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  • Title: Quantitative analysis of lumbar intervertebral disc abnormalities at 3.0 Tesla: value of T(2) texture features and geometric parameters.
    Author: Mayerhoefer ME, Stelzeneder D, Bachbauer W, Welsch GH, Mamisch TC, Szczypinski P, Weber M, Peters NH, Fruehwald-Pallamar J, Puchner S, Trattnig S.
    Journal: NMR Biomed; 2012 Jun; 25(6):866-72. PubMed ID: 22161807.
    Abstract:
    T(2) relaxation time mapping provides information about the biochemical status of intervertebral discs. The present study aimed to determine whether texture features extracted from T(2) maps or geometric parameters are sensitive to the presence of abnormalities at the posterior aspect of lumbar intervertebral discs, i.e. bulging and herniation. Thirty-one patients (21 women and 10 men; age range 18-51 years) with low back pain were enrolled. MRI of the lumbar spine at 3.0 Tesla included morphological T(1) - and T(2) -weighted fast spin-echo sequences, and multi-echo spin-echo sequences that were used to construct T(2) maps. On morphological MRI, discs were visually graded into 'normal', 'bulging' or 'herniation'. On T(2) maps, texture analysis (based on the co-occurrence matrix and wavelet transform) and geometry analysis of the discs were performed. The three T(2) texture features and geometric parameters best-suited for distinguishing between normal discs and discs with bulging or herniation were determined using Fisher coefficients. Statistical analysis comprised ANCOVA and post hoc t-tests. Eighty-two discs were classified as 'normal', 49 as 'bulging' and 20 showed 'herniation.' The T(2) texture features Entropy and Difference Variance, and all three pre-selected geometric parameters differed significantly between normal and bulging, normal and herniated, and bulging and herniated discs (p < 0.05). These findings suggest that T(2) texture features and geometric parameters are sensitive to the presence of abnormalities at the posterior aspect of lumbar intervertebral discs, and may thus be useful as quantitative biomarkers that predict disease.
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