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Knee. 2017 Aug;24(4):711-717. doi: 10.1016/j.knee.2017.04.009. Epub 2017 May 19.
2
Development of an Open-Source, Discrete Element Knee Model.一种开源离散元膝关节模型的开发。
IEEE Trans Biomed Eng. 2016 Oct;63(10):2056-67. doi: 10.1109/TBME.2016.2585926. Epub 2016 Jun 28.
3
Is There a Biomechanical Link Between Patellofemoral Pain and Osteoarthritis? A Narrative Review.髌股疼痛与骨关节炎之间是否存在生物力学联系?一项叙述性综述。
Sports Med. 2016 Dec;46(12):1797-1808. doi: 10.1007/s40279-016-0545-6.
4
Validation of a method for combining biplanar radiography and magnetic resonance imaging to estimate knee cartilage contact.一种结合双平面X线摄影和磁共振成像来估计膝关节软骨接触的方法的验证
Med Eng Phys. 2015 Oct;37(10):937-47. doi: 10.1016/j.medengphy.2015.07.002. Epub 2015 Aug 21.
5
Altered frontal and transverse plane tibiofemoral kinematics and patellofemoral malalignments during downhill gait in patients with mixed knee osteoarthritis.混合性膝关节骨关节炎患者下坡行走时,额状面和横断面胫股关节运动学改变及髌股关节排列不齐。
J Biomech. 2015 Jul 16;48(10):1707-12. doi: 10.1016/j.jbiomech.2015.05.015. Epub 2015 May 29.
6
Prediction and Validation of Load-Dependent Behavior of the Tibiofemoral and Patellofemoral Joints During Movement.运动过程中胫股关节和髌股关节负荷依赖行为的预测与验证
Ann Biomed Eng. 2015 Nov;43(11):2675-85. doi: 10.1007/s10439-015-1326-3. Epub 2015 Apr 28.
7
Is patellofemoral osteoarthritis common in middle-aged people with chronic patellofemoral pain?髌股关节炎在患有慢性髌股疼痛的中年人中常见吗?
Arthritis Care Res (Hoboken). 2014 Aug;66(8):1252-7. doi: 10.1002/acr.22274.
8
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J Biomech Eng. 2013 Aug;135(8):81011. doi: 10.1115/1.4024287.
9
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开发并验证髌股关节运动学驱动的离散元模型。

Development and validation of a kinematically-driven discrete element model of the patellofemoral joint.

机构信息

Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL, USA.

Department of Research, Cleveland Clinic Akron General, Akron, OH, USA.

出版信息

J Biomech. 2019 May 9;88:164-172. doi: 10.1016/j.jbiomech.2019.03.032. Epub 2019 Mar 28.

DOI:10.1016/j.jbiomech.2019.03.032
PMID:31003752
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7307598/
Abstract

Quantifying the complex loads at the patellofemoral joint (PFJ) is vital to understanding the development of PFJ pain and osteoarthritis. Discrete element analysis (DEA) is a computationally efficient method to estimate cartilage contact stresses with potential application at the PFJ to better understand PFJ mechanics. The current study validated a DEA modeling framework driven by PFJ kinematics to predict experimentally-measured PFJ contact stress distributions. Two cadaveric knee specimens underwent quadriceps muscle [215 N] and joint compression [350 N] forces at ten discrete knee positions representing PFJ positions during early gait while measured PFJ kinematics were used to drive specimen-specific DEA models. DEA-computed contact stress and area were compared to experimentally-measured data. There was good agreement between computed and measured mean and peak stress across the specimens and positions (r = 0.63-0.85). DEA-computed mean stress was within an average of 12% (range: 1-47%) of the experimentally-measured mean stress while DEA-computed peak stress was within an average of 22% (range: 1-40%). Stress magnitudes were within the ranges measured (0.17-1.26 MPa computationally vs 0.12-1.13 MPa experimentally). DEA-computed areas overestimated measured areas (average error = 60%; range: 4-117%) with magnitudes ranging from 139 to 307 mm computationally vs 74-194 mm experimentally. DEA estimates of the ratio of lateral to medial patellofemoral stress distribution predicted the experimental data well (mean error = 15%) with minimal measurement bias. These results indicate that kinematically-driven DEA models can provide good estimates of relative changes in PFJ contact stress.

摘要

量化髌股关节 (PFJ) 的复杂负荷对于了解 PFJ 疼痛和骨关节炎的发展至关重要。离散元分析 (DEA) 是一种计算效率高的方法,可用于估计软骨接触应力,并有可能应用于 PFJ 以更好地了解 PFJ 力学。本研究验证了一种由 PFJ 运动学驱动的 DEA 建模框架,以预测实验测量的 PFJ 接触应力分布。两个尸体膝关节标本在十个离散膝关节位置接受股四头肌 [215 N] 和关节压缩 [350 N] 力,这些位置代表了早期步态中的 PFJ 位置,同时使用测量的 PFJ 运动学来驱动特定于标本的 DEA 模型。将 DEA 计算的接触应力和面积与实验测量的数据进行比较。在标本和位置上,计算得到的平均和峰值应力与实验测量数据之间存在很好的一致性 (r=0.63-0.85)。DEA 计算得到的平均应力与实验测量得到的平均应力平均相差 12%(范围:1-47%),而 DEA 计算得到的峰值应力与实验测量得到的平均应力平均相差 22%(范围:1-40%)。测量得到的应力值在测量得到的范围内 (0.17-1.26 MPa 计算值与 0.12-1.13 MPa 实验值)。DEA 计算得到的面积比实验测量得到的面积高估了 (平均误差为 60%;范围:4-117%),计算值范围为 139-307mm2,实验值范围为 74-194mm2。DEA 估计的外侧与内侧髌股压力分布比很好地预测了实验数据(平均误差为 15%),测量偏差最小。这些结果表明,运动学驱动的 DEA 模型可以很好地估计 PFJ 接触应力的相对变化。