Automated tag tracking using Gabor filter bank, robust point matching, and deformable models

Authors:
Ting Chen;Sohae Chung;Leon Axel
Affiliations:
Radiology Department, New York University, School of Medicine, New York City, NY;Radiology Department, New York University, School of Medicine, New York City, NY;Radiology Department, New York University, School of Medicine, New York City, NY
Venue:
FIMH'07 Proceedings of the 4th international conference on Functional imaging and modeling of the heart
Year:
2007

Citing 4
Cited 2

A Graduated Assignment Algorithm for Graph Matching

IEEE Transactions on Pattern Analysis and Machine Intelligence
Physics-Based Deformable Models: Applications to Computer Vision, Graphics, and Medical Imaging

Physics-Based Deformable Models: Applications to Computer Vision, Graphics, and Medical Imaging
A new point matching algorithm for non-rigid registration

Computer Vision and Image Understanding - Special issue on nonrigid image registration
Dense myocardium deformation estimation for 2d tagged MRI

FIMH'05 Proceedings of the Third international conference on Functional Imaging and Modeling of the Heart

LV Motion and Strain Computation from tMRI Based on Meshless Deformable Models

MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Motion analysis with quadrature filter based registration of tagged MRI sequences

STACOM'11 Proceedings of the Second international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges

Quantified Score

Hi-index	0.00

Visualization

Abstract

Tagged Magnetic Resonance Imaging (tagged MRI or tMRI) provides a means of directly and noninvasively displaying the motion of the myocardium. Reconstruction of the motion field is needed for quantitative analysis of important clinical information, e.g., the myocardial strain. In this paper, we present a two-step method for this task. First, we use a Gabor filter bank to generate a corresponding phase map of tMRI images. Second, deformable models are initialized at the discontinuities in the wrapped phase map, and are deformed under the influence of the image gradient to track the motion of tags. Unlike previous approaches, a Robust Point Matching (RPM) module has been integrated into the model evolution to avoid false tracking results caused by 1) through-plane motion, and 2) small tag spacing. The method has been tested on a numeric phantom, as well as in vivo heart data. The experimental results show that the new method has a good performance on both synthetic and real data, and has the potential to be used in clinical applications.