Lung nodule volumetry accuracy and precision on energy-integrating and CdZnTe photon-counting CT technologies

Nodule volume doubling time is an important biomarker for lung cancer diagnosis, making accurate estimation of nodule volume in low-dose CT images crucial. Hence, the purpose of this work was to evaluate the precision and accuracy of volume measurements of solid nodules (SN) and ground glass opacities (GGO) in images acquired using conventional energy-integrating (EICT) and CdZnTe photon-counting CT (PCCT) systems, reconstructed using hybrid iterative (HIR) and deep-learning based (DLR) reconstructions. For this, a patient-based anthropomorphic 3D printed lung phantom, five printed SNs of different sizes, and four handmade GGOs were designed, constructed, and validated. To verify the real volumes (i.e., ground truth) of the nodules, ultra-high-resolution images were acquired using a µCT system. CT images of the phantom were acquired with a CTDIvol of 1.4 mGy. Ten acquisitions were performed per scanner/protocol combination with phantom repositioning to mimic clinical positioning variations. EICT images were reconstructed in normal resolution using HIR. The PCCT images were reconstructed in both normal and high resolution (HR) with HIR and DLR. The relative error (RE) and the coefficient of variation (CV) were used to evaluate the accuracy and precision of the volume estimates, respectively. The choice of reconstruction method and kernel combination is critical in both systems, affecting both precision and accuracy. When comparing EICT and PCCT systems using the combination DLR/Lung kernel, PCCT demonstrates better accuracy (RE < 14% for SN and < 8.1% for GGO) and precision (CV < 5% for SN).