J Korean Radiol Soc.  1996 Feb;34(2):263-268.

Value of Axial 3-D Fourier Transformation GRASS MR Images in Diagnosis of Meniscal Tears of the Knee

Affiliations
  • 1Department of Diagnostic Radiology, Chungnam National University College of Medicine, Korea.

Abstract

PURPOSE
The purpose of this study is to compare the sensitivity and specificity of axial 3-D imaging with those of conventional 2-D imaging in the diagnosis of meniscal injuries.
MATERIALS AND METHODS
Twenty-nine kneesof 27 patients with clinically suspected meniscal tears were imaged at a 1.5 T MR system. Conventional 2-D imagesand axial 3-D GRASS images were obtained in all cases. Arthroscopic or surgical confirmation was available in allcases and was used as the gold standard.
RESULTS
Among the 29 knee-joints evaluated, 21 of 23 meniscal tears and 33 of 35 normal menisci were correctly identified with axial 3-D imaging, yielding a sensitivity of 91.3% and specificity of 94.3%. Conversely, the sensitivity was 82.6% and the specificity was 97.1% for the conventional 2-Dtechnique. A combination of both techniques yielded 95.7% sensitivity and 100% specificity in the diagnosis of meniscal tears. Of the 23 meniscal tears proven at arthroscopy or surgery, there were four discrepancies between the two techniques ; three of the four represented false-negative 2-D images and one was a false-negative axial3-D image. Of 35 normal menisci, two false-positives occurred with axial 3-D imaging and one with 2-D imaging.
CONCLUSION
Despite the lack of a statistically significant difference between the efficacy of the two techniques, these results suggest that the sensitivity in the diagnosis of meniscal tears can be improved by adding thin-sliced axial 3-D GRASS imaging to conventional 2-D imaging in the limited cases with clinically suspected meniscal tears.

Keyword

Knee, injuries; Knee, MR; Knee, ligaments, menisci, and cantilage

MeSH Terms

Arthroscopy
Diagnosis*
Fourier Analysis*
Humans
Imaging, Three-Dimensional
Knee*
Poaceae*
Sensitivity and Specificity
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