Purpose: The development of dual energy CT (DECT) has made it possible to provide not only morphological characteristics but also a wide range of quantitative information. The purpose of this study is to differentiate between benign and malignant solitary pulmonary nodules (SPN) by using electron density values obtained from DECT. Methods: From the image data diagnosed as SPN, DECT images of 54 cases were selected, and the maximum electron density values of SPN were obtained. Electron density values were compared between benign and malignant cases by t-test. Comparisons between histopathological types and histological subtypes were performed by ANOVA. Logistic regression analysis was also applied to estimate the partial regression coefficients on electron density values. Results: Electron density values were 3.56×10²⁰/mm³ for malignant and 3.51×10²⁰/mm³ for benign, with malignancy being significantly higher (p<0.001). ROC analysis showed that the AUC was 0.77. In a comparison between histopathologic types, electron density values were significantly higher for adenocarcinoma and squamous cell carcinoma (p<0.05). There were no significant differences among subtypes. Logistic regression analysis showed a regression coefficient of 1.24 (p<0.01). Conclusion: Electron density values obtained from dual energy CT may serve as a useful quantitative parameter for differentiating between benign and malignant SPN. However, attention should be paid to certain histological subtypes, such as invasive mucinous adenocarcinoma, which may exhibit exceptionally low values.

Purpose: StarGuide (GE HealthCare, Haifa, Israel), a full-ring SPECT/CT system using Cadmium Zinc Telluride (CZT) technology, allows detectors to perform a pendulum motion (sweep) during SPECT acquisition. It offers two sweep modes: continuous and step and shoot, and we investigated the impact of different sweep modes on spatial resolution and image uniformity. Methods: Spatial resolution was evaluated using the full width half maximum (FWHM) of line source images. Image uniformity was assessed using the root mean square uniformity (%RMSU) of pool phantom images. Image reconstruction was performed using the 3D-OSEM method. Attenuation correction and spatial resolution correction were applied, and no filters were used during the reconstruction process. Results: FWHM at the center of rotation with update 500 was 5.60±0.13 mm for continuous and 4.40±0.15 mm for step and shoot. %RMSU with update 100 was 8.94±0.38% for continuous and 9.23±0.35% for step and shoot. Conclusion: Using the step-and-shoot sweep mode can maintain high spatial resolution.

Purpose: The purpose of this study was to compare the surface conformity of Clearfit Bolus II (Clearfit2) (Fujidenolo, Aichi) and Clearfit Bolus (Clearfit1) (Fujidenolo) using an anthropomorphic phantom. Methods: Clearfit1 and Clearfit2 boluses (5 mm thick) were placed in three anatomical regions of the anthropomorphic phantom: nose, breast, and abdomen, and CT scans were performed. Digital Imaging and Communications in Medicine (DICOM) structural data were used to measure the air gap between the bolus and the surface of the phantom. The surface conformity area ratio at the threshold (1–5 mm) was calculated. Statistical analysis was performed using the Mann–Whitney U test. Results: Clearfit2 had significantly reduced air gaps compared to Clearfit1: 2.9±2.0 mm in the nose, 1.8±2.1 mm in the breasts, and 0.1±0.1 mm in the abdomen, which were lower than Clearfit1’s 5.8±2.9 mm, 11.4±8.4 mm, and 2.2±2.1 mm, respectively (p<0.001). In the abdominal region, Clearfit2 achieved near-perfect surface conformity with over 99% of the surface showing gaps ≤1 mm. Conclusion: We demonstrated that Clearfit2 significantly improves the surface conformity by reducing air gaps between the bolus and the phantom surface compared to Clearfit1.

Purpose: The aim of this study was to attempt to make a decision on priorities in measures to prevent recurrence of previously reported low-level incidents using the priority monitoring score. Methods: A survey of the low-level incidents reported over the past 5 years in the department of radiation therapy was conducted among 7 radiological technologists. From the results of the survey, the priority monitoring score was calculated by multiplying each score of the potential incident level, the occurrence frequency, and the detectability. Additionally, the relationship between the priority monitoring score and each factor was investigated. Results: From the results of a survey of 67 low-level incidents, the mean+standard deviation (23.1) of the priority monitoring score was set as the threshold for a decision of priorities. The strongest correlation was observed between the priority monitoring score and the potential incident level (a correlation coefficient, r=0.74). Conclusion: It is suggested that the possibility of prioritizing the decision of low-level incidents that require measures to prevent recurrence be considered by setting the threshold for the priority monitoring score. On the other hand, the reporting requirements and the level classification for incidents vary among facilities. Therefore, the threshold for the decision of priorities needs to be considered in each facility.