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Flow Simulation & Analysis Group
IV Modeling and Analysis of Heart Murmurs
Rajat Mittal, Jung Hee Seo, Hani Bakhshae, Chi Zhu
Department of Mechanical Engineering & Division of Cardiology
Andreas Androu, Guillaume Garreau
Electrical Engineering
Johns Hopkins University
Cardiac Auscultation
2
Digital Stethoscope
BioSignetic Corporation
But… • Low specificity (high false positives) • Diagnosis is based on the empirical/statistical correlation • Source mechanism of murmurs is poorly understood • No modality provides simultaneous assessment of source and measurement
• 3000 year old technique • Cheap • Non-invasive, high sensitivity • Good as a screening tool
60% of all pediatric murmurs leading to referral are “innocent”
Computational Hemo-acoustics
3
Computational Hemo-acoustics (CHA) directly simulate the above procedure: • Prediction of murmur generation/propagation • Source mechanism of murmurs • Better Disease - Hemodynamics - Sound (Auscultation) relation Present Approach: -Immersed Boundary Method based Hybrid Approach • Blood Flow - IBM Incompressible Navier-Stokes solver • Flow induced sound - Linearized Perturbed Compressible Equations (LPCE) • Sound Propagation in tissue – Linear wave equation
Pressure Fluctuation in the Heart
Structural wave Propagation
Surface fluctuation on the chest
Can computational modeling provide the missing link between cause (pathology) and effect (sound)?
Computational Hemoacoustics
4
νρ
∇⋅ = + ∇ = ∇
20
0
10, DUU P UDt
Incompressible N-S Eqns.
Structural wave Eqns.
Hemodynamics Wave Propagation
( , )P x t
Hemodynamic Sound Source
Sound Signal
,1
ij jk iij p
k j i
ij ji iu i
j j j i
p uu u St x x x
p uu u St x x x x
λ δ µ
ηρ ρ
∂ ∂∂ ∂+ + + = ∂ ∂ ∂ ∂
∂ ∂∂ ∂∂+ = + + ∂ ∂ ∂ ∂ ∂
Murmur Associated with Aortic Stenosis
LV AO
Aortic Valve Stenosis
Aortic Stenosis Murmur
Simplified Hemodynamic Modeling
LV AO
75% Aortic valve stenosis
Cardio-Thoracic Phantom Studies
6
U
Stenosis
Flow fluctuation
Thoracic phantom (silicone gel)
D
Wave propagation
U=0.25 m/s D=1.5875 cm DT=9.84 cm Re=UD/ν=4000 St=fD/U
Material: EcoFlex-10 ρ=1040 kg/m3
E=55.16 kPa K=91.3 Mpa (cb=297.3 m/s) G=18.39 kPa (cs=4.2 m/s) µ=14 Pa s
DT
Acoustic Sensors
7
Biopac sensor attached to the Micromanipulator
HP sensor attached to the Micromanipulator
Silicone Rubber- Tissue Mimicking Material
• Silicone rubber, Ecoflex 010 (Smooth-on) – Easy to produce – Extremely stable – Non-toxic and – Negligible shrinkage
• Procedure to make
– Mixing Part A part B, – Adding Silicon thinner, – Degassing for 3-4 min in (-29 in Hg) to
remove air bubbles
Murmur Generating
9
3D printed Casts
U
Stenosis
Flow fluctuation
D
Wave propagatio
n
Fluid Flow Circuit
10
11
Biopac sensor attached to the Micromanipulator
HP sensor attached to the Micromanipulator
Cardiothoracic Phantom-2nd generation
12
• Adding lung to the phantom • Foam is used to model the lung • Non-axisymmetric model
z/DEn
ergy
0 1 2 3 40
5
10
15
20
25
30-6060-120120-480
×10-10
Experimental Measurements
Frequency spectrum
Outer-surface radial accelerations
Energy mapping
Frequency [Hz]
Rad
iala
ccel
erat
ion
100 101 102 10310-8
10-7
10-6
10-5
10-4
10-3
10-2
10-1
HPBIOPAC
Computational/Experimental Studies
U
Stenosis
Flow fluctuation
Thoracic phantom (silicone gel)
D
Wave propagation
Re=UD/ν=4000
Simple model for the aortic stenosis murmur
Material properties: Tissue mimicking, viscoelastic gel (EcoFlex-10) ρ=1040 kg/m3
K=1.04 GPa (cb=1000.0 m/s) G=18.39 kPa (cs=4.2 m/s) µ=14 Pa s Other parameters: U=0.25 m/s D=1.5875 cm DT=9.84 cm (gelA), 16.51 cm (gelB) c.f. Biological soft tissue: K=2.25 GPa (cb=1500 m/s) G=0.1 MPa (cs=10 m/s) µ=0.5 Pa s
DT
L=7D
Computational Modeling
15
Hemodynamics IBM, Incompressible N-S
Elastic wave eq. for viscoelastic material Generalized Hooke’s law Kelvin-Voigt model
0
1
λ δ µ
ηρ ρ
′ ′∂ ∂′ ′∂ ∂+ + + = ∂ ∂ ∂ ∂
′ ′∂ ∂′ ′∂ ∂∂+ = + ∂ ∂ ∂ ∂ ∂
ij jk iij
k j i
ij ji i
j j j i
p uu ut x x x
p uu ut x x x x
High-order IBM, 6th order Compact Finite Difference Scheme, 4 stage Runge-Kutta method
i ij jP n p n′ ′=
4~ (10 )iu O −′
0iu′ =
0iu′ =
210, ( ) νρ
∂∇ ⋅ = + ⋅∇ + ∇ = ∇
∂
UU U U P Ut
Freq [Hz]PS
D100 101 102 10310-7
10-6
10-5
10-4
10-3
10-2
10-1
100
-1.0D-0.5D00.5D1.0D1.5D2.0D2.5D3.0D3.5D4.0D
Flow Simulation
Axial velocity
Vorticity
Pressure
ReD=4000 Wall pressure spectrum
2D 0
3D Elastic Wave Simulation Radial velocity fluctuation contours
• 200x200x320 (12.8 M), about 60 hrs with 1024 cores for real time 0.8 sec
Comparison with Experimental Measurements
Frequency [Hz]
Rad
iala
ccel
erat
ion
[m/s
ec2 ]
Exp
100 101 102 10310-9
10-8
10-7
10-6
10-5
10-4
10-3
10-2
10-8
10-7
10-6
10-5
10-4
10-3
10-2
10-1
-1D01D2D3D4DExp-HPExp-BIOPAC
Frequency spectrum
Outer-surface radial accelerations
z/D
Sim
ulat
ion
Exp
0 1 2 3 40
1
2
3
0
5
10
15
20
25
30-6060-120120-480
×10-10×10-7
Energy mapping
Solid: simulation Dashed: measurement
Free-Space Green’s Tensor
d
( , ) ( , ) ( )i ij jU r G r Fω ω ω=
1( ) ( )21( ) ( )
2
i ti i
i ti i
u t U e d
f t F e d
ω
ω
ω ωπ
ω ωπ
−
−
=
=
∫
∫
( )2 2
2 ( ) ( ) ( )i lij kl ik jl il jk i
j k
u u f t rt x x
ρ λδ δ µ δ δ δ δ δ∂ ∂− + + =
∂ ∂ ∂
(1) (1)0 22
(1) (1)0 22
( , ) ( ) ( 3 ) ( )12 ( 2 )
2 ( ) ( 3 ) ( )12
p i jij ij p ij p
i jsij s ij s
ik x xG r h k r h k r
r
x xik h k r h k rr
ω δ δπ λ µ
δ δπµ
= + − +
− − + −
Green’s tensor (Ben-Menahem & Singh,1981)
/ , ( 2 ) /
/ , /p p p
s s s
k c c
k c c
ω λ µ ρ
ω µ ρ
= = +
= =
r
θ
Analytical estimation of elastic wave solution (no geometrical effects)
Evaluation of Radial Acceleration
d r
θ
2, , 2,( , ) ( ) ( , ) ( ), ( ) ( )ω ω ω ω ω ω= = ∆∑
m mn k k n k k kk
a r i G r F F P A
( )ωkP
2 ( )ωa
1x2x
Frequency [Hz]R
adia
lacc
eler
ati o
n[ m
/sec
2 ]100 101 102 10310-9
10-8
10-7
10-6
10-5
10-4
10-3
10-2
3D SimulationGreen-CompGreen-ShearGreen-Total
Oblique shear waves contribute significantly to the stethoscopic signal!
Source Localization
Surface measurements 1, 2, .... Mu u u
Source mapping
1, 2, .... NF F F
1( ; )
N
m m n nn
u G x x F=
=∑
1 1
M N
u F
u F
=
G
[ ] [ ]F u+= G
G: M by N complex matrix
G+: Pseudo inverse of G
Source Localization
z/D
Nor
mal
ized
Sour
ceEn
ergy
-1 0 1 2 3 4
0.2
0.4
0.6
0.8
1
EstimatedMeasured
*30-120 Hz
Proceeding towards using a multi-sensor stethoscopic array (StethoVest) for automatic murmur localization
23
Computational Modeling
Aortic stenosis Patient specific geometry Model
U=0.25 m/s
Re=UD/ν=4000
D=1.5875 cm
Epidermal surface
24
Fluid Solver:
Acoustic Solver:
vi – structure velocity. λ, μ – 1st and 2nd Lame’s constants. ρs – density. K – bulk modulus, = 1.04 GPa. G – shear modulus, = 18.39 KPa. η – viscosity, = 14.0 Pa s.
Vicar Code, sharp interface IBM
See: Mittal, R., et al., JCP, 2008
Interior nodes: 6th –order compact scheme Immersed boundary: approximating polynomial method Time advancement: 4th –order Runge-Kutta method
See: Seo, J. H., & Mittal, R. ,JCP, 2011
Numerical methods:
Computational Modeling
Hemodynamic Simulation Results
25
x component of vorticity
75% 90%
Same contour level
50%
Hemodynamic Simulation Results
26
Contour of surface pressure
75% 90%
5 times larger contour level
50%
Baseline 5 times smaller contour level
Source Location
27
75%
90% 100 times larger contour level
Surface Signal
50%
1000 times smaller contour level
Baseline
Realistic Thorax Model
28
CT scan data (Visible Human)
Material property (density and speed of sound) mapping
3D model of the thorax (density iso-surfaces)
Need to account or thoracic structures on sound propagation
Cited Paper - Hemoacoustics • Jung Hee Seo, Vijay Vedula, Theodore Abraham, and Rajat Mittal, “Multiphysics computational models for cardiac
flow and virtual cardiography”, Int. J. Num. Meth. Biomed. Eng., doi: 10.1002/cnm.2556, 2013.680, DOI: 10.1007/s10439-014-1018-4.
• Jung Hee Seo and Rajat Mittal, “A Coupled Flow-Acoustic Computational Study of Bruits from a Modeled Stenosed Artery”, Medical & Biological Engineering & Computing, Vol 50(10) pp 1025-35, 2012.
• Andreou, A.G.; Abraham, T.; Thompson, W.R.; Jung Hee Seo; Mittal, R., “Mapping the cardiac acousteome: An overview of technologies, tools and methods,” Information Sciences and Systems (CISS), 2015 49th Annual Conference on , vol., no., pp.1,6, 18-20 March 2015, doi: 10.1109/CISS.2015.7086899
• Bakhshaee, H.; Garreau, G.; Tognetti, G.; Shoele, K.; Carrero, R.; Kilmar, T.; Chi Zhu; Thompson, W.R.; Jung Hee Seo; Mittal, R.; Andreou, A.G., “Mechanical design, instrumentation and measurements from a hemoacoustic cardiac phantom,” Information Sciences and Systems (CISS), 2015 49th Annual Conference on , vol., no., pp.1,5, 18-20 March 2015, doi: 10.1109/CISS.2015.7086901.
• Jung Hee Seo and Rajat Mittal, “A Coupled Flow-Acoustic Computational Study of Bruits from a Modeled Stenosed Artery”, Medical & Biological Engineering & Computing, Vol 50(10) pp 1025-35, 2012.
• J. H. Seo and R. Mittal, “A Higher-Order Immersed Boundary Method for acoustic wave scattering and low-Mach number flow-induced sound in complex geometries”, Journal of Computational Physics, 2011, Vol. 230, Issue 4, pp. 1000-1019 .
• Jung Hee Seo, Vijay Vedula, Theodore Abraham, and Rajat Mittal, “Multiphysics computational models for cardiac flow and virtual cardiography”, Int. J. Num. Meth. Biomed. Eng., doi: 10.1002/cnm.2556, 2013.680,
29