摘要
针对人体头部组织电导率成像问题,提出了一种新的磁共振电阻抗成像技术(MREIT)算法.该算法仅利用单方向磁感应强度信息,利用基于自适应网络的模糊推理系统(ANFIS)对测量与计算得到的磁感应强度分布间不匹配程度的目标函数进行优化,通过函数寻优得到重建的目标电导率.在球状及真实头模型上的仿真实验证明,该算法可以准确地对颅骨-大脑的电导率比值进行重建,其估计误差分别小于1.10%与0.25%;对电极位置发生偏移情况进行仿真实验,其在真实头模型上的估计误差小于0.35%.通过与同类算法的结果比较发现,对于具有多层组织、电导率各向同性且分层连续体模型的阻抗重建,ANFIS-MREIT算法具有较大的优越性.
A new magnetic resonance electrical impedance tomography （MREIT） algorithm was developed to image the conductivity distribution within human head. Only one component of magnetic flux densities was utilized. The target conductivities were estimated by minimizing the dissimilarity between the measured and calculated magnetic flux densities based on adaptive neuro-fuzzy inference system （ANFIS）. Single-variable simulation on the sphere and realistic-geometry model estimated the skull-to-brain conductivity ratio. The relative error （RE） between the target and the estimated conductivity distribution was less than 1.10% on the sphere model and less than 0. 25% on the realistic-geometry head model; when the electrode＇s excursion was concerned, the RE was less than 0.35% on the realistic-geometry head model. Simulation shows that ANFIS-MREIT outperforms other MREIT algorithms in estimating the head volume conductivities for piece-wise homogeneous head volume-conductor models.
出处
《浙江大学学报（工学版）》
EI
CAS
CSCD
北大核心
2008年第7期1212-1217,共6页
Journal of Zhejiang University：Engineering Science
基金
国家自然科学基金资助项目(NSFC-50577055)
美国国家科学基金资助项目(NSFBES-0411898)
美国国立卫生院基金资助项目(NIHR01EB00178)
关键词
磁共振成像
磁共振电阻抗成像
有限元法
magnetic resonance imaging
magnetic resonance electrical impedance tomography（MREIT）
finite element method
作者简介
张孝通（1982-），男，山东济南人，博士生，从事磁共振阻抗成像方面的研究．E-mail：zhangxiaotong@zju．edu．cn
通讯联系人：朱善安，男，教授，博导．E-mail：zsa@zju．edu．cn
