@inproceedings{f1625bf4cb854ee3ba8585f4c1e50acd,
title = "Probabilistic fiber tracking using particle filtering",
abstract = "This paper presents a novel and fast probabilistic method for white matter fiber tracking from diffusion weighted MRI (DWI). We formulate fiber tracking on a nonlinear state space model which is able to capture both smoothness regularity of fibers and uncertainties of the local fiber orientations due to noise and partial volume effects. The global tracking model is implemented using particle filtering, which allows us to recursively compute the posterior distribution of the potential fibers. The fiber orientation distribution is theoretically formulated for prolate and oblate tensors separately. Fast and efficient sampling is realised using the von Mises-Fisher distribution on unit spheres. Given a seed point, the method is able to rapidly locate the global optimal fiber and also provide a connectivity map. The proposed method is demonstrated on a brain dataset.",
author = "Fan Zhang and Casey Goodlett and Edwin Hancock and Guido Gerig",
year = "2007",
doi = "10.1007/978-3-540-75759-7_18",
language = "English (US)",
isbn = "9783540757580",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
number = "PART 2",
pages = "144--152",
booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007 - 10th International Conference, Proceedings",
edition = "PART 2",
note = "10th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2007 ; Conference date: 29-10-2007 Through 02-11-2007",
}