Integrating material properties from magnetic resonance elastography into subject-specific computational models for the human brain.
Integrating material properties from magnetic resonance elastography into subject-specific computational models for the human brain.
复制标题
将磁共振弹性成像的材料特性集成到人脑特定学科的计算模型中。
DOI：
10.1016/j.brain.2021.100038
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发表时间：
2021
影响因子：
--
通讯作者：
Ramesh,KT
中科院分区：
文献类型：
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作者：
Alshareef,Ahmed;Knutsen,AndrewK;Johnson,CurtisL;Carass,Aaron;Upadhyay,Kshitiz;Bayly,PhilipV;Pham,DzungL;Prince,JerryL;Ramesh,KT
脑成像和计算方法的进步促进了特定于主题的计算脑模型的创建，帮助研究人员使用模拟冲击来研究脑损伤。磁共振弹性成像 (MRE) 作为一种非侵入性机械神经成像工具的出现，使得能够在低应变、谐波负载下体内估计材料特性。该领域的一个悬而未决的问题是如何将这些数据集成到计算模型中。本研究的目标是使用在人类志愿者中获取的新型 MRI 数据集来生成具有特定受试者解剖结构和材料特性的模型，然后将模拟的大脑变形与非伤害性负载下特定受试者的大脑变形数据进行比较。使用直接从 MRE 数据估计的线性粘弹性 (LVE) 材料特性对五个受试者的模型进行了模拟。将模型预测与使用标记 MRI 在同一受试者中获得的实验性脑变形进行比较。模型的结果与测量的峰值应变分量的空间分布和大小以及 0.005 mm/mm 以内的 95% 面内峰值应变和 0.012 mm/mm 以内的最大主应变相匹配。还研究了对材料异质性的敏感性。具有同质脑特性的模型和具有多达 10 个区域的离散化脑特性的模型所模拟的脑变形非常相似（峰值应变的平均绝对差小于 0.0015 mm/mm）。将来自 MRE 的材料特性直接纳入生物逼真的特定主题模型中，是未来研究高阶模型特征和在更严酷的负载条件下进行模拟的重要一步。意义声明该研究提出了一种使用先进磁共振成像 (MRI) 数据组合来校准和评估特定主题有限元脑模型的方法。成像数据是在人类志愿者身上采集的，包括解剖 MRI、磁共振弹性成像 (MRE) 和标记 MRI，以生成受试者特定的几何形状、校准受试者特定的材料属性，并使用受试者特定的大脑变形评估模拟反应。 MRE 和标记 MRI 的这个数据集可以对 MRE 的材料特性是否可用于创建人脑的生物仿真计算模型进行独特的评估。该研究开发了一种校准程序，可以根据 MRE 数据轻松计算线性粘弹性材料参数，然后提供大脑机械异质性对模拟响应影响的敏感性研究。校准后的计算模型用于模拟每个受试者的标记MRI实验；结果表明模拟应变场和实验应变场之间具有良好的一致性。所提出的研究和结果将为指导根据实验 MRE 数据校准特定于主题的计算大脑模型提供信息。处理后的 MRI、MRE 和标记的 MRI 数据可在 https://www.nitrc.org/projects/bbir/ 上公开获取。
Advances in brain imaging and computational methods have facilitated the creation of subject-specific computational brain models that aid researchers in investigating brain trauma using simulated impacts. The emergence of magnetic resonance elastography (MRE) as a non-invasive mechanical neuroimaging tool has enabled in vivo estimation of material properties at low-strain, harmonic loading. An open question in the field has been how this data can be integrated into computational models. The goals of this study were to use a novel MRI dataset acquired in human volunteers to generate models with subject-specific anatomy and material properties, and then to compare simulated brain deformations to subject-specific brain deformation data under non-injurious loading. Models of five subjects were simulated with linear viscoelastic (LVE) material properties estimated directly from MRE data. Model predictions were compared to experimental brain deformation acquired in the same subjects using tagged MRI. Outcomes from the models matched the spatial distribution and magnitude of the measured peak strain components as well as the 95thpercentile in-plane peak strains within 0.005 mm/mm and maximum principal strain within 0.012 mm/mm. Sensitivity to material heterogeneity was also investigated. Simulated brain deformations from a model with homogenous brain properties and a model with brain properties discretized with up to ten regions were very similar (a mean absolute difference less than 0.0015 mm/mm in peak strains). Incorporating material properties directly from MRE into a biofidelic subject-specific model is an important step toward future investigations of higher-order model features and simulations under more severe loading conditions.Statement of SignificanceThe study presents a method to calibrate and evaluate subject-specific finite element brain models using a combination of advanced magnetic resonance imaging (MRI) data. The imaging data is acquired in human volunteers and includes anatomical MRI, magnetic resonance elastography (MRE), and tagged MRI to generate subject-specific geometry, calibrate subject-specific material properties, and evaluate simulation response using subject-specific brain deformation. This dataset of MRE and tagged MRI allows for a unique evaluation of whether material properties from MRE can be used to create biofidelic computational models of the human brain. The study develops a calibration procedure to readily calculate linear viscoelastic material parameters from MRE data and then provides a sensitivity study of the effect of mechanical heterogeneity of the brain on simulation response. The calibrated computational models are used to simulate each subject's tagged MRI experiment; the results show good agreement between the simulated and experimental strain fields. The presented study and results will be informative in guiding the calibration of subject-specific computational brain model from experimental MRE data. The processed MRI, MRE, and tagged MRI data are publicly available at https://www.nitrc.org/projects/bbir/.