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Patient-tailored multimodal neuroimaging, visualization and quantification of human intra-cerebral hemorrhage

机译:患者量身定制的多模式神经影像,人类内出血的可视化和定量

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In traumatic brain injury (TBI) and intracerebral hemorrhage (ICH), the heterogeneity of lesion sizes and types necessitates a variety of imaging modalities to acquire a comprehensive perspective on injury extent. Although it is advantageous to combine imaging modalities and to leverage their complementary benefits, there are difficulties in integrating information across imaging types. Thus, it is important that efforts be dedicated to the creation and sustained refinement of resources for multimodal data integration. Here, we propose a novel approach to the integration of neuroimaging data acquired from human patients with TBI/ICH using various modalities; we also demonstrate the integrated use of multimodal magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) data for TBI analysis based on both visual observations and quantitative metrics. 3D models of healthy-appearing tissues and TBI-related pathology are generated, both of which are derived from multimodal imaging data. MRI volumes acquired using FLAIR, SWI, and T_2 GRE are used to segment pathology. Healthy tissues are segmented using user-supervised tools, and results are visualized using a novel graphical approach called a 'connectogram', where brain connectivity information is depicted within a circle of radially aligned elements. Inter-region connectivity and its strength are represented by links of variable opacities drawn between regions, where opacity reflects the percentage longitudinal change in brain connectivity density. Our method for integrating, analyzing and visualizing structural brain changes due to TBI and ICH can promote knowledge extraction and enhance the understanding of mechanisms underlying recovery.
机译:在创伤性脑损伤(TBI)和脑出血(ICH)中,病变尺寸和类型的异质性需要各种成像方式,以获得伤害程度的全面的视角。尽管将成像方式结合并利用它们的互补益处是有利的,但是遇到跨成像类型的信息存在困难。因此,重要的是,努力致力于为多模式数据集成的资源创建和持续细化。在这里,我们提出了一种新的方法,可以使用各种方式与人类TBI / ICH获取的神经影像动物数据的整合;我们还证明了基于视觉观测和定量度量的多峰磁共振成像(MRI)和扩散张量成像(DTI)数据的综合使用TBI分析。产生健康出现的组织和与TBI相关病理学的3D模型,两者都来自多式化成像数据。使用Flair,SWI和T_2 GRE获得的MRI体积用于分段病理学。使用用户监督的工具分割健康组织,并且使用称为“旋转图”的新颖的图形方法来对结果进行可视化,其中脑连接信息被描绘在径向对准元件的圆圈内。区域间连接及其强度由区域之间绘制的可变不透明度的链路表示,其中不透明度反映了脑连接密度的纵向变化百分比。我们为TBI和ICH引起的整合,分析和可视化结构脑变化的方法可以促进知识提取,并增强对恢复机制的理解。

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