MRI volumetric analysis of multiple sclerosis: Methodology and validation
MRI volumetric analysis of multiple sclerosis: Methodology and validation
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多发性硬化症的 MRI 体积分析：方法和验证
DOI：
10.1109/tns.2003.817334
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发表时间：
2003-10-01
影响因子：
1.8
通讯作者：
Liang, ZR
中科院分区：
文献类型：
--
作者：
Li, LH;Li, X;Liang, ZR
我们提出了一个自动混合为基础的算法分割脑组织（白色和灰色物质-WM和GM），脑脊液（CSF），和脑病变定量分析多发性硬化症。该方法使用基于随机模型的多谱磁共振（MR）图像执行基于强度的组织分类。由于采集的MR图像中存在白色高斯噪声和空间不变的模糊，采用Karhunen-Loeve（K-L）域Wiener滤波器对模糊和噪声图像进行精确降噪和分辨率恢复，以最大限度地减少部分体积效应（PVE），这是定量分析的主要限制因素。随后，我们利用马尔可夫随机场吉布斯模型将局部空间信息整合到成熟的期望最大化模型拟合算法中。然后通过最大后验（MAP）标准对每个体素进行分类，该标准指示其属于每个类别的概率，即，每个体素被标记为具有不同组织百分比的混合元素，从而进一步最小化PVE。从基于混合的分割中提取WM、GM、CSF和脑病变的体积，并计算相应的脑萎缩。在这项研究中，我们已经调查了包括噪声分析和点扩散函数的图像分辨率增强算法的准确性和可重复性。幻影，健康志愿者和病人的研究实验结果。
We present an automatic mixture-based algorithm for segmentation of brain tissues (white and gray matters-WM and GM), cerebral spinal fluid (CSF), and brain lesions to quantitatively analyze multiple sclerosis. The method performs intensity-based tissue classification using multispectral magnetic resonance (MR) images based on a stochastic model. With the existence of white Gaussian noise and spatially invariant blurring in acquired MR images, a Karhunen-Loeve (K-L) domain Wiener filter is applied for accurate noise reduction and resolution restoration on blurred and noisy images to minimize the partial volume effect (PVE), which is a major limiting factor for the quantitative analysis. Following that, we utilize a Markov random field Gibbs model to integrate the local spatial information into the well-established expectation-maximization model-fitting algorithm. Each voxel is then classified by a maximum a posterior (MAP) criterion, indicating its probabilities of belonging to each class, i.e., each voxel is labeled as a mixel with different tissue percentages, leading to further minimization of the PVE. The volumes of WM, GM, CSF, and brain lesions are extracted from the mixture-based segmentation and the corresponding brain atrophies are computed. In this study, we have investigated the accuracy and repeatability of the algorithm with inclusion of noise analysis and point spread function for image resolution enhancement. Experimental results on phantom, healthy volunteer, and patient studies are presented.