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, No.1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China.
Canon Medical System, Beijing, 100015, China.
Eur Radiol. 2022 Nov;32(11):7918-7926. doi: 10.1007/s00330-022-08796-2. Epub 2022 May 21.
To explore the impact of deep learning reconstruction (DLR) on image quality and machine learning-based coronary CT angiography (CTA)-derived fractional flow reserve (CT-FFR) values.
Thirty-three consecutive patients with known or suspected coronary artery disease who underwent coronary CTA and subsequent invasive coronary angiography were enrolled. DLR was compared with filtered back projection (FBP), statistical-based iterative reconstruction (SBIR), model-based iterative reconstruction (MBIR) Cardiac, and MBIR Cardiac sharp for objective image qualities of coronary CTA. Invasive fractional flow reserve (FFR) and quantitative flow ratio (QFR) were used as the reference standards. The diagnostic performances of different reconstruction approach-based CT-FFR were calculated.
A total of 182 lesions in 33 patients were enrolled for analysis. The image quality of DLR was superior to the others. There were no significant differences in the CT-FFR values among these five approaches (all p > 0.05). Of the 182 lesions, 17 had invasive FFR results, and 70 had QFR results. Using FFR as a reference, MBIR Cardiac, MBIR Cardiac sharp, and DLR achieved equal diagnostic performance, slightly higher than the other reconstruction approaches (MBIR Cardiac, MBIR Cardiac sharp, and DLR: AUC = 0.82, FBP and AIDR: AUC = 0.78, all p > 0.05). Using QFR as a reference, the AUCs of FBP, SBIR, MBIR Cardiac, MBIR Cardiac sharp, and DLR were 0.83, 0.81, 0.86, 0.84, and 0.83, respectively (all p > 0.05).
Our study showed that the DLR algorithm improved image quality, but there were no significant differences in the CT-FFR values and diagnostic performance among different reconstruction approaches.
• Deep learning-based image reconstruction (DLR) improves the image quality of coronary CTA. • CT-FFR values and diagnostic performance of DLR revealed no significant differences compared to other reconstruction approaches.
探讨深度学习重建（DLR）对图像质量和基于机器学习的冠状动脉 CT 血管造影（CTA）衍生的分数流量储备（CT-FFR）值的影响。
连续纳入 33 例已知或疑似冠状动脉疾病且接受冠状动脉 CTA 及随后有创冠状动脉造影的患者。比较 DLR 与滤波反投影（FBP）、基于统计学的迭代重建（SBIR）、基于模型的迭代重建（MBIR）心脏和 MBIR 心脏锐化对冠状动脉 CTA 的客观图像质量。以有创性血流储备分数（FFR）和定量血流比（QFR）为参考标准。计算不同重建方法的 CT-FFR 的诊断性能。
共纳入 33 例患者的 182 处病变进行分析。DLR 的图像质量优于其他方法。这五种方法的 CT-FFR 值均无显著差异（均 P＞0.05）。182 处病变中，17 处有有创 FFR 结果，70 处有 QFR 结果。以 FFR 为参考，MBIR 心脏、MBIR 心脏锐化和 DLR 获得了相等的诊断性能，略高于其他重建方法（MBIR 心脏、MBIR 心脏锐化和 DLR：AUC=0.82，FBP 和 AIDR：AUC=0.78，均 P＞0.05）。以 QFR 为参考，FBP、SBIR、MBIR 心脏、MBIR 心脏锐化和 DLR 的 AUC 分别为 0.83、0.81、0.86、0.84 和 0.83（均 P＞0.05）。
本研究表明 DLR 算法提高了冠状动脉 CTA 的图像质量，但不同重建方法之间的 CT-FFR 值和诊断性能无显著差异。
基于深度学习的图像重建（DLR）可改善冠状动脉 CTA 的图像质量。
与其他重建方法相比，DLR 的 CT-FFR 值和诊断性能无显著差异。
