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Updated: May 12, 2026

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
Published on: December 18, 2016
Quantitative MRI analysis of menisci using biexponential T2* fitting with a variable echo time sequence
Vladimir Juras1, Sebastian Apprich, Štefan Zbýň
1Center of Excellence for High field MR, Department of Radiology, Medical University of Vienna Waehringer Guertel 18-20, Vienna, Austria; Institute of Measurement Science, Department of Imaging Methods, Dubravska cesta 9, Bratislava, Slovakia.
Purpose:
The goal of this study was to differentiate between normal, degenerative meniscus, and meniscal tears using monoexponentially and biexponentially calculated T2*. Meniscal disease, characterized by an altered collagen fiber matrix, might be detectable in vivo using quantitative T2* mapping.
Methods:
A 3D Cartesian spoiled gradient echo technique was adapted to enable the use of a variable echo time approach in combination with a highly asymmetric readout. T2* was calculated monoexponentially and biexponentially using three- and five-parametric non-linear fits, respectively.
Results:
From a total of 68 evaluated menisci, 48 were normal, 12 were degenerated, and eight had tears. Mean values for the short (T2*s) and long (T2*l) T2* components were as follows: in normal menisci, 0.82 ± 0.38/15.0 ± 5.4 ms, respectively; in degenerated menisci, 1.29 ± 0.53/19.97 ± 5.59 ms, respectively; and, in meniscal tears, 2.05 ± 0.73 and 26.83 ± 7.72 ms, respectively. Biexponentially fitted T2* demonstrated a greater ability to distinguish normal and degenerated menisci using receiver operating characteristic (ROC) analysis (higher area under curve as well as higher specificity and sensitivity).
Conclusion:
This study suggests that biexponential fitting, used for T2* calculation in the menisci, provides better results compared to monoexponential fitting. Observed changes in T2* result from the matrix reorganization in degenerative processes in the menisci, which affects the collagen fiber orientation, as well as content.
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