Related Experiment Video
Updated: Jul 16, 2026
Imaging In-Stent Restenosis: An Inexpensive, Reliable, and Rapid Preclinical Model
Published on: September 14, 2009
Comparison of Super-Resolution Deep-Learning Reconstruction and Hybrid Iterative Reconstruction for Coronary Stent
Cheng Xu1, Limiao Zou1, Shaofei He2
1Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1, Shuaifuyuan, Dongcheng District, Beijing 100730, China.
Abstract:
Background: Conventional deep-learning reconstruction (DLR) CT methods provide noise reduction but limited improvements in spatial resolution. Objective: To compare a novel super-resolution DLR (SR-DLR) algorithm and a hybrid iterative reconstruction (HIR) algorithm in terms of stent visualization and diagnostic performance for in-stent restenosis on coronary CTA using invasive coronary angiography (ICA) as the reference. Methods: This prospective study enrolled patients undergoing coronary CTA at 11 centers in China from September 2023 to November 2024. Participants underwent coronary CTA using a normal-resolution 320-row MDCT scanner with reconstruction of HIR and SR-DLR images. The final study sample included participants with a coronary stent who underwent ICA within 2 months after CTA. CNRstent, stent-lumen attenuation increase ratio (SAIR), and stent edge sharpness were calculated. Two radiologists (R1, R2) independently assessed stents for subjective image quality (1-5 scale; 5=highest quality), diagnostic confidence (1-5 scale; 5=greatest confidence), and in-stent restenosis (defined as ≥50% stenosis). HIR and SR-DLR were compared using ICA as the reference for in-stent restenosis. Results: The analysis included 73 participants (62 men, 11 women; mean age, 65.5±10.3 years) with 110 stents. SR-DLR, compared with HIR, showed greater CNRstent (44.0±20.1 vs 33.6±15.7), lower SAIR (0.44±0.36 vs 0.71±0.55), and greater edge sharpness (469±261 vs 221±130 HU/mm) (all p<.05). SR-DLR, compared with HIR, showed greater subjective image quality (both readers: median, 4 vs 3) and greater diagnostic confidence (both readers: median, 4 vs 3) (all p<.001). SR-DLR, compared with HR, showed greater accuracy for in-stent restenosis among all stents in per-stent analysis (R1: 89.1% vs 79.1%; R2: 88.2% vs 77.3%) and per-patient analysis (R1: 89.0% vs 79.5%; R2: 87.7% vs 72.6%) and among stents with diameter ≤3.0 mm in per-stent analysis (R1: 91.5% vs 81.4%; R2: 93.2% vs 81.4%) and per-patient analysis (R1: 90.7% vs 76.7%; R2: 93.0% vs 76.7%). Conclusions: SR-DLR, compared with HIR, yielded objective and subjective improvements in stent visualization, including differences attributable to reduced blooming artifacts, and greater diagnostic performance for in-stent restenosis. Clinical Impact: The novel SR-DLR algorithm improves the performance of coronary CTA obtained on conventional 320-row MDCT scanners and could support expanded use of CTA for stent evaluation.
