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3D texture analyses within the substantia nigra of Parkinson's disease patients on quantitative susceptibility maps and R2∗ maps

定量磁化率图 方差分析 熵(时间箭头) 黑质 帕金森病 纹理(宇宙学) 相关性 核医学 核磁共振 模式识别(心理学) 数学 医学 人工智能 磁共振成像 物理 病理 内科学 疾病 放射科 计算机科学 几何学 量子力学 图像(数学)
作者
Gaiying Li,ZHAI Guo-qiang,Xinxin Zhao,Hedi An,Pascal Spincemaille,Kelly M. Gillen,Yixuan Ku,Yi Wang,Dongya Huang,Jianqi Li
出处
期刊:NeuroImage [Elsevier BV]
卷期号:188: 465-472 被引量:59
标识
DOI:10.1016/j.neuroimage.2018.12.041
摘要

Iron accumulation in the substantia nigra (SN) is spatially heterogeneous, yet no study has quantitatively evaluated how the texture of quantitative susceptibility maps (QSM) and R2∗ might evolve with Parkinson's disease (PD) and healthy controls (HC). The aim of this study was to discriminate between patients with PD and HC using texture analysis in the SN from QSM and R2∗ maps. QSM and R2∗ maps were obtained from 28 PD patients and 28 HC on a clinical 3T MR imaging scanner using 3D multi-echo gradient-echo sequence. The first- and second- order texture features of the QSM and R2∗ images were obtained to evaluate group differences using two-tailed t-test. After correction for multiple comparisons, for the first-order analysis, the susceptibility of SN from patients with PD was significantly greater (p = 0.017) compared with the SN from HC. For the second-order texture analysis, angular second moment, entropy, and sum of entropy showed significant differences in QSM (p < 0.001) and R2∗ maps (p < 0.01). In addition, correlation, contrast, sum of variance and difference of variance, significantly separated the subject groups in QSM maps (p < 0.05) but not in R2∗ images. Receiver operating characteristic analysis showed that entropy and sum of entropy of the QSM maps in the SN yielded the highest performance for differentiating PD patients from HC (area under the curve = 0.89). In conclusion, most first- and second- order QSM texture features successfully distinguished PD patients from HC and significantly outperformed R2∗ texture analysis. The second-order texture features were more accurate and sensitive than first-order texture features for classifying PD patients.
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