Validation of a Computed Tomography (CT) Based Fractional Flow Reserve (FFR) Software Using the 320 Detector Aquilion ONE CT Scanner.
Conditions: Atherosclerosis, Coronary
Sponsor: State University of New York at Buffalo

                                                                                                                                          
 
 
 
P a g e  1 | 13 
 
Validation of a Computed Tomography (CT) based Fractional Flow Reserve 
(FFR) software using the 320 Detectors Aquilion ONE CT Scanner 
 
NCT03149042  
 
September 24, 2019 
 
Research Team 
 
Dr. Ciprian N Ionita (Principal Investigator) 
 
Kelsey Sommer BS (Study Coordinator / Investigator) 
 
Dr. Erin Angel (Investigator) 
 
Dr. Vijay Iyer (Investigator) 
 
Dr. Kanako Kunishima Kumamaru (Investigator) 
 
Dr. Dimitris Mitsouras (Investigator) 
 
Dr. Frank Rybicki (Investigator) 
 
Dr. Michael F Wilson (Investigator) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

                                                                                                                                          
 
 
 
P a g e  2 | 13 
 
 
 
Contents 
 
Glossary…………………………………………………………………………….3 
Background and Rationale………………………………………………………….4 
Summary of Proposed Research……………………………………………………5 
Objectives…………………………………………………………………………..6 
Hypothesis………………………………………………………………………….6 
Study Design / Procedures.......................................……………………………..7-8 
Inclusion Criteria…………………………………………………………………...9 
Exclusion Criteria…………………………………………………………………..9 
Sample size…………………………………………………………………………9 
Study Flow Chart………………………………………………………………….10 
Statistical Analysis……………………………………………………………..…10 
References…………………………………………………………………..…11-13 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

                                                                                                                                          
 
 
 
P a g e  3 | 13 
 
 
 
Glossary 
 
FFR = Fractional Flow Reserve 
 
CCTA = Coronary Computed Tomography Angiography 
 
I-FFR = Invasive Fractional Flow Reserve 
 
CT-FFR = Computed Tomography Fractional Flow Reserve 
 
B-FFR = Benchtop Fractional Flow Reserve 
 
ROC = Receiver Operating Characteristic 
 
AUC = Area Under the Curve 
 
STL= Stereo Lithographic 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

                                                                                                                                          
 
 
 
P a g e  4 | 13 
 
Background and Rationale 
 
In the last few years there has been a significant push to develop and implement 
coronary blood flow estimation based on computed tomography angiography. 
Clinical trials such as FAME (Fractional Flow Reserve versus Angiography for 
Multivessel Evaluation) and FAME II demonstrated: “added benefit of physiologic 
information, specifically fractional flow reserve (FFR), above anatomic 
information alone, in guiding intervention in patients with multivessel coronary 
disease undergoing intervention and patients with stable coronary disease. 
Anatomic information with physiologic information obtained via FFR-CT, can 
help identify patients who would or would not benefit from revascularization. 
 
The results of these studies were very promising, however, some of these 
approaches failed to meet the required accuracy or required changes in acquisition 
protocols which increased the dose deliver to the patient. For example the 17-
center DeFACTO (Determination of Fractional Flow Reserve by Anatomic 
Computed Tomographic Angiography) study, showed that FFR-CT can improve 
diagnostic accuracy and discrimination versus CTA alone but failed to meet its 
pre-specified primary outcome goal for the level of per-patient diagnostic 
accuracy. We believe that these inaccuracies are a direct consequence of the 
arbitrary boundary conditions imposed in order to solve the Navier –Stokes 
equations and optimization of acquisition parameters.  
 
The main aim of this proposal is aligned with the studies above. We propose to 
improve the FFR-CT accuracy using the new opportunities offered by 3D printing. 
Namely we will develop very accurate coronary models using 3D printing and use 
these phantoms to optimize the FFR-CT approach. Next we propose to use the 
optimized approach to perform a small clinical study at Gates Vascular Institute 
where we will correlate the FFR-CT results with angio FFR and patient follow-up. 
 
 
 
 
 
 
 
 
 

                                                                                                                                          
 
 
 
P a g e  5 | 13 
 
Summary of Proposed Research 
 
Coronary Computed Tomography Angiography (CCTA) contrast opacification 
gradients and FFR-CT estimation can aid in the severity estimation of significant 
atherosclerotic lesions. Following this trend, we recently developed a collaboration 
between Brigham and Women’s Hospital (BWH) and Gates Vascular Institute 
(GVI). We 3D-printed patient specific coronary phantoms at (GVI) and scanned 
them with a Toshiba Aquilion scanner to test several aspects of the contrast 
opacification gradients using a method established at BWH. Our initial results 
showed strong correlation between the flow in the phantom and opacification 
gradients. We believe that this approach could be further developed to test and 
validate FFR-CT algorithms. Currently, FFR-CT algorithms can only be optimized 
using theoretical models and can only be validated in large multi-center clinical 
trials. This phantom approach would allow optimization of FFR-CT algorithms 
with a measured validation technique without the need for large clinical trials. 
Thus we believe that this study will result in a FFR-CT algorithm/method with a 
better predictability for arterial lesion severity than those existing on the market 
today.  Our approach is to use the infrastructure at GVI to perform a detailed 
validation of the FFR-CT method using 3D printed patient specific phantoms. Each 
patient will have a 3D phantom printed, containing the culprit lesion and used in a 
benchtop flow analysis. Flow measurements will be compared with: CT-FFR for 
both patients and phantoms, angio lab FFR measurements and 30 days follow-up. 
This pilot clinical study will include ~50 patients over a year and half at GVI. We 
are confident that this approach performed via 3D-phantom testing will prove the 
validity of FFR-CT based measurements as well as develop a new standard for 
validating FFR-CT algorithms 
 
 
 
 
 
 
 
 
 
 
 
 

                                                                                                                                          
 
 
 
P a g e  6 | 13 
 
Objectives 
 
o To build 50 3D printed patient specific phantoms for benchtop testing to 
determine pressure gradient along diseased vasculature 
 
o To perform a detailed validation of the FFR-CT method using 3D printed 
patient specific phantoms 
 
o Optimization of FFR-CT algorithms with a better predictability for arterial 
lesion severity without the need for large clinical trials 
 
 
 
Hypotheses 
 
Flow simulations in 3D printed patient specific phantoms will correlate with CT 
derived fractional flow reserve calculations. To demonstrate the concept feasibility, 
we propose to use 3D printed cardiac models for software validation using clinical 
evaluation as a gold standard. The clinical cases will be used both as ground truth 
to demonstrate the accuracy of the phantom validation as well as a pilot single-
center clinical evaluation to demonstrate similar results to previously published 
clinical trials for algorithms similar to those being tested. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

                                                                                                                                          
 
 
 
P a g e  7 | 13 
 
Study Design/ Procedures 
 
Patients will be consented after CCTA. First requirement is that the patient has at 
least one lesion with >50% stenosis or 30-50% if clinically driven. Second 
requirement is that the patient undergoes a cath lab intervention. If one of the 
above requirements is not satisfied the patient will be removed from the study. 
Viable candidates based on inclusion/exclusion criteria will undergo clinically 
indicated first generation 320-detector row CCTA (Aquilion ONE, Canon Medical 
Systems, Tustin, CA) with 0.5 mm slice thickness, automated tube current 
modulation, 100 kVp, and a reconstructed voxel size of 0.625x0.625x0.5 mm. 
Next, the patient will undergo catheter lab procedure as clinically warranted. The 
invasive FFR will be done on the culprit lesion(s) for clinical purposes. Invasive 
FFR (I-FFR) will be recorded via pressure wire at a distance of two lesion lengths 
past the distal end of the lesion. A FFR cutoff value of less than or equal to 0.8 was 
used to determine hemodynamic significance.  
 
For each patient a 3D printed phantom will be made and tested. Segmentation will 
be performed in Vitrea, vascular mesh sculpting in Autodesk Meshmixer, and 3D 
printed in a Stratasys Eden 260V printer (Stratasys, Eden Prairie, MN) printer. 
Figure 1 shows the model development process. Each 3D printed patient-specific 
model will be connected to a flow loop using the CardioFlow 5000 MR 
programmable physiological pulsatile flow pump (Shelley Medical Imaging 
Technologies, Toronto, Ontario, Canada) and a Benchtop FFR will be obtained. 
This pump introduced a physiologic human cardiac waveform and provided also an 
ECG signal triggered at points within the cardiac waveform. The viscosity of blood 
(approximately 3.7 cP) will be mimicked using a glycerol water solution of 40% 
water and 60% glycerol and measured using a viscometer. Figure 2 shows the 
benchtop flow system. 
 
Reconstructed CCTA images were imported into Vitrea segmentation software 
(Vital Images, Minnetonka, MN) using the research-based CT-FFR algorithm [26-
28]. The software analyzes four data volumes acquired a 70-99% of the R-R 
interval and computes the FFR based on the changes in vessel diameter and 
computational fluid dynamics.  

                                                                                                                                          
 
 
 
P a g e  8 | 13 
 
 
Figure 1:  Model development process. (a) The cardiac mesh was exported as a stereolithographic file from Vitrea into 
Autodesk Meshmixer. (b) The daughter branches off of the 3 main coronary arteries were cut off from the mesh. (c) The 
aortic root was extended at both the outlet and inlet with 30 mm diameter for later connections (red arrows).  Cylindrical 
meshes were appended (red circles) to the aortic root and each of the coronary arteries for later pressure sensor 
connections. (d) Each coronary artery was extended. (e) A plane cut was administered at the vessel outlets for parallel ends. 
(f) A 2 mm wall was generated while preserving the geometry of the arteries. (g) A support structure is positioned around 
the model and the Boolean union operation is administered. 
 
 
Figure 2:  Benchtop flow system. (a) A 3D printed patient-specific coronary model is connected to a flow loop using 
the Cardio Flow 5000 MR programmable physiological pulsatile flow pump (Shelley Medical Imaging Technologies, 
Toronto, Ontario, Canada). The flow loop consisted of two compliance chambers, at the aortic inlet (D1) and distal to 
the coronary arteries (D2) to control pressure oscillation and distal resistance. Aortic base pressure and distal flow 
rate were controlled by adjustable mechanical clamps (R1 and R2 respectively), and flow rate was recorded using an 
ultrasonic flow sensor (S). Flow direction is indicated by blue arrows. (b) Detailed side view of the 3D printed coronary 
model with pressure sensors connected to the aorta and the LAD. (c) Coronary waveform within the coronary flow 
pump software to determine the flow rate (mL/s) as a function of time in a single cardiac cycle. (d) 52 patient-specific 
3D printed coronary models assembled and ready to go through flow testing. 

                                                                                                                                          
 
 
 
P a g e  9 | 13 
 
Inclusion Criteria 
 
o Scheduled for clinically mandated elective invasive coronary angiography 
(ICA) or clinically mandated CTA 
o >18 yrs of age 
Exclusion Criteria 
o Less than 30 years of age 
o Atrial fibrillation 
o Renal insufficiency (estimated glomerular filtration rate (GFR)  <60 
ml/min/1.73 m2) 
o Active Bronchospasm prohibiting the use of beta blockers 
o Morbid obesity (body mass index 40 kg/m2) 
o Contraindications to iodinated contrast 
o Emergencies requiring immediate intervention or patients unable to consent 
o Patients not showing coronary calcium during Calcium Scoring procedures 
Sample Size 
To determine the numbers of phantoms needed for this study we treated 3D 
printing as a new diagnostic tool for which we needed to assess the accuracy of 
detecting significant FFR values. We used the sample size calculation proposed by 
Flahault et. al. [23].  
𝑛=
(𝑧𝛼/2√𝑉̂(𝐴̂))
2
𝐿2
 
Since this is a technical feasibility study, we wanted to estimate the ROC area to 
within ±0.6; thus, L=0.12 and the confidence level was set at 0.92 which 
corresponds to z_(α/2)=1.75 .  For the variance function we used the formulation 
indicated by Obuchhowski et al. [24] who proposed an estimate of the variance 
function for the Receiver Operator Characteristic based on an exponential 
distribution.  
𝑉̂(𝐴̂) = (0.0099 × 𝑒
𝑎2
2
⁄ ) × [(5𝑎2 + 8) + (5𝑎2 + 8)/𝑅] 
R denotes the ratio of the number of patients without the condition to patients with 
the condition in the study sample. Using results from previous reports on CT-FFR 
[25], we set R=1.2.   For an AUROC=0.8  the parameter may be set to 1.19 [24]. 
Thus the number of samples with FFR<=0.80 are approximatively 23 and the total 
number of subject given by N_total=n(1+R) is approximatively 52. 
 
 
 

                                                                                                                                          
 
 
 
P a g e  10 | 13 
 
Study Flow Chart 
 
 
 
 
 
Statistical Analysis 
 
The absolute error as a percentage with 95% confidence intervals and Pearson 
correlation values will be calculated to assess the results obtained from the 
benchtop flow testing and the CT-FFR software and how they compared to the I-
FFR reference standard. A ROC curve will also be generated to determine the area 
under the curve (AUC) for B-FFR and CT-FFR when compared to I-FFR. 
Statistical analysis for the development of the ROC curves and correlation analysis 
will be completed within NCSS Statistical Software (NCSS, v.12, LLC, Kaysville, 
Utah). 

                                                                                                                                          
 
 
 
P a g e  11 | 13 
 
References 
 
1. 
Brown, B.G., et al., Quantitative coronary arteriography: estimation of 
dimensions, hemodynamic resistance, and atheroma mass of coronary artery 
lesions using the arteriogram and digital computation. Circulation, 1977. 55(2): p. 
329-337. 
2. 
Qureshi, A.I., et al., Prevention and treatment of thromboembolic and 
ischemic complications associated with endovascular procedures: Part I—
Pathophysiological and pharmacological features. Neurosurgery, 2000. 46(6): p. 
1344-1359. 
3. 
Kumamaru, K.K., et al., Diagnostic accuracy of 3D deep-learning-based 
fully automated estimation of patient-level minimum fractional flow reserve from 
coronary computed tomography angiography. European Heart Journal - 
Cardiovascular Imaging, 2019. 
4. 
Tang, C.X., et al., Diagnostic performance of fractional flow reserve derived 
from coronary CT angiography for detection of lesion-specific ischemia: A multi-
center study and meta-analysis. Eur J Radiol, 2019. 116: p. 90-97. 
5. 
Morris, P.D., et al., Virtual fractional flow reserve from coronary 
angiography: modeling the significance of coronary lesions: results from the 
VIRTU-1 (VIRTUal Fractional Flow Reserve From Coronary Angiography) study. 
JACC Cardiovasc Interv, 2013. 6(2): p. 149-57. 
6. 
Morris, P.D., et al., Computational fluid dynamics modelling in 
cardiovascular medicine. Heart, 2016. 102(1): p. 18-28. 
7. 
Sankaran, S., et al., Uncertainty quantification in coronary blood flow 
simulations: Impact of geometry, boundary conditions and blood viscosity. J 
Biomech, 2016. 49(12): p. 2540-7. 
8. 
Sankaran, S., et al., Uncertainty quantification in coronary blood flow 
simulations: impact of geometry, boundary conditions and blood viscosity. Journal 
of biomechanics, 2016. 49(12): p. 2540-2547. 
9. 
Ko, B.S., et al., Noninvasive CT-derived FFR based on structural and fluid 
analysis: a comparison with invasive FFR for detection of functionally significant 
stenosis. JACC: Cardiovascular Imaging, 2017. 10(6): p. 663-673. 
10. 
Modi, B.N., et al., Optimal Application of Fractional Flow Reserve to 
Assess Serial Coronary Artery Disease: A 3D-Printed Experimental Study With 
Clinical Validation. J Am Heart Assoc, 2018. 7(20): p. e010279. 
11. 
Shepard, L.M., et al., Initial evaluation of three-dimensionally printed 
patient-specific coronary phantoms for CT-FFR software validation. J Med 
Imaging (Bellingham), 2019. 6(2): p. 021603. 

                                                                                                                                          
 
 
 
P a g e  12 | 13 
 
12. 
Sommer, K.N., et al. 3D printed cardiovascular patient specific phantoms 
used for clinical validation of a CT-derived FFR diagnostic software. in SPIE 
Medical Imaging. 2018. SPIE. 
13. 
Shepard, L., et al. Initial simulated FFR investigation using flow 
measurements in patient-specific 3D printed coronary phantoms. in Medical 
Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications. 
2017. International Society for Optics and Photonics. 
14. 
Rengier, F., et al., 3D printing based on imaging data: review of medical 
applications. International journal of computer assisted radiology and surgery, 
2010. 5(4): p. 335-341. 
15. 
Son, K.H., et al., Surgical planning by 3D printing for primary cardiac 
schwannoma resection. Yonsei medical journal, 2015. 56(6): p. 1735-1737. 
16. 
Chepelev, L., et al., Radiological Society of North America (RSNA) 3D 
printing Special Interest Group (SIG): guidelines for medical 3D printing and 
appropriateness for clinical scenarios. 3D printing in medicine, 2018. 4(1): p. 11. 
17. 
Kim, H.J., et al., Patient-specific modeling of blood flow and pressure in 
human coronary arteries. Ann Biomed Eng, 2010. 38(10): p. 3195-209. 
18. 
O’Rourke, M.F., et al., Clinical applications of arterial stiffness; definitions 
and reference values. American journal of hypertension, 2002. 15(5): p. 426-444. 
19. 
Rutishauser, W., Towards measurement of coronary blood flow in patients 
and its alteration by interventions. European heart journal, 1999. 20(15): p. 1076-
1083. 
20. 
Tabaczynski, J., Mechanical Assessment of 3D Printed Patient Specific 
Phantoms for Simulation of Minimally Invasive Image Guided Procedures. 2018, 
State University of New York at Buffalo. 
21. 
Tabaczynski, J.R., et al. Use of patient specific 3D printed (3DP) 
neurovascular phantoms for mechanical assessment of devices used in image 
guided minimally invasive procedures. in Medical Imaging 2018: Imaging 
Informatics for Healthcare, Research, and Applications. 2018. International 
Society for Optics and Photonics. 
22. 
Sommer, K., et al. Design optimization for accurate flow simulations in 3D 
printed vascular phantoms derived from computed tomography angiography. in 
Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and 
Applications. 2017. International Society for Optics and Photonics. 
23. 
Fahraeus, R. and T. Lindqvist, The viscosity of the blood in narrow capillary 
tubes. American Journal of Physiology-Legacy Content, 1931. 96(3): p. 562-568. 
24. 
Williams, B., et al., Differential impact of blood pressure-lowering drugs on 
central aortic pressure and clinical outcomes: principal results of the Conduit 

                                                                                                                                          
 
 
 
P a g e  13 | 13 
 
Artery Function Evaluation (CAFE) study. Circulation, 2006. 113(9): p. 1213-
1225. 
25. 
Kanaji, Y., et al., Effect of elective percutaneous coronary intervention on 
hyperemic absolute coronary blood flow volume and microvascular resistance. 
Circulation: Cardiovascular Interventions, 2017. 10(10): p. e005073. 
26. 
Ko, B., et al., Novel non-invasive CT-derived fractional flow reserve based 
on structural and fluid analysis (CT-FFR) for detection of functionally significant 
stenosis: a comparison with invasive fractional flow reserve. JACC Cardiovasc 
Imaging, 2017. 10: p. 663-73. 
27. 
Ihdayhid, A.R., et al., Performance of computed tomography-derived 
fractional flow reserve using reduced-order modelling and static computed 
tomography stress myocardial perfusion imaging for detection of 
haemodynamically significant coronary stenosis. European Heart Journal-
Cardiovascular Imaging, 2018. 19(11): p. 1234-1243. 
28. 
Fujimoto, S., et al., 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. European Heart 
Journal - Cardiovascular Imaging, 2018. 20(3): p. 343-352. 
29. 
Kishi, S., et al., Fractional flow reserve estimated at coronary CT 
angiography in intermediate lesions: comparison of diagnostic accuracy of 
different methods to determine coronary flow distribution. Radiology, 2017. 
287(1): p. 76-84. 
30. 
Li, P., et al., Blooming Artifact Reduction in Coronary Artery Calcification 
by A New De-blooming Algorithm: Initial Study. Scientific reports, 2018. 8(1): p. 
6945. 
31. 
Romano, S.M. and M. Pistolesi, Assessment of cardiac output from systemic 
arterial pressure in humans. Critical care medicine, 2002. 30(8): p. 1834-1841. 
32. 
Cotter, G., et al., The role of cardiac power and systemic vascular resistance 
in the pathophysiology and diagnosis of patients with acute congestive heart 
failure. European journal of heart failure, 2003. 5(4): p. 443-451. 
33. 
Moore, T.J., et al., Estimated costs of pivotal trials for novel therapeutic 
agents approved by the US Food and Drug Administration, 2015-2016. JAMA 
internal medicine, 2018. 178(11): p. 1451-1457.