脑磁源成像中的正则理论  

Regularization Theory in Magnetoencephalography(MEG) Source Imaging

作　　者：胡净[1] 胡洁[2] 

机构地区：[1]电子科技大学生命科学与技术学院,成都610054 [2]浙江大学医学院附属第一医院设备科,杭州310003

出　　处：《山东生物医学工程》2003年第1期1-4,共4页Shandong Journal of Biomedical Engineering

基　　金：浙江省自然科学基金资助项目 (编号 :60 2 111);浙江工业大学基金资助项目 (编号 :X30 35 )

摘　　要：从一个相对完整的正则理论角度对现有的各重建算法进行分析 ,把它们归属在确定性和随机性正则两大类中。脑内磁源成像中的正则理论 ,就是通过合理的神经活动特性的相关先验约束 ,以求得唯一的合理的鲁棒解。文中着重讨论了基于Tikhonov正则的最小模和基于Markov随机场模型的Bayesian的重建方法。最后 。We presented a relative complete regularization viewpoint on special image reconstruction. Regularization could be classified to two common approaches,i.e.,deterministic and stochastic. Their common feature is that they both make use of a priori constraints concerning the current density distribution. In this paper we reviewed several aspects of the application of regularization theory in MEG source imaging reconstruction, where the minimum norm estimation with Tikhonov regularization and Bayesian framework based on Markov random field model were introduced in detail. The characteristics, difference of these methods were also discussed. Finally, computer experiments were conducted in order to compare the performance of the two regularized reconstruction technique.

关 键 词：脑磁源成像 正则理论 确定性正则 随机性正则 仿真实验 鲁棒解 重建算法 

分 类 号：R445[医药卫生—影像医学与核医学] TP391.41[医药卫生—诊断学]