Analysis of normal-appearing white matter of multiple sclerosis by tensor-based two-compartment model of water diffusion

利用基于张量的双室水扩散模型分析多发性硬化症中外观正常的白质

阅读:1

Abstract

OBJECTIVES: To compare the significance of the two-compartment model, considering diffusional anisotropy with conventional diffusion analyzing methods regarding the detection of occult changes in normal-appearing white matter (NAWM) of multiple sclerosis (MS). METHODS: Diffusion-weighted images (nine b-values with six directions) were acquired from 12 healthy female volunteers (22-52 years old, median 33 years) and 13 female MS patients (24-48 years old, median 37 years). Diffusion parameters based on the two-compartment model of water diffusion considering diffusional anisotropy was calculated by a proposed method. Other parameters including diffusion tensor imaging and conventional apparent diffusion coefficient (ADC) were also obtained. They were compared statistically between the control and MS groups. RESULTS: Diffusion of the slow diffusion compartment in the radial direction of neuron fibers was elevated in MS patients (0.121 × 10(-3) mm2/s) in comparison to control (0.100 × 10(-3) mm(2)/s), the difference being significant (P = 0.001). The difference between the groups was not significant in other comparisons, including conventional ADC and fractional anisotropy (FA) of diffusion tensor imaging. CONCLUSION: The proposed method was applicable to clinically acceptable small data. The parameters obtained by this method improved the detectability of occult changes in NAWM compared to the conventional methods. KEY POINTS: • Water diffusion was compared between the controls and multiple sclerosis patients. • A two-compartment model, considering diffusional anisotropy was selected for water diffusion analysis. • Axial and radial diffusion of fast and slow diffusion components were evaluated. • A new method was developed to obtain the metrics stably. • The metrics indicated high detectability of slight differences between the groups.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。