OBJECTIVE: We evaluated the effect of various patient characteristics and time-density curve (TDC)-factors on the test bolus-affected vessel enhancement on coronary computed tomography angiography (CCTA). We also assessed the value of generalized linear regression models (GLMs) for predicting enhancement on CCTA. MATERIALS AND METHODS: We performed univariate and multivariate regression analysis to evaluate the effect of patient characteristics and to compare contrast enhancement per gram of iodine on test bolus (ΔHUTEST) and CCTA (ΔHUCCTA). We developed GLMs to predict ΔHUCCTA. GLMs including independent variables were validated with 6-fold cross-validation using the correlation coefficient and Bland–Altman analysis. RESULTS: In multivariate analysis, only total body weight (TBW) and ΔHUTEST maintained their independent predictive value (p < 0.001). In validation analysis, the highest correlation coefficient between ΔHUCCTA and the prediction values was seen in the GLM (r = 0.75), followed by TDC (r = 0.69) and TBW (r = 0.62). The lowest Bland–Altman limit of agreement was observed with GLM-3 (mean difference, −0.0 ± 5.1 Hounsfield units/grams of iodine [HU/gI]; 95% confidence interval [CI], −10.1, 10.1), followed by ΔHUCCTA (−0.0 ± 5.9 HU/gI; 95% CI, −11.9, 11.9) and TBW (1.1 ± 6.2 HU/gI; 95% CI, −11.2, 13.4). CONCLUSION: We demonstrated that the patient's TBW and ΔHUTEST significantly affected contrast enhancement on CCTA images and that the combined use of clinical information and test bolus results is useful for predicting aortic enhancement.
Angiography* ; Body Weight ; Cardiac Output ; Heart ; Humans ; Iodine ; Linear Models* ; Multivariate Analysis

OBJECTIVE: To evaluate the effect of patient characteristics on popliteal aortic contrast enhancement at lower extremity CT angiography (LE-CTA) scanning. MATERIALS AND METHODS: Prior informed consent to participate was obtained from all 158 patients. All were examined using a routine protocol; the scanning parameters were tube voltage 100 kVp, tube current 100 mA to 770 mA (noise index 12), 0.5-second rotation, 1.25-mm detector row width, 0.516 beam pitch, and 41.2-mm table movement, and the contrast material was 85.0 mL. Cardiac output (CO) was measured with a portable electrical velocimeter within 5 minutes of starting the CT scan. To evaluate the effects of age, sex, body size, CO, and scan delay on the CT number of popliteal artery, the researchers used multivariate regression analysis. RESULTS: A significant positive correlation was seen between the CT number of the popliteal artery and the patient age (r = 0.39, p < 0.01). A significant inverse correlation was observed between the CT number of the popliteal artery and the height (r = −0.48), total body weight (r = −0.52), body mass index (r = −0.33), body surface area (BSA) (r = −0.56), lean body weight (r = −0.56), and CO (r = −0.35) (p < 0.001 for all). There was no significant correlation between the enhancement and the scan delay (r = 0.06, p = 0.47). The BSA, CO, and age had significant effects on the CT number (standardized regression: BSA −0.42, CO −0.22, age 0.15; p < 0.05, respectively). CONCLUSION: The BSA, CO, and age are significantly correlated with the CT number of the popliteal artery on LE-CTA.
Angiography* ; Body Mass Index ; Body Size ; Body Surface Area ; Body Weight ; Cardiac Output ; Humans ; Informed Consent ; Lower Extremity* ; Popliteal Artery ; Tomography, X-Ray Computed

In accordance with the new model-core-curriculum for medical education, the current status of education about the science of radiation health was surveyed in all medical schools in Japan. Among the four learning points related to the “Biological effects of radiation and radiation hazards” , about half of the schools covered issues on “radiation and human body” and the “effect of medical radiation exposure” in one, or less than one, 60-minutes class, but did not touch on “radiation risk communication” and “radiological disaster medicine” . A significant deviation of human resources was also observed between schools. Learning tools such as presentation files and video content were preferred as education support materials. Therefore, development and distribution of the learning tools, especially in “radiation risk communication” and “radiological disaster medicine” , may be a first step to promoting high-quality education on the science of radiation health risk in each school’s curriculum.