In this study, the optimal monochromatic energy level in dual-energy spectral CT required for imaging coronary stents after percutaneous coronary intervention (PCI) was explored. Thirty-five consecutive patients after PCI were examined using the dual-energy spectral CT imaging mode. The original images were reconstructed at 40–140 keV (10-keV interval) monochromatic levels. The in-stent and out-stent CT values at each monochromatic level were measured to calculate the signal-to-noise ratio(SNR) and contrast-to-noise ratio (CNR) for the vessel and the CT value difference between the in-stent and out-stent lumen (dCT (in–out)), which reflects the artificial CT number increase due to the beam hardening effect caused by the stents. The subjective image quality of the stent and in-stent vessel was evaluated by two radiologists using a 5-point scale. With the increase in energy level, the CT value, SNR, CNR, and dCT (in–out) all decreased. At 80 keV, the mean CT value in-stent reached (345.24 ± 93.43) HU and dCT (in–out) started plateauing. In addition, the subjective image quality of the stents and vessels peaked at 80 keV. The 80 keV monochromatic images are optimal for imaging cardiac patients with stents after PCI, balancing the enhancement and SNR and CNR in the vessels while minimizing the beam hardening artifacts caused by the stents.

The purpose of this study was to establish a clinical prediction model for the differential diagnosis of pulmonary cystic echinococcosis (CE) and pulmonary abscess according to computed tomography (CT)-based radiomics signatures and clinical indicators. This is a retrospective single-centre study. A total of 117 patients, including 53 with pulmonary CE and 64 with pulmonary abscess, were included in our study and were randomly divided into a training set (n = 95) and validation set (n = 22). Radiomics features were extracted from CT images, a radiomics signature was constructed, and clinical indicators were evaluated to establish a clinical prediction model. Finally, a model combining imaging radiomics features and clinical indicators was constructed. The performance of the nomogram, radiomics signature and clinical prediction model was evaluated and validated with the training and test datasets, and then the three models were compared. The radiomics signature of this study was established by 25 features, and the radiomics nomogram was constructed by using clinical factors and the radiomics signature. Finally, the areas under the receiver operating characteristic curve (AUCs) for the training set and test set were 0.970 and 0.983, respectively. Decision curve analysis showed that the radiologic nomogram was better than the clinical prediction model and individual radiologic characteristic model in differentiating pulmonary CE from pulmonary abscess. The radiological nomogram and models based on clinical factors and individual radiomics features can distinguish pulmonary CE from pulmonary abscess and will be of great help to clinical diagnoses in the future.