Purpose: To prospectively evaluate, by optimizing image acquisition and introducing alternative image postprocessing techniques, dual-energy multidetector computed tomography (CT) for depiction of the lumens of coronary artery stents placed in a moving anthropomorphic heart phantom.
Materials and methods: Four coronary stents (2, 3, 4, and 5 mm) were examined at 64-section dual-energy multidetector CT by using a dual-detector "double-decker" imager with stacked high- and low-energy detector arrays, 0.67-mm section thickness, and 32 x 0.625-mm collimation. Simultaneous high- and low-energy data sets were acquired at 80 and 140 kVp and at 400 mAs. Cardiac motion was simulated in a moving anthropomorphic heart phantom. Stents were imaged longitudinally and axially with the phantom at rest and with it in motion. Use of an enhancement algorithm based on high- and low-energy absorption profiles was proposed. Stent lumen depiction and stent mesh delineation were quantified in terms of contrast-to-noise ratio (CNR) and kurtosis (kappa), respectively. Image quality was analyzed at univariate general linear model analysis in which peak voltage and data enhancement algorithm were dependent factors and stent orientation and cardiac motion were independent factors.
Results: Analysis of CNR and kappa revealed an interdependency between CNR and kappa and stent diameter: The CNR and kappa of smaller stents increased significantly when these stents were imaged at lower peak voltages in the axial plane and with the enhancement algorithm applied to the 80-kVp data sets (P < .001). The CNR and kappa of larger stents increased significantly when these stents were imaged at higher peak voltages in the longitudinal plane, and imaging of these stents benefited from the enhancement algorithm being applied to the 140-kVp data sets (P < .001).
Conclusion: Dual-energy multidetector CT performed with optimized acquisition parameters and alternative image postprocessing led to enhanced coronary stent lumen depiction to an extent beyond that achieved with single-energy multidetector CT.
(c) RSNA, 2008.