Armstrong Jeffrey R, Campbell J Quinn, Petrella Anthony J
Colorado School of Mines and works as a DRM/DFSS Program Manager for Medtronic Navigation, Louisville, CO, USA.
Jensen Hughes, Englewood, CO, USA.
Comput Methods Programs Biomed. 2021 Jun;204:106056. doi: 10.1016/j.cmpb.2021.106056. Epub 2021 Mar 19.
The purpose of this study was to compare two methods for quantifying differences in geometric shapes of human lumbar vertebra using statistical shape modeling (SSM).
A novel 3D implementation of a previously published 2D, nonlinear SSM was implemented and compared to a commonly used, Cartesian method of SSM. The nonlinear method, or Hybrid SSM, and Cartesian SSM were applied to lumbar vertebra shapes from a cohort of 18 full lumbar triangle meshes derived from CT scans. The comparison included traditional metrics for cumulative variance, generality, and specificity and results from application-based biomechanics using finite element simulation.
The Hybrid SSM has less compactness - likely due to the increased number of mathematical constraints in the SSM formulation. Similar results were found between methods for specificity and generality. Compared to the previously validated, manually-segmented FE model, both SSM methods produced similar and agreeable results.
Visual, statistical, and biomechanical findings did not convincingly support the superiority of the Hybrid SSM over the simpler Cartesian SSM.
This work suggests that, of the two methods compared, the Cartesian SSM is adequate to capture the variations in shape of the posterior spinal structures for biomechanical modeling applications.
本研究旨在使用统计形状建模（SSM）比较两种量化人类腰椎几何形状差异的方法。
实施了一种先前发表的二维非线性SSM的新型三维实现方式，并将其与常用的笛卡尔SSM方法进行比较。将非线性方法（即混合SSM）和笛卡尔SSM应用于来自18个源自CT扫描的完整腰椎三角形网格队列的腰椎形状。比较包括累积方差、通用性和特异性的传统指标，以及使用有限元模拟的基于应用的生物力学结果。
混合SSM的紧凑性较低——可能是由于SSM公式中数学约束数量的增加。在特异性和通用性方法之间发现了类似的结果。与先前经过验证的手动分割有限元模型相比，两种SSM方法产生了相似且一致的结果。
视觉、统计和生物力学结果并未令人信服地支持混合SSM优于更简单的笛卡尔SSM。
这项工作表明，在比较的两种方法中，笛卡尔SSM足以捕捉后脊柱结构形状的变化，用于生物力学建模应用。
