Tomizawa Nobuo, Fan Ruiheng, Fujimoto Shinichiro, Nozaki Yui O, Kawaguchi Yuko O, Takamura Kazuhisa, Hiki Makoto, Aikawa Tadao, Takahashi Norihito, Okai Iwao, Okazaki Shinya, Kumamaru Kanako K, Minamino Tohru, Aoki Shigeki
Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Eur Radiol. 2025 May 22. doi: 10.1007/s00330-025-11707-w.
This study aimed to compare the diagnostic performance of CT-derived fractional flow reserve (CT-FFR) using model-based iterative reconstruction (MBIR) and high-resolution deep learning reconstruction (HR-DLR) images to detect functionally significant stenosis with invasive FFR as the reference standard.
This single-center retrospective study included 79 consecutive patients (mean age, 70 ± 11 [SD] years; 57 male) who underwent coronary CT angiography followed by invasive FFR between February 2022 and March 2024. CT-FFR was calculated using a mesh-free simulation. The cutoff for functionally significant stenosis was defined as FFR ≤ 0.80. CT-FFR was compared with MBIR and HR-DLR using receiver operating characteristic curve analysis.
The mean invasive FFR value was 0.81 ± 0.09, and 46 of 98 vessels (47%) had FFR ≤ 0.80. The mean noise of HR-DLR was lower than that of MBIR (14.4 ± 1.7 vs 23.5 ± 3.1, p < 0.001). The area under the receiver operating characteristic curve for the diagnosis of functionally significant stenosis of HR-DLR (0.88; 95% CI: 0.80, 0.95) was higher than that of MBIR (0.76; 95% CI: 0.67, 0.86; p = 0.003). The diagnostic accuracy of HR-DLR (88%; 86 of 98 vessels; 95% CI: 80, 94) was higher than that of MBIR (70%; 69 of 98 vessels; 95% CI: 60, 79; p < 0.001).
HR-DLR improves image quality and the diagnostic performance of CT-FFR for the diagnosis of functionally significant stenosis.
Question The effect of HR-DLR on the diagnostic performance of CT-FFR has not been investigated. Findings HR-DLR improved the diagnostic performance of CT-FFR over MBIR for the diagnosis of functionally significant stenosis as assessed by invasive FFR. Clinical relevance HR-DLR would further enhance the clinical utility of CT-FFR in diagnosing the functional significance of coronary stenosis.
本研究旨在比较基于模型的迭代重建（MBIR）和高分辨率深度学习重建（HR-DLR）图像的CT衍生血流储备分数（CT-FFR）检测功能上有意义狭窄的诊断性能，以有创FFR作为参考标准。
这项单中心回顾性研究纳入了2022年2月至2024年3月期间连续79例患者（平均年龄70±11[标准差]岁；57例男性），这些患者接受了冠状动脉CT血管造影，随后进行了有创FFR检查。使用无网格模拟计算CT-FFR。功能上有意义狭窄的截断值定义为FFR≤0.80。使用受试者操作特征曲线分析比较MBIR和HR-DLR的CT-FFR。
平均有创FFR值为0.81±0.09，98支血管中有46支（47%）的FFR≤0.80。HR-DLR的平均噪声低于MBIR（14.4±1.7对23.5±3.1，p<0.001）。HR-DLR诊断功能上有意义狭窄的受试者操作特征曲线下面积（0.88；95%CI：0.80，0.95）高于MBIR（0.76；95%CI：0.67，0.86；p=0.003）。HR-DLR的诊断准确性（88%；98支血管中的86支；95%CI：80，94）高于MBIR（70%；98支血管中的69支；95%CI：60，79；p<0.001）。
HR-DLR提高了图像质量以及CT-FFR诊断功能上有意义狭窄的诊断性能。
问题 尚未研究HR-DLR对CT-FFR诊断性能的影响。发现 通过有创FFR评估，HR-DLR在诊断功能上有意义狭窄方面比MBIR提高了CT-FFR的诊断性能。临床意义 HR-DLR将进一步提高CT-FFR在诊断冠状动脉狭窄功能意义方面的临床效用。
