Chun Xiang Tang1, Chun Yu Liu1, Meng Jie Lu1, U Joseph Schoepf2, Christian Tesche3, Richard R Bayer3, H Todd Hudson3, Xiao Lei Zhang1, Jian Hua Li4, Yi Ning Wang5, Chang Sheng Zhou1, Jia Yin Zhang6, Meng Meng Yu6, Yang Hou7, Min Wen Zheng8, Bo Zhang9, Dai Min Zhang10, Yan Yi5, Yuan Ren11, Chen Wei Li11, Xi Zhao11, Guang Ming Lu1, Xiu Hua Hu12, Lei Xu13, Long Jiang Zhang14. 1. Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China. 2. Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China; Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina. 3. Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina. 4. Department of Cardiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China. 5. Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. 6. Institute of Diagnostic and Interventional Radiology and Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China. 7. Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China. 8. Department of Radiology, The First Affiliated Hospital of Fourth Military Medical University, Xi'an, China. 9. Department of Radiology, Jiangsu Taizhou People's Haspital, Taizhou, China. 10. Department of Cardiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China. 11. Shanghai United Imaging Healthcare, Shanghai, China. 12. Department of Radiology, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang University, 3 East Qingchun Road, Hangzhou 310006, Zhejiang, People's Republic of China. Electronic address: medhxh@163.com. 13. Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China. Electronic address: leixu2001@hotmail.com. 14. Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China. Electronic address: kevinzhlj@163.com.
Abstract
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.
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.
Authors: Yi Xue; Min Wen Zheng; Yang Hou; Fan Zhou; Jian Hua Li; Yi Ning Wang; Chun Yu Liu; Chang Sheng Zhou; Jia Yin Zhang; Meng Meng Yu; Bo Zhang; Dai Min Zhang; Yan Yi; Lei Xu; Xiu Hua Hu; Guang Ming Lu; Chun Xiang Tang; Long Jiang Zhang Journal: Eur Radiol Date: 2022-01-12 Impact factor: 5.315
Authors: Jagat Narula; Y Chandrashekhar; Amir Ahmadi; Suhny Abbara; Daniel S Berman; Ron Blankstein; Jonathon Leipsic; David Newby; Edward D Nicol; Koen Nieman; Leslee Shaw; Todd C Villines; Michelle Williams; Harvey S Hecht Journal: J Cardiovasc Comput Tomogr Date: 2020-11-20
Authors: Giuseppe Muscogiuri; Marly Van Assen; Christian Tesche; Carlo N De Cecco; Mattia Chiesa; Stefano Scafuri; Marco Guglielmo; Andrea Baggiano; Laura Fusini; Andrea I Guaricci; Mark G Rabbat; Gianluca Pontone Journal: Biomed Res Int Date: 2020-12-16 Impact factor: 3.411
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