• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

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

膝关节关节软骨的术前 MRI:一种实用方法。

Preoperative MRI of Articular Cartilage in the Knee: A Practical Approach.

机构信息

National Orthopaedic Imaging Associates, Greenbrae, California.

Department of Radiology, Stanford University, Stanford, California.

出版信息

J Knee Surg. 2020 Nov;33(11):1088-1099. doi: 10.1055/s-0040-1716719. Epub 2020 Oct 29.

DOI:10.1055/s-0040-1716719
PMID:33124010
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8601109/
Abstract

Articular cartilage of the knee can be evaluated with high accuracy by magnetic resonance imaging (MRI) in preoperative patients with knee pain, but image quality and reporting are variable. This article discusses the normal MRI appearance of articular cartilage as well as the common MRI abnormalities of knee cartilage that may be considered for operative treatment. This article focuses on a practical approach to preoperative MRI of knee articular cartilage using routine MRI techniques. Current and future directions of knee MRI related to articular cartilage are also discussed.

摘要

膝关节的关节软骨可以通过术前膝关节疼痛患者的磁共振成像(MRI)进行高精度评估,但图像质量和报告结果存在差异。本文讨论了关节软骨的正常 MRI 表现以及可能考虑手术治疗的常见膝关节软骨 MRI 异常。本文重点介绍了使用常规 MRI 技术进行膝关节关节软骨术前 MRI 的实用方法。还讨论了与关节软骨相关的膝关节 MRI 的当前和未来方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/d15c81d4f14e/nihms-1754140-f0021.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/6b99ab5286d3/nihms-1754140-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/76b85acc3809/nihms-1754140-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/5341d94fad80/nihms-1754140-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/3522beaccd12/nihms-1754140-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/1a8d5dd0488b/nihms-1754140-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/a83bba1afe81/nihms-1754140-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/8deea76ba0d4/nihms-1754140-f0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/dfd1a7c6cdf6/nihms-1754140-f0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/94c07c19f67f/nihms-1754140-f0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/f874e955606d/nihms-1754140-f0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/b5c12b14e750/nihms-1754140-f0011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/075b23e95cdd/nihms-1754140-f0012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/54d5914bfe94/nihms-1754140-f0013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/340f522de851/nihms-1754140-f0014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/e092fd037bbe/nihms-1754140-f0015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/8e03e553fa05/nihms-1754140-f0016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/97e6b8d50f65/nihms-1754140-f0017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/d8f47a88dcbc/nihms-1754140-f0018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/56cc591ee9ea/nihms-1754140-f0019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/cdc96408dda8/nihms-1754140-f0020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/d15c81d4f14e/nihms-1754140-f0021.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/6b99ab5286d3/nihms-1754140-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/76b85acc3809/nihms-1754140-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/5341d94fad80/nihms-1754140-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/3522beaccd12/nihms-1754140-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/1a8d5dd0488b/nihms-1754140-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/a83bba1afe81/nihms-1754140-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/8deea76ba0d4/nihms-1754140-f0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/dfd1a7c6cdf6/nihms-1754140-f0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/94c07c19f67f/nihms-1754140-f0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/f874e955606d/nihms-1754140-f0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/b5c12b14e750/nihms-1754140-f0011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/075b23e95cdd/nihms-1754140-f0012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/54d5914bfe94/nihms-1754140-f0013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/340f522de851/nihms-1754140-f0014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/e092fd037bbe/nihms-1754140-f0015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/8e03e553fa05/nihms-1754140-f0016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/97e6b8d50f65/nihms-1754140-f0017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/d8f47a88dcbc/nihms-1754140-f0018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/56cc591ee9ea/nihms-1754140-f0019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/cdc96408dda8/nihms-1754140-f0020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e45c/8601109/d15c81d4f14e/nihms-1754140-f0021.jpg

相似文献

1
Preoperative MRI of Articular Cartilage in the Knee: A Practical Approach.膝关节关节软骨的术前 MRI:一种实用方法。
J Knee Surg. 2020 Nov;33(11):1088-1099. doi: 10.1055/s-0040-1716719. Epub 2020 Oct 29.
2
Articular Cartilage Repair in the Knee: Postoperative Imaging.膝关节软骨修复:术后影像学表现。
J Knee Surg. 2021 Jan;34(1):2-10. doi: 10.1055/s-0040-1716357. Epub 2020 Sep 8.
3
MR Imaging of Knee Cartilage Injury and Repair Surgeries.磁共振成像在膝关节软骨损伤及修复手术中的应用
Magn Reson Imaging Clin N Am. 2022 May;30(2):227-239. doi: 10.1016/j.mric.2021.11.004. Epub 2022 Apr 13.
4
Imaging of the Knee Following Repair of Focal Articular Cartilage Lesions.局灶性关节软骨损伤修复后的膝关节影像学检查
Semin Musculoskelet Radiol. 2018 Sep;22(4):377-385. doi: 10.1055/s-0038-1667301. Epub 2018 Aug 22.
5
Articular Cartilage Lesion Characteristic Reporting Is Highly Variable in Clinical Outcomes Studies of the Knee.关节软骨病变特征报告在膝关节临床结局研究中具有高度变异性。
Cartilage. 2019 Jul;10(3):299-304. doi: 10.1177/1947603518756464. Epub 2018 Feb 6.
6
Long-Term Clinical and MRI Results of Matrix-Assisted Autologous Chondrocyte Implantation for Articular Cartilage Defects of the Knee.膝关节软骨缺损的基质辅助自体软骨细胞移植的长期临床和 MRI 结果。
Cartilage. 2019 Jul;10(3):305-313. doi: 10.1177/1947603518756463. Epub 2018 Feb 11.
7
Magnetic Resonance Imaging of Articular Cartilage within the Knee.膝关节内关节软骨的磁共振成像
J Knee Surg. 2018 Feb;31(2):155-165. doi: 10.1055/s-0037-1620233. Epub 2018 Jan 18.
8
Understanding Magnetic Resonance Imaging of Knee Cartilage Repair: A Focus on Clinical Relevance.了解膝关节软骨修复的磁共振成像:关注临床相关性。
Cartilage. 2018 Jul;9(3):223-236. doi: 10.1177/1947603517710309. Epub 2017 Jun 5.
9
Intraoperative validation of quantitative T2 mapping in patients with articular cartilage lesions of the knee.膝关节关节软骨病变患者定量 T2 映射的术中验证。
Osteoarthritis Cartilage. 2017 Nov;25(11):1841-1849. doi: 10.1016/j.joca.2017.07.021. Epub 2017 Aug 8.
10
Deep Learning Approach for Evaluating Knee MR Images: Achieving High Diagnostic Performance for Cartilage Lesion Detection.深度学习方法评估膝关节磁共振成像:实现软骨病变检测的高诊断性能。
Radiology. 2018 Oct;289(1):160-169. doi: 10.1148/radiol.2018172986. Epub 2018 Jul 31.

引用本文的文献

1
Preoperative diagnosis of knee cartilage, meniscal, and ligament injuries by magnetic resonance imaging.通过磁共振成像对膝关节软骨、半月板和韧带损伤进行术前诊断。
J Exp Orthop. 2023 Apr 20;10(1):47. doi: 10.1186/s40634-023-00595-y.
2
Can the MRI based AMADEUS score accurately assess pre-surgery chondral defect severity according to the ICRS arthroscopic classification system?基于MRI的AMADEUS评分能否根据ICRS关节镜分类系统准确评估术前软骨损伤的严重程度?
J Exp Orthop. 2022 Aug 19;9(1):83. doi: 10.1186/s40634-022-00511-w.

本文引用的文献

1
Improving the Speed of MRI with Artificial Intelligence.利用人工智能提高磁共振成像速度
Semin Musculoskelet Radiol. 2020 Feb;24(1):12-20. doi: 10.1055/s-0039-3400265. Epub 2020 Jan 28.
2
Time-saving opportunities in knee osteoarthritis: T mapping and structural imaging of the knee using a single 5-min MRI scan.膝关节骨关节炎的省时机会:使用单次 5 分钟 MRI 扫描进行膝关节 T 映射和结构成像。
Eur Radiol. 2020 Apr;30(4):2231-2240. doi: 10.1007/s00330-019-06542-9. Epub 2019 Dec 16.
3
Rapid Knee MRI Acquisition and Analysis Techniques for Imaging Osteoarthritis.用于骨关节炎成像的快速膝关节磁共振成像采集与分析技术
J Magn Reson Imaging. 2020 Nov;52(5):1321-1339. doi: 10.1002/jmri.26991. Epub 2019 Nov 21.
4
Utility of deep learning super-resolution in the context of osteoarthritis MRI biomarkers.深度学习超分辨率在骨关节炎 MRI 生物标志物中的应用。
J Magn Reson Imaging. 2020 Mar;51(3):768-779. doi: 10.1002/jmri.26872. Epub 2019 Jul 16.
5
Motion in Magnetic Resonance: New Paradigms for Improved Clinical Diagnosis.磁共振中的运动:改善临床诊断的新范例。
Invest Radiol. 2019 Jul;54(7):383-395. doi: 10.1097/RLI.0000000000000566.
6
Combined 5-minute double-echo in steady-state with separated echoes and 2-minute proton-density-weighted 2D FSE sequence for comprehensive whole-joint knee MRI assessment.联合稳态 5 分钟双回波分离回波和 2 分钟质子密度加权二维 FSE 序列进行全面的膝关节 MRI 评估。
J Magn Reson Imaging. 2019 Jun;49(7):e183-e194. doi: 10.1002/jmri.26582. Epub 2018 Dec 23.
7
Comparison of 1.5- and 3.0-T magnetic resonance imaging for evaluating lesions of the knee: A systematic review and meta-analysis (PRISMA-compliant article).1.5T与3.0T磁共振成像用于评估膝关节病变的比较:一项系统评价和荟萃分析(遵循PRISMA的文章)
Medicine (Baltimore). 2018 Sep;97(38):e12401. doi: 10.1097/MD.0000000000012401.
8
Deep Learning Approach for Evaluating Knee MR Images: Achieving High Diagnostic Performance for Cartilage Lesion Detection.深度学习方法评估膝关节磁共振成像:实现软骨病变检测的高诊断性能。
Radiology. 2018 Oct;289(1):160-169. doi: 10.1148/radiol.2018172986. Epub 2018 Jul 31.
9
Diagnostic Performance of Three-dimensional MRI for Depicting Cartilage Defects in the Knee: A Meta-Analysis.三维 MRI 诊断膝关节软骨缺损的性能:一项荟萃分析。
Radiology. 2018 Oct;289(1):71-82. doi: 10.1148/radiol.2018180426. Epub 2018 Jul 17.
10
Use of 2D U-Net Convolutional Neural Networks for Automated Cartilage and Meniscus Segmentation of Knee MR Imaging Data to Determine Relaxometry and Morphometry.使用 2D U-Net 卷积神经网络对膝关节 MRI 数据进行自动软骨和半月板分割以确定弛豫度和形态测量学。
Radiology. 2018 Jul;288(1):177-185. doi: 10.1148/radiol.2018172322. Epub 2018 Mar 27.