Patient-specific modeling for left ventricular mechanics using data-driven boundary energies

L. Asner; M. Hadjicharalambous; R. Chabiniok; D. Peressutti; E. Sammut; J. Wong; G. Carr-White; R. Razavi; A. P. King; N. Smith; J. Lee; D. Nordsletten

doi:10.1016/j.cma.2016.08.002

Patient-specific modeling for left ventricular mechanics using data-driven boundary energies

L. Asner, M. Hadjicharalambous, R. Chabiniok, D. Peressutti, E. Sammut, J. Wong, G. Carr-White, R. Razavi, A. P. King, N. Smith, J. Lee, D. Nordsletten^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

27 Citations (Scopus)

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Abstract

Supported by the wide range of available medical data available, cardiac biomechanical modeling has exhibited significant potential to improve our understanding of heart function and to assisting in patient diagnosis and treatment. A critical step towards the development of accurate patient-specific models is the deployment of boundary conditions capable of integrating data into the model to enhance model fidelity. This step is often hindered by sparse or noisy data that, if applied directly, can introduce non-physiological forces and artifacts into the model. To address these issues, in this paper we propose novel boundary conditions which aim to balance the accurate use of data with physiological boundary forces and model outcomes through the use of data-derived boundary energies. The introduced techniques employ Lagrange multipliers, penalty methods and moment-based constraints to achieve robustness to data of varying quality and quantity. The proposed methods are compared with commonly used boundary conditions over an idealized left ventricle as well as over . in vivo models, exhibiting significant improvement in model accuracy. The boundary conditions are also employed in . in vivo full-cycle models of healthy and diseased hearts, demonstrating the ability of the proposed approaches to reproduce data-derived deformation and physiological boundary forces over a varied range of cardiac function.

Original language	English
Pages (from-to)	269-295
Journal	COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
Volume	314
Early online date	11 Aug 2016
DOIs	https://doi.org/10.1016/j.cma.2016.08.002
Publication status	Published - 1 Feb 2017

Keywords

Cardiac mechanic
Finite element method
Medical imaging
Patient-specific boundary conditions
Patient-specific modeling

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Accepted author manuscript, 38.2 MBLicence: CC BY-NC-ND

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Asner, L., Hadjicharalambous, M., Chabiniok, R., Peressutti, D., Sammut, E., Wong, J., Carr-White, G., Razavi, R., King, A. P., Smith, N., Lee, J., & Nordsletten, D. (2017). Patient-specific modeling for left ventricular mechanics using data-driven boundary energies. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 314, 269-295. https://doi.org/10.1016/j.cma.2016.08.002

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abstract = "Supported by the wide range of available medical data available, cardiac biomechanical modeling has exhibited significant potential to improve our understanding of heart function and to assisting in patient diagnosis and treatment. A critical step towards the development of accurate patient-specific models is the deployment of boundary conditions capable of integrating data into the model to enhance model fidelity. This step is often hindered by sparse or noisy data that, if applied directly, can introduce non-physiological forces and artifacts into the model. To address these issues, in this paper we propose novel boundary conditions which aim to balance the accurate use of data with physiological boundary forces and model outcomes through the use of data-derived boundary energies. The introduced techniques employ Lagrange multipliers, penalty methods and moment-based constraints to achieve robustness to data of varying quality and quantity. The proposed methods are compared with commonly used boundary conditions over an idealized left ventricle as well as over . in vivo models, exhibiting significant improvement in model accuracy. The boundary conditions are also employed in . in vivo full-cycle models of healthy and diseased hearts, demonstrating the ability of the proposed approaches to reproduce data-derived deformation and physiological boundary forces over a varied range of cardiac function.",

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author = "L. Asner and M. Hadjicharalambous and R. Chabiniok and D. Peressutti and E. Sammut and J. Wong and G. Carr-White and R. Razavi and King, {A. P.} and N. Smith and J. Lee and D. Nordsletten",

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Asner, L , Hadjicharalambous, M , Chabiniok, R , Peressutti, D , Sammut, E , Wong, J , Carr-White, G , Razavi, R , King, AP , Smith, N , Lee, J & Nordsletten, D 2017, 'Patient-specific modeling for left ventricular mechanics using data-driven boundary energies', COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, vol. 314, pp. 269-295. https://doi.org/10.1016/j.cma.2016.08.002

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AU - Asner, L.

AU - Hadjicharalambous, M.

AU - Chabiniok, R.

AU - Peressutti, D.

AU - Sammut, E.

AU - Wong, J.

AU - Carr-White, G.

AU - Razavi, R.

AU - King, A. P.

AU - Smith, N.

AU - Lee, J.

AU - Nordsletten, D.

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N2 - Supported by the wide range of available medical data available, cardiac biomechanical modeling has exhibited significant potential to improve our understanding of heart function and to assisting in patient diagnosis and treatment. A critical step towards the development of accurate patient-specific models is the deployment of boundary conditions capable of integrating data into the model to enhance model fidelity. This step is often hindered by sparse or noisy data that, if applied directly, can introduce non-physiological forces and artifacts into the model. To address these issues, in this paper we propose novel boundary conditions which aim to balance the accurate use of data with physiological boundary forces and model outcomes through the use of data-derived boundary energies. The introduced techniques employ Lagrange multipliers, penalty methods and moment-based constraints to achieve robustness to data of varying quality and quantity. The proposed methods are compared with commonly used boundary conditions over an idealized left ventricle as well as over . in vivo models, exhibiting significant improvement in model accuracy. The boundary conditions are also employed in . in vivo full-cycle models of healthy and diseased hearts, demonstrating the ability of the proposed approaches to reproduce data-derived deformation and physiological boundary forces over a varied range of cardiac function.

AB - Supported by the wide range of available medical data available, cardiac biomechanical modeling has exhibited significant potential to improve our understanding of heart function and to assisting in patient diagnosis and treatment. A critical step towards the development of accurate patient-specific models is the deployment of boundary conditions capable of integrating data into the model to enhance model fidelity. This step is often hindered by sparse or noisy data that, if applied directly, can introduce non-physiological forces and artifacts into the model. To address these issues, in this paper we propose novel boundary conditions which aim to balance the accurate use of data with physiological boundary forces and model outcomes through the use of data-derived boundary energies. The introduced techniques employ Lagrange multipliers, penalty methods and moment-based constraints to achieve robustness to data of varying quality and quantity. The proposed methods are compared with commonly used boundary conditions over an idealized left ventricle as well as over . in vivo models, exhibiting significant improvement in model accuracy. The boundary conditions are also employed in . in vivo full-cycle models of healthy and diseased hearts, demonstrating the ability of the proposed approaches to reproduce data-derived deformation and physiological boundary forces over a varied range of cardiac function.

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Patient-specific modeling for left ventricular mechanics using data-driven boundary energies

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Keywords

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