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)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: July 21, 2022
Accepted:
June 24,
2022
Received in revised form:
June 17,
2022
Received:
January 6,
2022
Identification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
