Marai G Elisabeta, Grimm Cindy M, Laidlaw David H
Computer Science Department, Brown University, Providence, RI 02912, USA.
IEEE Trans Vis Comput Graph. 2007 Sep-Oct;13(5):1095-104. doi: 10.1109/TVCG.2007.1063.
Abstract-Orthopedists invest significant amounts of effort and time trying to understand the biomechanics of arthrodial (gliding) joints. Although new image acquisition and processing methods currently generate richer-than-ever geometry and kinematic data sets that are individual specific, the computational and visualization tools needed to enable the comparative analysis and exploration of these data sets lag behind. In this paper, we present a framework that enables the cross-data-set visual exploration and analysis of arthrodial joint biomechanics. Central to our approach is a computer-vision-inspired markerless method for establishing pairwise correspondences between individual-specific geometry. Manifold models are subsequently defined and deformed from one individual-specific geometry to another such that the markerless correspondences are preserved while minimizing model distortion. The resulting mutually consistent parameterization and visualization allow the users to explore the similarities and differences between two data sets and to define meaningful quantitative measures. We present two applications of this framework to human-wrist data: articular cartilage transfer from cadaver data to in vivo data and cross-data-set kinematics analysis. The method allows our users to combine complementary geometries acquired through different modalities and thus overcome current imaging limitations. The results demonstrate that the technique is useful in the study of normal and injured anatomy and kinematics of arthrodial joints. In principle, the pairwise cross-parameterization method applies to all spherical topology data from the same class and should be particularly beneficial in instances where identifying salient object features is a nontrivial task.
摘要——骨科医生投入了大量的精力和时间来试图理解动关节（滑动关节）的生物力学。尽管目前新的图像采集和处理方法能够生成比以往更丰富的、针对个体的几何和运动学数据集，但对这些数据集进行比较分析和探索所需的计算和可视化工具却滞后了。在本文中，我们提出了一个框架，用于对动关节生物力学进行跨数据集的可视化探索和分析。我们方法的核心是一种受计算机视觉启发的无标记方法，用于在个体特定的几何形状之间建立成对对应关系。随后定义流形模型，并将其从一种个体特定的几何形状变形为另一种，以便在最小化模型失真的同时保留无标记对应关系。由此产生的相互一致的参数化和可视化使用户能够探索两个数据集之间的异同，并定义有意义的定量测量方法。我们展示了该框架在人体腕部数据上的两个应用：将尸体数据中的关节软骨转移到活体数据以及跨数据集运动学分析。该方法允许我们的用户组合通过不同模态获取的互补几何形状，从而克服当前成像的局限性。结果表明，该技术在研究动关节的正常和损伤解剖结构及运动学方面是有用的。原则上，成对交叉参数化方法适用于同一类别的所有球形拓扑数据，并且在识别显著物体特征是一项艰巨任务的情况下应该特别有用。
