Simulation of MRI cluster plots and application to neurological segmentation

Simulation of MRI cluster plots and application to neurological segmentation
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DOI:
10.1016/0730-725x(95)02040-z
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发表时间:
1996-01-01
影响因子:
2.5
通讯作者:
Williams, SCR
Williams, SCR
中科院分区:
医学4区
文献类型:
--
作者:
Simmons, A;Arridge, SR;Williams, SCR

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磁共振成像的出现为组织的体积测量提供了新的机会,近年来应用急剧增加。聚类分类技术已被证明是最受欢迎的体积测量,但很少有人注意到如何选择的图像分析影响的质量和容易分割。为了解决这个问题,我们已经开发了一个系统来模拟MRI集群图使用多房室拟人软件模型的解剖结构,和组件的图像对比度,信噪比,图像不均匀性,组织异质性,成像场强,部分体积效应,质子密度之间的相关性,T-1和T-2,以及各种数据预处理技术。证明了这些组分对组织簇大小、形状、方向和分离的影响。模拟允许的脉冲序列,采集参数和数据预处理的聚类分类要作出明智的选择,以及提供一个援助,以解释所获得的数据聚类图和一个有价值的教育工具。该系统已被用于选择合适的图像,以使用自旋回波、反转恢复和梯度回波脉冲序列对灰质、白色物质、CSF和多发性硬化病变进行神经学分割。图像选择的约束进行了讨论。
The advent of magnetic resonance imaging has provided new opportunities for volume measurement of tissues, with applications increasing dramatically in recent years. Cluster classification techniques have proved the most popular for volume measurement, yet little attention has been paid to how the choice of images for analysis affects the quality and ease of segmentation. To address this issue, we have developed a system to simulate MRI cluster plots using multicompartmental anthropomorphic software models of anatomy, and components for image contrast, signal-to-noise ratio, image nonuniformity, tissue heterogeneity, imager field strength, the partial volume effect, correlation between proton density, T-1 and T-2, and a variety of data preprocessing techniques. The effect of these components on tissue cluster size, shape, orientation, and separation is demonstrated. The simulation allows an informed choice of pulse sequence, acquisition parameters, and data preprocessing for cluster classification to be made as well as providing an aid to interpretation of acquired data cluster plots and a valuable educational tool. The system has been used to choose suitable images for neurological segmentation of grey matter, white matter, CSF, and multiple sclerosis lesions using spin-echo, inversion recovery, and gradient-echo pulse sequences. Constraints on image selection are discussed.