Computed tomography coronary angiography image-based noninvasive virtual fractional flow reserve (vFFR) is a promising clinical practice to determine the physiological condition of coronary lesions. vFFR is the key factor in the diagnosis of coronary plaque. The purpose of this study is to detect the coronary main arteries lesion severity by using 1D (one-dimensional) hemodynamic factors compared to 3D (three-dimensional) heart flow computational models. The results provide the hemodynamic properties of the intraluminal condition by evaluating the vFFR. The computational burden of 3D hemodynamic simulations is one of the important drawbacks in most clinical cases. At first, we have established comparative results between vFFR3D (vFFR in 3D) and published results in the maximal hyperemic flow conditions. Then, we have employed statistical analysis including Pearson correlation test, Bland–Altman test, and computation time test for vFFR3D compared with the vFFR1D (vFFR in 1D) results. We have observed that the vFFR3D and vFFR1D results do not significantly differ as a function of stenosis length, type (concentric or eccentric), or location in the coronary artery. Pearson's product moment was found as r=0.9661,p<0.0001 illustrating a strong correlation between vFFR1D and vFFR3D. In both 3D and 1D cases, the results suggest that proximal stenosis is more severe compared to a distal one, even if they provide the same reduction in lumen (80% or 90% area of stenosis). The 1D inexpensive computational results vFFR1D can be used to predict the severity of atherosclerotic plaque in clinical procedures.

1.
A.
Passos
,
J. M.
Sherwood
,
E.
Kaliviotis
,
R.
Agrawal
,
C.
Pavesio
, and
S.
Balabani
, “
The effect of deformability on the microscale flow behavior of red blood cell suspensions
,”
Phys. Fluids
31
(
9
),
091903
(
2019
).
2.
U.
Siebert
 et al, “
Improving the quality of percutaneous revascularisation in patients with multivessel disease in Australia: Cost-effectiveness, public health implications, and budget impact of FFR-guided PCI
,”
Heart. Lung Circ.
23
(
6
),
527
533
(
2014
).
3.
C.
Costopoulos
 et al, “
Impact of combined plaque structural stress and wall shear stress on coronary plaque progression, regression, and changes in composition
,”
Eur. Heart J.
40
(
18
),
1411
1422
(
2019
).
4.
R. C.
Gosling
 et al, “
Effect of side branch flow upon physiological indices in coronary artery disease
,”
J. Biomech.
103
,
109698
(
2020
).
5.
N.
Zaman
,
M.
Ferdows
,
M. A.
Xenos
,
K. E.
Hoque
, and
E. E.
Tzirtzilakis
, “
Effect of angle bifurcation and stenosis in coronary arteries: An idealized model study
,”
BioMed Res. J.
4
(
3
),
214
228
(
2020
).
6.
K. E.
Hoque
,
S.
Sawall
,
M. A.
Hoque
, and
M. S.
Hossain
, “
Hemodynamic simulations to identify irregularities in coronary artery models
,”
J. Adv. Math. Comput. Sci.
28
(
5
),
1
19
(
2018
).
7.
L.
Papamanolis
 et al, “
Myocardial perfusion simulation for coronary artery disease: A coupled patient-specific multiscale model
,”
Ann. Biomed. Eng.
49
,
1432
(
2020
).
8.
K. E.
Hoque
,
M.
Ferdows
,
S.
Sawall
,
E. E.
Tzirtzilakis
, and
M. A.
Xenos
, “
The impact of hemodynamic factors in a coronary main artery to detect the atherosclerotic severity: Single and multiple sequential stenosis cases The impact of hemodynamic factors in a coronary main artery to detect the atherosclerotic severity: Single
,”
Phys. Fluids
33
(
33
),
031903
(
2021
).
9.
X.
Xie
,
M.
Zheng
,
D.
Wen
,
Y.
Li
, and
S.
Xie
, “
A new CFD based non-invasive method for functional diagnosis of coronary stenosis
,”
Biomed. Eng. OnLine
17
(
1
),
36
(
2018
).
10.
N.
Freidoonimehr
,
R.
Chin
,
A.
Zander
, and
M.
Arjomandi
, “
Effect of shape of the stenosis on the hemodynamics of a stenosed coronary artery
,”
Phys. Fluids
33
(
8
),
081914
(
2021
).
11.
F.
Gijsen
 et al, “
Expert recommendations on the assessment of wall shear stress in human coronary arteries: Existing methodologies, technical considerations, and clinical applications
,”
Eur. Heart J.
40
(
41
),
3421
3433
(
2019
).
12.
N. P.
Johnson
,
K. L.
Gould
,
M. F.
Di Carli
, and
V. R.
Taqueti
, “
Invasive FFR and noninvasive CFR in the evaluation of ischemia: What is the future?
,”
J. Am. College Cardiol.
67
(
23
),
2772
(
2016
).
13.
N. H. J.
Pijls
 et al, “
Coronary pressure measurement to assess the hemodynamic significance of serial stenoses within one coronary artery: Validation in humans
,”
Circulation
102
(
19
),
2371
2377
(
2000
).
14.
N.
Freidoonimehr
,
R.
Chin
,
A.
Zander
, and
M.
Arjomandi
, “
An experimental model for pressure drop evaluation in a stenosed coronary artery
,”
Phys. Fluids
32
(
2
),
021901
(
2020
).
15.
P.
Eslami
 et al, “
Effect of wall elasticity on hemodynamics and wall shear stress in patient-specific simulations in the coronary arteries
,”
J. Biomech. Eng.
142
(
2
),
024503
(
2020
).
16.
Z.
Malota
,
J.
Glowacki
,
W.
Sadowski
, and
M.
Kostur
, “
Numerical analysis of the impact of flow rate, heart rate, vessel geometry, and degree of stenosis on coronary hemodynamic indices
,”
BMC Cardiovasc. Disord.
18
(
1
),
1
16
(
2018
).
17.
C. M.
Fleeter
,
G.
Geraci
,
D. E.
Schiavazzi
,
A. M.
Kahn
, and
A. L.
Marsden
, “
Multilevel and multifidelity uncertainty quantification for cardiovascular hemodynamics
,”
Comput. Methods Appl. Mech. Eng.
365
,
113030
(
2020
).
18.
K. K. L.
Wong
,
J.
Wu
,
G.
Liu
,
W.
Huang
, and
D. N.
Ghista
, “
Coronary arteries hemodynamics: Effect of arterial geometry on hemodynamic parameters causing atherosclerosis
,”
Med. Biol. Eng. Comput.
58
(
8
),
1831
1843
(
2020
).
19.
P.
Pant
,
R.
Doshi
,
P.
Bahl
, and
A. B.
Farimani
, “
Deep learning for reduced order modelling and efficient temporal evolution of fluid simulations
,”
Phys. Fluids
33
,
107101
(
2021
).
20.
C.
Collet
 et al, “
Impact of coronary remodeling on fractional flow reserve
,”
Circulation
137
(
7
),
747
749
(
2018
).
21.
X.
Wang
, “
1D modeling of blood flow in networks: Numerical computing and applications
,” Ph.D. thesis (Université Pierre et Marie Curie-Paris VI,
2014
).
22.
Z.
Duanmu
,
W.
Chen
,
H.
Gao
,
X.
Yang
,
X.
Luo
, and
N. A.
Hill
, “
A one-dimensional hemodynamic model of the coronary arterial tree
,”
Front. Physiol.
10
,
1
12
(
2019
).
23.
P. J.
Blanco
 et al, “
Comparison of 1D and 3D models for the estimation of fractional flow reserve
,”
Sci. Rep.
8
(
1
),
17275
(
2018
).
24.
M.
Mirramezani
,
S. L.
Diamond
,
H. I.
Litt
, and
S. C.
Shadden
, “
Reduced order models for transstenotic pressure drop in the coronary arteries
,”
J. Biomech. Eng.
141
(
3
),
0310051
(
2019
).
25.
A.
Uus
,
P.
Liatsis
,
R.
Rajani
, and
L.
Mandic
, “
The impact of boundary conditions in patient-specific coronary blood flow simulation
,” in
IWSSIP 2014 Proceedings
(
IEEE Xplore
,
2014
), pp.
35
38
.
26.
E. W. C.
Lo
,
L. J.
Menezes
, and
R.
Torii
, “
Impact of inflow boundary conditions on the calculation of CT-based FFR
,”
Fluids
4
(
2
),
60
–16 (
2019
).
27.
M.
Yin
,
A.
Yazdani
, and
G. E.
Karniadakis
, “
One-dimensional modeling of fractional flow reserve in coronary artery disease: Uncertainty quantification and Bayesian optimization
,”
Comput. Methods Appl. Mech. Eng.
353
,
66
85
(
2019
).
28.
R.
Maulik
,
B.
Lusch
, and
P.
Balaprakash
, “
Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders
,”
Phys. Fluids
33
(
3
),
037106
(
2021
).
29.
L.
Pekker
, “
A one-dimensional model of liquid laminar flows with large Reynolds numbers in tapered microchannels
,”
Phys. Fluids
33
(
4
),
042003
(
2021
).
30.
C. F.
Jekel
and
G.
Venter
, “
PieceWise linear fitting: A Python library for fitting 1D continuous piecewise linear functions
,” https://github.com/cjekel/piecewise_linear_fit_py (
2019
).
31.
S.
Saha
,
T.
Purushotham
, and
K. A.
Prakash
, “
Comparison of fractional flow reserve value of patient-specific left anterior descending artery using 1D and 3D CFD analysis
,”
Int. J. Adv. Eng. Sci. Appl. Math.
11
(
4
),
244
253
(
2019
).
32.
P. D.
Morris
,
J.
Iqbal
,
C.
Chiastra
,
W.
Wu
,
F.
Migliavacca
, and
J. P.
Gunn
, “
Simultaneous kissing stents to treat unprotected left main stem coronary artery bifurcation disease; stent expansion, vessel injury, hemodynamics, tissue healing, restenosis, and repeat revascularization
,”
Catheterization Cardiovasc. Interventions
92
,
1
12
(
2018
).
33.
S. H.
Kueh
 et al, “
Fractional flow reserve derived from coronary computed tomography angiography reclassification rate using value distal to lesion compared to lowest value
,”
J. Cardiovasc. Comput. Tomogr.
11
(
6
),
462
467
(
2017
).
34.
K. E.
Hoque
,
M.
Ferdows
,
S.
Sawall
, and
E. E.
Tzirtzilakis
, “
The effect of hemodynamic parameters in patient-based coronary artery models with serial stenoses: Normal and hypertension cases
,”
Comput. Methods Biomech. Biomed. Eng.
23
(
9
),
467
475
(
2020
).
35.
A.
Updegrove
,
N. M.
Wilson
,
J.
Merkow
,
H.
Lan
,
A. L.
Marsden
, and
S. C.
Shadden
, “
SimVascular: An open source pipeline for cardiovascular simulation
,”
Ann. Biomed. Eng.
45
(
3
),
525
541
(
2017
).
36.
J.
Ahrens
,
B.
Geveci
, and
C.
Law
, “
ParaView: An end-user tool for large-data visualization
,” in
Visualization Handbook
(
Elsevier
,
2005
), Vol.
836
, pp.
717
731
.
37.
M.
Gottsauner-Wolf
,
H.
Sochor
,
D.
Moertl
,
M.
Gwechenberger
,
F.
Stockenhuber
, and
P.
Probst
, “
Assessing coronary stenosis. Quantitative coronary angiography versus visual estimation from cine-film or pharmacological stress perfusion images
,”
Eur. Heart J.
17
(
8
),
1167
1174
(
1996
).
38.
B. F.
Waller
, “
The eccentric coronary atherosclerotic plaque: Morphologic observations and clinical relevance
,”
Clin. Cardiol.
12
(
1
),
14
20
(
1989
).
39.
L.
Antiga
,
M.
Piccinelli
,
L.
Botti
,
B.
Ene-Iordache
,
A.
Remuzzi
, and
D. A.
Steinman
, “
An image-based modeling framework for patient-specific computational hemodynamics
,”
Med. Biol. Eng. Comput.
46
(
11
),
1097
1112
(
2008
).
40.
G. J.
Gassner
and
A. R.
Winters
, “
A novel robust strategy for discontinuous Galerkin methods in computational fluid mechanics: Why? When? What? Where?
,”
Front. Phys.
8
,
1
24
(
2021
).
41.
J.
Alastruey
,
K. H.
Parker
,
J.
Peiró
, and
S. J.
Sherwin
, “
Lumped parameter outflow models for 1-D blood flow simulations: Effect on pulse waves and parameter estimation
,”
Commun. Comput. Phys.
4
(
2
),
317
336
(
2008
).
42.
L.
Formaggia
,
A.
Quarteroni
, and
C.
Vergara
, “
On the physical consistency between three-dimensional and one-dimensional models in haemodynamics
,”
J. Comput. Phys.
244
,
97
112
(
2013
).
43.
M. S.
Olufsen
,
C. S.
Peskin
,
W. Y.
Kim
,
E. M.
Pedersen
,
A.
Nadim
, and
J.
Larsen
, “
Numerical simulation and experimental validation of blood flow in arteries with structured-tree outflow conditions
,”
Ann. Biomed. Eng.
28
(
11
),
1281
1299
(
2000
).
44.
S.
Madhavan
and
E. M. C.
Kemmerling
, “
The effect of inlet and outlet boundary conditions in image-based CFD modeling of aortic flow
,”
Biomed. Eng. OnLine
17
(
1
),
66
(
2018
).
45.
B. C.
Konala
,
A.
Das
, and
R. K.
Banerjee
, “
Influence of arterial wall-stenosis compliance on the coronary diagnostic parameters
,”
J. Biomech.
44
(
5
),
842
847
(
2011
).
46.
S.
Kamangar
 et al, “
Effect of stenosis on hemodynamics in left coronary artery based on patient-specific CT scan
,”
Biomed. Mater. Eng.
30
(
4
),
463
473
(
2019
).
47.
P. D.
Morris
 et al, “
Fast virtual fractional flow reserve based upon steady-state computational fluid dynamics analysis: Results from the VIRTU-fast study
,”
JACC
2
(
4
),
434
446
(
2017
).
48.
B. K.
Koo
 et al, “
Diagnosis of ischemia-causing coronary stenoses by noninvasive fractional flow reserve computed from coronary computed tomographic angiograms: Results from the prospective multicenter DISCOVER-FLOW (diagnosis of ischemia-causing stenoses obtained via noninvasive fractional flow reserve) study
,”
J. Am. Coll. Cardiol.
58
(
19
),
1989
1997
(
2011
).
49.
M. H.
Hesamian
,
W.
Jia
,
X.
He
, and
P.
Kennedy
, “
Deep learning techniques for medical image segmentation: Achievements and challenges
,”
J. Digital Imaging
32
(
4
),
582
596
(
2019
).
50.
O.
Furat
 et al, “
Machine learning techniques for the segmentation of tomographic image data of functional materials
,”
Front. Mater.
6
,
145
(
2019
).
You do not currently have access to this content.