中文标题#
基于规则的关键点提取用于脊柱磁共振引导的生物力学数字孪生
英文标题#
Rule-based Key-Point Extraction for MR-Guided Biomechanical Digital Twins of the Spine
中文摘要#
数字孪生为特定个体的仿真和临床决策支持提供了一个强大的框架，但其开发通常依赖于准确的个性化解剖建模。 在本研究中，我们提出了一种基于规则的方法，用于从 MRI 中提取亚像素精度的关键点，该方法改编自之前的基于 CT 的方法。 我们的方法结合了鲁棒的图像配准和椎体特异性方向估计，以生成具有解剖学意义的地标，这些地标作为边界条件和力作用点，如生物力学模型中的肌肉和韧带附着点。 这些模型能够考虑受试者的个体解剖结构来模拟脊柱力学，从而支持在临床诊断和治疗计划中开发定制化的方法。 通过利用 MRI 成像，我们的方法无需辐射，非常适合大规模研究和在代表性不足的人群中使用。 本研究通过弥合精确的医学图像分析与生物力学仿真的差距，为数字孪生生态系统做出了贡献，并与个性化建模在医疗保健中的关键主题相一致。
英文摘要#
Digital twins offer a powerful framework for subject-specific simulation and clinical decision support, yet their development often hinges on accurate, individualized anatomical modeling. In this work, we present a rule-based approach for subpixel-accurate key-point extraction from MRI, adapted from prior CT-based methods. Our approach incorporates robust image alignment and vertebra-specific orientation estimation to generate anatomically meaningful landmarks that serve as boundary conditions and force application points, like muscle and ligament insertions in biomechanical models. These models enable the simulation of spinal mechanics considering the subject's individual anatomy, and thus support the development of tailored approaches in clinical diagnostics and treatment planning. By leveraging MR imaging, our method is radiation-free and well-suited for large-scale studies and use in underrepresented populations. This work contributes to the digital twin ecosystem by bridging the gap between precise medical image analysis with biomechanical simulation, and aligns with key themes in personalized modeling for healthcare.
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