中文标题#
基於規則的關鍵點提取用於脊柱磁共振引導的生物力學數位雙胞胎
英文标题#
Rule-based Key-Point Extraction for MR-Guided Biomechanical Digital Twins of the Spine
中文摘要#
數位雙胞胎為特定個體的仿真和臨床決策支持提供了一個強大的框架，但其開發通常依賴於準確的個性化解剖建模。 在本研究中，我們提出了一種基於規則的方法，用於從 MRI 中提取亞像素精度的關鍵點，該方法改編自之前的基於 CT 的方法。 我們的方法結合了魯棒的圖像配準和椎體特異性方向估計，以生成具有解剖學意義的地標，這些地標作為邊界條件和力作用點，如生物力學模型中的肌肉和韌帶附著點。 這些模型能夠考慮受試者的個體解剖結構來模擬脊柱力學，從而支持在臨床診斷和治療計劃中開發定制化的方法。 通過利用 MRI 成像，我們的方法無需輻射，非常適合大規模研究和在代表性不足的人群中使用。 本研究通過彌合精確的醫學圖像分析與生物力學仿真的差距，為數位雙胞胎生態系統做出了貢獻，並與個性化建模在醫療保健中的關鍵主題相一致。
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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