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

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

In this paper, we describe a new approach for reconstructing 3D strains in the myocardium using tagged MR images. We first segment the myocardium using a 3D deformable model driven by image gradients and Gabor filter responses. Tags are automatically detected and tracked as deformable thin plates during systole and early diastole. To keep the tracking results more stable and consistent, we use a combination of gradient information, an intensity probabilistic model, the phase information, and a temporal-spatial smoothness constraint. Based on the tag deformation, we compute a dense displacement in the myocardium around both ventricles. The displacements in x-, y-, and z- directions are calculated separately and are combined to form the final displacement maps. We do not use the information outside the segmented surface of the myocardium to avoid displacement errors caused by noises, artifacts, and correlations between different regions in the myocardium. The strain in the myocardium during the heart cycle is derived from the displacement. This method accepts images of either a tag grid or separate horizontal and vertical tag lines as its input. Experimental results on phantom and real data demonstrate good performance of this method in calculating the myocardial strain