Studholme Colin
Department of Radiology, University of California San Francisco, Northern California Institute for Research and Education, VAMC San Francisco, San Francisco, USA.
Inf Process Med Imaging. 2007;20:223-32. doi: 10.1007/978-3-540-73273-0_19.
Deformation tensor morphometry provides a sensitive approach to detecting and mapping subtle volume changes in the brain from conventional high resolution T1W MRI data. However, it is limited in its ability to localize volume changes within sub-regions of uniform white matter in T1W MRI. In contrast, lower resolution DTI data provides valuable complementary microstructural information within white matter. An approach to incorporating information from DTI data into deformation tensor morphometry of conventional high resolution T1W imaging is described. A novel mutual information (MI) derived criteria is proposed, termed diffusion paired MI, using an approximation to collective many-channel MI between all images. This approximation avoids the evaluation of high dimensional joint probability distributions, but allows a combination of conventional and diffusion data in a single registration criteria. The local gradient of this measure is used to drive a viscous fluid registration between repeated DTI-MRI imaging studies. Results on example data from clinical studies of Alzheimer's disease illustrate the improved localization of tissue loss patterns within regions of white matter.
变形张量形态测量学提供了一种灵敏的方法，可从传统高分辨率T1加权磁共振成像（MRI）数据中检测和绘制大脑中细微的体积变化。然而，它在定位T1加权MRI中均匀白质亚区域内体积变化的能力方面存在局限。相比之下，低分辨率扩散张量成像（DTI）数据在白质内提供了有价值的补充微观结构信息。本文描述了一种将DTI数据信息纳入传统高分辨率T1加权成像的变形张量形态测量学的方法。提出了一种新的基于互信息（MI）的准则，称为扩散配对MI，它使用了所有图像之间集体多通道MI的近似值。这种近似避免了对高维联合概率分布的评估，但允许在单一配准准则中结合传统数据和扩散数据。该测量的局部梯度用于驱动重复DTI-MRI成像研究之间的粘性流体配准。来自阿尔茨海默病临床研究的示例数据结果表明，白质区域内组织损失模式的定位得到了改善。
