A novel intrinsic unscented Kalman filter for tractography from HARDI

The unscented Kalman filter (UKF) was recently introduced in literature for simultaneous multi-tensor estimation and tractography. This UKF however was not intrinsic to the space of diffusion tensors. Lack of this key property leads to inaccuracies in the multi-tensor estimation as well as in tractography. In this paper, we propose an novel intrinsic unscented Kalman filter (IUKF) in the space of symmetric positive definite matrices, which can be used for simultaneous recursive estimation of multi-tensors and tractography from diffusion weighted MR data. In addition to being more accurate, IUKF retains all the advantages of UKF for instance, multi-tensor estimation is only performed in the places where it is needed for tractography, which would be much more efficient than the two stage process involved in methods that do tracking post diffusion tensor estimation. The accuracy and effectiveness of the proposed method is demonstrated via real data experiments.