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Intensity Robust Viscous Fluid Deformation Based Morphometry Using Regionally Adapted Mutual Information

机译:基于区域适应性互信息的基于强度鲁棒粘性流体变形的形态学

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This paper describes an approach to fine scale non-rigid registration for mapping patterns of tissue volume loss in serial MRI studies of the brain. Specifically it addresses the important confound of diffuse tissue contrast changes which can influence local sub-voxel estimates of volume change. Such changes can be induced by neurodegenerative or neurodevelopmental processes, which not only modify apparent tissue volume, but also modify tissue integrity and its resulting MRI contrast parameters. We derive an approach to the voxel-wise maximization of regional mutual information (RMI) and use this to drive a viscous fluid deformation model between images. This provides a topology preserving map of local changes in volume between time points that is robust to regional changes in tissue contrast. Comparisons with current methodology are included showing that the approach provides a significant reduction in errors when tissue contrast varies locally between MRI acquisitions.
机译:本文描述了一种精细的非刚性配准方法,用于在脑部MRI研究中映射组织体积损失的模式。具体来说,它解决了弥漫性组织对比度变化的重要混淆,后者可能影响局部亚体素对体积变化的估计。这种变化可以由神经变性或神经发育过程引起,其不仅改变表观组织体积,而且改变组织完整性及其产生的MRI对比参数。我们推导了区域互信息(RMI)的体素最大化的方法,并使用它来驱动图像之间的粘性流体变形模型。这提供了在时间点之间的局部体积变化的拓扑保留图,该拓扑图对于组织对比度的区域变化是鲁棒的。包括与当前方法的比较,表明当MRI采集之间组织对比度局部变化时,该方法可显着减少错误。

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