基于MRI的脊柱数字孪生关键点提取，实现无辐射精准建模。
Rule-based Key-Point Extraction for MR-Guided Biomechanical Digital Twins of the Spine
- 通过规则化流程从MRI中提取亚像素级解剖标志点。
- 生成可用于生物力学模型的肌肉韧带附着点等边界条件。
- 适用于个性化诊疗与大规模研究，尤其适合低辐射场景。
数字孪生为个体化模拟与临床决策支持提供了强大框架，但其发展依赖于精确的个体解剖建模。本文提出一种基于规则的方法，从磁共振成像（MRI）中提取亚像素精度的关键点，借鉴了先前基于CT的方法。该方法结合鲁棒图像配准与椎体特异性方向估计，生成具有解剖学意义的地标，用作生物力学模型中的边界条件和力施加点（如肌肉与韧带附着点）。这些模型可考虑个体解剖特征，模拟脊柱力学行为，从而支持个性化诊断与治疗规划。得益于使用MRI，本方法无辐射，适合大规模研究及代表性不足人群的应用。本工作推动了医学影像分析与生物力学仿真之间的融合，契合个性化医疗建模的核心主题。
原文摘要 · Abstract (English)
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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