The aim of this phantom study was to assess the detectability and volumetric accuracy of pulmonary nodules on photon-counting detector CT (PCD-CT) at different low-dose levels compared to conventional energy-integrating detector CT (EID-CT). In-house fabricated artificial nodules of different shapes (spherical, lobulated, spiculated), sizes (2.5–10 mm and 5–1222 mm³), and densities (−330 HU and 100 HU) were randomly inserted into an anthropomorphic thorax phantom. The phantom was scanned with a low-dose chest protocol with PCD-CT and EID-CT, in which the dose with PCD-CT was lowered from 100% to 10% with respect to the EID-CT reference dose. Two blinded observers independently assessed the CT examinations of the nodules. A third observer measured the nodule volumes using commercial software. The influence of the scanner type, dose, observer, physical nodule volume, shape, and density on the detectability and volumetric accuracy was assessed by a multivariable regression analysis. In 120 CT examinations, 642 nodules were present. Observer 1 and 2 detected 367 (57%) and 289 nodules (45%), respectively. With PCD-CT and EID-CT, the nodule detectability was similar. The physical nodule volumes were underestimated by 20% (range 8–52%) with PCD-CT and 24% (range 9–52%) with EID-CT. With PCD-CT, no significant decrease in the detectability and volumetric accuracy was found at dose reductions down to 10% of the reference dose (p > 0.05). The detectability and volumetric accuracy were significantly influenced by the observer, nodule volume, and a spiculated nodule shape (p < 0.05), but not by dose, CT scanner type, and nodule density (p > 0.05). Low-dose PCD-CT demonstrates potential to detect and assess the volumes of pulmonary nodules, even with a radiation dose reduction of up to 90%.

Background: Thorax phantoms for computed tomography (CT) imaging often lack realistic lung parenchyma and bronchovascular anatomy. To improve anatomical accuracy, 3D‐printed chest phantoms have been developed as more realistic alternatives to existing models. Purpose: To evaluate whether an in‐house developed anthropomorphic phantom insert realistically represents the anatomical structures and attenuation characteristics compared to the original phantom and human CT data. Methods: The anthropomorphic chest phantom “Lungman” was modified by integrating a 3D‐printed insert, cork‐based lung parenchyma, and lung nodules. The phantom was scanned on a CT system and evaluated using qualitative and quantitative CT analyses, comparing attenuation values and histogram distributions to human CT data. Subjective radiologist assessments were conducted to compare anatomical realism between the modified and unmodified phantom. Results: Qualitative assessment of CT value distribution showed strong similarity between the modified phantom and human lung parenchyma, although the radiodensity characteristics of the 3D‐printed bronchovascular insert still require further refinement. Quantitative analysis confirmed that the modified phantom's parenchymal attenuation (−854 Hounsfield unit [HU]) closely matched human lung parenchyma (−872 HU, p > 0.05), whereas the unmodified phantom showed lower attenuation (−997 HU, p < 0.05). However, the bronchovascular insert showed lower attenuation than human vasculature (−41 HU vs. 42 HU, p < 0.05). Radiologists rated the modified phantom higher for anatomical realism. Conclusions: This study presents a modified, anthropomorphic thorax phantom that integrates realistic 3D‐printed bronchial and vascular structures, lung parenchyma, and lung nodules.

Objectives: To compare lung nodule volumetric accuracy and precision between photon-counting detector (PCD) computed tomography (CT) and energy-integrating detector (EID) CT using low-dose lung cancer screening protocols, and to optimize reconstruction parameters for lung nodule volumetry. Materials and Methods: An anthropomorphic chest phantom with 12 artificial lung nodules of varying size, shape, and radiodensity was scanned using EID-CT and PCD-CT with reference and optimized low-dose lung cancer screening protocols. PCD-CT reconstruction parameters (slice thickness, matrix size, kernel, iterative reconstruction, and virtual monoenergetic imaging energy) were varied. Each protocol was scanned 3 times with nodule repositioning. Nodule volumes were independently measured semiautomatically by 2 observers. Interobserver agreement and test-retest reliability were assessed using the intraclass correlation coefficient (ICC) and Bland-Altman plots. Volumetric accuracy and precision were calculated relative to ground-truth volumes. Volumetric accuracy was compared between PCD-CT and EID-CT using one-way analysis of variance, and across PCD-CT reconstructions using univariable linear regression. Volumetric precision was assessed based on the SD of mean volume differences. Noise was compared across scanners and reconstructions using one-way analysis of variance. Results: A total of 1224 nodule measurements demonstrated excellent volumetric interobserver agreement (ICC: 0.99) and test-retest reliability (ICCs of 0.96 for both observers). Volumetric accuracy improved from −16.6% and −15.2% with the reference and optimized EID-CT protocols to −12.5% and −10.2% with the reference and optimized PCD-CT protocols ( P < 0.05). Volumetric precision remained comparable between reference and optimized EID-CT (6.3 and 8.2 mm ³ ) and PCD-CT (6.4 and 6.2 mm ³ ) protocols. On PCD-CT, ultra-thin slices (0.2 mm) and an ultra-sharp kernel (Qr76) worsened volumetric accuracy by 2.9% and 2.8%, respectively ( P < 0.05). Image noise was lower on PCD-CT than on EID-CT ( P < 0.05) and varied significantly across reconstruction settings on PCD-CT. Conclusions: PCD-CT improved lung nodule volumetric accuracy and reduced volume underestimation by up to 6% compared with EID-CT using low-dose screening protocols, while maintaining similar volumetric precision. Ultra-thin slices and an ultra-sharp kernel worsened volumetric accuracy. By reducing volume underestimation, PCD-CT may shift a larger proportion of nodules to higher baseline risk categories, potentially increasing the number of screening participants requiring clinical referral or short-term follow-up CT.

Background/Objectives: To determine the optimal reconstruction parameters for accurate visualization of peripheral in-stent restenosis using photon-counting detector CT (PCD-CT), and to evaluate its potential advantages over energy-integrated detector CT (EID-CT). Methods: Endovascular peripheral stents with varying degrees of in-stent restenosis were scanned in a custom-made phantom using EID-CT (Somatom Force) and PCD-CT (Naeotom Alpha) under clinical acquisition protocols. EID-CT images were reconstructed with Bv40 and Bv59 kernels at 512 matrices. PCD-CT data were acquired in standard-resolution (SR) and ultra-high-resolution (UHR) modes. In both modes, images were reconstructed with multiple kernels (Bv40, Bv56 and Bv72) and matrix sizes (512 and 1024 matrix). In SR mode, additional virtual monoenergetic images (40–100 keV) were generated, while UHR mode included only polychromatic reconstructions. Quantitative image quality (noise, contrast, contrast-to-noise ratio [CNR]) was measured, and two blinded readers performed qualitative assessments of restenosis visualization. Results: PCD-CT with SR mode at VMI 40 keV achieved the highest image contrast and CNR, significantly outperforming EID-CT and PCD-CT_UHR under matched conditions (all p < 0.05). The sharper reconstruction kernel further enhanced the image contrast and improved subjective visualization despite increased image noise. Both readers ranked PCD-CT_{SR-Bv72-40keV} at 1024 matrix highest for detecting all degrees of restenosis, with excellent inter-reader agreement (ρ > 0.80). Conclusions: PCD-CT in SR mode at VMI 40 keV, specifically using the Bv72 kernel with a 1024 matrix, optimizes the visualization of peripheral in-stent restenosis. Compared to EID-CT, PCD-CT provides superior image quality and detectability of restenosis.