Diagnostic accuracy of in-stent restenosis using model-based iterative reconstruction at coronary CT angiography: initial experience.

Objective: The purpose of our study was to compare the diagnostic performance of coronary CT angiography (CTA) subjected to model-based iterative reconstruction (IR) or hybrid IR to rule out coronary in-stent restenosis. Methods: We enrolled 16 patients who harboured 22 coronary stents. They underwent coronary CTA on a 320-slice CT scanner. The images were reconstructed with hybrid IR (AIDR 3D) and model-based IR (FIRST) algorithms. We calculated the stent lumen attenuation increase ratio and measured the visible stent lumen diameter. Two blinded observers visually graded the likelihood of in-stent restenosis (lesions >= 50%) on hybrid IR and FIRST images. Results: The stent lumen attenuation increase ratio on FIRST-was lower than on AIDR 3D images (0.20 vs 0.32). The ratio of the visible-compared to the true stent lumen diameter was higher on FIRST-than AIDR 3D images (52.5 vs 47.5%). Invasive coronary angiography identified five stents (22.7%) with significant in-stent restenosis. The use of FIRST improved the sensitivity (60 vs 100%), positive (75.0 vs 83.3%) and negative predictive value (88.9 vs 100%) and the accuracy (86.4 vs 95.5%) for the detection of in-stent restenosis. Specificity was 94.1% for both reconstruction methods. Conclusion: The model- based IR algorithm may improve diagnostic performance for the detection of in-stent restenosis. Advances in knowledge: Compared to hybrid IR, the new model- based IR algorithm reduced blooming artefacts and improved the image quality. It can be expected to improve diagnostic performance for the detection of in-stent restenosis on coronary CTA images.