The establishment of static digital humans and the integration with spinal models
Journal:
arXiv
Published Date:
Feb 11, 2025
Abstract
Adolescent idiopathic scoliosis (AIS), a prevalent spinal deformity,
significantly affects individuals' health and quality of life. Conventional
imaging techniques, such as X - rays, computed tomography (CT), and magnetic
resonance imaging (MRI), offer static views of the spine. However, they are
restricted in capturing the dynamic changes of the spine and its interactions
with overall body motion. Therefore, developing new techniques to address these
limitations has become extremely important. Dynamic digital human modeling
represents a major breakthrough in digital medicine. It enables a three -
dimensional (3D) view of the spine as it changes during daily activities,
assisting clinicians in detecting deformities that might be missed in static
imaging. Although dynamic modeling holds great potential, constructing an
accurate static digital human model is a crucial initial step for high -
precision simulations. In this study, our focus is on constructing an accurate
static digital human model integrating the spine, which is vital for subsequent
dynamic digital human research on AIS. First, we generate human point - cloud
data by combining the 3D Gaussian method with the Skinned Multi - Person Linear
(SMPL) model from the patient's multi - view images. Then, we fit a standard
skeletal model to the generated human model. Next, we align the real spine
model reconstructed from CT images with the standard skeletal model. We
validated the resulting personalized spine model using X - ray data from six
AIS patients, with Cobb angles (used to measure the severity of scoliosis) as
evaluation metrics. The results indicate that the model's error was within 1
degree of the actual measurements. This study presents an important method for
constructing digital humans.
