Zhou Minhao, Ramil Gabriella, Yu Isabel, Sadoughi Saghi, Saeed Isra, Fan Bo, Burghardt Andrew J, Kim Tiffany Y, Ix Joachim H, Kazakia Galateia J
Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, United States.
Department of Medicine, University of California, San Francisco, CA 94158, United States.
J Bone Miner Res. 2025 Jun 25;40(7):868-880. doi: 10.1093/jbmr/zjaf060.
High-resolution peripheral quantitative computed tomography (HR-pQCT), combined with micro-finite element (μFE) models, provide a powerful clinical research tool for evaluating bone structure-function relationships with musculoskeletal disorders and bone-targeting treatments. Based on ex vivo cadaveric phantom scans, the Laplace-Hamming (LH) segmentation approach, compared to the standard segmentation approach, improves the accuracy and precision of microarchitecture evaluation using second-generation HR-pQCT scanners. However, the effect of LH segmentation on in vivo scans acquired from clinically relevant cohorts with disease-specific bone microarchitecture (eg, patients with end-stage kidney disease) has not been investigated. Additionally, the effect of LH segmentation on μFE biomechanical estimations remains unexplored, defining the objectives of the current study. Findings from the current study demonstrated that LH segmentation, compared to standard segmentation, reduced structure-dependent bias in HR-pQCT microarchitectural and μFE biomechanical metrics. Specifically, trabecular bone volume fraction (Tb.BV/TV), trabecular thickness (Tb.Th), and cortical pore diameter (Ct.Po.Dm) were particularly sensitive to segmentation strategy. Due to the structure dependence of the standard segmentation approach, applying LH segmentation can alter the results of between-cohort comparisons, potentially leading to different clinical interpretations. For example, differences in cortical porosity (Ct.Po) between healthy participants and patients with end-stage kidney disease were only significant when evaluated using the standard segmentation approach. Thus, it is important that investigators consider the segmentation approach utilized when interpreting HR-pQCT outcome metrics for disease progression or drug effects assessments. Additionally, a structure-based parameter (Tb.Th$\times$Ct.Po.Dm) that robustly predicted the effect of LH segmentation on μFE biomechanical estimations was established. The predictive power of this parameter highlights the importance of incorporating LH segmentation when evaluating cohorts with documented disease-specific alterations in bone microstructure (eg, changes in Tb.Th and Ct.Po.Dm), such as patients with type 2 diabetes.
高分辨率外周定量计算机断层扫描（HR-pQCT）与微观有限元（μFE）模型相结合，为评估骨骼结构与肌肉骨骼疾病及骨靶向治疗之间的功能关系提供了强大的临床研究工具。基于离体尸体模型扫描，与标准分割方法相比，拉普拉斯-汉明（LH）分割方法提高了使用第二代HR-pQCT扫描仪进行微观结构评估的准确性和精确性。然而，LH分割对从具有疾病特异性骨微观结构的临床相关队列（如终末期肾病患者）获取的体内扫描的影响尚未得到研究。此外，LH分割对μFE生物力学估计的影响仍未得到探索，这确定了本研究的目标。本研究结果表明，与标准分割相比，LH分割减少了HR-pQCT微观结构和μFE生物力学指标中与结构相关的偏差。具体而言，骨小梁骨体积分数（Tb.BV/TV）、骨小梁厚度（Tb.Th）和皮质孔隙直径（Ct.Po.Dm）对分割策略特别敏感。由于标准分割方法的结构依赖性，应用LH分割可能会改变队列间比较的结果，可能导致不同的临床解释。例如，健康参与者和终末期肾病患者之间的皮质孔隙率（Ct.Po）差异仅在使用标准分割方法评估时才显著。因此，研究人员在解释用于疾病进展或药物效果评估的HR-pQCT结果指标时，考虑所使用的分割方法非常重要。此外，还建立了一个基于结构的参数（Tb.Th×Ct.Po.Dm），该参数能可靠地预测LH分割对μFE生物力学估计的影响。该参数的预测能力突出了在评估具有记录在案的疾病特异性骨微结构改变（如Tb.Th和Ct.Po.Dm变化）的队列时纳入LH分割的重要性，如2型糖尿病患者。
