Massive particles for brain tractography

Authors:
Ramón Aranda;Mariano Rivera;Alonso Ramírez-Manzanares;Manzar Ashtari;James C. Gee
Affiliations:
Centro De Invertigación en Matemáticas, Guanajuato, Gto, México;Centro De Invertigación en Matemáticas, Guanajuato, Gto, México;Universidad de Guanajuato, Departemento de Matemáticas, Guanajuato, Gto, México;Diffusion Tensor Imaging and Brain Morphometry Lab, Childrens Hospital of Philadelphia, Philadelphia, PA;Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, PA
Venue:
MICAI'10 Proceedings of the 9th Mexican international conference on Advances in artificial intelligence: Part I
Year:
2010

Citing 1
Cited 1

Basis Tensor Decomposition for Restoring Intra-Voxel Structure and Stochastic Walks for Inferring Brain Connectivity in DT-MRI

International Journal of Computer Vision

Improved diffusion basis functions fitting and metric distance for brain axon fiber estimation

PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part II

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Abstract

We propose a method for estimating axonal fiber connectivity pathways (cerebral connectivity fibers) from Multi-Tensor Diffusion Magnetic Resonance Imaging (MTD-MRI). Our method uses multiple local orientation information provided by MTD-MRI for leading stochastic walks of particles. We perform stochastic walks on particles with mass which introduce inertia and gravitational forces that result in filtered trajectories. Afterwards, the fiber bunches are estimated with a clustering procedure based on terminal points that allows us to eliminate outliers. The method's performance is evaluated on MTD-MRI from realistic synthetic data, a diffusion phantom and demonstrated in real human brain data.