Department of Integrated Systems Engineering, The Ohio State University, Columbus, Ohio, USA.
Department of Biomedical Engineering, The Ohio State University, Columbus, Ohio, USA.
Department of Radiation Oncology, The Ohio State University, Columbus, Ohio, USA.
Department of Neurological Surgery, The Ohio State University, Columbus, Ohio, USA.
Department of Radiology, The Ohio State University, Columbus, Ohio, USA.
Department of Neurology, The Ohio State University, Columbus, Ohio, USA.
Department of Orthopedics, The Ohio State University, Columbus, Ohio, USA.
Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, Ohio, USA.
Department of Materials Science and Engineering, The Ohio State University, Columbus, Ohio, USA.
本文介绍了构建人工智能 (AI) 辅助框架的努力，该框架被称为 ReconGAN，用于创建人类椎骨的真实数字双胞胎并预测椎骨骨折 (VF) 的风险。ReconGAN 由深度卷积生成对抗网络 (DCGAN)、图像处理步骤和基于有限元 (FE) 的形状优化组成，以重建椎骨模型。该 DCGAN 模型使用一组从尸体样本中获得的小梁骨定量微计算机断层扫描 (micro-QCT) 图像进行训练。使用 DCGAN 生成的合成小梁模型的质量通过将其一组统计微结构描述符与成像数据的描述符进行比较来验证。然后使用基于 FE 的形状优化方法将合成的小梁微结构注入从患者的诊断 CT 扫描中提取的椎骨皮质壳中，以实现小梁区域与皮质区域之间的平滑过渡。椎骨的最终几何模型被转换为高保真有限元模型，以使用压缩和屈曲载荷条件下的连续损伤模型来模拟 VF 响应。提出了一项可行性研究，以证明使用这种 AI 辅助框架生成的数字双胞胎在预测患有脊柱转移的癌症患者中 VF 风险的适用性。椎骨的最终几何模型被转换为高保真有限元模型，以使用压缩和屈曲载荷条件下的连续损伤模型来模拟 VF 响应。提出了一项可行性研究，以证明使用这种 AI 辅助框架生成的数字双胞胎在预测患有脊柱转移的癌症患者中 VF 风险的适用性。椎骨的最终几何模型被转换为高保真有限元模型，以使用压缩和屈曲载荷条件下的连续损伤模型来模拟 VF 响应。提出了一项可行性研究，以证明使用这种 AI 辅助框架生成的数字双胞胎在预测患有脊柱转移的癌症患者中 VF 风险的适用性。
This article presents an effort toward building an artificial intelligence (AI) assisted framework, coined ReconGAN, for creating a realistic digital twin of the human vertebra and predicting the risk of vertebral fracture (VF). ReconGAN consists of a deep convolutional generative adversarial network (DCGAN), image-processing steps, and finite element (FE) based shape optimization to reconstruct the vertebra model. This DCGAN model is trained using a set of quantitative micro-computed tomography (micro-QCT) images of the trabecular bone obtained from cadaveric samples. The quality of synthetic trabecular models generated using DCGAN are verified by comparing a set of its statistical microstructural descriptors with those of the imaging data. The synthesized trabecular microstructure is then infused into the vertebra cortical shell extracted from the patient's diagnostic CT scans using an FE-based shape optimization approach to achieve a smooth transition between trabecular to cortical regions. The final geometrical model of the vertebra is converted into a high-fidelity FE model to simulate the VF response using a continuum damage model under compression and flexion loading conditions. A feasibility study is presented to demonstrate the applicability of digital twins generated using this AI-assisted framework to predict the risk of VF in a cancer patient with spinal metastasis.
