OBJECTIVES: The objective of this study was to assess the robustness of a novel test bolus (TB)-based computed tomographic angiography (CTA) contrast-enhancement-prediction (CEP) algorithm by retrospectively quantifying the systematic and random errors between the predicted and true enhancements. MATERIALS AND METHODS: All local institutional review boards approved this retrospective study, in which a total of 72 (3 × 24) anonymized cardiac CTA examinations were collected from 3 hospitals. All patients (46 men; median age, 62 years [range, 31-81 years]) underwent a TB scan and a cardiac CTA according to local scan and injection protocols. For each patient, a shorter TB signal and TB signals with lower temporal resolution were derived from the original TB signal. The CEP algorithm predicted the enhancement in the descending aorta (DAo) on the basis of the TB signals in the DAo, the injection protocols and kilovolt settings, as well as population-averaged blood circulation characteristics. The true enhancement was extracted with a region of interest along the DAo centerline. For each patient, the errors in timing and amplitude were calculated; differences between the hospitals were assessed using the 1-way analysis of variance (P < 0.05) and variations between the TB signals were assessed using the within-subject standard deviation. RESULTS: No significant differences were found between the 3 hospitals for any of the TB signals. With errors in the amplitude and timing of 0.3% ± 15.6% and -0.2 ± 2.0 seconds, respectively, no clinically relevant systematic errors existed. Shorter- and coarser-time-sampled TB signals introduced a within-subject standard deviation of 4.0% and 0.5 seconds, respectively. CONCLUSIONS: This TB-based CEP algorithm has no systematic errors in the timing and amplitude of predicted enhancements and is robust against coarser-time-sampled and incomplete TB scans.

§Institut für Radiologie und Nuklearmedizin Deutsches Herzzentrum München Klinik an der Technischen Universität München

¶Department of Diagnostic Radiology Eberhard Karls Universität Tübingen Tübingen

†Department of Diagnostic and Interventional Radiology University Hospital Philipps University of Marburg Marburg Germany

‡Department of Imaging Methods Charles University and University Hospital Pilsen Pilsen Czech Republic

∥1 Medizinische Klinik Ludwig Maximilians Universität München Munich

From the *Siemens AG Healthcare Sector Imaging and Therapy Division Computed Tomography Forchheim

Institute of Medical Physics Friedrich Alexander University Erlangen Nürnberg Germany

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