Institute of Orthopaedic Research and Biomechanics, University of Ulm, Ulm, Germany.
J Anat. 2010 Mar;216(3):320-8. doi: 10.1111/j.1469-7580.2009.01187.x. Epub 2009 Dec 21.
Statistical correlations between anatomical dimensions of human vertebral structures have indicated a potential for the prediction of vertebral morphometry, which could be applied to the creation of simplified geometrical models of the spine excluding the need for preliminary processing of medical images. The aim of this study was to perform linear and nonlinear regressions with published anatomical data to generate prediction equations for 20 vertebral parameters of the human thoracic and lumbar spine as a function of only one given parameter that was measured by X-ray. Each parameter was considered individually as a potential predictor variable in terms of its correlation with all of the other parameters, together with the readiness with which lateral X-rays could be obtained. Based on this, the parameter vertebral body height posterior was chosen and the statistical analyses described here are related to this parameter. Our linear, exponential and logarithmic regressions provided significant predictions of anterior vertebral structures. However, third-order polynomial prediction equations allowed an improvement on these predictions (P-values < 0.001), e.g. endplates and spinal canal (R(2), 0.970-0.995) as well as pedicle heights and the spinous process (R(2), 0.811-0.882), in addition to a reasonable prediction of the posterior vertebral structures, which have shown a low or no correlation in previous studies, e.g. pedicle inclination and transverse process (R(2), 0.514-0.693) (anova). Comparisons of the theoretical predictions with two other sets of experimental data indicated that the predictions generally agree well with the experimental data. A time-efficient approach for obtaining anatomical data for the description of human thoracic and lumbar geometry was provided by this method, which requires the measurement of only one parameter per vertebra (vertebral body height posterior) from a lateral X-ray and the set of developed prediction equations. Vertebral models based on this type of parameterized geometry could be used in biomechanical studies that require geometry variation, such as in spinal deformations, including scoliosis.
人体脊椎结构的解剖学尺寸的统计相关性表明，脊椎形态学的预测具有一定潜力，这可以应用于创建脊柱的简化几何模型，而无需对医学图像进行初步处理。本研究的目的是利用已发表的解剖学数据进行线性和非线性回归，生成 20 个胸椎和腰椎参数的预测方程，这些方程是作为一个给定参数的函数，而该参数仅通过 X 射线测量。每个参数都被单独考虑为一个潜在的预测变量，根据其与所有其他参数的相关性，以及获得侧位 X 射线的难易程度。基于此，选择了椎体后高这一参数，本文描述的统计分析与该参数相关。我们的线性、指数和对数回归对前位脊椎结构提供了显著的预测。然而，三阶多项式预测方程可以提高这些预测的精度（P 值<0.001），例如终板和椎管（R(2)，0.970-0.995）以及椎弓根高度和棘突（R(2)，0.811-0.882），此外还可以合理地预测以前研究中相关性较低或没有相关性的后位脊椎结构，例如椎弓根倾斜和横突（R(2)，0.514-0.693）（anova）。将理论预测与另外两组实验数据进行比较表明，预测结果与实验数据总体上吻合较好。该方法提供了一种高效的方法来获取描述人体胸椎和腰椎几何形状的解剖学数据，该方法仅需要从侧位 X 射线中测量每个椎体（椎体后高）的一个参数，以及一组开发的预测方程。基于这种参数化几何模型的椎体模型可以用于需要几何形状变化的生物力学研究，例如脊柱畸形，包括脊柱侧凸。
