Faculty Bibliography

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

In this paper we propose a novel hybrid 3D segmentation framework which combines Gibbs models, marching cubes and deformable models. In the framework, first we construct a new Gibbs model whose energy function is defined on a high order clique system. The new model includes both region and boundary information during segmentation. Next we improve the original marching cubes method to construct 3D meshes from Gibbs models' output. The 3D mesh serves as the initial geometry of the deformable model. Then we deform the deformable model using external image forces so that the model converges to the object surface. We run the Gibbs model and the deformable model recursively by updating the Gibbs model's parameters using the region and boundary information in the deformable model segmentation result. In our approach, the hybrid combination of region-based methods and boundary-based methods results in improved segmentations of complex structures. The benefit of the methodology is that it produces high quality segmentations of 3D structures using little prior information and minimal user intervention. The modules in this segmentation methodology are developed within the context of the Insight ToolKit (ITK). We present experimental segmentation results of brain tumors and evaluate our method by comparing experimental results with expert manual segmentations. The evaluation results show that the methodology achieves high quality segmentation results with computational efficiency. We also present segmentation results of other clinical objects to illustrate the strength of the methodology as a generic segmentation framework.

Magnetic resonance tagging technique measures the deformation of the heart wall by overlying darker tag lines onto the brighter myocardiurn and tracking their motion during the heart cycle. In this paper, we propose a new spline-based methodology for constructing a dense cardiac displacement map based on the tag tracking result. In this new approach, the deformed tags are tracked using a Gabor filter-based technique and smoothed using implicit splines. Then we measure the displacement in the myocardium of both ventricles using a new spline interpolation model. This model uses rough segmentation results to set up break points along tag tracking spline so that the local myocardium deformation will not be influenced by the tag information in the blood or the deformation in other parts of the myocardium. The displacements in x- and y- directions are calculated separately and are combined later to form the final displacement map. This method accepts either a tag grid or separate horizontal and vertical tag lines as its input by adjusting the offsets of images taken at different breath hold. The method can compute dense displacement maps of the myocardiurn for time phases during systole and diastole. The approach has been quantatively validated on phantom images and been tested on more than 20 sets of in-vivo heart data

Recent advances in CT technology have allowed the development of systems with multiple rows of detectors and rapid rotation. These new imaging systems have permitted the acquisition of high resolution, spatially registered, and cardiac gated 3D heart data. In this paper, we present a framework that makes use of these data to reconstruct the 3D cardiac anatomy with resolutions that were not previously possible. We use an improved 3D hybrid segmentation framework which integrates Gibbs prior models, deformable models, and the marching cubes method to achieve a sub-pixel accuracy of the reconstruction of cardiac objects. To improve the convergence at concavities on the object surface, we introduce a new type of external force, which we call the scalar gradient. The scalar gradient is derived from a gray level edge map using local configuration information and can help the deformable models converge into deep concavities on object's surface. The 3D segmentation and reconstruction have been conducted on 8 high quality CT data sets. Important features, such as the structure of papillary muscles, have been well captured, which may lead to a new understanding of the cardiac anatomy and function. All experimental results have been evaluated by clinical experts and the validation shows the method has a very strong performance

Myocardial tagging is a non-invasive MR imaging technique; it generates a periodic tag pattern in the magnetization that deforms with the tissue during the cardiac cycle. It can be used to assess regional myocardial function, including tissue displacement and strain. Most image analysis methods require labor-intensive tag detection and tracking. We have developed an accurate and automated method for tag detection in order to calculate strain from tagged magnetic resonance images of the heart. It detects the local spatial frequency and phase of the tags using a bank of Gabor filters with varying frequency and phase. This variation in tag frequency is then used to calculate the local myocardial strain. The method is validated using computer simulations