A Riemannian Framework for Tensor Computing
International Journal of Computer Vision
Joint LMMSE Estimation of DWI Data for DTI Processing
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Non-local means variants for denoising of diffusion-weighted and diffusion tensor MRI
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
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A methodology to build a realistic phantom for the assessment of filtering performance in Diffusion Weighted Images (DWI) is presented. From a real DWI data---set, a regularization process is carried out taking into account the diffusion model. This process drives to a model which accurately preserves the structural characteristics of actual DWI volumes, being in addition regular enough to be considered as a noise---free data---set and therefore to be used as a ground---truth. We compare our phantom with a kind of simplified phantoms commonly used in the literature (those based on homogeneous cross sections), concluding that the latter may introduce important biases in common quality measures used in the filtering performance assessment, and even drive to erroneous conclusions in the comparison of different filtering techniques.