A Two-step MREIT Algorithm for Head Tissues Based on Radial Basic Function Neural Network
- VernacularTitle:基于径向基函数神经网络的两步核磁共振头部组织电阻抗成像算法
- Author:
Dandan YAN
;
Xiaotong ZHANG
;
Shanan ZHU
;
He BIN
- Publication Type:Journal Article
- Keywords:
magnetic resonance electrical impedance tomography;
magnetic flux density measurement;
radius basic function;
neural network;
genetic algorithm
- From:Space Medicine & Medical Engineering
2006;0(02):-
- CountryChina
- Language:Chinese
-
Abstract:
Objective To develop a new Two-step magnetic resonance electrical impedance tomography(MREIT)algorithm based on radial basic function(RBF)neural network for imaging electrical impedance distribution of a head.Methods Firstly,the magnetic resonance imaging(MRI)system with high resolution was used to set up 3D model of the object and to identify the boundaries of different tissues.Then RBF MREIT algorithm was applied to estimate piece-wise homogeneous impedance values of those tissues,respectively.Furthermore,the impedance of each element within each region of the FEM model was estimated according to the RBF genetic algorithm method based on the piece-wise constant impedance.Results Computer simulations were conducted in a three-sphere head model(scalp-skull-brain,SSB)and the simulation results showed the applicability and feasibility of the present Two-step MREIT algorithm in imaging continuous electrical impedance distribution within the head.Conclusion The present Two-step MREIT algorithm is an effective method for imaging the continuous electrical impedance distribution within the human head.
