Improvement of Lung Nodule Volumetric Accuracy with Photon-counting Computed Tomography Over Energy-integrating Computed Tomography in Low-dose Screening: A Phantom Study.

Abstract

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 mm3) and PCD-CT (6.4 and 6.2 mm3) 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.