Institute of Mechanics, Structural Analysis and Dynamics, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Pfaffenwaldring 27, 70569 Stuttgart, Germany; Biomechanics Lab, Institute of Mechanics, Structural Analysis and Dynamics, Faculty of Aerospace Engineering and Geodesy, University of Stuttgart, Pfaffenwaldring 27, 70569 Stuttgart, Germany.
Faculty of Pharmacy, Istinye University, Maltepe, Cirpici Yolu B Ck. No. 9, 34010 Zeytinburnu, Istanbul, Turkey.
J Mech Behav Biomed Mater. 2022 Feb;126:104963. doi: 10.1016/j.jmbbm.2021.104963. Epub 2021 Nov 26.
MRI-based mathematical and computational modeling studies can contribute to a better understanding of the mechanisms governing cartilage's mechanical performance and cartilage disease. In addition, distinct modeling of cartilage is needed to optimize artificial cartilage production. These studies have opened up the prospect of further deepening our understanding of cartilage function. Furthermore, these studies reveal the initiation of an engineering-level approach to how cartilage disease affects material properties and cartilage function. Aimed at researchers in the field of MRI-based cartilage simulation, research articles pertinent to MRI-based cartilage modeling were identified, reviewed, and summarized systematically. Various MRI applications for cartilage modeling are highlighted, and the limitations of different constitutive models used are addressed. In addition, the clinical application of simulations and studied diseases are discussed. The paper's quality, based on the developed questionnaire, was assessed, and out of 79 reviewed papers, 34 papers were determined as high-quality. Due to the lack of the best constitutive models for various clinical conditions, researchers may consider the effect of constitutive material models on the cartilage disease simulation. In the future, research groups may incorporate various aspects of machine learning into constitutive models and MRI data extraction to further refine the study methodology. Moreover, researchers should strive for further reproducibility and rigorous model validation and verification, such as gait analysis.
基于 MRI 的数学和计算建模研究有助于更好地理解控制软骨机械性能和软骨疾病的机制。此外，需要对软骨进行独特的建模，以优化人工软骨的生产。这些研究开辟了进一步加深我们对软骨功能理解的前景。此外，这些研究揭示了一种工程层面的方法，即软骨疾病如何影响材料特性和软骨功能。针对基于 MRI 的软骨模拟领域的研究人员，本文系统地识别、审查和总结了与基于 MRI 的软骨建模相关的研究文章。强调了各种用于软骨建模的 MRI 应用，并讨论了不同本构模型的局限性。此外，还讨论了模拟和研究疾病的临床应用。根据制定的问卷评估了论文的质量，在所审查的 79 篇论文中，有 34 篇被确定为高质量论文。由于缺乏各种临床情况下的最佳本构模型，研究人员可能会考虑本构材料模型对软骨疾病模拟的影响。在未来，研究小组可能会将机器学习的各个方面纳入本构模型和 MRI 数据提取中，以进一步完善研究方法。此外，研究人员应努力提高可重复性和严格的模型验证和验证，如步态分析。
