1. 基本知识

WMH的全程：White matter hyperintensity，白质高信号，是Leukoaraiosis（脑白质疏松）的影像体现
White matter hyperintensities (WMH) have been linked to cognitive dysfunction and dementia, although the reasons are unclear. One possibility is that WMH promote neurodegeneration, which, in turn, affects cognition.

图像特征

T2WI((typically created using 3D FLAIR)中发亮的高信号

高信号出现在脑白质内，就叫WMH
高信号出现在subcortical gray matter内，就叫GMH

Leukoaraiosis
WMH

出现部位和分类

Hyperintensities are commonly divided into 3 types depending on the region of the brain where they are found.

Deep white matter hyperintensites occur deep within white matter
Periventricular white matter hyperintensities occur adjacent to the lateral ventricles
Subcortical hyperintensities occur in the basal ganglia

生理意义

白质高信号就是病变，是脑血管病（cerebrovascular disease, CVD）的一个指标。

WMH和抑郁症、双相障碍和痴呆都有关。

2. WMH的计算

定性描述

借助一些量表对WMH分类描述，比如Fazekas scale

Fazekas scale is a four-level one, where the range of hyperintense white matter changes in the paraventricular and subcortical areas of the brain varies from 0 to 3, where 0 means no white matter lesions, 1 – single changes, 2 – numerous changes, 3 – confluent areas of WMH.

定量计算

严谨高分的文章通常使用的是WMH volume而不是一个宽泛的量表分。如果有MRI图像，可以借助现有工具计算出WMH体积(WMH volume，WMHV)。

已知可以算WMHV的工具有

FSL BIANCA (常用)
SPM - LST (简单)
DSI Studio
Analyze software

下面是工具的不完全使用教程

LST

Lesion segmentation toolbox是matlab的SPM里面一个自动化的工具包, 需要T1和T2 FLAIR图像

你的电脑要安装matlab和SPM
LST官网
 LST Documentation
使用参考这里

BIANCA

BIANCA是Brain Intensity AbNormality Classification Algorithm的缩写

BIANCA is a fully automated, supervised method for white matter hyperintensities (WMH) detection, based on the k-nearest neighbour (k-NN) algorithm. BIANCA classifies the image’s voxels based on their intensity and spatial features, and the output image represents the probability per voxel of being WMH. BIANCA is very flexible in terms of MRI modalities to use and offers different options for weighting the spatial information, local spatial intensity averaging, and different options for the choice of the number and location of the training points.

具体的使用我放在这篇帖子
)里面