Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
Med Image Anal. 2010 Oct;14(5):666-73. doi: 10.1016/j.media.2010.05.002. Epub 2010 May 26.
Diffusion tensor imaging plays a key role in our understanding of white matter both in normal populations and in populations with brain disorders. Existing techniques focus primarily on using diffusivity-based quantities derived from diffusion tensor as surrogate measures of microstructural tissue properties of white matter. In this paper, we describe a novel tract-specific framework that enables the examination of white matter morphometry at both the macroscopic and microscopic scales. The framework leverages the skeleton-based modeling of sheet-like white matter fasciculi using the continuous medial representation, which gives a natural definition of thickness and supports its comparison across subjects. The thickness measure provides a macroscopic characterization of white matter fasciculi that complements existing analysis of microstructural features. The utility of the framework is demonstrated in quantifying white matter atrophy in Amyotrophic Lateral Sclerosis, a severe neurodegenerative disease of motor neurons. We show that, compared to using microscopic features alone, combining the macroscopic and microscopic features gives a more complete characterization of the disease.
扩散张量成像在我们理解正常人群和脑疾病人群的白质方面起着关键作用。现有的技术主要侧重于使用从扩散张量中得出的基于扩散率的量作为白质微观结构组织特性的替代测量。在本文中,我们描述了一种新的束流特定框架,使我们能够在宏观和微观尺度上检查白质形态计量学。该框架利用基于骨架的连续中轴表示对片状白质束进行建模,这为厚度提供了自然的定义,并支持在不同对象之间进行比较。厚度测量为白质束提供了宏观特征,补充了现有对微观结构特征的分析。该框架的实用性在量化肌萎缩侧索硬化症(一种严重的运动神经元神经退行性疾病)的白质萎缩方面得到了证明。我们表明,与单独使用微观特征相比,将宏观和微观特征相结合可以更完整地描述该疾病。