Adolescent idiopathic scoliosis (AIS) significantly affects health and quality of life. Traditional imaging methods (X-ray, CT, MRI) are unable to capture the dynamic changes of the spine. In this study, we developed a static digital human model that integrates the body surface geometry of AIS patients with their personalized spinal structure. Our approach combines multi-view images, from which the skinned multi-person linear (SMPL) model is learned using a 3D Gaussian methodology and constrained to estimate the skeletal structure, with a spine model derived from CT scans. The alignment is achieved through feature point-based coarse registration followed by as-rigid-as-possible (ARAP) optimization. Validation on eight AIS patients showed a Cobb angle error of less than \(1\%\) . This model provides a crucial foundation for accurate dynamic simulation and clinical applications.

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The Establishment of Static Digital Humans and Integration with Spinal Models

  • Fujiao Ju,
  • Yuxuan Wang,
  • Shuo Wang,
  • Chengyin Wang,
  • Yinbo Chen,
  • Jianfeng Li,
  • Mingjie Dong,
  • Bin Fang,
  • Jianguo Zhang,
  • Qianyu Zhuang

摘要

Adolescent idiopathic scoliosis (AIS) significantly affects health and quality of life. Traditional imaging methods (X-ray, CT, MRI) are unable to capture the dynamic changes of the spine. In this study, we developed a static digital human model that integrates the body surface geometry of AIS patients with their personalized spinal structure. Our approach combines multi-view images, from which the skinned multi-person linear (SMPL) model is learned using a 3D Gaussian methodology and constrained to estimate the skeletal structure, with a spine model derived from CT scans. The alignment is achieved through feature point-based coarse registration followed by as-rigid-as-possible (ARAP) optimization. Validation on eight AIS patients showed a Cobb angle error of less than \(1\%\) . This model provides a crucial foundation for accurate dynamic simulation and clinical applications.