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大脑的纹理分析:从动物模型到人类应用

Texture analysis of the brain: from animal models to human applications.

作者信息

Nedelec Jean-François J, Yu Olivier, Chambron Jacques, Macher Jean-Paul

机构信息

FORENAP, Rouffach, France.

出版信息

Dialogues Clin Neurosci. 2004 Jun;6(2):227-33.

PMID:22034451
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3181803/
Abstract

Magnetic resonance imaging (MRI) is widely used to image brain in vivo both in studies in animal models and for human diagnosis. A large part of the value of MRI is due to the fact that soft tissue contrast is enhanced by the substantial variation in the T(1) and T(2) relaxation times between tissues. It may be possible to use an alternative approach, which does not rely on the absolute measurement of relaxation times. Generally speaking, textures are complex visual patterns composed of entities, or subpatterns, that have characteristic brightness, color, slope, size, etc. Thus, texture can be regarded as a similarity grouping in an image. The properties of the local subpattern give rise to the perceived lightness, uniformity, density, roughness, regularity, linearity, frequency, phase, directionality, coarseness, randomness, fineness, smoothness, and granulation. The purpose here is to illustrate how texture analysis can be used in animal models and in human clinical applications, as well as in the search for further pharmacological applications in humans. Thus, this article summarzes three different MRI studies in (i) rats, using the lipocarpine epileptic rat model as an animal model; (ii) patients with Alzheimer's disease; and (iii) patients with schizophrenia.

摘要

磁共振成像(MRI)在动物模型研究和人类诊断中都被广泛用于活体脑成像。MRI的很大一部分价值在于,组织间T(1)和T(2)弛豫时间的显著差异增强了软组织对比度。有可能使用一种不依赖于弛豫时间绝对测量的替代方法。一般来说,纹理是由具有特征亮度、颜色、斜率、大小等的实体或子模式组成的复杂视觉模式。因此,纹理可被视为图像中的相似性分组。局部子模式的属性产生了感知到的亮度、均匀性、密度、粗糙度、规则性、线性、频率、相位、方向性、粗糙度、随机性、精细度、平滑度和颗粒度。本文的目的是说明纹理分析如何用于动物模型和人类临床应用,以及在寻找人类进一步的药理学应用中如何使用。因此,本文总结了三项不同的MRI研究:(i)以脂卡品癫痫大鼠模型作为动物模型的大鼠研究;(ii)阿尔茨海默病患者的研究;以及(iii)精神分裂症患者的研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6589/3181803/e397e065d626/DialoguesClinNeurosci-6-227-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6589/3181803/238826e7701d/DialoguesClinNeurosci-6-227-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6589/3181803/647aa016bbab/DialoguesClinNeurosci-6-227-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6589/3181803/452eafbf5207/DialoguesClinNeurosci-6-227-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6589/3181803/e397e065d626/DialoguesClinNeurosci-6-227-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6589/3181803/238826e7701d/DialoguesClinNeurosci-6-227-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6589/3181803/647aa016bbab/DialoguesClinNeurosci-6-227-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6589/3181803/452eafbf5207/DialoguesClinNeurosci-6-227-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6589/3181803/e397e065d626/DialoguesClinNeurosci-6-227-g004.jpg

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