Purpose Iodine-containing contrast agent (CA) used in contrast-enhanced CT angiography (CTA) can pose a health risk for patients. A system that adjusts the frequently used standard CA dose for individual patients based on their clinical parameters can be useful. As basis the quality of the image contrast in CTA volumes has to be determined, especially to recognize excessive contrast induced by CA overdosing. However, a manual assessment with a ROI-based image contrast classification is a time-consuming step in everyday clinical practice. Methods We propose a method to automate the contrast measurement of aortic CTA volumes. The proposed algorithm is based on the mean HU values in selected ROIs that were automatically positioned in the CTA volume. First, an automatic localization algorithm determines the CTA image slices for certain ROIs followed by the localization of these ROIs. A rule-based classification using the mean HU values in the ROIs categorizes images with insufficient, optimal and excessive contrast. Results In 95.89% (70 out of 73 CTAs obtained with the ulrich medical CT motion contrast media injector) the algorithm chose the same image contrast class as the radiological expert. The critical case of missing an overdose did not occur with a positive predicative value of 100%. Conclusion The resulting system works well within our range of considered scan protocols detecting enhanced areas in CTA volumes. Our work automized an assessment for classifying CA-induced image contrast which reduces the time needed for medical practitioners to perform such an assessment manually.

This work aims to recognize the patient individual possibility of contrast dose reduction in CT angiography. This system should help to identify whether the dose of contrast agent in CT angiography can be reduced to avoid side effects. In a clinical study, 263 CT angiographies were performed and, in addition, 21 clinical parameters were recorded for each patient before contrast agent administration. The resulting images were labeled according to their contrast quality. It is assumed that the contrast dose could be reduced for CT angiography images with excessive contrast. These data was used to develop a model for predicting excessive contrast based on the clinical parameters using logistic regression, random forest, and gradient boosted trees. In addition, the minimization of clinical parameters required was investigated to reduce the overall effort. Therefore, models were tested with all subsets of clinical parameters and each parameter’s importance was examined. In predicting excessive contrast in CT angiography images covering the aortic region, a maximum accuracy of 0.84 was achieved by a random forest with 11 clinical parameters; for the leg-pelvis region data, an accuracy of 0.87 was achieved by a random forest with 7 parameters; and for the entire data set, an accuracy of 0.74 was achieved by gradient boosted trees with 9 parameters.