Herlidou S, Grebe R, Grados F, Leuyer N, Fardellone P, Meyer M-E
Unité de Génie Biophysique et Médical, Faculte de Médecine, Centre Hospitalier Universitaire Nord, Amiens, France.
Magn Reson Imaging. 2004 Feb;22(2):237-43. doi: 10.1016/j.mri.2003.07.007.
Recent developments in high-resolution MR imaging techniques have opened up new perspectives for structural characterization of trabecular bone by non-invasive methods. In this study, 3-D MR imaging was performed on 17 healthy volunteers and 6 osteoporotic patients. Two different MR sequences were used to evaluate the impact on MR acquisition on texture analysis results. Images were analyzed with four automated methods of texture analysis (grey level histogram, cooccurrence, runlength and gradient matrices) enabling quantitative analysis of grey level intensity and distribution within three different regions of interest (ROI). Texture analysis is not very frequently used since the interpretation of the large number of calculated parameters is difficult. We applied multiparametric data analyses such as principal component analysis (CFA) and hierarchical ascending classification (HAC) to determine the relevant parameters to differentiate between three sets of images (healthy young volunteers, healthy postmenopaused and osteoporotic patients). The results suggest that relevant texture information (depending on the ROI localization in the calcaneus) can be extracted from calcaneus MR images to evaluate osteoporosis and age effects on trabecular bone structure if strictly the same acquisition sequences are used for all patients' examination.
高分辨率磁共振成像技术的最新进展为通过非侵入性方法对小梁骨进行结构表征开辟了新的前景。在本研究中,对17名健康志愿者和6名骨质疏松患者进行了三维磁共振成像。使用两种不同的磁共振序列来评估磁共振采集对纹理分析结果的影响。采用四种自动纹理分析方法(灰度直方图、共生矩阵、游程长度矩阵和梯度矩阵)对图像进行分析,从而能够对三个不同感兴趣区域(ROI)内的灰度强度和分布进行定量分析。由于难以解释大量计算参数,纹理分析并不常用。我们应用主成分分析(CFA)和层次上升分类(HAC)等多参数数据分析来确定区分三组图像(健康年轻志愿者、健康绝经后女性和骨质疏松患者)的相关参数。结果表明,如果对所有患者的检查都严格使用相同的采集序列,那么可以从跟骨磁共振图像中提取相关纹理信息(取决于跟骨中ROI的定位),以评估骨质疏松和年龄对小梁骨结构的影响。