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软骨下骨生物力学的进展:马模型中的计算机断层扫描和微计算机断层扫描成像的见解。

Advancements in Subchondral Bone Biomechanics: Insights from Computed Tomography and Micro-Computed Tomography Imaging in Equine Models.

机构信息

Department of Biomedical Engineering, University of Melbourne, Parkville, VIC, 3010, Australia.

Equine Centre, Department of Veterinary Clinical Sciences, University of Melbourne, Werribee, VIC, 3030, Australia.

出版信息

Curr Osteoporos Rep. 2024 Dec;22(6):544-552. doi: 10.1007/s11914-024-00886-y. Epub 2024 Sep 14.

Abstract

PURPOSE OF REVIEW

This review synthesizes recent advancements in understanding subchondral bone (SCB) biomechanics using computed tomography (CT) and micro-computed tomography (micro-CT) imaging in large animal models, particularly horses.

RECENT FINDINGS

Recent studies highlight the complexity of SCB biomechanics, revealing variability in density, microstructure, and biomechanical properties across the depth of SCB from the joint surface, as well as at different joint locations. Early SCB abnormalities have been identified as predictive markers for both osteoarthritis (OA) and stress fractures. The development of standing CT systems has improved the practicality and accuracy of live animal imaging, aiding early diagnosis of SCB pathologies. While imaging advancements have enhanced our understanding of SCB, further research is required to elucidate the underlying mechanisms of joint disease and articular surface failure. Combining imaging with mechanical testing, computational modelling, and artificial intelligence (AI) promises earlier detection and better management of joint disease. Future research should refine these modalities and integrate them into clinical practice to enhance joint health outcomes in veterinary and human medicine.

摘要

目的综述

本综述综合了利用大型动物模型(尤其是马)中的计算机断层扫描(CT)和微计算机断层扫描(micro-CT)成像技术,研究理解软骨下骨(SCB)生物力学的最新进展。

最近的发现

最近的研究强调了 SCB 生物力学的复杂性,揭示了 SCB 从关节表面到深部以及在不同关节位置的密度、微观结构和生物力学特性的可变性。早期 SCB 异常已被确定为骨关节炎(OA)和应力性骨折的预测标志物。站立 CT 系统的发展提高了活体动物成像的实用性和准确性,有助于早期诊断 SCB 病变。虽然成像技术的进步提高了我们对 SCB 的理解,但仍需要进一步研究来阐明关节疾病和关节表面失效的潜在机制。将成像与机械测试、计算建模和人工智能(AI)相结合,有望更早地发现关节疾病并进行更好的管理。未来的研究应完善这些方式,并将其整合到临床实践中,以提高兽医和人类医学中的关节健康结果。

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