应用DTI技术对妊娠女性盆底肌损伤诊断的研究  被引量:2

Study of DTI in diagnosis of pelvic floor muscle injury for pregnant women

在线阅读下载全文

作  者:马洪舟[1] 

机构地区:[1]山东省菏泽市立医院MRI室,山东菏泽274000

出  处:《中国民康医学》2014年第14期10-12,共3页Medical Journal of Chinese People’s Health

摘  要:目的:分析采用磁共振弥散张量成像(DTI)观察妊娠对女性盆底肌损伤的可行性。方法:筛选35名初产妇和35名无孕育史无盆底疾病的年轻志愿者,分别对其盆底肌进行常规MRI扫描和DTI扫描,测量肛提肌和肛门括约肌复合体的部分各向异性分数值(FA),计算其平均值。并使用计算机软件描绘肛提肌和肛门括约肌复合体的三维纤维示踪图。结果:35名初产妇肛提肌FA值均值是0.31±0.02,35名志愿者肛提肌FA值均值是0.41±0.02,两者比较有统计学意义(P<0.05);初产妇肛门括约肌复合体FA值均值是0.67±0.03,志愿者肛门括约肌复合体FA值均值是0.70±0.03,两者比较有统计学意义(P<0.05)。结论:磁共振弥散张量成像(DTI)可以对女性盆底肌进行量化分析和三维形态学观察,为女性盆底功能障碍的早期预防和早期诊断提供了新的影像学工具。Objective: To analyze feasibility of magnetic resonance diffusion tensor imaging( DTI) in diagnosis of pelvic floor muscle injury for pregnant women. Methods: 35 primiparas and 35 young volunteers without an inoculation history and pelvic floor disorders were selected. The pelvic floor muscles underwent routine MRI and DTI scanning,and fractional anisotropy( FA) scores of levator ani and anal sphincter complex fraction were measured and the average value was calculated. Three dimensional fiber tractography depicting the levator ani and anal sphincter complex was performed through a computer software. Results: The levator ani muscle average FA of the 35 primipara was 0. 31 ± 0. 02,that of 35 volunteers was 0. 41 ± 0. 02,and the difference was statistically significant( P〈0. 05). Primipara anal sphincter complex average FA was 0. 67 ± 0. 03,volunteer's anal sphincter complex average FA was 0. 70 ±0. 03,and the difference was statistically significant( P〈0. 05). Conclusions: The magnetic resonance diffusion tensor imaging can quantifiably analyze and 3D morphologically observe female pelvic floor muscle,and provides a new imaging tool for the early prevention and early diagnosis of female pelvic floor dysfunction.

关 键 词:妊娠 盆底 弥散张量成像 

分 类 号:R445.2[医药卫生—影像医学与核医学]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象