Suppr超能文献

在猪模型中,基于 T2*-加权 MRI 的原位、非侵入性参数可预测前交叉韧带重建或生物增强性初次修复的体外结构特性。

In Situ, noninvasive, T2*-weighted MRI-derived parameters predict ex vivo structural properties of an anterior cruciate ligament reconstruction or bioenhanced primary repair in a porcine model.

机构信息

Department of Orthopaedics, Warren Alpert Medical School, Brown University/Rhode Island Hospital, Providence, RI 02903, USA.

出版信息

Am J Sports Med. 2013 Mar;41(3):560-6. doi: 10.1177/0363546512472978. Epub 2013 Jan 24.

Abstract

BACKGROUND

Magnetic resonance imaging (MRI) is a noninvasive technology that can quantitatively assess anterior cruciate ligament (ACL) graft size and signal intensity. However, how those properties relate to reconstructed or repaired ligament strength during the healing process is yet unknown.

HYPOTHESIS

Magnetic resonance imaging-derived measures of graft volume and signal intensity are significant predictors of the structural properties of an ACL or ACL graft after 15 weeks and 52 weeks of healing.

STUDY DESIGN

Controlled laboratory study.

METHODS

The current data were gathered from 2 experiments evaluating ACL reconstruction and repair techniques. In the first experiment, pigs underwent unilateral ACL transection and received (1) ACL reconstruction, (2) ACL reconstruction with collagen-platelet composite (CPC), or (3) no treatment. The surgical legs were harvested after 15 weeks of healing. In the second experiment, pigs underwent ACL transection and received (1) ACL reconstruction, (2) ACL reconstruction with CPC, (3) bioenhanced ACL primary repair with CPC, or (4) no treatment. The surgical legs were harvested after 52 weeks. The harvested knees were imaged using a T2*-weighted 3-dimensional constructive interference in steady state (CISS) sequence. Each ligament was segmented from the scans, and the intra-articular volume and the median grayscale values were determined. Mechanical testing was performed to establish the ligament structural properties.

RESULTS

Volume significantly predicted the structural properties (maximum load, yield load, and linear stiffness) of the ligaments and grafts (R (2) = 0.56, 0.56, and 0.49, respectively; P ≤ .001). Likewise, the median grayscale values (ie, signal intensity) significantly predicted the structural properties of the ligaments and grafts (R (2) = 0.42, 0.37, and 0.40, respectively; P < .001). The combination of these 2 parameters in a multiple regression model improved the predictions (R (2) = 0.73, 0.72, and 0.68, respectively; P ≤ .001).

CONCLUSION

Volume and grayscale values from high-resolution T2*-weighted MRI scans are predictive of structural properties of the healing ligament or graft in a porcine model.

CLINICAL RELEVANCE

This study provides a critical step in the development of a noninvasive method to predict the structural properties of the healing ACL graft or repair. This technique may prove beneficial as a surrogate outcome measure in preclinical animal and clinical studies.

摘要

背景

磁共振成像(MRI)是一种非侵入性技术,可定量评估前交叉韧带(ACL)移植物的大小和信号强度。然而,在愈合过程中,这些特性与重建或修复后的韧带强度之间的关系尚不清楚。

假设

MRI 衍生的移植物体积和信号强度测量值是 ACL 或 ACL 移植物在 15 周和 52 周愈合后结构特性的重要预测指标。

研究设计

对照实验室研究。

方法

目前的数据来自两个评估 ACL 重建和修复技术的实验。在第一个实验中,猪接受了单侧 ACL 切断,并接受了(1)ACL 重建、(2)ACL 重建加胶原蛋白-血小板复合物(CPC)或(3)无治疗。愈合 15 周后取出手术侧的腿。在第二个实验中,猪接受了 ACL 切断,并接受了(1)ACL 重建、(2)ACL 重建加 CPC、(3)生物增强 ACL 初级修复加 CPC 或(4)无治疗。愈合 52 周后取出手术侧的腿。使用 T2*-加权三维稳态建设性干扰(CISS)序列对采集的膝关节进行成像。从扫描中对每条韧带进行分割,确定关节内体积和中位数灰度值。进行力学测试以确定韧带的结构特性。

结果

体积显著预测了韧带和移植物的结构特性(最大载荷、屈服载荷和线性刚度)(R²=0.56、0.56 和 0.49;P≤0.001)。同样,中位数灰度值(即信号强度)显著预测了韧带和移植物的结构特性(R²=0.42、0.37 和 0.40;P<0.001)。在多元回归模型中结合这两个参数可提高预测值(R²=0.73、0.72 和 0.68;P≤0.001)。

结论

在猪模型中,高分辨率 T2*-加权 MRI 扫描的体积和灰度值可预测愈合韧带或移植物的结构特性。

临床相关性

本研究为开发一种非侵入性方法预测愈合 ACL 移植物或修复的结构特性提供了关键步骤。该技术可能作为临床前动物和临床试验中替代终点测量指标有益。

相似文献

引用本文的文献

本文引用的文献

3
The potential for primary repair of the ACL.前交叉韧带一期修复的可能性。
Sports Med Arthrosc Rev. 2011 Mar;19(1):44-9. doi: 10.1097/JSA.0b013e3182095e5d.

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验