Faculty Bibliography

We propose a novel meshless deformable model for in vivo Left Ventricle (LV) 3D motion estimation and analysis based on tagged MRI (tMRI). The meshless deformable model can capture global deformations such as contraction and torsion with a few parameters, while track local deformations with Laplacian representation. In particular, the model performs well even when the control points (tag intersections) are relatively sparse. We test the performance of the meshless model on a numeric phantom, as well as in vivo heart data of healthy subjects and patients. The experimental results show that the meshless deformable model can fully recover the myocardial motion and strain in 3D

Fast tracking of motion is the key step towards tagged MRI-based quantitative cardiac analysis. Existing motion tracking approaches, including the widely used HARP method, are either time consuming or qualitatively inconsistent, or both. We present in this paper a new fast motion tracking method based on a meshless kernel. For MR image sequences containing multiple image frames, tag intersections are automatically detected in all frames and indexed in the first frame. Then a thin plate spline approach is used to establish a point-to-point correspondence between tag intersections in the initial and the current frame. Lastly, we use a meshless registration kernel to generate a dense displacement map that minimizes the residual of sparse motion at intersections. To further improve the motion tracking, we develop a special technique to preserve tangential angles of tags at tag intersections. We tested our new method on both numerical phantoms and in vivo heart data. The motion tracking results are evaluated against the ground truth and manually drawn tags. Clinical application potential is demonstrated by cardiac strain analysis based on the proposed methodology

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