Optimization of Tensor Reconstruction by Excluding Outliers from DWIs

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 DWIs (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.