Probabilistic white matter fiber tracking using particle filtering and von Mises–Fisher sampling

Abstract

Standard particle filtering technique have previously been applied to the problem of fiber tracking by Brun et al. (2002) and Bjornemo et al. (2002). However, these previous attempts have not utilised the full power of the technique, and as a result the fiber paths were tracked in a goal directed way. In this paper we provide an advanced technique by presenting a fast and novel probabilistic method for white matter fiber tracking in diffusion weighted MRI (DWI), which takes advantage of the weighting and resampling mechanism of particle filtering. We formulate fiber tracking using a nonlinear state space model which captures both smoothness regularity of the fibers and the uncertainties in the local fiber orientations due to noise and partial volume effects. Global fiber tracking is then posed as a problem of particle filtering. To model the posterior distribution, we classify voxels of the white matter as either prolate or oblate tensors. We then construct the orientation distributions for prolate and oblate tensors separately. Finally, the importance density function for particle filtering is modeled using the von Mises-Fisher distribution on a unit sphere. Fast and efficient sampling is achieved using Ulrich-Wood's simulation algorithm. Given a seed point, the method is able to rapidly locate the globally optimal fiber and also provides a probability map for potential connections. The proposed method is validated and compared to alternative methods both on synthetic data and real-world brain MRI datasets.

Automated assessment and tracking of human body thermal variations using unsupervised clustering

“…A tracking algorithm needs to be reasonably robust since a surface is rarely uniform. This approach uses a particle filter tracker [81] to track and adapt to the tracking task (particle tracking also used many times in medical image analysis for 400 example [82,83,84]). It provides a robust system to face the changes in ROI Using a Bayesian method tracking algorithm, the particle filter works in the time t and approximates the tracking recursively of the target by a finite set of posterior distribution weighted samples.…”

“…There is extensive literature regarding different materials and their thermal properties such as emissivity and transmissivity. For instant, one of them presented by Ohman 1982, approached emissivity finding the sophisticated practical way by gathering the radiations of object, reflection and atmosphere [82].…”

“…To convert this relationship from radiation into thermal value, I, the S must be replaced with I/C that C is an empirical instrument factor [82].…”

A Novel Global Tractography Algorithm Based on an Adaptive Spin Glass Model

“…Moreover, further decomposition of fibers into morphological descriptors (e.g., length, curvature) for shape analysis becomes difficult. Recently, global alternatives to tractography were developed [4,5]. In those, the entire neural pathway is the parameter to be optimized, which elegantly adds robustness to deterministic tractography.…”

Brain Connectivity Using Geodesics in HARDI

“…A large number of tractography algorithms have been developed for DTI, which are limited in regions of fiber crossings. While HARDI-based extensions of streamline deterministic [5,6,7,4] and probabilistic [8,9,10,11,12,13,4] tracking algorithms have flourished in the last few years (the list is not exhaustive), [14] was the only attempt to generalize DTI geodesic tracking [15,16] for HARDI measurements.…”