Estimation of passive and active properties in the human heart using 3D tagged MRI

TY - JOUR

T1 - Estimation of passive and active properties in the human heart using 3D tagged MRI

AU - Asner, Liya

AU - Hadjicharalambous, Myrianthi

AU - Chabiniok, Radomir

AU - Peresutti, Devis

AU - Sammut, Eva

AU - Wong, James

AU - Carr-White, Gerald

AU - Chowienczyk, Philip

AU - Lee, Jack

AU - King, Andrew

AU - Smith, Nicolas

AU - Razavi, Reza

AU - Nordsletten, David

PY - 2015/11/26

Y1 - 2015/11/26

N2 - Advances in medical imaging and image processing are paving the way for personalised cardiac biomechanical modelling. Models provide the capacity to relate kinematics to dynamics and-through patient-specific modelling-derived material parameters to underlying cardiac muscle pathologies. However, for clinical utility to be achieved, model-based analyses mandate robust model selection and parameterisation. In this paper, we introduce a patient-specific biomechanical model for the left ventricle aiming to balance model fidelity with parameter identifiability. Using non-invasive data and common clinical surrogates, we illustrate unique identifiability of passive and active parameters over the full cardiac cycle. Identifiability and accuracy of the estimates in the presence of controlled noise are verified with a number of in silico datasets. Unique parametrisation is then obtained for three datasets acquired in vivo. The model predictions show good agreement with the data extracted from the images providing a pipeline for personalised biomechanical analysis.

AB - Advances in medical imaging and image processing are paving the way for personalised cardiac biomechanical modelling. Models provide the capacity to relate kinematics to dynamics and-through patient-specific modelling-derived material parameters to underlying cardiac muscle pathologies. However, for clinical utility to be achieved, model-based analyses mandate robust model selection and parameterisation. In this paper, we introduce a patient-specific biomechanical model for the left ventricle aiming to balance model fidelity with parameter identifiability. Using non-invasive data and common clinical surrogates, we illustrate unique identifiability of passive and active parameters over the full cardiac cycle. Identifiability and accuracy of the estimates in the presence of controlled noise are verified with a number of in silico datasets. Unique parametrisation is then obtained for three datasets acquired in vivo. The model predictions show good agreement with the data extracted from the images providing a pipeline for personalised biomechanical analysis.

U2 - 10.1007/s10237-015-0748-z

DO - 10.1007/s10237-015-0748-z

M3 - Article

C2 - 26611908

JO - Biomechanics and Modeling in Mechanobiology

JF - Biomechanics and Modeling in Mechanobiology

ER -