Canonical polyadic decomposition for principal diffusion direction extraction in diffusion weighted imaging
Diffusion weighted imaging is a non-invasive method for investigation of brain fiber bundles. In diffusion tensor imaging (DTI), the diffusion of water molecules is assumed Gaussian, therefore, it can just show a single fiber direction in a voxel. To overcome this limitation, a number of high angular resolution diffusion imaging methods have been proposed. One of these techniques is Q-ball imaging. Using this method, we can extract orientation distribution function (ODF) that shows the orientations of multiple fibers in a voxel. For extracting the fiber directions, the maxima of the ODFs are conventionally determined. However, the results of this approach are sensitive to noise. To improve the results, we present a method for extracting principal diffusion directions using a tensor decomposition method. The results obtained using simulated and real data show that our proposed method has a better performance compared to the local maxima. For SNRs less than 30 dB, the similarity value of our method is 0.3 larger than that of the local maxima method.