Zou Li-Miao, Xu Cheng, Xu Min, Xu Ke-Ting, Wang Ming, Wang Yun, Wang Yi-Ning
Department 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, Beijing, China.
Canon Medical Systems, Beijing, China.
Quant Imaging Med Surg. 2025 Sep 1;15(9):8541-8552. doi: 10.21037/qims-24-2075. Epub 2025 Aug 12.
Super-resolution deep learning reconstruction (SR-DLR) algorithm has emerged as a promising image reconstruction technique for improving the image quality of coronary computed tomography angiography (CCTA) and ensuring accurate CCTA-derived fractional flow reserve (CT-FFR) assessments even in problematic scenarios (e.g., the presence of heavily calcified plaque and stent implantation). Therefore, the purposes of this study were to evaluate the image quality of CCTA obtained with SR-DLR in comparison with conventional reconstruction methods and to investigate the diagnostic performances of different reconstruction approaches based on CT-FFR.
Fifty patients who underwent CCTA and subsequent invasive coronary angiography (ICA) were retrospectively included. All images were reconstructed with hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), conventional deep learning reconstruction (C-DLR), and SR-DLR algorithms. Objective parameters and subjective scores were compared. Among the patients, 22-comprising 45 lesions-had invasive FFR results as a reference, and the diagnostic performance of different reconstruction approaches based on CT-FFR were compared.
SR-DLR achieved the lowest image noise, highest signal-to-noise ratio (SNR), and best edge sharpness (all P values <0.05), as well as the best subjective scores from both reviewers (all P values <0.001). With FFR serving as a reference, the specificity and positive predictive value (PPV) were improved as compared with HIR and C-DLR (72% 36-44% and 73% 53-58%, respectively); moreover, SR-DLR improved the sensitivity and negative predictive value (NPV) as compared to MBIR (95% 70% and 95% 68%, respectively; all P values <0.05). The overall diagnostic accuracy and area under the curve (AUC) for SR-DLR were significantly higher than those of the HIR, MBIR, and C-DLR algorithms (82% 60-67% and 0.84 0.61-0.70, respectively; all P values <0.05).
SR-DLR had the best image quality for both objective and subjective evaluation. The diagnostic performances of CT-FFR were improved by SR-DLR, enabling more accurate assessment of flow-limiting lesions.
超分辨率深度学习重建（SR-DLR）算法已成为一种很有前景的图像重建技术，可用于提高冠状动脉计算机断层扫描血管造影（CCTA）的图像质量，并确保即使在存在问题的情况下（例如，存在严重钙化斑块和支架植入）也能准确进行基于CCTA的血流储备分数（CT-FFR）评估。因此，本研究的目的是比较SR-DLR与传统重建方法获得的CCTA图像质量，并基于CT-FFR研究不同重建方法的诊断性能。
回顾性纳入50例行CCTA及随后的有创冠状动脉造影（ICA）的患者。所有图像均采用混合迭代重建（HIR）、基于模型的迭代重建（MBIR）、传统深度学习重建（C-DLR）和SR-DLR算法进行重建。比较客观参数和主观评分。在这些患者中，22例（包含45个病变）有有创FFR结果作为参考，并比较基于CT-FFR的不同重建方法的诊断性能。
SR-DLR实现了最低的图像噪声、最高的信噪比（SNR）和最佳的边缘清晰度（所有P值<0.05），以及两位阅片者给出的最佳主观评分（所有P值<0.001）。以FFR作为参考，与HIR和C-DLR相比，特异性和阳性预测值（PPV）有所提高（分别为72%对36%-44%和73%对53%-58%）；此外，与MBIR相比，SR-DLR提高了敏感性和阴性预测值（NPV）（分别为95%对70%和95%对68%；所有P值<0.05）。SR-DLR的总体诊断准确性和曲线下面积（AUC）显著高于HIR、MBIR和C-DLR算法（分别为82%对60%-67%和0.84对0.61-0.70；所有P值<0.05）。
SR-DLR在客观和主观评估方面均具有最佳图像质量。SR-DLR提高了CT-FFR的诊断性能，能够更准确地评估限流性病变。
