finite element simulation of the deformation of the female breast … · 2016. 2. 20. ·...

Optimization of material parameters for the constitutive modelling of the female breast based

on MRI data, 3-D surface scanning and finite element simulation

S. Raith1, M.Eder1, F. v Waldenfels1, J. Jalali2, A.Volf1, L. Kovacs1 1Research Group CAPS (Computer Aided Plastic Surgery) - Klinik und Poliklinik für Plastische

Chirurgie und Handchirurgie, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, D-81675 Munich, Germany

2Institute of Medical Engineering at the Technische Universität München (IMETUM), Boltzmannstr. 11, D-85748 Garching, Germany

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Optimization of material parameters for the constitutive modelling of the female breast based on MRI data, 3-D surface scanning and finite element simulations S. Raith, M. Eder, F. v Waldenfels, J. Jalali, A. Volf, L. Kovacs

9. Weimarer Optimierungs- und Stochastiktage 29-30 November 2012

Clinic for Plastic Surgery and Handsurgery

Klinikum rechts der Isar

Technische Universität München

medical staff: engineering staff:

Who is research group CAPS? Computer Aided Plastic Surgery

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Klinikum rechts der Isar IMETUM Garching

PD Dr. Laszlo Kovacs Head of the group

Dr. M. Eder Dr. F.v.Waldenfels A. Volf J. Jalali S. Raith

Interdisciplinary research between medicine and engineering



• Breast cancer is very common: 140.337 surgical interventions in Germany in 2010 [source: S. B. Deutschland, „Statistisches Bundesamt,“ [Online]. Available: https://www.destatis.de/DE/ZahlenFakten/GesellschaftStaat/Gesundheit/Krankenhaeuser/Tabellen/DiagnosenWeiblich.html. [4 9 2012].]

• Additionally there are reductions, asymmetry corrections, reconstructions and augmentations

• There is a need for better planning in breast surgery to overcome todays ad-hoc heuristic approaches – Software tools from engineering science might be used for planning

Simulations of the mechanical behavior of the breast’s soft tissues is evident

Current Situation and Motivation

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Krouskop, T., Wheeler, T., Kallel, K., Garra, B., Hall, T.,

1998. Elastic moduli of breast and prostate tissues under

compression. Ultrason Imaging. 20, 260-274.

Wellman, P., Howe, R., Dalton, E., Kern, K., 1999. Breast

tissue stiffness in compression is correlated to histological

diagnosis. Harvard Biorobotics Laboratory; Availabe at

http://biorobotics.harvard.edu/pubs/1999/mechprops.pdf

Accessed March 01, 2012

Azar, F., Metaxas, D., Schnall, M., 2001. A deformable

finite element model of the breast for predicting mechanical

deformations under external perturbations. Acad Radiol. 8,

965-975.

Samani, A., Bishop, J., Yaffe, M.J., Pelwes, D., 2001.

Biomechanical 3D finite element modeling of the Human

Breast using MRI data. IEEE Trans Med Imaging. 20, 271-

279.

Samani, A., Plewes, D., 2004. A method to measure the

hyperelastic parameters of ex vivo breast tissue samples.

Phys Med Biol. 49, 4395-4405.

Tanner, C., Schnabel, J.A., Hill, D.L., Hawkes, D.J., Leach,

M.O., Hose, D.R., 2006. Factors influencing the accuracy of

biomechanical breast models. Med Phys. 33, 1758-1769.

Samani, A., Zubovits, J., Plewes, D., 2007. Elastic moduli

of normal and pathological human breast tissues: an

inversion-technique-based investigation of 169 samples.

Phys. Med Biol. 52, 1565-1576.

Rajagopal, V., Lee, A., Chung, J., Warren, R., Highnam R.,

2008. Creating individual-specific biomechanical models of

the breast for medical image analysis. Acad Radiol. 15,

1425–1436.

Del Palomar, A.P., Calvo, B., Herrero, J., López, J.,

Doblaré, M., 2008. A finite element model to accurately

predict real deformations of the breast. Med Eng Phys. 30,

1089-1097.

Lapuebla-Ferri, A., Del Palomar, A.P., Herrero, J., Jiménez-

Mocholí, A.J., 2011. A patient-specific FE-based

methodology to simulate prosthesis insertion during an

augmentation mammoplasty. Med Eng Phys. 33, 1094-

1102.

Publications dealing with mechanical behaviour of female breast soft tissue

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Publication Krouskop et al. (1998) Wellman et al. (1999) Azar et al. (2001) Samani et al. (2001) Samani et al. (2004) Samani et al. (2007) Tanner et al. (2006) Del Palomar et al. (2008) Rajagopal et al. (2008) Lapuebla-F. et al. (2011)

Theoretical constitutive models utilized by the different authors

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Constitutive Model Piecewise linear elastic Piecewise linear elastic Exponential Hyperelastic Polynomial Elastic Mooney-Rivlin Linear Elastic Various Material Models Neo-Hookean Neo-Hookean Neo-Hookean

Different testing methods such as ex vivo material tests (uni- or biaxial tension, shear) or numerical simulations(FEA)



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• Taking advantage of modern imaging technologies – MRI imaging

• inner anatomy of the test persons’ chests

• possible in prone or supine position

– 3-D surface scanning

• possible in standing position

Method for in vivo calculation of the breast’s material parameters

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• Taking advantage of modern imaging technologies

Method for in vivo calculation of the breast’s material parameters

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3-D surface scan Standing position

MRI prone position MRI supine position



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Principle workflow of the material model validation

Machbarkeitsstudie

D D



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3-D Laser Scan im Brustbereich

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scanning device: Konica Minolta Vivid 900i



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stitching and merging of the 3 separate surface scan data

Utilized software tool:

3-D laser scans of the breast region

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• 3 different compartments are defined:

• thorax wall, pectoral muscle and soft tissue (fat and glandular)

Segmentation of the relevant anatomical compartments

Prob 10



Finite element mesh for simulation

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Finite element mesh for simulation

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System boundaries (green) -> fixed boundary condition Thorax treated as rigid -> fixed boundary condition Muscle also considered to be rigid -> fixed boundary condition



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• Apparent deformation due to gravity loading is non negligible

• -> for physically meaningful calculations a stress free starting configuration has to be found

Consideration of initial deformations



Inverse algorithm for the determination of the unloaded breast shape

deformed configuration segmented from MRI data

First estimation of the unloaded configuration

new forward calculation of the deformed shape

g g

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Comparison

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better approximation of the undeformed configuration

reaction forces may be determined form the comparison to the undeformed configuration

comparison via prescribed displacements

!F

!D

Prob 04

g forward calculation

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Iterativer Prozess

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variation of material parameters according to Tanner et al. and further

Neo Hookean (Young’s modulus varying, PR=0.49)

Simulation results with different sets of material properties

* Tanner et al., Factors influencing the accuracy of biomechanical breast models, Am. Assoc. Phys. Med. 33 (2006) 1758-1769

stiff soft



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Registration of surfaces in one common coordinate system



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• Coordinate systems of simulation an scans have to be registered for the comparison.

• Calculation of 3D deviation (so called Hausdorff distance) and summed over the surface as one scalar value

3-D surface comparison

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Comparison of different simulation results with

material parameters too stiff matching material parameters

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Principle workflow of the parameter identification loop

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D

Optimization loop Preprocessing

MR Imaging

3-D surface scan

FEA Mesh

merged surface

D

Material parameters

3D compare

scalar value of accordance

FEA result



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Results: clearly defined optimal set of parameters



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Patient individual optimal material parameters:

Young‘s modulus: 0.000827 MPa (Factor 1.06 to Tanner et al.)

Poisson‘s ratio: 0.5 (perfectly incompressible materail)




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Material parameters: Poisson‘s ratio always at 0.5 Young‘s Modulus as a factor to the parameters of Tanner at al. Output: average deviation in mm



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• In vivo approximation of material properties is feasible – Based on a combination of:

• 3-D surface scans of standing position

• MRI in prone lying position – non-invasive approach for material parameter determination

– Patient individual determination of material parameters is possible

• Acquired material parameters may be used for surgical planning and operation simulations

Conclusion



• application to training set of test persons to get better statistical evidence (18 are yet available)

• using more complex material models – Mooney-Rivlin, Ogden etc…

– with more parameters than E and nu (or G and K, respectively)

• material is yet considered to be homogeneous and isotropic – glandular tissue is not considered

Outlook

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Determined material parameters may be used in clinical surgery planning e.g. breast reconstruction



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Thank you for your attention

www.caps.me.tum.de

Contact: Stefan Raith [email protected] Tel.: 089/289-10858

mailto:[email protected]

mailto:[email protected]

finite element simulation of the deformation of the female breast … · 2016. 2. 20. ·...

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