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Model-based pulse pileup and charge sharing compensation for photon counting detectors: A simulation study.

Katsuyuki Taguchi1, Christoph Polster2, W Paul Segars3

  • 1The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

Medical Physics
|June 20, 2022
PubMed
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The new PCP algorithm effectively corrects for pulse pileup (PP) and charge sharing (CS) in X-ray imaging, showing minimal bias and noise. This method outperforms the LCP algorithm, especially at high count rates, ensuring accurate image reconstruction.

Area of Science:

  • Medical Physics
  • Image Reconstruction
  • Detector Science

Background:

  • Pulse pileup (PP) and charge sharing (CS) are significant sources of spectral distortion in X-ray detectors.
  • Accurate compensation for these effects is crucial for quantitative imaging, particularly in computed tomography (CT).
  • Existing methods may introduce biases, especially under high count-rate conditions.

Purpose of the Study:

  • To develop and evaluate a model-based algorithm (PCP) for compensating PP and CS effects.
  • To assess the performance of the PCP algorithm against a comparative algorithm (LCP) using computer simulations.

Main Methods:

  • Developed the PCP algorithm using cascaded models for CS and PP, maximizing Poisson log-likelihood with an exhaustive search.
  • Developed a comparative LCP algorithm modeling loss of counts (LCs) and CS.
Keywords:
charge sharingphoton counting CTphoton counting detectorspulse pileup

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  • Performed slab-based and CT-based simulations using a cadmium telluride detector, varying X-ray intensity and assessing bias and noise.

    • Main Results:

    • PCP demonstrated minimal bias (<0.15 PCL) and noise (within 8% of CRB) in slab simulations, even at high probabilities of count loss (PCL up to 0.8).
    • LCP exhibited significant biases (>±2 cm adipose) when PCL exceeded 0.15.
    • CT simulations confirmed PCP's accuracy in reconstructing basis line integrals, density maps, and monoenergetic images, while LCP showed biases, especially in high-attenuation regions.

    Conclusions:

    • The PCP algorithm effectively compensates for PP and CS, providing statistically efficient and unbiased results for quantitative X-ray imaging.
    • PCP is accurate across various count rates, outperforming LCP which introduces severe biases at high incident count rates (PCL ≥ 0.15).
    • The developed PCP algorithm is suitable for applications requiring precise spectral compensation in CT and other X-ray imaging modalities.