Optimization of Tensor Reconstruction by Excluding Outliers from DWIs

摘要：

　　Outliers in Diffusion Weighted Imaging (DWI) data appear frequently due to subjects motion and the system noise,which are deleterious to the accuracy of diffusion tensor (DT) reconstruction.By detecting artifacts in the resulting DT data and minimizing a criteria score in the consequent FA map and positive definite map,we propose an optimization algorithm for Tensor Reconstruction by Excluding Outliers from DWls (TREOD) that effectively improves the quality of tensor data reconstructed based on a selected subset of the raw DWI data in which outliers are excluded.Extensive experiments with both simulated and real datasets demonstrate the correctness and effectiveness of our proposed method.

关键词： Tensor reconstruction DWI outliers Least Square FA factor Positive definite

作者: CUI Jiali CUI Yanwei WANG Yiding

作者单位: North China University of Technology, Beijing 100144, P.R.China

会议类型: 国际会议

会议名称: 2012 IEEE 11th International Conference on Signal Processing (第11届IEEE信号处理国际会议)

会议地点: 北京

会议语种:英文

页码: 1672-1677

在线出版日期: 2012-10-21（万方平台首次上网日期，不代表论文的发表时间）