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Can photon-counting CT improve estimation accuracy of morphological radiomics features? A simulation study for assessing the quantitative benefits from improved spatial resolution in deep silicon-based photon-counting CT

  • Shobhit Sharma
    Correspondence
    Address Correspondence to: SS.
    Affiliations
    Center for Virtual Imaging Trials and Carl E. Ravin Advanced Imaging Laboratories, Durham, NC

    Department of Physics, Duke University, Durham, NC
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  • Debashish Pal
    Affiliations
    GE Healthcare, Waukesha, WI
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  • Ehsan Abadi
    Affiliations
    Center for Virtual Imaging Trials and Carl E. Ravin Advanced Imaging Laboratories, Durham, NC

    Department of Radiology, Duke University, Durham, NC
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  • Thomas Sauer
    Affiliations
    Center for Virtual Imaging Trials and Carl E. Ravin Advanced Imaging Laboratories, Durham, NC
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  • Paul Segars
    Affiliations
    Center for Virtual Imaging Trials and Carl E. Ravin Advanced Imaging Laboratories, Durham, NC

    Department of Radiology, Duke University, Durham, NC
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  • Author Footnotes
    + currently at Department of Medical Physics, University of Wisconsin School of Medicine and Public Health Medical Physics, 1111 Highland Ave #1005, Madison, WI
    Jiang Hsieh
    Footnotes
    + currently at Department of Medical Physics, University of Wisconsin School of Medicine and Public Health Medical Physics, 1111 Highland Ave #1005, Madison, WI
    Affiliations
    GE Healthcare, Waukesha, WI
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  • Ehsan Samei
    Affiliations
    Center for Virtual Imaging Trials and Carl E. Ravin Advanced Imaging Laboratories, Durham, NC

    Department of Physics, Duke University, Durham, NC

    Department of Radiology, Duke University, Durham, NC
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  • Author Footnotes
    + currently at Department of Medical Physics, University of Wisconsin School of Medicine and Public Health Medical Physics, 1111 Highland Ave #1005, Madison, WI

      Rationale and Objectives

      Deep silicon-based photon-counting CT (Si-PCCT) is an emerging detector technology that provides improved spatial resolution by virtue of its reduced pixel sizes. This article reports the outcomes of the first simulation study evaluating the impact of this advantage over energy-integrating CT (ECT) for estimation of morphological radiomics features in lung lesions.

      Materials and Methods

      A dynamic nutrient-access-based stochastic model was utilized to generate three distinct morphologies for lung lesions. The lesions were inserted into the lung parenchyma of an anthropomorphic phantom (XCAT - 50th percentile BMI) at 50, 70, and 90 mm from isocenter. The phantom was virtually imaged with an imaging simulator (DukeSim) modeling a Si-PCCT and a conventional ECT system using varying imaging conditions (dose, reconstruction kernel, and pixel size). The imaged lesions were segmented using a commercial segmentation tool (AutoContour, Advantage Workstation Server 3.2, GE Healthcare) followed by extraction of morphological radiomics features using an open-source radiomics package (pyradiomics). The estimation errors for both systems were computed as percent differences from corresponding feature values estimated for the ground-truth lesions.

      Results

      Compared to ECT, the mean estimation error was lower for Si-PCCT (independent features: 35.9% vs. 54.0%, all features: 54.5% vs. 68.1%) with statistically significant reductions in errors for 8/14 features. For both systems, the estimation accuracy was minimally affected by dose and distance from the isocenter while reconstruction kernel and pixel size were observed to have a relatively stronger effect.

      Conclusion

      For all lesions and imaging conditions considered, Si-PCCT exhibited improved estimation accuracy for morphological radiomics features over a conventional ECT system, demonstrating the potential of this technology for improved quantitative imaging.

      Key Words

      Abbreviations:

      CT (Computed Tomography), PCCT (Photon-Counting Computed Tomography), ECT (Energy-integrating Computed Tomography), Si-PCCT (Deep Silicon-based Photon-Counting Computed Tomography), XCAT (Extended Cardiac-Torso), PCD (Photon-Counting Detector)
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