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Abstract
In population-oriented ergonomics product design and musculoskeletal kinetics analysis, digital spine models of different shape, pose and material property are in great demand. The purpose of this study was to construct a parameterized finite element spine model with adjustable spine shape and material property. We used statistical shape model approach to learn inter-subject shape variations from 65 CT images of training subjects. Second order polynomial regression was used to model the age-dependent changes in vertebral material property derived from spatially aligned CT images. Finally, a parametric spine generator was developed to create finite element instances of different shapes and material properties. For quantitative analysis, the generalization ability to emulate spine shapes of different people was evaluated by fitting into 17 test CT images. The median fitting accuracy was 0.8 for Dice coefficient and 0.43 mm for average surface distance. The age-dependent bone density regression curve was also proved to well agree with large population statistics data. Finite element simulation was performed to compare how shape parameters influenced the biomechanics distribution of spine. The proposed parametric finite element whole spine model will assist the design process of new devices and biomechanical simulation towards a wide range of population.
Highlights
S PINE controls the movement of the trunk, provides mechanical stability for vital organs and hosts major nerve routes through the spinal canal
To provide a generative spine model for populationoriented biomechanical simulation, this study developed parametric finite element (FE) modelling of spine shape and bone material property based on a training set of 65 CT images
Since our model was developed for population-oriented biomechanical simulation, it was important to know how well the model characterized the shapes and bone material property of different people
Summary
S PINE controls the movement of the trunk, provides mechanical stability for vital organs and hosts major nerve routes through the spinal canal. It is critical to perform a thorough biomechanical assessment before the product design for spine structure. Biomechanical simulation [1]–[3] has become a standard procedure for spine-related ergonomics product design, musculoskeletal kinetics simulation [4], orthopaedic implant development [5], medical image analysis [6], [7]. Patient-specific models can be obtained via segmenting spinal structure from a tomographic image and determining Young’s modulus from CT voxel intensity [8], these subject-specific models are not suitable for population-oriented studies. For population-oriented modelling, it has been more than 20 years since the first construction of the generative spine model. Works mainly focused on local spine modelling [3], [9], while later studies paid attention
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