Guo Weihao, Rezasefat Mohammad, Rabey Karyne N, Ouellet Simon, Westover Lindsey, Hogan James David
Department of Mechanical Engineering, The University of Alberta, Edmonton, AB T6G 2R3, Canada.
Department of Mechanical Engineering, The University of Alberta, Edmonton, AB T6G 2R3, Canada.
J Mech Behav Biomed Mater. 2025 Aug;168:107007. doi: 10.1016/j.jmbbm.2025.107007. Epub 2025 Apr 24.
Cranial bone injuries significantly impact human health, potentially leading to death or permanent disability, and mechanical responses are crucial predictors of cranial damage. Predicting mechanical responses through medical imaging is an efficient method that streamlines the process by eliminating the need for intermediate steps, such as diagnostic testing and biomedical analysis. Although previous studies have successfully predicted 1D sequences or 2D sectional mechanical attributes from medical imaging, these approaches are limited in their ability to capture the anisotropic characteristics of cranial bone. As a complex osseous material, cranial bone has significant directional dependencies in its microstructural features, which directly influence its macroscopic mechanical responses under loading conditions. In this study, we aim to introduce a deep learning-based high-throughput framework to correlate the three-dimensional mechanical responses and three-dimensional cranial microstructures derived from medical images. First, micro-CT scans of 40 human cranial samples, spanning an average age of 82.5 years, were performed to capture microstructural information. Next, 2000 representative volume element (RVE) units were randomly and automatically extracted from these scans to characterize the cranial microstructures. Following this, 2000 stress and 2000 strain fields were derived from finite element simulations based on these RVE units, and subsequently validated through quasi-static compression experiments. An optimized U-Net-based deep learning network was employed to link the macro-property stress-strain response with the three-dimensional cranial microstructures. The proposed framework demonstrates robust performance in predicting the spatial mechanical behavior based on microstructural inputs, showing high and consistent similarity between the predictions and ground truth. Overall, the high-throughput nature of this framework facilitates the handling of large-scale data, enabling comprehensive and efficient analysis that is crucial for predicting mechanical responses. By elucidating structure-property relationships, this approach can enhance the accuracy of injury diagnosis and aid in the development of tailored treatment plans, effectively bridging the gap between structural morphology and mechanical functionality.
颅骨损伤对人类健康有重大影响，可能导致死亡或永久性残疾，而力学响应是颅骨损伤的关键预测指标。通过医学成像预测力学响应是一种有效的方法，它通过消除诸如诊断测试和生物医学分析等中间步骤来简化流程。尽管先前的研究已经成功地从医学成像中预测了一维序列或二维截面力学属性，但这些方法在捕捉颅骨各向异性特征方面存在局限性。作为一种复杂的骨材料，颅骨在其微观结构特征上具有显著的方向依赖性，这直接影响其在加载条件下的宏观力学响应。在本研究中，我们旨在引入一个基于深度学习的高通量框架，以关联从医学图像中获取的三维力学响应和三维颅骨微观结构。首先，对40个平均年龄为82.5岁的人类颅骨样本进行了微观计算机断层扫描（micro-CT），以获取微观结构信息。接下来，从这些扫描中随机自动提取2000个代表性体积单元（RVE），以表征颅骨微观结构。随后，基于这些RVE单元通过有限元模拟得出2000个应力场和2000个应变场，并通过准静态压缩实验进行验证。采用了一种基于优化U-Net的深度学习网络，将宏观属性应力-应变响应与三维颅骨微观结构联系起来。所提出的框架在基于微观结构输入预测空间力学行为方面表现出强大的性能，预测结果与真实情况之间显示出高度且一致的相似性。总体而言，该框架的高通量特性便于处理大规模数据，实现全面而高效的分析，这对于预测力学响应至关重要。通过阐明结构-属性关系，这种方法可以提高损伤诊断的准确性，并有助于制定量身定制的治疗方案，有效地弥合结构形态与力学功能之间의差距。
