目的·采用基于相位线性度拟合的磁化率定量成像（quantitative susceptibility mapping，QSM）技术，定量化帕金森病（Parkinson′s disease，PD）患者脑铁含量，结合纹理分析方法，多参数、多维度定量分析帕金森病患者脑灰质核团磁化率分布特征，并结合临床评分评估纹理特征的敏感性。方法·对20名PD患者以及20名健康对照组（health control，HC）的定量磁化率图像信息进行回顾性分析，手动分割的大脑灰质核团感兴趣区域进行基于灰度游程矩阵（gray level run-length matrix，GLRLM）的三维纹理分析。使用单因素方差分析（one-way ANOVA）比较2组之间的差异，并计算双侧皮尔逊线性相关系数（ r），以研究纹理参数与统一帕金森病评定量表（Unified Parkinson′s Disease Rating Scale，UPDRS）-Ⅲ临床评分的相关性。结果·纹理特征参数分析表明，PD组与HC组在灰质核团存在诸多差异性。在GLRLM的所有纹理特征参数中，LngREmph在所测量的5个灰质核团中，均显示PD组与HC组具有显著性差异。灰质核团的磁化率平均值与GLRLM纹理参数均具有较好区分PD与HC的价值（AUC>0.5）。其中RLNonUni、LngREmph、ShrtREmp以及Fraction的AUC均大于磁化率平均值的AUC。各灰质核团的GLRLM纹理特征参数与UPDRS-Ⅲ评分的相关性分析结果显示，尾状核（caudate nucleus，CN）的RLNonUni和GLevNonU以及红核（red nucleus，RN）的GLevNonU和ShrtRenp均与UPDRS-Ⅲ评分具有显著相关性，其余特征参数未发现显著临床评分相关性。结论·相较于灰质核团磁化率平均值，GLRLM纹理特征参数能够更好地从健康对照组中区分出PD。纹理多参数分析方法是QSM技术在多参数定量脑铁含量方面的一个新思路，可为PD的无创诊断提供更多维度的定量信息。

Objective ·To quantify brain iron content in Parkinson′s disease (PD) patients by using quantitative susceptibility mapping (QSM) based on phase linearity fitting. Combined with texture analysis methods, the magnetic susceptibility distribution characteristics of gray matter nuclei in PD patients were quantitatively analyzed with multiple parameters and dimensions, and the sensitivity of texture features was evaluated with clinical scoring. Methods ·Quantitative susceptibility images from 20 PD patients and 20 healthy controls (HC) were analyzed retrospectively. Regions of interest in basal ganglia were manually segmented, followed by three-dimensional texture analysis by using gray-level run-length matrix (GLRLM). One-way analysis of variance (ANOVA) was performed to compare differences between the two groups, and the bilateral Pearson linear correlation coefficient ( r) was calculated to evaluate the correlation between texture parameters and UPDRS-III clinical scores. Results ·The analysis of texture feature parameters showed that there were significant differences between the PD and HC groups in the gray matter nuclei. Among all the texture feature parameters of GLRLM, LngREnch showed significant differences between the PD group and the HC group in the five gray matter nuclei measured. The average magnetic susceptibility of gray matter nuclei and GLRLM texture parameters were sensitive in distinguishing PD from HC (AUC>0.5). The AUC values of RLNonUni, LngREnch, ShrtREmp, and Fraction were higher than that of the average magnetization susceptibiliyt. The correlation analysis showed that RLNonUni and GLevNonU in the caudate nucleus (CN), as well as GLevNonU in the red nucleus (RN), were significantly correlated with UPDRS-III scores, while no significant clinical correlations were found for the remaining parameters. Conclusion ·Compared to the mean magnetic susceptibility values, GLRLM texture parameters provide better differentiation between the PD and HC groups. Multiparameter texture analysis offers a novel approach to QSM-based quantitative assessment of brain iron content, which can provide additional multidimensional quantitative information for the non-invasive diagnosis of PD.