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计算机辅助成像用于评估健康和患病大脑的脑结构。

Computer-assisted imaging to assess brain structure in healthy and diseased brains.

作者信息

Ashburner John, Csernansky John G, Davatzikos Christos, Fox Nick C, Frisoni Giovanni B, Thompson Paul M

机构信息

The Wellcome Department of Imaging Neuroscience, Institute of Neurology, London, UK.

出版信息

Lancet Neurol. 2003 Feb;2(2):79-88. doi: 10.1016/s1474-4422(03)00304-1.

Abstract

Neuroanatomical structures may be profoundly or subtly affected by the interplay of genetic and environmental factors, age, and disease. Such effects are particularly true in healthy ageing individuals and in those who have neurodegenerative diseases. The ability to use imaging to identify structural brain changes associated with different neurodegenerative disease states would be useful for diagnosis and treatment. However, early in the progression of such diseases, neuroanatomical changes may be too mild, diffuse, or topologically complex to be detected by simple visual inspection or manually traced measurements of regions of interest. Computerised methods are being developed that can capture the extraordinary morphological variability of the human brain. These methods use mathematical models sensitive to subtle changes in the size, position, shape, and tissue characteristics of brain structures affected by neurodegenerative diseases. Neuroanatomical features can be compared within and between groups of individuals, taking into account age, sex, genetic background, and disease state, to assess the structural basis of normality and disease. In this review, we describe the strengths and limitations of algorithms of existing computer-assisted tools at the most advanced stage of development, together with available and foreseeable evidence of their usefulness at the clinical and research level.

摘要

神经解剖结构可能会受到遗传和环境因素、年龄及疾病之间相互作用的深刻或微妙影响。这种影响在健康的老龄个体以及患有神经退行性疾病的个体中尤为明显。利用成像技术识别与不同神经退行性疾病状态相关的脑结构变化的能力,将有助于疾病的诊断和治疗。然而,在这些疾病进展的早期,神经解剖学变化可能过于轻微、弥散或拓扑结构复杂,以至于无法通过简单的目视检查或手动追踪感兴趣区域的测量来检测。正在开发的计算机化方法能够捕捉人类大脑非凡的形态变异性。这些方法使用对受神经退行性疾病影响的脑结构的大小、位置、形状和组织特征的细微变化敏感的数学模型。可以在个体组内和个体组之间比较神经解剖特征,同时考虑年龄、性别、遗传背景和疾病状态,以评估正常和疾病的结构基础。在这篇综述中,我们描述了处于最先进发展阶段的现有计算机辅助工具算法的优势和局限性,以及它们在临床和研究层面有用性的现有和可预见证据。

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