Matsuyama Takahiro, Nagata Hiroyuki, Ozawa Yoshiyuki, Ito Yuya, Kimata Hirona, Fujii Kenji, Akino Naruomi, Ueda Takahiro, Nomura Masahiko, Yoshikawa Takeshi, Takenaka Daisuke, Kawai Hideki, Sarai Masayoshi, Izawa Hideo, Ohno Yoshiharu
Department of Radiology, Fujita Health University School of Medicine, Toyoake, Japan.
Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake, Japan.
Eur Radiol. 2025 Feb 4. doi: 10.1007/s00330-025-11376-9.
To directly compare coronary arterial stenosis evaluations by hybrid-type iterative reconstruction (IR), model-based IR (MBIR), deep learning reconstruction (DLR), and high-resolution deep learning reconstruction (HR-DLR) on coronary computed tomography angiography (CCTA) in both in vitro and in vivo studies.
For the in vitro study, a total of three-vessel tube phantoms with diameters of 3 mm, 4 mm, and 5 mm and with simulated non-calcified stepped stenosis plaques with degrees of 0%, 25%, 50%, and 75% stenosis were scanned with area-detector CT (ADCT) and ultra-high-resolution CT (UHR-CT). Then, ADCT data were reconstructed using all methods, although UHR-CT data were reconstructed with hybrid-type IR, MBIR, and DLR. For the in vivo study, patients who had undergone CCTA at ADCT were retrospectively selected, and each CCTA data set was reconstructed with all methods. To compare the image noise and measurement accuracy at each of the stenosis levels, image noise, and inner diameter were evaluated and statistically compared. To determine the effect of HR-DLR on CAD-RADS evaluation accuracy, the accuracy of CAD-RADS categorization of all CCTAs was compared by using McNemar's test.
The image noise of HR-DLR was significantly lower than that of others on ADCT and UHR-CT (p < 0.0001). At a 50% and 75% stenosis level for each phantom, hybrid-type IR showed a significantly larger mean difference on ADCT than did others (p < 0.05). At in vivo study, 31 patients were included. Accuracy on HR-DLR was significantly higher than that on hybrid-type IR, MBIR, or DLR (p < 0.0001).
HR-DLR is potentially superior for coronary arterial stenosis evaluations to hybrid-type IR, MBIR, or DLR shown on CCTA.
Question How do coronary arterial stenosis evaluations by hybrid-type IR, MBIR, DLR, and HR-DLR compare to coronary CT angiography? Findings HR-DLR showed significantly lower image noise and more accurate coronary artery disease reporting and data system (CAD-RADS) evaluation than others. Clinical relevance HR-DLR is potentially superior to other reconstruction methods for coronary arterial stenosis evaluations, as demonstrated by coronary CT angiography results on ADCT and as shown in both in vitro and in vivo studies.
在体外和体内研究中，直接比较混合型迭代重建（IR）、基于模型的IR（MBIR）、深度学习重建（DLR）和高分辨率深度学习重建（HR-DLR）在冠状动脉计算机断层扫描血管造影（CCTA）上对冠状动脉狭窄的评估。
在体外研究中，使用面积探测器CT（ADCT）和超高分辨率CT（UHR-CT）扫描了总共三个血管的管模，其直径分别为3毫米、4毫米和5毫米，并带有模拟的非钙化阶梯状狭窄斑块，狭窄程度分别为0%、25%、50%和75%。然后，使用所有方法重建ADCT数据，而UHR-CT数据则使用混合型IR、MBIR和DLR进行重建。在体内研究中，回顾性选择了在ADCT接受CCTA检查的患者，并使用所有方法重建每个CCTA数据集。为了比较每个狭窄水平的图像噪声和测量准确性，对图像噪声和内径进行了评估并进行统计学比较。为了确定HR-DLR对CAD-RADS评估准确性的影响，使用McNemar检验比较了所有CCTA的CAD-RADS分类准确性。
在ADCT和UHR-CT上，HR-DLR的图像噪声明显低于其他方法（p < 0.0001）。对于每个管模，在狭窄程度为50%和75%时，混合型IR在ADCT上显示出比其他方法更大的平均差异（p < 0.05）。在体内研究中，纳入了31名患者。HR-DLR的准确性明显高于混合型IR、MBIR或DLR（p < 0.0001）。
在CCTA上显示，HR-DLR在冠状动脉狭窄评估方面可能优于混合型IR、MBIR或DLR。
问题 混合型IR、MBIR、DLR和HR-DLR对冠状动脉狭窄的评估与冠状动脉CT血管造影相比如何？ 发现 HR-DLR显示出比其他方法明显更低的图像噪声以及更准确的冠状动脉疾病报告和数据系统（CAD-RADS）评估。 临床意义 如ADCT上的冠状动脉CT血管造影结果以及体外和体内研究所示，HR-DLR在冠状动脉狭窄评估方面可能优于其他重建方法。
