Comparison of retrospectively ECG-gated and nongated MDCT of the chest in an emergency setting regarding workflow, image quality, and diagnostic certainty
- 137 Downloads
This study aims to assess the influence of ECG-gated acquisition on workflow and to compare image quality and diagnostic certainty for retrospectively ECG-gated and nongated multidetector computed tomography of the chest in the emergency suite.
Materials and methods
Thirty-two consecutive patients were referred for both an ECG-gated and a nongated CT to rule out traumatic thoracic injury (n=15) or acute aortic dissection (n=17). The time from the start of the transportation from the emergency suite to the CT room until the start of the CT scan was recorded. Using a scoring system, the image quality of axial images and multiplanar reformats, the presence of disease, and the subjective diagnostic certainty were assessed with regard to the vascular structures, the bone structures, and the lung parenchyma.
The time needed for transportation and patient preparation was 12.1±1.7 min (8.1–14.5 min). The motion artifacts of the thoracic aorta and the supra-aortic vessels were significantly reduced in the ECG-gated data acquisition compared with the nongated technique (P<0.001). Subjective diagnostic certainty for assessment of the aorta was significantly better using ECG gating. The image quality of the lung parenchyma (P<0.005), the spine (P<0.005), and the ribs (P<0.002) was inferior in the ECG-gated data sets but did not compromise the detection rate of traumatic lesions and fractures.
Performing ECG gating in the emergency room did not slow down the diagnostic workup. ECG-gated acquisition performed better in the assessment of the aorta, but image quality for lung and bone structures was slightly reduced. Further studies are required to assess the influence of the imaging technique on the diagnostic outcome.
KeywordsECG gating Chest trauma Aortic dissection Blunt aortic injury
This research has been supported by the NCCR CO-ME of the Swiss National Science Foundation. We thank Ulrich Helfenstein, MD, and Burkhardt Seifert, PhD, for their contributions to the statistical analyses.