Coronary stent imaging with dual-source CT: assessment of lumen visibility using different convolution kernels and postprocessing filters

Background Assesment of the coronary arteries after stent placement using coronary computed tomography angiography (CCTA) currently requires reconstruction of images with soft kernels for the assessment of atherosclerotic plaques and dedicated edge enhancing kernels for the evaluation of the stent lumen. Purpose To evaluate a two-dimensional filter tool that provides instant postprocessing of images reconstructed with soft kernels into edge-enhanced images and vice versa and thus may eliminate the need for two separate reconstrcutions for the assessment of coronary artery stents using CCTA. Material and Methods Twenty stents with a diameter of 3.0 mm placed in a vascular phantom were scanned with a dual-source CT using standard parameters. Images were reconstructed with a soft B30f and an edge-enhancing B46f kernel and postprocessed with the corresponding filter algorithm (F30 for B30f images; F46 for B46f images). The resulting four data-sets were evaluated for lumen visibility, intraluminal attenuation, and image noise by two independent readers. Results were validated in vivo against invasive coronary angiography in data-sets from patients with coronary artery stents. Results Average intraluminal attenuation was 552.6 HU, 527.3 HU, 207.9 HU, and 267.5 HU for B30f, F30, B46f, and F46 images, respectively (P < 0.0001). Average image noise was 11.3, 10.6, 19.2, and 15.0 HU, respectively (P < 0.0001). The visible stent diameter was significantly higher in the B46f (59.6%) and F46 images (54%) compared to the B30f (48.3%) and F30 (51.5%) images (P < 0.0001). In the patient study, lumen assessability was significantly better in B46f images than in F46 images. Sensitivity for stenosis detection was best in the original B46f images with a sensitivity of 67% and a specificity of 94%. Conclusion The postprocessing filter reduces image noise, however currently it does not offer an alternative to image reconstruction using the edge-enhancing kernels for the evaluation of the stent lumen.