Rule-based Key-Point Extraction for MR-Guided Biomechanical Digital Twins of the Spine

TL;DR

This paper introduces a rule-based method for extracting anatomically meaningful landmarks from MRI to improve subject-specific biomechanical models of the spine, supporting personalized clinical diagnostics and treatment planning.

Contribution

It presents a novel MRI-based landmark extraction technique that enhances the development of personalized digital twins for spinal biomechanics, bridging image analysis and simulation.

Findings

Accurate landmark extraction from MRI enables subject-specific spinal models.

The method supports radiation-free, large-scale studies in diverse populations.

It improves the integration of medical imaging with biomechanical simulation.

Abstract

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.