Rationale and Objectives
Hepatocellular carcinoma (HCC) is the only tumor entity that allows non-invasive diagnosis
based on imaging without further histological proof. Therefore, excellent image quality
is of utmost importance for HCC diagnosis. Novel photon-counting detector (PCD) CT
improves image quality via noise reduction and higher spatial resolution, inherently
providing spectral information. The aim of this study was to investigate these improvements
for HCC imaging with triple-phase liver PCD-CT in a phantom and patient population
study focusing on identification of the optimal reconstruction kernel.
Materials and Methods
Phantom experiments were performed to analyze objective quality characteristics of
the regular body and quantitative reconstruction kernels, each with four sharpness
levels (36–40–44–48). For 24 patients with viable HCC lesions on PCD-CT, virtual monoenergetic
images at 50 keV were reconstructed using these kernels. Quantitative image analysis
included contrast-to-noise ratio (CNR) and edge sharpness. Three raters performed
qualitative analyses evaluating noise, contrast, lesion conspicuity, and overall image
quality.
Results
In all contrast phases, the CNR was highest using the kernels with a sharpness level
of 36 (all p < 0.05), with no significant influence on lesion sharpness. Softer reconstruction
kernels were also rated better regarding noise and image quality (all p < 0.05). No
significant differences were found in image contrast and lesion conspicuity. Comparing
body and quantitative kernels with equal sharpness levels, there was no difference
in image quality criteria, neither regarding in vitro nor in vivo analysis.
Conclusion
Soft reconstruction kernels yield the best overall quality for the evaluation of HCC
in PCD-CT. As the image quality of quantitative kernels with potential for spectral
post-processing is not restricted compared to regular body kernels, they should be
preferred.
Abbreviations:
PCD (Photon-counting detector), EID (Energy-integrating detector), HCC (Hepatocellular carcinoma), CNR (Contrast-to-noise ratio), DECT (Dual-energy CT), VMI (Virtual monoenergetic image), MTF (Modulation transfer function), CTDI (CT dose index), NPS (Noise power spectrum), ROI (Region of interest), IQR (Interquartile range), DLP (Dose length product)Key Words
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Article info
Publication history
Published online: April 22, 2023
Accepted:
March 20,
2023
Received in revised form:
March 8,
2023
Received:
January 21,
2023
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved All rights reserved.
