标题： 静态数字人的建立与脊柱模型的整合 显示英文标题

标题： The establishment of static digital humans and the integration with spinal models

摘要： 青少年特发性脊柱侧凸（AIS），是一种常见的脊柱畸形，对个体的健康和生活质量有显著影响。传统的成像技术，如X光、计算机断层扫描（CT）和磁共振成像（MRI），提供脊柱的静态视图。然而，它们在捕捉脊柱的动态变化及其与整体身体运动的相互作用方面受到限制。因此，开发新技术以解决这些局限性变得非常重要。动态数字人体建模是数字医学的重大突破。它能够显示日常活动中脊柱的三维（3D）视图，帮助临床医生检测在静态成像中可能被忽略的畸形。尽管动态建模具有巨大潜力，但构建一个精确的静态数字人体模型是高精度模拟的关键初始步骤。在本研究中，我们的重点是构建一个整合脊柱的精确静态数字人体模型，这对于后续针对AIS的动态数字人体研究至关重要。首先，我们通过将三维高斯方法与从患者多视角图像中获得的皮肤多人线性（SMPL）模型相结合，生成人体点云数据。然后，我们将标准骨骼模型拟合到生成的人体模型上。接下来，我们将从CT图像重建的真实脊柱模型与标准骨骼模型对齐。我们使用六名AIS患者的X光数据验证了最终的个性化脊柱模型，Cobb角度（用于测量脊柱侧凸的严重程度）作为评估指标。结果表明，模型的误差在实际测量值的1度以内。本研究提出了一种构建数字人体的重要方法。 显示英文摘要

摘要： 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.