Morphing an Existing Open Source Human Body Model into a Personalized Model for Seating Discomfort Investigation

Liu, Shenghui; Beillas, Philippe; Wang, Xuguang

doi:10.4271/2020-01-0874

Cited by 9 publications

(17 citation statements)

References 22 publications

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“…Another technique for efficient generation of subject-specific models is by mesh morphing (also called warping). The concept has been used extensively in many biomechanics fields on different organs (Couteau et al 2000;Castellano-Smith et al 2001;Fernandez et al 2004;Sigal et al 2008;Bucki et al 2010;Bijar et al 2016;Park et al 2017), fullbody models (Davis et al 2016;Beillas and Berthet 2017;Liu et al 2020), as well as smooth brain models (Hu et al 2007;Ji et al 2011Ji et al , 2015Wu et al 2019), showing promising results. A typical procedure includes image registration (rigid/affine and/or followed by nonlinear registration algorithms), from which displacement field representing the geometrical difference between the subject and baseline mesh is obtained.…”

Section: Introductionmentioning

confidence: 99%

An anatomically detailed and personalizable head injury model: Significance of brain and white matter tract morphological variability on strain

Zhou

Kleiven

2020

Biomech Model Mechanobiol

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Finite element head (FE) models are important numerical tools to study head injuries and develop protection systems. The generation of anatomically accurate and subject-specific head models with conforming hexahedral meshes remains a significant challenge. The focus of this study is to present two developmental works: first, an anatomically detailed FE head model with conforming hexahedral meshes that has smooth interfaces between the brain and the cerebrospinal fluid, embedded with white matter (WM) fiber tracts; second, a morphing approach for subject-specific head model generation via a new hierarchical image registration pipeline integrating Demons and Dramms deformable registration algorithms. The performance of the head model is evaluated by comparing model predictions with experimental data of brain–skull relative motion, brain strain, and intracranial pressure. To demonstrate the applicability of the head model and the pipeline, six subject-specific head models of largely varying intracranial volume and shape are generated, incorporated with subject-specific WM fiber tracts. DICE similarity coefficients for cranial, brain mask, local brain regions, and lateral ventricles are calculated to evaluate personalization accuracy, demonstrating the efficiency of the pipeline in generating detailed subject-specific head models achieving satisfactory element quality without further mesh repairing. The six head models are then subjected to the same concussive loading to study the sensitivity of brain strain to inter-subject variability of the brain and WM fiber morphology. The simulation results show significant differences in maximum principal strain and axonal strain in local brain regions (one-way ANOVA test, p < 0.001), as well as their locations also vary among the subjects, demonstrating the need to further investigate the significance of subject-specific models. The techniques developed in this study may contribute to better evaluation of individual brain injury and the development of individualized head protection systems in the future. This study also contains general aspects the research community may find useful: on the use of experimental brain strain close to or at injury level for head model validation; the hierarchical image registration pipeline can be used to morph other head models, such as smoothed-voxel models.

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Section: Introductionmentioning

confidence: 99%

An anatomically detailed and personalizable head injury model: Significance of brain and white matter tract morphological variability on strain

Zhou

Kleiven

2020

Biomech Model Mechanobiol

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“…Mesh morphing is an approach for efficient generation of subject-specific models. It has been used extensively in many biomechanics fields on different organs [48][49][50][51][52][53][54], full-body models [55][56][57], as well as has for brain models with smoothed surfaces [18,[58][59][60]. A typical procedure includes image registration (rigid or affine and followed by nonlinear registration algorithms), from which a displacement field representing the geometrical difference between the subject and baseline mesh is obtained.…”

Section: Introductionmentioning

confidence: 99%

Head model personalization: A framework for morphing lifespan brain images and brains with substantial anatomical changes

2021

Preprint

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Finite element (FE) head models have emerged as a powerful tool in many fields within neuroscience, especially for studying the biomechanics of traumatic brain injury (TBI). Personalized head models are needed to account for geometric variations among subjects for more reliable predictions. However, the generation of subject-specific head models with conforming hexahedral elements suitable for studying the biomechanics of TBIs remains a significant challenge, which has been a bottleneck hindering personalized simulations. This study presents a framework capable of generating lifespan brain models and pathological brains with substantial anatomical changes, morphed from a previously developed baseline model. The framework combines hierarchical multiple feature and multimodality imaging registrations with mesh grouping, which is shown to be efficient with a heterogeneous dataset of seven brains, including a newborn, 1-year-old (1Y), 2Y, 6Y, adult, 92Y, and a hydrocephalus brain. The personalized models of the seven subjects show competitive registration accuracy, demonstrating the potential of the framework for generating personalized models for almost any brains with substantial anatomical changes. The family of head injury models generated in this study opens vast opportunities for studying age-dependent and groupwise brain injury mechanisms. The framework is equally applicable for personalizing head models in other fields, e.g., in tDCS, TMS, TUS, as an efficient approach for generating subject-specific head models than from scratch.

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“…Traumatic brain injury (TBI) is a leading cause of injury-related death and disability, with a devastating impact on the patients and their families (Maas et al 2017). TBI causes a substantial threat to global public health and enormous economic burden for the society, with an estimated number of 50-60 million new TBI cases occur annually (Feigin et al 2013).…”

Section: Introductionmentioning

confidence: 99%

“…TBI influences all age groups, including children, adolescents, and the elderly, which can happen due to traffic accidents, sports injuries, and falls. Studies also suggest that TBI might represent an important modifiable risk factor for epilepsy, stroke, and latelife neurodegenerative diseases such as dementia and Parkinson's disease (Maas et al 2017). Biomechanical studies, including experimental and computational studies, have long been carried out to understand brain injury mechanisms due to external mechanical input (Meaney et al 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Another technique for efficient generation of subject-specific models is by mesh morphing (also called warping). The concept has been used extensively in many biomechanics fields on different organs (Couteau et al 2000;Castellano-Smith et al 2001;Fernandez et al 2004;Sigal et al 2008;Bucki et al 2010;Bijar et al 2016;Park et al 2017), full-body models (Davis et al 2016;Beillas and Berthet 2017;Liu et al 2020), as well as smooth brain models (Hu et al 2007;Ji et al 2011;Ji et al 2015;Wu et al 2019), showing promising results. A typical procedure includes image registration (rigid/affine and/or followed by nonlinear registration algorithms), from which displacement field representing the geometrical difference between the subject and baseline mesh is obtained.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An anatomically accurate and personalizable head injury model: Significance of brain and white matter tract morphological variability on strain

Zhou

Kleiven

2020

Preprint

View full text Add to dashboard Cite

Finite element head (FE) models are important numerical tools to study head injuries and develop protection systems. The generation of anatomically accurate and subject-specific head models with conforming hexahedral meshes remains a significant challenge. The focus of this study is to present two developmental work: First, an anatomically detailed FE head model with conforming hexahedral meshes that has smooth interfaces between the brain and the cerebrospinal fluid, embedded with white matter (WM) fiber tracts; Second, a morphing approach for subject-specific head model generation via a new hierarchical image registration pipeline integrating Demons and Dramms deformable registration algorithms. The performance of the head model is evaluated by comparing model predictions with experimental data of brain-skull relative motion, brain strain, and intracranial pressure. To demonstrate the applicability of the head model and the pipeline, six subject-specific head models of largely varying intracranial volume and shape are generated, incorporated with subject-specific WM fiber tracts. DICE similarity coefficients for cranial, brain mask, local brain regions, and lateral ventricles are calculated to evaluate personalization accuracy, demonstrating the efficiency of the pipeline in generating detailed subject-specific head models achieving satisfactory element quality without further mesh repairing. The six head models are then subjected to the same concussive loading to study sensitivity of brain strain to inter-subject variability of the brain and WM fiber morphology. The simulation results show significant differences in maximum principal strain (MPS) and axonal strain (MAS) in local brain regions (one-way ANOVA test, p<0.001), as well as their locations also vary among the subjects, demonstrating the need to further investigate the significance of subject-specific models. The techniques developed in this study may contribute to better evaluation of individual brain injury and development of individualized head protection systems in the future. This study also contains general aspects the research community may find useful: on the use of experimental brain strain close to or at injury level for head model validation; the hierarchical image registration pipeline can be used to morph other head models, such as smoothed-voxel models.

show abstract

Morphing an Existing Open Source Human Body Model into a Personalized Model for Seating Discomfort Investigation

Cited by 9 publications

References 22 publications

An anatomically detailed and personalizable head injury model: Significance of brain and white matter tract morphological variability on strain

An anatomically detailed and personalizable head injury model: Significance of brain and white matter tract morphological variability on strain

Head model personalization: A framework for morphing lifespan brain images and brains with substantial anatomical changes

An anatomically accurate and personalizable head injury model: Significance of brain and white matter tract morphological variability on strain

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