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Biomedical subjects

Adam Wittek

Publications and source records attributed to Adam Wittek.

3 recordsLinked to original sources

Patient-specific model of brain deformation: application to medical image registration.

This contribution presents finite element computation of the deformation field within the brain during craniotomy-induced brain shift. The results were used to illustrate the capabilities of non-linear (i.e. accounting for both geometric and material non-linearities) finite element analysis in non-rigid registration of pre- and intra-operative magnetic resonance images of the brain. We used patient-specific hexahedron-dominant finite element mesh, together with realistic material properties for the brain tissue and appropriate contact conditions at boundaries. The model was loaded by the enforced motion of nodes (i.e. through prescribed motion of a boundary) at the brain surface in the craniotomy area. We suggest using explicit time-integration scheme for discretised equations of motion, as the computational times are much shorter and accuracy, for practical purposes, the same as in the case of implicit integration schemes. Application of the computed deformation field to register (i.e. align) the pre-operative images with the intra-operative ones indicated that the model very accurately predicts the displacements of the tumour and the lateral ventricles even for limited information about the brain surface deformation. The prediction accuracy improves when information about deformation of not only exposed (during craniotomy) but also unexposed parts of the brain surface is used when prescribing loading. However, it appears that the accuracy achieved using information only about the deformation of the exposed surface, that can be determined without intra-operative imaging, is acceptable. The presented results show that non-linear biomechanical models can complement medical image processing techniques when conducting non-rigid registration. Important advantage of such models over the previously used linear ones is that they do not require unrealistic assumptions that brain deformations are infinitesimally small and brain stress-strain relationship is linear.

Brain↗

Brain shift computation using a fully nonlinear biomechanical model.

In the present study, fully nonlinear (i.e. accounting for both geometric and material nonlinearities) patient specific finite element brain model was applied to predict deformation field within the brain during the craniotomy-induced brain shift. Deformation of brain surface was used as displacement boundary conditions. Application of the computed deformation field to align (i.e. register) the preoperative images with the intraoperative ones indicated that the model very accurately predicts the displacements of gravity centers of the lateral ventricles and tumor even for very limited information about the brain surface deformation. These results are sufficient to suggest that nonlinear biomechanical models can be regarded as one possible way of complementing medical image processing techniques when conducting nonrigid registration. Important advantage of such models over the linear ones is that they do not require unrealistic assumptions that brain deformations are infinitesimally small and brain tissue stress-strain relationship is linear.

Biomechanical Phenomena↗

Mathematical Modelling of Muscle Effect on the Kinematics of the Head-Neck Complex in a Frontal Car Collision: A Parameter Study.

A 2-dimensional multibody model of the head-neck complex with muscle elements was developed to estimate the influence of muscles on the kinematics of the head-neck complex in a frontal car collision. With this model the authors evaluated how strongly the calculated influence of muscles depends on 3 important factors: (a) impact severity, (b) reflex time, and (c) parameters that determine characteristics of different components of the muscle model. When muscles were triggered at the beginning of impact, the maximum angle of the head flexion was decreased by the muscles by 40% in a frontal collision with an acceleration of 15g. The influence of muscles was significant for reflex times lower than 60 (80) ms. The calculated influence of muscles was not sensitive to most parameters of the muscle model.

Hill-type muscle models↗