An Image Registration-Based Morphing Technique for Generating Subject-Specific Brain Finite Element Models

Giudice, J. Sebastian; Alshareef, Ahmed; Wu, Taotao; Gancayco, Christina; Reynier, Kristen A.; Tustison, Nicholas J.; Druzgal, T. Jason; Panzer, Matthew B.

doi:10.1007/s10439-020-02584-z

Cited by 32 publications

(20 citation statements)

References 61 publications

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“…The mesh of the CAB-20MSym template model was chosen to represent the anatomy of the CAB-20MSym template image developed by Giudice et al (2020) . This template was constructed from T1-weighted MRI scans obtained from 20 young, healthy adult males (22 ± 3 years).…”

Section: Methodsmentioning

confidence: 99%

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Calibration of a Heterogeneous Brain Model Using a Subject-Specific Inverse Finite Element Approach

Giudice

Alshareef

et al. 2021

Front. Bioeng. Biotechnol.

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Central to the investigation of the biomechanics of traumatic brain injury (TBI) and the assessment of injury risk from head impact are finite element (FE) models of the human brain. However, many existing FE human brain models have been developed with simplified representations of the parenchyma, which may limit their applicability as an injury prediction tool. Recent advances in neuroimaging techniques and brain biomechanics provide new and necessary experimental data that can improve the biofidelity of FE brain models. In this study, the CAB-20MSym template model was developed, calibrated, and extensively verified. To implement material heterogeneity, a magnetic resonance elastography (MRE) template image was leveraged to define the relative stiffness gradient of the brain model. A multi-stage inverse FE (iFE) approach was used to calibrate the material parameters that defined the underlying non-linear deviatoric response by minimizing the error between model-predicted brain displacements and experimental displacement data. This process involved calibrating the infinitesimal shear modulus of the material using low-severity, low-deformation impact cases and the material non-linearity using high-severity, high-deformation cases from a dataset of in situ brain displacements obtained from cadaveric specimens. To minimize the geometric discrepancy between the FE models used in the iFE calibration and the cadaveric specimens from which the experimental data were obtained, subject-specific models of these cadaveric brain specimens were developed and used in the calibration process. Finally, the calibrated material parameters were extensively verified using independent brain displacement data from 33 rotational head impacts, spanning multiple loading directions (sagittal, coronal, axial), magnitudes (20–40 rad/s), durations (30–60 ms), and severity. Overall, the heterogeneous CAB-20MSym template model demonstrated good biofidelity with a mean overall CORA score of 0.63 ± 0.06 when compared to in situ brain displacement data. Strains predicted by the calibrated model under non-injurious rotational impacts in human volunteers (N = 6) also demonstrated similar biofidelity compared to in vivo measurements obtained from tagged magnetic resonance imaging studies. In addition to serving as an anatomically accurate model for further investigations of TBI biomechanics, the MRE-based framework for implementing material heterogeneity could serve as a foundation for incorporating subject-specific material properties in future models.

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Section: Methodsmentioning

confidence: 99%

“…This template was constructed from T1-weighted MRI scans obtained from 20 young, healthy adult males (22 ± 3 years). Details regarding these images are provided elsewhere ( Giudice et al, 2020 ; Reynier et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Calibration of a Heterogeneous Brain Model Using a Subject-Specific Inverse Finite Element Approach

Giudice

Alshareef

et al. 2021

Front. Bioeng. Biotechnol.

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“…Future studies can employ infant Freesurfer [77] for more accurate segmentation for infant brain images, thus allows more objective evaluation of personalization accuracy. For adult brain mesh morphing, a recent study presents an image registration-based approach showing promising for morphing smooth-voxel healthy adult brain FE models, using affine and deformable registration algorithms implemented in Advanced Normalization Tools (ANTs) [61]. However, the personalization accuracy hasn't been quantified.…”

Section: Discussionmentioning

confidence: 99%

“…In particular, deformable image registration-based mesh morphing has been shown promising to personalize healthy brain models due to its capacity to capture the anatomical difference between the baseline and the subject image [35,61]. However, despite intensive efforts for decades, deformable image registration for subjects with substantial anatomical changes is still challenging and with limited registration performance [62].…”

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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“…In recent years, a large number of non-rigid registration models for medical images has been reported, such as the free form deformation (FFD) models based on B-spline [9][10][11], finite elements model (FEM) [12][13][14][15], viscous fluid model [16,17], and Demons [4,5,[18][19][20][21][22] etc. Since FFD based on B-spline employs the cubic B-spline to model the elastic deformation and each B-spline curve is only related to four adjacent control points, it can provide a high degree of flexibility for estimating the local motions of human tissues and organs.…”

Section: Related Workmentioning

confidence: 99%

HNSF Log‐Demons: Diffeomorphic demons registration using hierarchical neighbourhood spectral features

Lei

et al. 2021

IET Image Processing

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Many biomedical applications require accurate non-rigid image registration that can cope with complex deformations. However, popular diffeomorphic Demons registration algorithms suffer from difficulties for complex and serious distortions since they only use image greyscale and gradient information. To address these difficulties, a new diffeomorphic Demons registration algorithm is proposed using hierarchical neighbourhood spectral features namely HNSF Log-Demons in this paper. In view of three important properties of hierarchical neighbourhood spectral features based on line graph such as rotation invariance, invariance of linear changes of brightness, and robustness to noise, the hierarchical neighbourhood spectral features of a reference image and a moving image is first extracted and these novel spectral features are incorporated into the energy function of the diffeomorphic registration framework to improve the capability of capturing complex distortions. Secondly, the Nyström approximation based on random singular value decomposition is employed to effectively enhance the computational efficiency of HNSF Log-Demons. Finally, the hybrid multi-resolution strategy based on wavelet decomposition in the registration process is utilised to further improve the registration accuracy and efficiency. Experimental results show that the proposed HNSF Log-Demons not only effectively ensures the generation of smooth and reversible deformation field, but also achieves better performance than state-of-the-art algorithms.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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An Image Registration-Based Morphing Technique for Generating Subject-Specific Brain Finite Element Models

Cited by 32 publications

References 61 publications

Calibration of a Heterogeneous Brain Model Using a Subject-Specific Inverse Finite Element Approach

Calibration of a Heterogeneous Brain Model Using a Subject-Specific Inverse Finite Element Approach

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

HNSF Log‐Demons: Diffeomorphic demons registration using hierarchical neighbourhood spectral features

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