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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.

Design and Construction of a Realistic DWI Phantom for Filtering Performance Assessment