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

Abstract

<b>BACKGROUND.</b> A super-resolution deep learning reconstruction (SR-DLR) algorithm may provide better image sharpness than earlier reconstruction algorithms and thereby improve coronary stent assessment on coronary CTA. <b>OBJECTIVE.</b> The purpose of our study was to compare SR-DLR and other reconstruction algorithms in terms of image quality measures related to coronary stent evaluation in patients undergoing coronary CTA. <b>METHODS.</b> 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 were 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 (4-point scale: 1 = worst reconstruction, 4 = best reconstruction) for qualitative measures and to score diagnostic confidence (5-point scale: score ≥ 3 indicating an assessable stent). The assessability rate was calculated for stents with a diameter of 3.0 mm or less. <b>RESULTS.</b> The sample included 24 patients (18 men, six women; mean age, 72.5 ± 9.8 [SD] 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 (2.4 vs 1.7-1.9 mm), stent strut sharpness (327 vs 147-210 ΔHU/mm), and CNR (30.0 vs 16.0-25.6) (all <i>p</i> < .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 <i>p</i> < .001). The assessability rate for stents with a diameter of 3.0 mm or less (<i>n</i> = 37) was higher for SR-DLR (86.5% for observer 1 and 89.2% for observer 2) than for HIR (35.1% and 43.2%), MBIR (59.5% and 62.2%), and NR-DLR (62.2% and 64.9%) (all <i>p</i> < .05). <b>CONCLUSION.</b> 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. <b>CLINICAL IMPACT.</b> SR-DLR may facilitate coronary stent assessment on a 320-row normal-resolution scanner, particularly for small-diameter stents.