T2图在脊柱结核中的应用价值
The application of T2 relaxation time mapping in spinal tuberculosis
  
DOI:10.3969/j.issn.1006-7108.2017.01.011
中文关键词:  脊柱结核  椎体  T2弛豫时间图  骨质疏松
英文关键词:Spinal tuberculosis  Vertebra  T2 relaxation time mapping  Osteoporosis
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杨培培 郭辉 姚娟 熊鑫鑫 李白艳* 新疆医科大学第一附属医院影像中心放射科新疆 乌鲁木齐835400 
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中文摘要:
      目的 结合磁共振成像常规序列成像,应用T2弛豫时间图(T2 relaxation time mapping)成像测量脊柱结核病变,分析脊柱结核的特征表现、T2值范围及其诊断价值。方法病例组25名脊柱结核患者60个病变椎体、自身对照组60个病变临近正常椎体及对照组25名健康志愿者60个正常椎体行脊柱矢状面自旋回波(fast spin echo,FSE)序列8回波扫描,自动生成T2图,并划定感兴趣区(region ofinterest,ROI)测量T2值。病例组分别与自身对照组和对照组之间采用配对t检验和两独立样本t检验,以P <0.01为差异有统计学意义。结果 病变椎体及椎旁冷脓肿在T2图上均显示为红绿蓝的混杂色阶,且病变椎体、病变临近正常椎体、对照组正常椎体的T2值分别为(110. 32±13. 84)ms、(70. 06 ±9. 03)ms、(77. 29 ±4. 19)ms,病例组分别与自身对照组和对照组之间有统计学差异(P <0. 01)。结论T2弛豫时间图技术可以量化评估脊柱结核病变内组织成分的变化,对脊柱结核的诊断及鉴别诊断有一定的指导意义。
英文摘要:
      Objective Combining T2 relaxation time mapping with the conventional Magnetic resonance imaging,we explored the T2 mapping features,range of T2 values and its value of diagnosis in spinal tuberculosis. Methods Twenty-five cases of spinal tuberculosis was the case group with 60 tuberculosis affected vertebrae. Sixty vertebrae of 25 healthy volunteers and 60 vertebral bodies adjacent to lesion acted as controls and self-controls. T2 values were measured for each of the vertebra from the T2 mapping with MR 8 echo FSE sequence of 1.5 T and statistical analyses were performed using pair-sample and two-independent samples t- test,with significance level set at P <0. 01. Results Vertebral and paravertebral cold abscess on T2 mapping are shown as mixed red,green and blue. The mean T2 value of the lesion vertebrae,the surrounding lesion vertebra and normal vertebra was (110. 32 土 13. 84) ms,(70.06 ±9. 03) ms,(77.29 ±4. 19) ms,respectively. The value of the lesion vertebrae was significantly different from that of the adjacent normal vertebrae and healthy controls vertebrae,both P <0.01. Conclusion The T2 relaxation time mapping can provide valuable information in spinal tuberculosis from a quantitative point of view and is useful for early detection.
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