Coronary Artery Stent Evaluation by CTA: Impact of Deep Learning Reconstruction and Subtraction Technique

Coronary CTA with hybrid iterative reconstruction (HIR) is prone to false-positive results for in-stent restenosis due to stent-related blooming artifact. The purpose of this study is to assess the impact of deep learning reconstruction (DLR), subtraction images, and the combination of DLR and subtraction images on the diagnostic performance of coronary CTA for the detection of in-stent restenosis. This prospective study included patients with coronary stents who underwent coronary CTA between March 2020 and August 2021. CTA used a technique with two breath-holds (noncontrast and contrast-enhanced acquisitions). Conventional and subtraction images were reconstructed for HIR and DLR. The maximum visible instent lumen diameter was measured. Two readers independently evaluated images for in-stent restenosis (≥ 50% stenosis). A simulated assessment of combined conventional and subtraction images was generated, reflecting assessment of conventional and subtraction images in the presence or absence of severe misregistration artifact, respectively. Invasive angiography served as reference standard. The study enrolled 30 patients (22 men and eight women; mean age, 63.6 ± 7.4 [SD] years) with a total of 59 stents; severe misregistration artifact was present for 32 stents. Maximum visible in-stent lumen diameter was higher for DLR than for HIR (2.3 ± 0.5 vs 2.1 ± 0.5 mm, < .001), and among stents without severe misregistration artifact, it was higher for subtraction than conventional DLR (3.0 ± 0.5 vs 2.4 ± 0.5, < .001). Among conventional CTA with HIR, conventional CTA with DLR, combination (conventional and subtraction) approach with HIR, and combination (conventional and subtraction) approach with DLR, the highest patient-level diagnostic performance measures were as follows: for reader 1, sensitivity was identical (62.5%), specificity was highest for combination with DLR (90.1%), PPV was highest for combination with DLR (71.4%), NPV was highest for combination with DLR (87.0%), and accuracy was highest for combination with DLR (83.3%); for reader 2, sensitivity was identical (50.0%), specificity was highest for combination with HIR or DLR (both 95.5%), PPV was highest for combination with HIR or DLR (both 80.0%), NPV was highest for combination with HIR or DLR (84.0%), and accuracy was highest for combination with HIR or DLR (both 83.3%). The combined DLR and subtraction technique yielded optimal diagnostic performance for detecting in-stent restenosis by corona