The impact of image resolution on computation of fractional flow reserve: coronary computed tomography angiography versus 3-dimensional quantitative coronary angiography.
Calculation of fractional flow reserve (FFR) based on computational fluid dynamics (CFD) requires reconstruction of patient-specific coronary geometry and estimation of hyperemic flow rate. Coronary computed tomography angiography (CCTA) and invasive coronary angiography (ICA) are two dominating imaging modalities used for the geometrical reconstruction. Our aim was to investigate the impact of image resolution as inherently associated with these two imaging modalities on geometrical reconstruction and subsequent FFR calculation. Patients with mild or intermediate coronary stenoses who underwent both CCTA and ICA were included. CCTA images were acquired either by 320-row area detector CT or by 128-slice dual-source CT. Two geometrical models were reconstructed separately from CCTA and ICA, from which FFRCTA and FFRQCA were subsequently calculated using CFD simulations, applying the same hyperemic flow rate derived from the ICA images at the inlet boundaries. A total of 57 vessels in 41 patients were analyzed. Average diameter stenosis was 43.4 ± 10.8 % by 3D QCA. Reasonably good correlation between FFRCTA and FFRQCA was observed (r = 0.71, p < 0.001). The difference between FFRCTA and FFRQCA was correlated with the deviation between minimal lumen areas by CCTA and by ICA (ρ = 0.34, p = 0.01), but not with plaque volume (ρ = -0.09, p = 0.51) or calcified plaque volume (ρ = 0.01, p = 0.95). Applying the cutoff value of ≤0.8 to both FFRCTA and FFRQCA, the agreement between FFRCTA and FFRQCA in discriminating functional significant stenoses was moderate (kappa 0.47, p < 0.001). Disagreement was found in 10 (17.5 %) vessels. Acceptable correlation between FFRCTA and FFRQCA was observed, while their agreement in distinguishing functional significant stenosis was moderate. Our results suggest that image resolution has a significant impact on FFR computation.
Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Lili
L
Liu
Department of Radiology, Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Wenjie
W
Yang
Department of Cardiology, Fujita Health University Hospital, Toyoake, Japan.
Yasuomi
Y
Nagahara
Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
Yingguang
Y
Li
Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Saeb R
SR
Lamooki
Department of Cardiology, Fujita Health University Hospital, Toyoake, Japan.
Takashi
T
Muramatsu
Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
Pieter
P
Kitslaar
Department of Cardiology, Fujita Health University Hospital, Toyoake, Japan.
Masayoshi
M
Sarai
Department of Cardiology, Fujita Health University Hospital, Toyoake, Japan.
Yukio
Y
Ozaki
Department of Medicine, Faculty of Medicine, Dentistry & Health Sciences, The University of Melbourne, Melbourne, Victoria, Australia.
Peter
P
Barlis
Department of Radiology, Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Fuhua
F
Yan
Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
Johan H C
JH
Reiber
Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China. sxtu@sjtu.edu.cn.
Shengxian
S
Tu
D000368:Aged; D000465:Algorithms; D017023:Coronary Angiography / Q000379:methods*; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging* / Q000503:physiopathology; D023921:Coronary Stenosis / Q000000981:diagnostic imaging* / Q000503:physiopathology; D003331:Coronary Vessels / Q000000981:diagnostic imaging* / Q000503:physiopathology; D005260:Female; D053805:Fractional Flow Reserve, Myocardial; D006801:Humans; D006940:Hyperemia / Q000000981:diagnostic imaging / Q000503:physiopathology; D007090:Image Interpretation, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008297:Male; D008875:Middle Aged; D008955:Models, Cardiovascular; D061330:Multidetector Computed Tomography; D055414:Myocardial Perfusion Imaging / Q000379:methods*; D015588:Observer Variation; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012720:Severity of Illness Index
Computational fluid dynamics; Coronary computed tomography angiography; Fractional flow reserve; Quantitative coronary angiography