Automatic quality measurement of aortic contrast-enhanced CT angiographies for patient-specific dose optimization

René Pallenberg, Marja Fleitmann, Kira Soika, Andreas Stroth, Jan Gerlach, Alexander Fürschke, Jörg Barkhausen, Arpad Bischof, Heinz Handels

In: International Journal of Computer Assisted Radiology and Surgery (IJCARS) 15 Seiten 1611-1617 Springer 7/2020.


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.

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Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence