Analysis reveals photon-counting detector outperforms energy-integrating detector for lung imaging, indicating better accuracy at low X-ray doses.
Photon-counting detector (PCD) computed tomography (CT) promises improved resolution and contrast at reduced X-ray dose compared to energy-integrating detector (EID) CT. To determine the parameters that achieve robust accuracy of quantitative measures in chest PCD-CT studies compared to quantitative EID-CT at low CT dose. The Kyoto LUNGMAN chest phantom with preserved lung tissue core and NIST-calibrated foam density standards (4-20 lbs.), and the COPD Lung Phantom II with six airways of various outer and inner diameters, were scanned using PCD-CT (NAEOTOM Alpha) and EID-CT (SOMATOM Force) with a target CT dose index (CTDIvol) of 2.2 mGy to match that specified for ongoing longitudinal quantitative chest CT studies of chronic lung disease. Mean density of foam inserts and mean lumen area (LA) and wall thickness (WT) of COPD Lung Phantom II were automatically segmented, analyzed, and compared using the root mean squared error (RMSE). Contrast-to-noise (CNR) and signal-to-noise (SNR) ratios were also automatically calculated. Large (11.2 mm) and small (5.5 mm) airway LA and WT in the lung tissue core were semi-automatically measured. PCD-CT with the Qr40 kernel yielded superior foam density accuracy (RMSE: 6.1–7.8 HU) compared to EID-CT (RMSE: 9.7 HU). Q+UHR mode with Qr64 and a 1024×1024 matrix achieved the highest airway accuracy (RMSE <1.8 mm² for LA and <0.3 mm for WT). However, these protocols showed increased variability in tracheal air measurements (SD up to 9 HU), indicating a trade-off between higher spatial resolution and measurement repeatability. At equivalent low radiation dose (2.2 mGy CTDIvol), PCD-CT outperforms EID-CT in quantitative accuracy for foam density and airway measurements, with comparable SNR and CNR. These results support the use of PCD-CT for quantitative lung imaging in longitudinal studies, provided reconstruction settings are selected to balance accuracy and repeatability.
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AlArab et al. (2025) studied this question.
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