Miller M I, Christensen G E, Amit Y, Grenander U
Department of Electrical Engineering, Washington University, St. Louis, MO 63130.
Proc Natl Acad Sci U S A. 1993 Dec 15;90(24):11944-8. doi: 10.1073/pnas.90.24.11944.
Mathematical techniques are presented for the transformation of digital anatomical textbooks from the ideal to the individual, allowing for the representation of the variabilities manifest in normal human anatomies. The ideal textbook is constructed on a fixed coordinate system to contain all of the information currently available about the physical properties of neuroanatomies. This information is obtained via sensor probes such as magnetic resonance, as well as computed axial and emission tomography, along with symbolic information such as white- and gray-matter tracts, nuclei, etc. Human variability associated with individuals is accommodated by defining probabilistic transformations on the textbook coordinate system, the transformations forming mathematical translation groups of high dimension. The ideal is applied to the individual patient by finding the transformation which is consistent with physical properties of deformable elastic solids and which brings the coordinate system of the textbook to that of the patient. Registration, segmentation, and fusion all result automatically because the textbook carries symbolic values as well as multisensor features.
本文介绍了将数字解剖学教科书从理想化转变为个体化的数学技术，从而能够呈现正常人体解剖结构中表现出的变异性。理想化教科书构建于固定坐标系之上，用以包含目前所有关于神经解剖学物理特性的可用信息。这些信息通过诸如磁共振、计算机轴向断层扫描和发射断层扫描等传感探头获取，同时还包括诸如白质和灰质束、神经核等符号信息。通过在教科书坐标系上定义概率变换来适应个体相关的人类变异性，这些变换形成高维数学平移群。通过找到与可变形弹性固体物理特性一致且能将教科书坐标系转换为患者坐标系的变换，将理想化应用于个体患者。由于教科书既包含符号值又包含多传感器特征，配准、分割和融合都能自动实现。
