摘要
目的通过对子宫腺肌病病例高强度聚焦超声(HIFU)治疗前后扩散张量成像(DTI)的各种参数进行分析,探讨DTI评估子宫腺肌病HIFU治疗的意义。方法 40例患者均于HIFU术前、术后依次行盆腔MRI平扫、DTI、DCE-MRI检查。记录并分析HIFU治疗前后子宫腺肌病病变的以下参数:ADC、FA、VRA、T2-weighted trace。结果 HIFU治疗前和治疗后的ADC值分别为(1.51±0.26)×10^(-9)mm^2/s和(1.42±0.20)×10^(-9)mm^2/s,FA值分别为0.22±0.04和0.18±0.04,VRA值分别为0.06±0.02和0.04±0.02,T2-weighted trace值分别为225.53±81.22和245.86±78.86。HIFU治疗后,ADC值、FA值、VRA值降低,T2-weighted trace值升高。HIFU治疗前后,ADC值和T2-weighted trace值差异无统计学意义(P> 0.05),FA值和VRA值差异具有统计学意义(P <0.05)。结论 DTI对评估子宫腺肌病HIFU治疗前后的组织学差异有一定的意义。
Objective To explore the value of diffusion tensor imaging(DTI)in the treatment of adenomyosis with high intensity focused ultrasound( HIFU) by analyzing the characteristics of DTI before and after surgery. Method All 40 patients were performed MRI plain scan and DTI、DCE-MRI examination before and after HIFU. Parameters of ADC、FA、VRA and T2-weighted tracewere recorded and analyzed before and after HIFU treatment. Result The preoperative and postoperative values of ADC were(1.51 ± 0.26)× 10^-9 mm^2/s and( 1.42 ± 0.20)× 10^-9 mm^2/s,FA values were 0.22 ± 0.04 and 0.18 ± 0.04,VRA values were 0.06 ± 0.02 and 0.04 ± 0.02,T2-weighted trace values were 225.53 ± 81.22 and 245.86 ± 78.86. After HIFU,the value of ADC、FA and VRA wasdecreased,while the value of T2-weighted trace was increased. There was no significant difference between the value of ADC and T2-weighted trace before and after HIFU treatment(P > 0.05). The difference between FA value and VRA value was statistically significant(P < 0.05). Conclusion DTI can provide more information about histopathological changes of adenomyosis after HIFU treatment.
作者
吴准
李凤致
王宗英
王习
王滨
王锡臻
WU Zhun;LI Fengzhi;WANG Zongying;WANG Xi;WANG Bin;WANG Xizhen(Imaging Center of Affiliated Hospital of Weifang Medical University,Weifang 261042,China)
出处
《实用医学杂志》
CAS
北大核心
2019年第10期1652-1654,1658,共4页
The Journal of Practical Medicine
基金
国家自然科学基金项目(编号:81641074
81171303
30470518
81771828)
关键词
子宫腺肌病
HIFU
磁共振成像
扩散张量成像
adenomyosis
high intensity focused ultrasound
magnetic resonance imaging
diffusion tensor imaging