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Construction of a neuroanatomical shape complex atlas from 3D MRI brain structures

机译:从3D MRI脑结构构建神经解剖形状复杂图谱

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Brain atlas construction has attracted significant attention lately in the neuroimaging community due to its application to the characterization of neuroanatomical shape abnormalities associated with various neurodegenerative diseases or neuropsychiatric disorders. Existing shape atlas construction techniques usually focus on the analysis of a single anatomical structure in which the important inter-structural information is lost. This paper proposes a novel technique for constructing a neuroanatomical shape complex atlas based on an information geometry framework. A shape complex is a collection of neighboring shapes - for example, the thalamus, amygdala and the hippocampus circuit - which may exhibit changes in shape across multiple structures during the progression of a disease. In this paper, we represent the boundaries of the entire shape complex using the zero level set of a distance transform function S(x). We then re-derive the relationship between the stationary state wave function ψ(x) of the Schr?dinger equation -{Planck constant} 2?; 2ψ+ψ=0 and the eikonal equation ||?;S||=1 satisfied by any distance function. This leads to a one-to-one map (up to scale) between ψ(x) and S(x) via an explicit relationship. We further exploit this relationship by mapping ψ(x) to a unit hypersphere whose Riemannian structure is fully known, thus effectively turn ψ(x) into the square-root of a probability density function. This allows us to make comparisons - using elegant, closed-form analytic expressions - between shape complexes represented as square-root densities. A shape complex atlas is constructed by computing the Karcher mean ψ-(x) in the space of square-root densities and then inversely mapping it back to the space of distance transforms in order to realize the atlas shape. We demonstrate the shape complex atlas computation technique via a set of experiments on a population of brain MRI scans including controls and epilepsy patients with either right anterior medial temporal or left anterior medial temporal lobectomies.
机译:由于其在表征与各种神经退行性疾病或神经精神疾病相关的神经解剖形状异常中的应用,最近在神经影像学界引起了人们的关注。现有的形状图谱构建技术通常集中于单个解剖结构的分析,在该解剖结构中重要的结构间信息丢失了。本文提出了一种基于信息几何框架构建神经解剖形状复杂图谱的新技术。形状复合体是相邻形状的集合,例如丘脑,杏仁核和海马回路,它们在疾病发展过程中可能在多个结构上显示出形状变化。在本文中,我们使用距离变换函数S(x)的零级集表示整个形状复杂的边界。然后,我们重新推导薛定er方程-{Planck constant} 2?的稳态波函数ψ(x)之间的关系; 2ψ+ψ= 0且任何距离函数都满足本征方程||?; S || = 1。这通过显式关系得出了ψ(x)和S(x)之间的一对一映射(按比例绘制)。我们通过将ψ(x)映射到完全已知其黎曼结构的单位超球体来进一步利用这种关系,从而有效地将ψ(x)转换为概率密度函数的平方根。这使我们能够使用优雅的闭式分析表达式在以平方根密度表示的形状复合体之间进行比较。通过计算平方根密度空间中的Karcher均值ψ-(x),然后将其反向映射回距离变换空间,以构造图集形状,从而构造形状复杂的图集。我们通过一组大脑MRI扫描实验(包括对照组和患有右前内侧颞叶或左前内侧颞叶切除术的癫痫患者)的一组实验证明了形状复杂的图集计算技术。

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