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Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: 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.
摘要：
■尽管冠状动脉计算机断层扫描血管造影（CTA）取得了进展，由于空间分辨率有限，在阳性预测值和特异性方面仍然存在挑战。这项实验研究的目的是研究第二代基于深度学习的重建（DLR）对冠状动脉CTA中定量和定性图像质量的影响。
■使用320探测器CT扫描具有逐步非钙化斑块的血管模型。使用四种技术进行图像重建：混合迭代重建（HIR），基于模型的迭代重建(MBIR)，DLR,第二代DLR。管腔峰值CT数,对比噪声比(CNR)，通过轮廓曲线分析对边缘上升斜率（ERS）进行了定量评估。两位观察者对颗粒性进行了定性分级，管腔锐度，和总体管腔能见度的基础上的信心程度的狭窄严重程度，使用五点量表。
■具有HIR的图像噪声，MBIR,DLR,第二代DLR分别为23.0、21.0、16.9和9.5HU，分别。相应的CNR(25%狭窄)分别为15.5、15.9、22.1和38.3。相应的ERS（25%狭窄）为203.2、198.6、228.9和262.4HU/mm，分别。在四种重建方法中，第二代DLR实现了显着最高的CNR和ERS值。第二代DLR在所有评估点中的得分(颗粒度，清晰度,和总体管腔可见性)高于其他方法(总体血管可见性评分，2.6±0.5,3.8±0.6,3.7±0.5,4.6±0.5,HIR,MBIR,DLR,第二代DLR，分别)。
■第二代DLR在冠状动脉CTA中提供的CNR和ERS优于HIR，MBIR,和上一代DLR，在血管狭窄的评估中导致最高的主观图像质量。
■使用320探测器CT扫描具有逐步非钙化斑块的血管模型。使用四种技术进行图像重建：混合迭代重建（HIR），基于模型的迭代重建(MBIR)，DLR,第二代DLR。管腔峰值CT数,对比噪声比(CNR)，通过轮廓曲线分析对边缘上升斜率（ERS）进行了定量评估。两位观察者对颗粒性进行了定性分级，管腔锐度，和总体管腔能见度的基础上的信心程度的狭窄严重程度，使用五点量表。
■具有HIR的图像噪声，MBIR,DLR,第二代DLR分别为23.0、21.0、16.9和9.5HU，分别。相应的CNR(25%狭窄)分别为15.5、15.9、22.1和38.3。相应的ERS（25%狭窄）为203.2、198.6、228.9和262.4HU/mm，分别。在四种重建方法中，第二代DLR实现了显着最高的CNR和ERS值。第二代DLR在所有评估点中的得分(颗粒度，清晰度,和总体管腔可见性)高于其他方法(总体血管可见性评分，2.6±0.5,3.8±0.6,3.7±0.5,4.6±0.5,HIR,MBIR,DLR,第二代DLR，分别)。
■第二代DLR在冠状动脉CTA中提供的CNR和ERS优于HIR，MBIR,和上一代DLR，在血管狭窄的评估中导致最高的主观图像质量。
