Abstract
Lumbar degeneration leads to changes in geometry and density distribution of vertebrae, which could further influence the mechanical property and behavior. This study aimed to quantitatively describe the variations in shape and density distribution for degenerated vertebrae by statistical models, and utilized the specific statistical shape model (SSM)/statistical appearance model (SAM) modes to assess compressive strength and fracture behavior. Highly detailed SSM and SAM were developed based on the 75 L1 vertebrae of elderly men, and their variations in shape and density distribution were quantified with principal component (PC) modes. All vertebrae were classified into mild (n = 22), moderate (n = 29), and severe (n = 24) groups according to the overall degree of degeneration. Quantitative computed tomography-based finite element analysis was used to calculate compressive strength for each L1 vertebra, and the associations between compressive strength and PC modes were evaluated by multivariable linear regression (MLR). Moreover, the distributions of equivalent plastic strain (PEEQ) for the vertebrae assigned with the first modes of SSM and SAM at mean ± 3SD were investigated. The Leave-One-Out analysis showed that our SSM and SAM had good performance, with mean absolute errors of 0.335±0.084 mm and 64.610±26.620 mg/cm3, respectively. A reasonable accuracy of bone strength prediction was achieved by using four PC modes (SSM 1, SAM 1, SAM 4, and SAM 5) to construct the MLR model. Furthermore, the PEEQ values were more sensitive to degeneration-related variations of density distribution than those of morphology. The density variations may change the deformity type (compression deformity or wedge deformity), which further affects the fracture pattern. Statistical models can identify the morphology and density variations in degenerative vertebrae, and the SSM/SAM modes could be used to assess compressive strength and fracture behavior. The above findings have implications for assisting clinicians in pathological diagnosis, fracture risk assessment, implant design, and preoperative planning.
摘要
腰椎退行性病变可导致椎骨形状和密度分布发生改变, 进而影响其力学性能和行为. 本研究旨在通过统计形状模型(SSM)和统 计外观模型(SAM)定量描述退变椎骨形状和密度分布的变化, 并利用特定主成分模式来评估椎骨的强度和断裂行为. 基于75名老年男 性L1椎骨建立详细的SSM和SAM, 提取其主成分模式来定量描述退变椎骨形状和密度分布特征. 根据L1椎骨的退变程度, 将所有受试 者分为轻度退变组(n = 22)、中度退变组(n = 29)和重度退变组(n = 24). 基于定量CT的有限元分析计算每个椎骨的抗压强度, 并利用多 元线性回归分析来评估椎骨强度与主成分模式之间的关系. 对比分析SSM和SAM第一模式(平均值±3倍标准差)下等效塑性应变 (PEEQ)的分布以评估退变椎骨的断裂模式. 留一法结果显示所建立的SSM和SAM具有良好的性能, 用它们对未知椎骨的形状和密度 分布进行重建, 其平均绝对误差分别为0.335±0.084 mm和64.610±26.620 mg/cm3. 使用4个主成分模式(SSM 1、SAM 1、SAM 4和SAM 5)构建的多元线性回归模型, 其强度预测性能良好. 此外, 与椎骨退变相关的形态变化相比, PEEQ的数值对密度分布的变化更为敏感. 椎骨密度分布的变化可能会影响其受力后的变形类型(压缩变形和楔形变形), 进而影响其骨折模式. 统计模型可以识别退变椎骨形状 和密度分布的变化, 并且SSM/SAM模式可以用来评估椎骨的抗压强度和断裂行为. 上述研究结果对协助临床医生进行病理诊断、骨 折风险评估、植入体的设计和术前规划具有重要意义.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant No. 12272029).
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Author contributions Meng Zhang: Formal analysis, Investigation, Methodology, and Writing — original draft. He Gong: Conceptualization, Funding acquisition, Project administration, Supervision, and Writing — review & editing. Ming Zhang: Resources.
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Zhang, M., Gong, H. & Zhang, M. Assessment of bone strength and fracture behavior of degenerative vertebrae through quantifying morphology and density distribution. Acta Mech. Sin. 41, 624016 (2025). https://doi.org/10.1007/s10409-024-24016-x
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DOI: https://doi.org/10.1007/s10409-024-24016-x