Probabilistic fiber tracking using particle filtering.
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.
Dept. of Computer Science, University of York, York YO10 5DD, UK.
Fan
F
Zhang
Casey
C
Goodlett
Edwin
E
Hancock
Guido
G
Gerig
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000166:cytology*; D003627:Data Interpretation, Statistical; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D009413:Nerve Fibers, Myelinated / Q000648:ultrastructure*; D009434:Neural Pathways / Q000166:cytology; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity