Blood flow computation in phase-contrast MRI by minimal paths in anisotropic media

Authors:
Michael Schwenke;Anja Hennemuth;Bernd Fischer;Ola Friman
Affiliations:
Fraunhofer MEVIS, Institute for Medical Image Computing, Bremen, Germany;Fraunhofer MEVIS, Institute for Medical Image Computing, Bremen, Germany;Fraunhofer MEVIS, Project Group Image Registration and Institute of Mathematics and Image Computing, University of Lübeck, Germany;Fraunhofer MEVIS, Institute for Medical Image Computing, Bremen, Germany
Venue:
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Year:
2011

Citing 7
Cited 0

An analysis of 3D particle path integration algorithms

Journal of Computational Physics
Global Minimum for Active Contour Models: A Minimal Path Approach

International Journal of Computer Vision
Control Theory and Fast Marching Techniques for Brain Connectivity Mapping

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Accurate anisotropic fast marching for diffusion-based geodesic tractography

Journal of Biomedical Imaging - Recent Advances in Neuroimaging Methodology
A recursive anisotropic fast marching approach to reaction diffusion equation: application to tumor growth modeling

IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Probabilistic 4D blood flow mapping

MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Tubular Structure Segmentation Based on Minimal Path Method and Anisotropic Enhancement

International Journal of Computer Vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, anisotropic Fast Marching is employed to compute blood flow trajectories as minimal paths in 3D phase-contrast MRI images. Uncertainty in the estimated blood flow vectors is incorporated in a tensor which is used as metric for the anisotropic Fast Marching. A flow connectivity distribution is computed simultaneously to the Fast Marching. Based on the connectivity distribution the most likely flow trajectories can be identified. Results are presented for several PC MRI data sets and the capability of the method to indicate uncertainty of the flow trajectories is shown.