Nispel Kati, Lerchl Tanja, Gruber Gabriel, Moeller Hendrik, Graf Robert, Senner Veit, Kirschke Jan S
Institute for Neuroradiology, TUM University Hospital, School of Medicine and Health, Technical University of Munich, Munich, Germany.
Associate Professorship of Sport Equipment and Sport Materials, School of Engineering and Design, Technical University of Munich, Garching, Germany.
Front Bioeng Biotechnol. 2025 Jan 3;12:1485115. doi: 10.3389/fbioe.2024.1485115. eCollection 2024.
Biomechanical simulations can enhance our understanding of spinal disorders. Applied to large cohorts, they can reveal complex mechanisms beyond conventional imaging. Therefore, automating the patient-specific modeling process is essential.
We developed an automated and robust pipeline that generates and simulates biofidelic vertebrae and intervertebral disc finite element method (FEM) models based on automated magnetic resonance imaging (MRI) segmentations. In a first step, anatomically-constrained smoothing approaches were implemented to ensure seamless contact surfaces between vertebrae and discs with shared nodes. Subsequently, surface meshes were filled isotropically with tetrahedral elements. Lastly, simulations were executed. The performance of our pipeline was evaluated using a set of 30 patients from an in-house dataset that comprised an overall of 637 vertebrae and 600 intervertebral discs. We rated mesh quality metrics and processing times.
With an average number of 21 vertebrae and 20 IVDs per subject, the average processing time was 4.4 min for a vertebra and 31 s for an IVD. The average percentage of poor quality elements stayed below 2% in all generated FEM models, measured by their aspect ratio. Ten vertebra and seven IVD FE simulations failed to converge.
The main goal of our work was to automate the modeling and FEM simulation of both patient-specific vertebrae and intervertebral discs with shared-node surfaces directly from MRI segmentations. The biofidelity, robustness and time-efficacy of our pipeline marks an important step towards investigating large patient cohorts for statistically relevant, biomechanical insight.
生物力学模拟能够增进我们对脊柱疾病的理解。应用于大规模队列研究时,它们可以揭示传统成像技术之外的复杂机制。因此,实现患者特异性建模过程的自动化至关重要。
我们开发了一种自动化且稳健的流程,该流程基于自动磁共振成像(MRI)分割生成并模拟生物逼真的椎体和椎间盘有限元方法(FEM)模型。第一步,实施解剖学约束平滑方法,以确保椎体和椎间盘之间具有共享节点的无缝接触表面。随后,用四面体单元各向同性地填充表面网格。最后,执行模拟。我们使用来自内部数据集的一组30名患者对我们的流程性能进行了评估,该数据集总共包含637个椎体和600个椎间盘。我们对网格质量指标和处理时间进行了评级。
每位受试者平均有21个椎体和20个椎间盘,一个椎体的平均处理时间为4.4分钟,一个椎间盘的平均处理时间为31秒。在所有生成的有限元模型中,通过长宽比测量,质量较差单元的平均百分比保持在2%以下。十个椎体和七个椎间盘有限元模拟未能收敛。
我们工作的主要目标是直接从MRI分割自动对具有共享节点表面的患者特异性椎体和椎间盘进行建模和有限元模拟。我们流程的生物逼真度、稳健性和时间效率标志着朝着研究大型患者队列以获得具有统计学意义的生物力学见解迈出了重要一步。