biostudies-literatureGranados ANational Institute for Health Research (NIHR)Wellcome/EPSRC Centre for Medical EngineeringWellcome TrustEngineering and Physical Sciences Research Council141-150https://www.ebi.ac.uk/biostudies/studies/S-EPMC7822772biostudies-literatureUnknown16(1)<h4>Purpose</h4>Estimation of brain deformation is crucial during neurosurgery. Whilst mechanical characterisation captures stress-strain relationships of tissue, biomechanical models are limited by experimental conditions. This results in variability reported in the literature. The aim of this work was to demonstrate a generative model of strain energy density functions can estimate the elastic properties of tissue using observed brain deformation.<h4>Methods</h4>For the generative model a Gaussian Process regression learns elastic potentials from 73 manuscripts. We evaluate the use of neo-Hookean, Mooney-Rivlin and 1-term Ogden meta-models to guarantee stability. Single and multiple tissue experiments validate the ability of our generative model to estimate tissue properties on a synthetic brain model and in eight temporal lobe resection cases where deformation is observed between pre- and post-operative images.<h4>Results</h4>Estimated parameters on a synthetic model are close to the known reference with a root-mean-square error (RMSE) of 0.1 mm and 0.2 mm between surface nodes for single and multiple tissue experiments. In clinical cases, we were able to recover brain deformation from pre- to post-operative images reducing RMSE of differences from 1.37 to 1.08 mm on the ventricle surface and from 5.89 to 4.84 mm on the resection cavity surface.<h4>Conclusion</h4>Our generative model can capture uncertainties related to mechanical characterisation of tissue. When fitting samples from elastography and linear studies, all meta-models performed similarly. The Ogden meta-model performed the best on hyperelastic studies. We were able to predict elastic parameters in a reference model on a synthetic phantom. However, deformation observed in clinical cases is only partly explained using our generative model.International journal of computer assisted radiology and surgeryA generative model of hyperelastic strain energy density functions for multiple tissue brain deformation.PMC7822772EP/M020533/1WT106882NF-SI-0515-10000(WT203148/Z/16/Z)1931393(Health Innovation Challenge Fund (WT106882))Granados AMcEvoy AWSparks RVos SBOurselin SVakharia VMiserocchi ADuncan JSSchweiger MPerez-Garcia FfalseA generative model of hyperelastic strain energy density functions for multiple tissue brain deformation.<h4>Purpose</h4>Estimation of brain deformation is crucial during neurosurgery. Whilst mechanical characterisation captures stress-strain relationships of tissue, biomechanical models are limited by experimental conditions. This results in variability reported in the literature. The aim of this work was to demonstrate a generative model of strain energy density functions can estimate the elastic properties of tissue using observed brain deformation.<h4>Methods</h4>For the generative model a Gaussian Process regression learns elastic potentials from 73 manuscripts. We evaluate the use of neo-Hookean, Mooney-Rivlin and 1-term Ogden meta-models to guarantee stability. Single and multiple tissue experiments validate the ability of our generative model to estimate tissue properties on a synthetic brain model and in eight temporal lobe resection cases where deformation is observed between pre- and post-operative images.<h4>Results</h4>Estimated parameters on a synthetic model are close to the known reference with a root-mean-square error (RMSE) of 0.1 mm and 0.2 mm between surface nodes for single and multiple tissue experiments. In clinical cases, we were able to recover brain deformation from pre- to post-operative images reducing RMSE of differences from 1.37 to 1.08 mm on the ventricle surface and from 5.89 to 4.84 mm on the resection cavity surface.<h4>Conclusion</h4>Our generative model can capture uncertainties related to mechanical characterisation of tissue. When fitting samples from elastography and linear studies, all meta-models performed similarly. The Ogden meta-model performed the best on hyperelastic studies. We were able to predict elastic parameters in a reference model on a synthetic phantom. However, deformation observed in clinical cases is only partly explained using our generative model.2021-01-01T00:00:00Z2021 Jan2024-11-11T20:08:21.165Z2021-02-21T04:34:07ZS-EPMC78227723316570510.1007/s11548-020-02284-y