Photon-counting computed tomography versus energy-integrating computed tomography for detection of small liver lesions: comparison using a virtual framework imaging

Nicholas Felice; Benjamin Wildman-Tobriner; William Paul Segars; Mustafa R Bashir; Daniele Marin; Ehsan Samei; Ehsan Abadi

doi:10.1117/1.JMI.11.5.053502

Photon-counting computed tomography versus energy-integrating computed tomography for detection of small liver lesions: comparison using a virtual framework imaging

J Med Imaging (Bellingham). 2024 Sep;11(5):053502. doi: 10.1117/1.JMI.11.5.053502. Epub 2024 Oct 17.

Authors

Nicholas Felice^{1

2}, Benjamin Wildman-Tobriner³, William Paul Segars^{1

2

4}, Mustafa R Bashir³, Daniele Marin³, Ehsan Samei^{1

2

4

5

6}, Ehsan Abadi^{1

2

4}

Affiliations

¹ Duke University, Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Durham, North Carolina, United States.
² Duke University, Medical Physics Graduate Program, Durham, North Carolina, United States.
³ Duke University, Department of Radiology, Durham, North Carolina, United States.
⁴ Duke University, Department of Biomedical Engineering, Durham, North Carolina, United States.
⁵ Duke University, Department of Electrical and Computer Engineering, Durham, North Carolina, United States.
⁶ Duke University, Department of Physics, Durham, North Carolina, United States.

PMID: 39430123
PMCID: PMC11486217 (available on 2025-10-17)
DOI: 10.1117/1.JMI.11.5.053502

Abstract

Purpose: Photon-counting computed tomography (PCCT) has the potential to provide superior image quality to energy-integrating CT (EICT). We objectively compare PCCT to EICT for liver lesion detection.

Approach: Fifty anthropomorphic, computational phantoms with inserted liver lesions were generated. Contrast-enhanced scans of each phantom were simulated at the portal venous phase. The acquisitions were done using DukeSim, a validated CT simulation platform. Scans were simulated at two dose levels ( ${CTDI}_{vol}$ 1.5 to 6.0 mGy) modeling PCCT (NAEOTOM Alpha, Siemens, Erlangen, Germany) and EICT (SOMATOM Flash, Siemens). Images were reconstructed with varying levels of kernel sharpness (soft, medium, sharp). To provide a quantitative estimate of image quality, the modulation transfer function (MTF), frequency at 50% of the MTF ( $f_{50}$ ), noise magnitude, contrast-to-noise ratio (CNR, per lesion), and detectability index ( $d^{'}$ , per lesion) were measured.

Results: Across all studied conditions, the best detection performance, measured by $d^{'}$ , was for PCCT images with the highest dose level and softest kernel. With soft kernel reconstruction, PCCT demonstrated improved lesion CNR and $d^{'}$ compared with EICT, with a mean increase in CNR of 35.0% ( $p < 0.001$ ) and 21% ( $p < 0.001$ ) and a mean increase in $d^{'}$ of 41.0% ( $p < 0.001$ ) and 23.3% ( $p = 0.007$ ) for the 1.5 and 6.0 mGy acquisitions, respectively. The improvements were greatest for larger phantoms, low-contrast lesions, and low-dose scans.

Conclusions: PCCT demonstrated objective improvement in liver lesion detection and image quality metrics compared with EICT. These advances may lead to earlier and more accurate liver lesion detection, thus improving patient care.

Keywords: in silico; liver lesions; photon-counting computed tomography; virtual trials.