バイオスーパーコンピューティング東北2014
２０１４年６月２日

予測医療を目指して－マルチモダリティと血流シミュレーションの融合－Marie Oshima
The University of Tokyo, Japan

OutlineIntroduction
 Patient-specific modeling of vascular geometry
 Numerical method

1D-0D Simulation of the entire circulation including thecerebrovascular system

Patient-Specific simulation for Cerebrovascular
circulation after carotid stenting
Measurement data & 3D-1D-0D simulation
 Measurement data & 1D-0D simulation

Summary and Future Work21

Background : the situation of cardiovasculardiseases in Japan
Main causes of death in Japan
Cancer 28.5%• Heart disease 15.6%• Cerebrovascular disorder 9.9%•■Cancer■Heart disease■pneumonia■Cerebrovascular disorder

Cardiovascular disease

Statics according toMinistry of Health, Labour and Welfare （２０12）

Since Japan is becoming an aging society, the number of patientswith cardiovascular diseases would increase.
There is high possibility for a patient to become paralysis
or bedridden.
Quality of life of the patient becomes low.
The family experience the burden of long-term care.

3Background & Motivation － Atherosclerosis－
It is a main cause of stroke and heart attack Characteristics of atherosclerosis formed at
preferential locations such as bifurcation areas
with low WSS( wall shear stress)
 Treatment :Carotid artery stenting is becoming widely used
- restenosis
Carotid artery stenting- intracranial hemorrhage (ICH)
How can we predict the influences of the surgery
on individuals in the preoperative stage ?

Patient-specificcomputer simulation42

Objectives
How to correctly prescribe boundary conditions?
Place a 3-D model into a model of the entire system

Multi-scale modeling of the cardiovascular system
0D model1D model3D model[Liang F.Y., Takagi S., et al. Journal of Biomechanics. 2009]

The system determines wherethe blood to go in the body!

Account for blood distribution over the entire system

Geometrical multi-scale modeling & simulation ofthe cardiovascular system[Numerical Issues]*Patient-Specific Modeling
Ｇｅｏｍｅｔｒｉｃ Ｍｏｄｅｌｉｎｇ*Modeling of Boundary Conditions
Ｂ ｌｏｏｄ Ｆｌｏｗ
Ｓｉｍ ｕｌａｔｉｏｎ

Fluid-Structure Interaction
Peripehral Network- Even though a simulation is conducted
for a local region, it is necessary toconsider the rest of circulatory system- How ?
mutiscale (multi-domain) approach

Mass Transport/Mechanotransduction*Blood Flow–Arterial Wall Interaction
Visualization- It is important to examine wave
propagation & influence of
hemodynamics on arterial wall
[Blood simulation for cerebral aneurysms & atherosclerosis]
・Shoujima, M. et al., Stroke, (2004,2005)
・Meng H, et al., Stroke (2007).
・Cebral, J . Et al., Am/, J. Neuroradiol (2005)
・Oshima, M., Current Pharmaceutical Biotechnology (2011)3

Patient-Specific Modeling of
Vascular Modeling7

Geometric modeling of lumen and arterial walls
Lumen (fluid domain)

Segmentation
Region Growing

3-D Surface Construction
based on B-spline
Meshgeneration

CT Images
MRI

Blood vessel (structure domain)

Extraction of a medial axis using the voxel
reduction technique,“ Thinning’’

Integration oflumen and arterial wallsby overlapping the medial
axes of both domains

84

V-Modeler – Interactive operation based on GUI
・78 years old, male: The arterial circular of Willis
・Medial axis
・Identify the radius& length

・Categorize a typeof artery according
to the anatomy

・possible to conduct
virtual surgery suchas stent andbypass surgery

9Multi-Scale Simulation
1D-0D simulation
3D-1D-0D simulation105Schematic Illustration of multi-scale simulationof the circulatory system
Patient-Specific 3D Simulation

Cerebral Artery

1DSimulation

Circulation ofHead+ Neck

Outflow B.C

0D
Simulation

3D modelPulmonarycirculation

Inflow B.C

Left
ventricle

Carotid ArteryLeft
atriumRightventricle

RightatriumUpper body

Outflow B.C

Superior
vena cava

Lower body

Inferior
vena cava

Inflow B.C

Cerebrovascular circulation
Liang’s Model（Liang＋ Cerebral Circulation） Model
1D

0D

0D

0D

0D

0D

0D

Vertebral artery
ExternalInternal
1D
Carotid Artery
Carotid Artery

1D

1DThe cerebrovascular circulation including
distal brunches of ECAs
126

1D-0D modeling of the entire cardiovascular system<0D model><1D model>+=

Closed-loop circulation
model

Roles of model 1D model describes
pulse wave propagationin the arterial system

 0D model closes the1D model to account forsystemic hemodynamic
behaviors.
[F.Y. Liang et al. Medical & Biological Engineering & Computing. 2009]

Coupling of a 1-D compliant model and a
3-D rigid model
3-D rigid model
1-D compliant model

1-D compliant model

Impedance
discontinuity

Impedance
discontinuityA direct coupling results in unrealistic wave reflection(Passerini et al. Journal of Engineering Mathematics.
2009)

0-D interface model
1-D compliant model
W1(u)P1d(u)

0-D interface model
3-D rigid model*P1d(d)*1-D compliant modelLu
Qu1*Cu(Vu)

Cd(Vd)

Qd2* W2(d)7How to couple 3D-1D-0D model
3D and 1D-0D computations
are implemented separately
for each cardiac cycle

InitialCondition

Computeiteratively

3D model

1D-0D model

1D-0D model0D modelstart
4 cycles4 cycles2 cyclesend

3D model

1D-0D computation

3D computationYes

1D model0D modelFull 1D computation

Converged?

1D model0D modelNoAlgorithm

The role of interfaces isto represent the inherent
compliance of the 3D model.

1D modelFlow

15

Patient-Specific Modeling ＆Simulation＊Measurement data & 3D-1D-0D(MRI & PC-MRI)＊Measurement data&1D-0D(MRI, PC-MRI,SPECT)168

Clinical record of a patient undergoing carotid surgery
Patient: 78 years old male
Preoperative symptoms: Stroke induced by a severe stenosis present in theleft internal carotid artery (ICA).

Surgery: Carotid artery stenting (CAS)
Postoperative syndrome: Intracranial hemorrhage due to hyperperfusion
Pre-operative MRA

Post-operative CT
StenosisHemorrhage
Ischemic region

Cerebral arteries

Carotid bifurcation

Brain scanning

Circle of Wills

Patient clinical data
1. Flow rate by PC-MRI & Blood Pressure
Averaged FR(ml/min) Pre
Rt. ICA 47
251.1
Lt. ICA
76.4
40
BA
154.9
56
Rt. MCA 61
71.2
Lt. MCA 67
23.1
Max.
Averaged BL (mmHg) 100.0

Post231.8
186.9
142.0
50.3
59.3
Min95.04740616756
Inlet
Outlet

2. Medical Images
MRACTA

189Optimization of parameters
Step 1： Measurement of diameter & length of artery

Medical images

Extraction of medial axis
lengthdiameterStep 2： Application of measurement data to simulation
Step 3： Optimization of peripheral resistanceStep 4： Estimation of occlusion (in case of pre-operation)
L.ICA
Post_datar0

r1

L:84.4

Lr0:1.05
r1:1.02

19

Application of measurement dataand optimization of Parameters
Step 1： Measurement of diameter & length of arteryStep 2： Application of measurement data to simulation
Step 3： Optimization of peripheral resistanceResistance in 0D model of
cerebrovascular circulation

Post-operation
309.4/2? 63*Q(ml/min)

TQ :measurement data

309.4/2?
65

Estimation
Measurement

α=1.2
67

61

n

Q : simulated value in the nth step

The rest of parameters are optimized
using BP in the arm

50.3

59.3

142.0/2? 58

70

142.0/2?

入口

出口
α=0.9

T

P n :Measurement data
P : simulated value in the nth stepStep 4： Estimation of occlusion (in case of pre-operation)

20

10

Estimation of parameters for stenosis
Step 1： Measurement of diameter & length of arteryStep 2： Application of measurement data to simulation
Step 3： Optimization of peripheral resistanceStep 4： Estimation of occlusion (in case of pre-operation)Less than 1 pixel
da: 0.297mm < 1 pixel of CT image
Dc: 1.56mm
Stenosis : 0.81~1.00

Due to sever stenosis,
L. ICA is not shown

Stenosis
ＮＡＳＣＥＴ法

21Results◆ Stenosis◆ Resistance (mmHg・s・ml(-1))
血管番号5858707061
61676763
6365

No.1(pre)

43.8

37.7

49.4

143.1

24.5

13.9

No.2(post)

14.5

19.4

71.4

59.6

10.6

12.6

血管番号

58706167

6365

No.1(pre)

77.5

77.5

71.2

23.1

116.6

Measusment(pre)

77.5

77.5

71.2

23.1

No.2(post)

71.0

71.0

50.0

Measurement （post）

71.0

71.0

50.3

7063

Right

◆ Flowrate(ml/min)
58

0.85（0.81～1.00）

61◆Averaged B P (mmHg)
No.1

99.7（100）No.2

95.0（95.0）

65

47

47

40

116.6

251.1

76.4

116.6

116.6

253.7

75.2

59.3

154.7

154.7

233.9

187.0

59.3

154.7

154.7

231.8

186.9

67

63

65

Left

40
47

40

61

67

58

70
InletOut let◆Intracranial flow rate (ml/s) from that of heartNo.1(pre)8.05ml/s11.2%

No.2(Post)

9.35ml/s

12.7%
2211

Estimation of flow distribution from pre-operative
data□Simulation has been conducted using the pre-operative data
◆ Resistance (mmHg・s・ml(-1))
血管番号

5870
70

6167
67

63
6365

Measurement(post)------

No.1(pre)

43.8

37.7

49.4

143.1

24.5

13.9

No.2(post)

14.5

19.4

71.4

59.6

10.6

12.6

RightLeft
63

65

4740

6167

◆ Flowrate(ml/min)
血管番号

5870
70

6167

63

65

47
47

4040

71.0 71.0

50.3

59.3

154.7

154.7

231.8

186.9

No.1

96.4 93.7

60.5

32.296.2

107.8

142.0

157.1

No.2

71.0 71.0

50.0

59.3

154.7

154.7

233.9

187.0

58

Due to low flow rate, some arteries
can not be measured from theimages□Discussion
Simulation

r1r0

Real Geometry

67

L.MCA(67)70

r0:1.04mm
r1:0.80mm

r1r0

It is necessary to use more realistic vascular geometry

23(1) Incorporation of medical image data
Cerebral artery model
3D modeling3D grid model (829,084 cells)

ACA
MCA

1D model parameters

Parameters ofstenosis
ICA

Geometric model of theMCA bifurcation after region
subtraction and smoothing

MRA parameters
Geometricof each cerebral artery

Segmentation

12

Results of 3D
model
3.0

ICA
ACAMCA

Flow rate (ml/s)

2.5
2.0
1.5
1.0
0.5
0.0
0.0

0.2

0.4

0.6

0.8

1.0

Time (s)Flow waveforms and streamlines
Case 1 (pre-operative)

Case 2 (post-operative)

3.5
3.0

a b

ICAACA
MCA

c

3.0

2.0
1.5
1.0
0.5
0.0
-0.5
0.0

a b

ICA
ACA
MCA

c

2.5

Flow rate (ml/s)

2.5

Flow rate (ml/s)

3.5

2.0
1.5
1.0
0.5
0.0

0.2

0.4

0.6

Time (s)ACA

0.8

1.0-0.5
0.0

0.2

0.4

0.6

0.8

1.0Time (s)

MCA

ICA13

Mean wall shear stress (MWSS) and Oscillatory shear index (OSI)
Case 1 (pre-operative)

Case 2 (post-operative)

ACA
MCA

t =0.25st =0.25s

ICA

Challenges of 3D-1D-0D multi-scale simulation
What can we do for medical doctors to use
simulations in the clinical study ?
Requirement for applying simulation to an individual
patient

Requirement for allotted timeCombination of medical image dataHow long can they wait to get results ?

Requirement for usability
Can they handle different types of software for modeling, simulation,and visualization ?
 PC?
How can we combine patient-specific data &simulation in a practical manner ?

2814

Summary A general cardiovascular model can be personalized tosimulate the hemodynamic conditions for a patient.
- The cerebrovascular circulation including distal brunches of ECAs isadded on 1D-0D simulation- Estimation of both pressure & velocity is improved by optimizing theperipheral resistance of the cerebrovascular circulation with theultrasound data
- Patient-Specific 3D simulation is conducted under the boundary conditions
given by 1D-0D simulation of the entire circulatory system- Patient-specific 1D simulation is incorporated with the entire circulatory
system using patient-specific image data such MRI, CT, and SPECT

A personalized cardiovascular model has the potential to predictthe risk of a surgery based on clinical data acquired during
preoperative diagnosis.

Application of simulation to Clinical Study
Advancement in
Medical images &measurement

Mainly for
Visualization
use

Simulation + Clinical Data
Prediction ofprogress on
an individualbasis

Surgical planning

Contributing to development of medicine and clinical treatment 3015Acknowledgement
This research is supported by the ‘Research and Development of the NextGeneration Integrated Simulation of Living Matter’, as a part of the‘Development and Use of the Next-Generation Supercomputer Project’ of theMinistry of Education, Culture, Sports, Science and Technology (MEXT),
Japan.

Professor Fuyou Liang： Shanghai Jiao Tong University, Shanghai, China
Professor Shu Takagi: The University of Tokyo, Japan
Dr. Ryutaro Himeno: Riken
Professor Kiyomi Niki： Tokyo City University
Mr. Ryota Abe
Ms. Shiori Yauchi
Professor Motoaki Sugawara: Hemeji Dokyo University
Dr. Shigeki Yamada: Hamamatsu Rosai Hospital
Mr. Masaharu Kobayashi The University of Tokyo
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Thank you for your attention

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