RESUMO
Background: Despite advancements in coronary computed tomography angiography (CTA), challenges in positive predictive value and specificity remain due to limited spatial resolution. The purpose of this experimental study was to investigate the effect of 2nd generation deep learning-based reconstruction (DLR) on the quantitative and qualitative image quality in coronary CTA. Methods: A vessel model with stepwise non-calcified plaque was scanned using 320-detector CT. Image reconstruction was performed using four techniques: hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), DLR, and 2nd generation DLR. The luminal peak CT number, contrast-to-noise ratio (CNR), and edge rise slope (ERS) were quantitatively evaluated via profile curve analysis. Two observers qualitatively graded the graininess, lumen sharpness, and overall lumen visibility on the basis of the degree of confidence for the stenosis severity using a five-point scale. Results: The image noise with HIR, MBIR, DLR, and 2nd generation DLR was 23.0, 21.0, 16.9, and 9.5 HU, respectively. The corresponding CNR (25% stenosis) was 15.5, 15.9, 22.1, and 38.3, respectively. The corresponding ERS (25% stenosis) was 203.2, 198.6, 228.9, and 262.4 HU/mm, respectively. Among the four reconstruction methods, the 2nd generation DLR achieved the significantly highest CNR and ERS values. The score of 2nd generation DLR in all evaluation points (graininess, sharpness, and overall lumen visibility) was higher than those of the other methods (overall vessel visibility score, 2.6±0.5, 3.8±0.6, 3.7±0.5, and 4.6±0.5 with HIR, MBIR, DLR, and 2nd generation DLR, respectively). Conclusions: 2nd generation DLR provided better CNR and ERS in coronary CTA than HIR, MBIR, and previous-generation DLR, leading to the highest subjective image quality in the assessment of vessel stenosis.
RESUMO
PURPOSE: The purposes of this experimental study were to compare the quantitative and qualitative visibility of in-stent restenosis between conventional-resolution CT (CRCT) and ultra-high-resolution CT (U-HRCT) and to investigate the effects of the image reconstruction techniques on the visualization of in-stent restenosis. MATERIALS AND METHODS: A vessel tube with non-calcified plaque in a 3.0-mm stent was scanned by using CRCT and U-HRCT at 4 stent directions (0, 30, 60, and 90 degrees) to the through-plane direction. Hybrid iterative reconstruction (HIR); model-based iterative reconstruction (MBIR); deep-learning-based reconstruction (DLR) were used as reconstruction methods. The lumen size was assessed using the full width at half maximum method, and image quality was visually evaluated using 4-point scale. RESULTS: U-HRCT had the significantly wider lumen sizes and narrower stent strut thickness than CRCT in three types of the reconstruction methods (P < 0.01). The lumen sizes for U-HRCT with 90 degrees were narrower than those with the other angle directions regardless of the reconstruction methods. Visual score was significantly higher for U-HRCT than CRCT (3.2 ± 0.7 vs 2.0 ± 0.4, P < 0.001). CONCLUSIONS: U-HRCT quantitatively and qualitatively provided better visualization of in-stent restenosis compared to CRCT. Image quality of U-HRCT may be affected by stent angle.
