Abstract:Purpose
Brain shift that occurs during neurosurgery disturbs the brain’s anatomy. Prediction of the brain shift is essential for accurate localisation of the surgical target. Biomechanical models have been envisaged as a possible tool for such predictions. In this study, we created a framework to automate the workflow for predicting intra-operative brain deformations.
Methods
We created our framework by uniquely combining our meshless total Lagrangian expl… Show more
“…Computational mechanics models are a versatile tool to predict the response of nearly arbitrary geometries under mechanical loading. Exemplary applications from the field of brain biomechanics are the simulation of the human head under impact ( Ji et al, 2022 ), cortical folding during brain development ( Garcia et al, 2018 ; Zarzor et al, 2021 ), and brain deformation during surgery ( Safdar et al, 2023 ). All these models depend on the availability of constitutive models and corresponding parameters that accurately characterize the material behavior.…”
Inverse mechanical parameter identification enables the characterization of ultrasoft materials, for which it is difficult to achieve homogeneous deformation states. However, this usually involves high computational costs that are mainly determined by the complexity of the forward model. While simulation methods like finite element models can capture nearly arbitrary geometries and implement involved constitutive equations, they are also computationally expensive. Machine learning models, such as neural networks, can help mitigate this problem when they are used as surrogate models replacing the complex high fidelity models. Thereby, they serve as a reduced order model after an initial training phase, where they learn the relation of in- and outputs of the high fidelity model. The generation of the required training data is computationally expensive due to the necessary simulation runs. Here, active learning techniques enable the selection of the “most rewarding” training points in terms of estimated gained accuracy for the trained model. In this work, we present a recurrent neural network that can well approximate the output of a viscoelastic finite element simulation while significantly speeding up the evaluation times. Additionally, we use Monte-Carlo dropout based active learning to identify highly informative training data. Finally, we showcase the potential of the developed pipeline by identifying viscoelastic material parameters for human brain tissue.
“…Computational mechanics models are a versatile tool to predict the response of nearly arbitrary geometries under mechanical loading. Exemplary applications from the field of brain biomechanics are the simulation of the human head under impact ( Ji et al, 2022 ), cortical folding during brain development ( Garcia et al, 2018 ; Zarzor et al, 2021 ), and brain deformation during surgery ( Safdar et al, 2023 ). All these models depend on the availability of constitutive models and corresponding parameters that accurately characterize the material behavior.…”
Inverse mechanical parameter identification enables the characterization of ultrasoft materials, for which it is difficult to achieve homogeneous deformation states. However, this usually involves high computational costs that are mainly determined by the complexity of the forward model. While simulation methods like finite element models can capture nearly arbitrary geometries and implement involved constitutive equations, they are also computationally expensive. Machine learning models, such as neural networks, can help mitigate this problem when they are used as surrogate models replacing the complex high fidelity models. Thereby, they serve as a reduced order model after an initial training phase, where they learn the relation of in- and outputs of the high fidelity model. The generation of the required training data is computationally expensive due to the necessary simulation runs. Here, active learning techniques enable the selection of the “most rewarding” training points in terms of estimated gained accuracy for the trained model. In this work, we present a recurrent neural network that can well approximate the output of a viscoelastic finite element simulation while significantly speeding up the evaluation times. Additionally, we use Monte-Carlo dropout based active learning to identify highly informative training data. Finally, we showcase the potential of the developed pipeline by identifying viscoelastic material parameters for human brain tissue.
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