    FIGURE 6.

    Expected percent improvement in NEC by using singles-based estimation of random coincidences. (A) ECAT ART scanner: 61 patient studies. (B) ECAT HR+ scanner: 23 patient studies.

Tables

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    TABLE 1

    Methods for Estimating Random Coincidences

    MethodComments
    Delayed coincidencesAccurate. Higher noise (Eq. 1). Lowest processing requirements.
    Smoothed delayed coincidencesAccurate. Lower noise (0 ≤ k ≪ 1, Eq. 1). Higher processing requirements.
    Calculated from single photon ratesPotential for bias if scanner is not properly calibrated. Lower noise (0 ≤ k ≪ 1, Eq. 1). Low processing requirements.
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    TABLE 2

    Count Rates Used to Simulate 5 Whole-Body Acquisitions with Different Activity Concentrations

    Injected dose (MBq)Activity (kBq/cm3)Mcps
    TrueScatteredRandom
    741.18.19.93.5
    1853.321.526.224.7
    3707.037.045.090.0
    55511.547.758.3197.7
    77714.751.763.2283.4
    • Count levels were based on the estimated patient rates (7).

    • View popup
    TABLE 3

    Comparison of Smoothed Delayed Coincidences and Singles-Based Random Coincidence Estimations Averaged over All Sphere Diameters

    Parameter% improvement in SNR compared with delayed coincidences
    Smoothed delayed coincidencesSingles-based estimation
    Background mean2.31.3
    Background SNRI8.37.4
    Hot spheres SNRI17.415.4
    Hot spheres CNR9.43.5
    Cold spheres SNRI23.923.0
    Cold spheres CNR12.613.5
    • Results are expressed as the percent difference from nonsmoothed delayed coincidences.

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Journal of Nuclear Medicine: 46 (5)
Journal of Nuclear Medicine
Vol. 46, Issue 5
May 1, 2005
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Correction Methods for Random Coincidences in Fully 3D Whole-Body PET: Impact on Data and Image Quality
David Brasse, Paul E. Kinahan, Carole Lartizien, Claude Comtat, Mike Casey, Christian Michel
Journal of Nuclear Medicine May 2005, 46 (5) 859-867;

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Correction Methods for Random Coincidences in Fully 3D Whole-Body PET: Impact on Data and Image Quality
David Brasse, Paul E. Kinahan, Carole Lartizien, Claude Comtat, Mike Casey, Christian Michel
Journal of Nuclear Medicine May 2005, 46 (5) 859-867;
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