Diagnostic performance of on-site computed CT-fractional flow reserve based on fluid structure interactions: comparison with invasive fractional flow reserve and instantaneous wave-free ratio

Fujimoto, Shinichiro; Kawasaki, Tomonori; Kumamaru, Kanako K.; Kawaguchi, Yuko; Dohi, Tomotaka; Okonogi, Taichi; K, Ri; Yamada, Sou; Takamura, Kazuhisa; Katô, Etsurô; Kato, Yoshiteru; Hiki, Makoto; Okazaki, Shinya; Aoki, Shigeki; Mitsouras, Dimitris; Rybicki, Frank J.; Daida, Hiroyuki

doi:10.1093/ehjci/jey104

Cited by 43 publications

(37 citation statements)

References 33 publications

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“…9). On-site vendor-based platforms are also available in some institutions, including a machine learning-based algorithm [102,103], four-dimensional CT image tracking (registration) and structural and fluid analysis [104,105], and patientspecific lumped parameter models [106,107]. However, the off-site CT-FFR using a remote analysis service is recently received with national reimbursement approval in Japan, but the available facilities are strictly limited by requirements.…”

Section: Technology Of Ct-ffrmentioning

confidence: 99%

Computed tomographic evaluation of myocardial ischemia

et al. 2020

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Myocardial ischemia is caused by a mismatch between myocardial oxygen consumption and oxygen delivery in coronary artery disease (CAD). Stratification and decision-making based on ischemia improves the prognosis in patients with CAD. Non-invasive tests used to evaluate myocardial ischemia include stress electrocardiography, echocardiography, single-photon emission computed tomography, and magnetic resonance imaging. Invasive fractional flow reserve is considered the reference standard for assessment of the hemodynamic significance of CAD. Computed tomography (CT) angiography has emerged as a first-line imaging modality for evaluation of CAD, particularly in the population at low to intermediate risk, because of its high negative predictive value; however, CT angiography does not provide information on the hemodynamic significance of stenosis, which lowers its specificity. Emerging techniques, e.g., CT perfusion and CT-fractional flow reserve, help to address this limitation of CT, by determining the hemodynamic significance of coronary artery stenosis. CT perfusion involves acquisition during the first pass of contrast medium through the myocardium following pharmacological stress. CTfractional flow reserve uses computational fluid dynamics to model coronary flow, pressure, and resistance. In this article, we review these two functional CT techniques in the evaluation of myocardial ischemia, including their principles, technology, advantages, limitations, pitfalls, and the current evidence.

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Section: Technology Of Ct-ffrmentioning

confidence: 99%

Computed tomographic evaluation of myocardial ischemia

et al. 2020

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“…CT-FFR has been reported to have high diagnostic accuracy for detecting hemodynamically significant CAD assessed by invasive FFR (22) and to be an effective gatekeeper for invasive coronary angiography in clinical settings (23). The on-site CT-FFR used in the present study relied on fluid-structure interaction for calculation of patient-specific CT-FFR (11), and a previous study indicated a close correlation between CT-FFR and invasive FFR that was highly reproducible (12). As a result, it was reasonable that the functional index of CT-FFR was more important than other anatomical indices, including anatomical stenosis, plaque characteristics, and coronary artery calcification.…”

Section: Discussionmentioning

confidence: 81%

“…Two operators consensually edit the vessel contour and centerline especially in the calcified segments. After the edit, the CT-FFR value was estimated using the following steps: (1) measurement of the shape and changes in the cross-sectional area of the coronary artery and aorta in four volumes reconstructed at different time-points during 70%À99% of the R-R interval extraction of luminal deformation data for the coronary arteries and aorta; (2) application of Bayesian hierarchical modeling and the Markov chain Monte Carlo method to determine the analysis conditions; (3) fluid simulation using a reduced order fluid model; and (4) determination of pressure and flow through the coronary artery tree and estimation of the CT-FFR value (11,12). In the process of CT-FFR analysis, the machine-learning technique was not employed.…”

Section: On-site Ct-ffr Analysismentioning

confidence: 99%

Evaluation of Significant Coronary Artery Disease Based on CT Fractional Flow Reserve and Plaque Characteristics Using Random Forest Analysis in Machine Learning

et al. 2020

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Rationale and Objectives: Fractional flow reserve (FFR) is an established technique for detecting lesion-specific ischemia but is invasive. Our objective was to investigate the effects of combined assessment of coronary CT angiography (CCTA) imaging features and CT-FFR on detecting lesion-specific ischemia by comparing with invasive FFR. Materials and Methods: Forty-seven patients who had 60 coronary vessels with 30%À90% stenosis were included. Six anatomic CCTA descriptors (Agatston score, stenosis severity, mean plaque CT attenuation value, noncalcified and calcified plaque volumes, remodeling index) and a functional descriptor (CT-FFR) were measured. Random forest was used to identify which descriptors were useful to identify ischemia-related lesion. Receiver-operating characteristic (ROC) curves were calculated for 2 models: i.e. Model-1 for anatomical CT descriptors and Model-2 for anatomical CT descriptors plus CT-FFR. Results: Stenosis severity (40.8 § 15.7% vs 57.6 § 14.1%), noncalcified plaque volume (190 § 100 vs 254.8 § 133.3), and remodeling index (1.04 § 0.12 vs 1.11 § 0.13) were significantly higher in ischemia-related lesions than nonischemia-related lesions. CT-FFR was 0.84 § 0.14 and 0.71 § 0.14, respectively, for ischemia-related and nonischemia-related lesions, and the difference was significant. The area under the ROC curve was 0.738 and 0.835 in Model-1 and Model-2, respectively. Reclassification of ischemic lesion risk was significantly improved after adding CT-FFR: net reclassification improvement was 0.297 and integrated discrimination improvement was 0.254. Conclusion: Combined assessment of anatomical CCTA features and functional CT-FFR was helpful for detecting lesion-specific ischemia.

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“…A potential advantage of these techniques is that processing and analysis can be performed using a standard desktop computer at the point-of-care. The time required for analysis has been reported to range between 30 minutes and 2.5 hours [9,10].…”

Section: On-site Reducd-order Modelsmentioning

confidence: 99%

CT-Derived Fractional Flow Reserve (CT-FFR) in the Evaluation of Coronary Artery Disease

Khav

Ihdayhid

2020

Heart, Lung and Circulation

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Diagnostic performance of on-site computed CT-fractional flow reserve based on fluid structure interactions: comparison with invasive fractional flow reserve and instantaneous wave-free ratio

Abstract: Locally computed CT-FFR based on fluid structure interaction has excellent diagnostic accuracy to detect a significant FFR ≤0.8 compared with conventional CCTA and high reproducibility.

Cited by 43 publications

References 33 publications

Computed tomographic evaluation of myocardial ischemia

Computed tomographic evaluation of myocardial ischemia

Evaluation of Significant Coronary Artery Disease Based on CT Fractional Flow Reserve and Plaque Characteristics Using Random Forest Analysis in Machine Learning

CT-Derived Fractional Flow Reserve (CT-FFR) in the Evaluation of Coronary Artery Disease

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