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
ZHARP: three-dimensional motion tracking from a single image plane
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
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
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The tracking and reconstruction of myocardial motion is critical to the diagnosis and treatment of heart disease. Currently, little has been done for the analysis of motion in long axis (LA) cardiac images. We propose a new fully automated motion reconstruction method for grid- tagged MRI that combines Gabor filters and deformable models. First, we use a Gabor filter bank to generate the corresponding phase map in the myocardium and estimate the location of grid tag intersections. Second, we use a non-rigid registration module driven by thin plate splines (TPS) to generate a transformation function between tag intersections in two consecutive images. Third, deformable spline models are initialized using Fourier domain analysis and tracked during the cardiac cycle using the TPS generated transformation function. The splines will then locally deform under the influence of gradient flow and image phase information. The final motion is decomposed into tangential and normal components corresponding to the local orientation of the heart wall. The new method has been tested on LA phantoms and in vivo heart data, and its performance has been quantitatively validated. The results show that our method can reconstruct the motion field in LA cardiac tagged MR images accurately and efficiently.