Name
Abstract | ||
|---|---|---|
We approach the problem of fiber tractography from the viewpoint that a computational theory should relate to the underlying quantity that is being measured - the diffusion of water molecules. We characterize the brownian motion of water by a 3D random walk described by a stochastic non- linear differential equation. We show that the maximum- likelihood trajectories are 3D elastica, or curves of least energy. We illustrate the model with Monte-Carlo (sequen- tial) simulations and then develop a more efficient (local, parallelizable) implementation, based on the Fokker-Planck equation. The final algorithm allows us to efficiently com- pute stochastic completion fields to connect a source region to a sink region, while taking into account the underlying diffusion MRI data. We demonstrate promising tractogra- phy results using high angular resolution diffusion data as input. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CVPRW.2009.5204044 | computer vision and pattern recognition |
Keywords | Field | DocType |
brownian motion,fokker-planck equation,monte carlo methods,biodiffusion,biomedical mri,computation theory,nonlinear differential equations,3d random walk,3d stochastic completion field,monte-carlo simulation,computational theory,diffusion mri data,fiber tractography,high angular resolution diffusion data,maximum-likelihood trajectory,sink region,source region,stochastic nonlinear differential equation,water molecule diffusion | Parallelizable manifold,Diffusion MRI,Random walk,Computer science,Artificial intelligence,Statistical physics,Fokker–Planck equation,Differential equation,Mathematical optimization,Pattern recognition,Stochastic process,Brownian motion,Tractography | Conference |
Volume | Issue | ISSN |
2009 | 1 | 2160-7508 |
ISBN | Citations | PageRank |
978-1-4244-3994-2 | 2 | 0.39 |
References | Authors | |
14 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Parya Momayyez | 1 | 5 | 0.82 |
| Kaleem Siddiqi | 2 | 3259 | 242.07 |