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髌骨软化症:一项体外研究。磁共振成像标准与组织学及宏观检查结果的比较。

Chondromalacia patellae: an in vitro study. Comparison of MR criteria with histologic and macroscopic findings.

作者信息

van Leersum M, Schweitzer M E, Gannon F, Finkel G, Vinitski S, Mitchell D G

机构信息

Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, PA 19107, USA.

出版信息

Skeletal Radiol. 1996 Nov;25(8):727-32. doi: 10.1007/s002560050169.

Abstract

OBJECTIVE

To develop MR criteria for grades of chondromalacia patellae and to assess the accuracy of these grades.

DESIGN

Fat-suppressed T2-weighted double-echo, fat-suppressed T2-weighted fast spin echo, fat-suppressed T1-weighted, and gradient echo sequences were performed at 1.5 T for the evaluation of chondromalacia. A total of 1000 MR, 200 histologic, and 200 surface locations were graded for chondromalacia and statistically compared.

RESULTS

Compared with gross inspection as well as with histology the most accurate sequences were fat-suppressed T2-weighted conventional spin echo and fat suppressed T2-weighted fast spin echo, although the T1-weighted and proton density images also correlated well. The most accurate MR criteria applied to the severe grades of chondromalacia, with less accurate results for lesser grades.

CONCLUSIONS

This study demonstrates that fat-suppressed routine T2-weighted and fast spin echo T2-weighted sequences seem to be more accurate than proton density, T1-weighted, and gradient echo sequences in grading chondromalacia. Good histologic and macroscopic correlation was seen in more severe grades of chondromalacia, but problems remain for the early grades in all sequences studied.

摘要

目的

制定髌骨软化症分级的磁共振成像(MR)标准,并评估这些分级的准确性。

设计

在1.5T条件下,采用脂肪抑制T2加权双回波、脂肪抑制T2加权快速自旋回波、脂肪抑制T1加权及梯度回波序列来评估髌骨软化症。总共对1000例MR图像、200例组织学标本以及200个表面位置进行了髌骨软化症分级,并进行统计学比较。

结果

与大体检查及组织学检查相比,最准确的序列是脂肪抑制T2加权传统自旋回波序列和脂肪抑制T2加权快速自旋回波序列,尽管T1加权图像和质子密度图像也有较好的相关性。应用于重度髌骨软化症的MR标准最为准确,而对于较轻程度的分级,结果准确性较低。

结论

本研究表明,在髌骨软化症分级方面,脂肪抑制常规T2加权序列和快速自旋回波T2加权序列似乎比质子密度、T1加权及梯度回波序列更准确。在重度髌骨软化症中,观察到与组织学和大体表现有良好的相关性,但在所研究的所有序列中,早期分级仍存在问题。

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