Coronary Stent Evaluation by CTA: Image Quality Comparison Between Super-Resolution Deep-Learning Reconstruction and Other Reconstruction Algorithms.

Abstract

Background: A super-resolution deep-learning based reconstruction (SR-DLR) algorithm may provide better image sharpness than earlier reconstruction algorithms and thereby improve coronary stent assessment on coronary CTA. Objective: To compare SR-DLR and other reconstruction algorithms in terms of image quality measures related to coronary stent evaluation in patients undergoing coronary CTA. Methods: This retrospective study included patients with at least one coronary artery stent who underwent coronary CTA between January 2020 and December 2020. Examinations were performed using a 320-row normal-resolution scanner and reconstructed with hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), normal-resolution deep-learning reconstruction (NR-DLR), and SR-DLR algorithms. Quantitative image quality measures were determined. Two radiologists independently reviewed images to rank the four reconstructions (1=worst reconstruction, 4=best reconstruction) for qualitative measures, and to score diagnostic confidence (5-point scale; score≥3 indicating an assessable stent). Assessability rate was calculated for stents with diameter ≤3.0 mm. Results: The sample included 24 patients (18 men, 6 women; mean age, 72.8±9.8 years), with 51 stents. SR-DLR, in comparison with the other reconstructions, yielded lower stent-related blooming artifacts (median, 40.3 vs 53.4-58.2), stent-induced attenuation increase ratio (0.17 vs 0.27-0.31), and quantitative image noise (18.1 vs 20.9-30.4 HU), and higher in-stent lumen diameter (2.41 vs. 1.74-1.94 mm), stent strut sharpness (327 vs 147-210 ΔHU/mm), and CNR (30.0 vs 16.0-25.6), (all p<.001). For both observers, all ranked measures (image sharpness; image noise; noise texture; delineation of stent strut, in-stent lumen, coronary artery wall, and calcified plaque surrounding the stent) and diagnostic confidence showed a higher score for SR-DLR (median, 4.0 for all features) than for the other reconstructions (range, 1.0-3.0) (all p<.001). Assessability rate for stents with diameter ≤3.0 mm (n=37) was higher for SR-DLR (observer 1: 86.5%; observer 2: 89.2%) than for HIR (35.1%, 43.2%), MBIR (59.5%, 62.2%), or NR-DLR (62.2%, 64.9%) (all p<.05). Conclusion: SR-DLR yielded improved delineation of the stent strut and in-stent lumen, with better image sharpness and less image noise and blooming artifacts, in comparison with HIR, MBIR, and NR-DLR. Clinical Impact: SR-DLR may facilitate coronary stent assessment on a 320-row normal-resolution scanner, particularly for small-diameter stents.