mechanobiologyof the

(1)

Institut Mines-Télécom

Mechanics and

mechanobiology of the wall of the thoracic aorta

Stéphane AVRIL

(2)

ascending aorta

descending aorta

(thoracic aorta and abdominal aorta)

arch of aorta ▶ a local dilation of the aorta

due to aortic wall weakening

AORTIC ANEURISMS

MODMAD - 2015/11/09 - Stéphane AVRIL 2

(3)

SOCIETAL AND MEDICAL ISSUES

How can we predict the aneurysm’s rupture?

Thoracic aortic aneurysms per year: 15000 people in the US, +30000 people in Europe with a male preponderance.

50-60% involves the ascending aorta.

(4)

Institut Mines-Télécom MODMAD - 2015/11/09 - Stéphane AVRIL 4

I) Aneurysm excised specimen.

II) Separation of Media and Adventitia.

Adventitia Media

Circumferential

A x ia l

Circumferential

A x ia l

III) Inflation test device. Speckle pattern is made before starting the

inflation test Circumferential

A x ia l

Adventitia & Media

Circumferential

A x ia l

II) Media and Adventitia.

Characterization of the modes of rupture

Romo et al. Journal of Biomechanics -

2014.

(5)

camera

Instron machine protector

Undeformed Deformed

x y

Full-field measurements

(6)

Local stress reconstruction

𝐴 ∙ 𝝈 = [𝐵]

𝑑𝑖𝑣 𝝈 + 𝑓 = 0

(7)

Rupture picture and area of interest

(AOI) Local thickness

evolution (mm)

= NodeMAX

= NodeTOP

= NodeRUP Mesh

A. Romo, S. Avril, P. Badel, A Duprey, J.P.

Favre. In vitro analysis of localized aneurism rupture. Journal of Biomechanics -2014, vol 47, N°3, pp 607-616.

Local analysis of rupture

(8)

55% of the ultimate stress

Local damage initiation

(9)

Rupture

modes

(10)

𝜎 ^𝑅𝑢𝑝 = (𝜎 ∙ 𝑞 _𝜃 ) ∙ 𝑞 _𝜃

1.31 MPa 1.13 MPa 1.05 MPa

0.88 MPa

Ultimate stress values

(11)

Fit a strain energy density function at every point

𝛽 𝛽

Strain energy function:

(12)

MATLAB ®

Identification of a hyperelastic constitutive model

Davis et al. Biomechanics and Modeling in

Mechanobiology - 2015.

(13)

Results

(14)

Results

(15)

Application to computational retrospective predictions

 Image acquisition and 3D reconstruction

5 patients: Dynamic preoperative scanners during cardiac cycle ( 0.92 s) = 10 phases.

CT: (resolution 512x512, slice thickness of 0.5 mm)

Isolation of the aneurysm 1 phase superposition of

10 phases

Systolic and diastolic geometries

Trabelsi et al. Journal of Biomechanics - 2015.

(16)

Good prediction of in vivo volume variations

CT image geometry (diastolic geometry) Zero-pressure geometry

Patient 1

Patient 2

Patient 3

Patient 4

Patient 5 0

2 4 6 8 10 12 14 16 18 20

1 2 3 4 5

Relative volume variation

Patients

Comparison between actual and predicted relative volumes variation of the aneurysm

Relative FEM volume variation Relative CT volume variation

(17)

Identification of hyperelastic parameters by calibrating the volume changes

A

C B

D

-40000

-40000 -200

00

-2 0000

-20000

-200 00 0

0

0 20000

20000

20000 20000

20000

40000

40000 ⁴⁰⁰⁰⁰

60000 D1 (kPa)

D2

Patient 1: Variation between predicted and CT systolic and mid-cycle volumes (mm3)

2 3 4 5 6 7 8 9 10

2 3 4 5 6 7 8 9

F(sys) F(mid)

Trabelsi et al. Journal of Biomechanics - 2015.

INVERSE ANALYSIS

(18)

Stress analysis and rupture risk estimation

0 0.2 0.4 0.6 0.8 1 1.2 1.4

1 2 3 4 5

R el at iv e Ris k

Patient ID

Relative diameter

Rupture risk index (regression propreties)

Rupture risk index (Experimental properties)

Overpressure Risk (Experimental propreties)

Overpressure Risk (Regression

properties)

(19)

Future work: predicting and stopping aneurism growth

4D MRI

(20)