Simmons A, Arridge S R, Barker G J, Williams S C
Department of Neurology, Institute of Psychiatry, De Crespigny Park, London, UK.
Magn Reson Imaging. 1996;14(1):73-92. doi: 10.1016/0730-725x(95)02040-z.
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, T1 and T2, 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.
磁共振成像的出现为组织体积测量提供了新的机遇，近年来其应用急剧增加。聚类分类技术已被证明是体积测量中最受欢迎的方法，但对于分析图像的选择如何影响分割的质量和难易程度却很少有人关注。为了解决这个问题，我们开发了一个系统，使用多房室人体解剖软件模型以及图像对比度、信噪比、图像不均匀性、组织异质性、成像场强、部分容积效应、质子密度、T1和T2之间的相关性等组件，以及各种数据预处理技术来模拟MRI聚类图。展示了这些组件对组织聚类大小、形状、方向和分离的影响。该模拟允许为聚类分类明智地选择脉冲序列、采集参数和数据预处理，同时有助于解释采集到的数据聚类图，并成为一个有价值的教育工具。该系统已被用于使用自旋回波、反转恢复和梯度回波脉冲序列为灰质、白质、脑脊液和多发性硬化症病变的神经分割选择合适的图像。讨论了图像选择的限制因素。
