Rule-based Key-Point Extraction for MR-Guided Biomechanical Digital Twins of the Spine
This work addresses the need for precise anatomical modeling in spinal digital twins for clinical diagnostics and treatment planning, representing an incremental adaptation of existing methods to MRI data.
The authors tackled the problem of creating accurate anatomical models for spinal digital twins by developing a rule-based method for extracting subpixel-accurate key points from MRI scans, adapted from prior CT-based approaches, which enables radiation-free biomechanical simulations tailored to individual patient anatomy.
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.