Author + information
- Received February 24, 2019
- Revision received May 13, 2019
- Accepted June 20, 2019
- Published online August 14, 2019.
- Chun Xiang Tang, MSca,∗,
- Chun Yu Liu, BSa,∗,
- Meng Jie Lu, MSca,
- U. Joseph Schoepf, MDa,c,
- Christian Tesche, MDc,
- Richard R. Bayer II, MDc,
- H. Todd Hudson Jr., MSc,
- Xiao Lei Zhang, BSa,
- Jian Hua Li, MDb,
- Yi Ning Wang, MDd,
- Chang Sheng Zhou, MSca,
- Jia Yin Zhang, MD, PhDe,
- Meng Meng Yu, MSce,
- Yang Hou, MD, PhDf,
- Min Wen Zheng, MDg,
- Bo Zhang, MDh,
- Dai Min Zhang, MD, PhDi,
- Yan Yi, MD, PhDd,
- Yuan Ren, MScj,
- Chen Wei Li, MScj,
- Xi Zhao, PhDj,
- Guang Ming Lu, MDa,
- Xiu Hua Hu, MD, PhDk,∗∗∗∗ (, )
- Lei Xu, MD, PhDl,∗∗∗ ( and )
- Long Jiang Zhang, MD, PhDa,∗∗ ()
- aDepartment of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
- bDepartment of Cardiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
- cDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina
- dDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- eInstitute of Diagnostic and Interventional Radiology and Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
- fDepartment of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
- gDepartment of Radiology, Xijing Hospital, Fourth Military Medical University, Xi’an, China
- hDepartment of Radiology, Jiangsu Taizhou People’s Haspital, Taizhou, China
- iDepartment of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
- jShanghai United Imaging Healthcare, Shanghai, China
- kShaoyifu Hospital Affiliated to Medical College of Zhejiang University, Hangzhou, China
- lDepartment of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
- ↵∗Address for correspondence:
Dr. Long Jiang Zhang, Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, China.
- ↵∗∗Dr. Lei Xu, Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing 10029, China.
- ↵∗∗∗Dr. Xiu Hua Hu, Shaoyifu Hospital Affiliated to Medical College of Zhejiang University, Hangzhou, 310016, China.
Objectives The aim of this study was to validate the feasibility of a novel structural and computational fluid dynamics–based fractional flow reserve (FFR) algorithm for coronary computed tomography angiography (CTA), using alternative boundary conditions to detect lesion-specific ischemia.
Background A new model of computed tomographic (CT) FFR relying on boundary conditions derived from structural deformation of the coronary lumen and aorta with transluminal attenuation gradient and assumptions regarding microvascular resistance has been developed, but its accuracy has not yet been validated.
Methods A total of 338 consecutive patients with 422 vessels from 9 Chinese medical centers undergoing CTA and invasive FFR were retrospectively analyzed. CT FFR values were obtained on a novel on-site computational fluid dynamics–based CT FFR (uCT-FFR [version 1.5, United-Imaging Healthcare, Shanghai, China]). Performance characteristics of uCT-FFR and CTA in detecting lesion-specific ischemia in all lesions, intermediate lesions (luminal stenosis 30% to 70%), and “gray zone” lesions (FFR 0.75 to 0.80) were calculated with invasive FFR as the reference standard. The effect of coronary calcification on uCT-FFR measurements was also assessed.
Results Per vessel sensitivities, specificities, and accuracies of 0.89, 0.91, and 0.91 with uCT-FFR, 0.92, 0.34, and 0.55 with CTA, and 0.94, 0.37, and 0.58 with invasive coronary angiography, respectively, were found. There was higher specificity, accuracy, and AUC for uCT-FFR compared with CTA and qualitative invasive coronary angiography in all lesions, including intermediate lesions (p < 0.001 for all). No significant difference in diagnostic accuracy was observed in the “gray zone” range versus the other 2 lesion groups (FFR ≤0.75 and >0.80; p = 0.397) and in patients with “gray zone” versus FFR ≤0.75 (p = 0.633) and versus FFR >0.80 (p = 0.364), respectively. No significant difference in the diagnostic performance of uCT-FFR was found between patients with calcium scores ≥400 and <400 (p = 0.393).
Conclusions This novel computational fluid dynamics–based CT FFR approach demonstrates good performance in detecting lesion-specific ischemia. Additionally, it outperforms CTA and qualitative invasive coronary angiography, most notably in intermediate lesions, and may potentially have diagnostic power in gray zone and highly calcified lesions.
↵∗ Drs. Tang, Liu, L.J. Zhang, L. Xu, and Hu have contributed equally to this study.
This work was supported by The National Key Research and Development Program of China (grant 2017YFC0113400 to Dr. L.J. Zhang, grant 2016YFC1300300 for Dr. L. Xu). Dr. Schoepf receives institutional research support from Astellas, Bayer, General Electric, and Siemens Healthineers; and has received honoraria for speaking and consulting from Bayer, Guerbet, HeartFlow, and Siemens Healthineers. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
- Received February 24, 2019.
- Revision received May 13, 2019.
- Accepted June 20, 2019.
- 2019 American College of Cardiology Foundation
This article requires a subscription or purchase to view the full text. If you are a subscriber or member, click Login or the Subscribe link (top menu above) to access this article.