Faculty Bibliography

In this paper we present a fully automatic and accurate segmentation framework for 2D tagged cardiac MR images. This scheme consists of three learning methods: a) an active shape model is implemented to model the heart shape variations, b) an Adaboost learning method is applied to learn confidence-rated boundary criterions from the local appearance features at each landmark point on the shape model, and c) an Adaboost detection technique is used to initialize the segmentation. The set of boundary statistics learned by Adaboost is the weighted combination of all the useful appearance features, and results in more reliable and accurate image forces compared to using only edge or region information. Our experimental results show that given similar imaging techniques, our method can achieve a highly accurate performance without any human interaction

Magnetic resonance imaging (MRI) of the heart with magnetization tagging provides a potentially useful new way to assess cardiac mechanical function, through revealing the local motion of otherwise indistinguishable portions of the heart wall. While still an evolving area, tagged cardiac MRI is already able to provide novel quantitative information on cardiac function. Exploiting this potential requires developing tailored methods for both imaging and image analysis. In this paper, we review some of the progress that has been made in developing such methods for tagged cardiac MRI, as well as some of the ways these methods have been applied to the study of cardiac function

Heart ventricular mechanics has been investigated intensively in the last four decades. The passive material properties, the ventricular geometry and muscular architecture, and the myocardial activation are among the most important determinants of cardiac mechanics. The heart muscle is anisotropic, inhomogeneous, and highly nonlinear. The heart ventricular geometry is irregular and object dependent. The muscular architecture includes the organization of the fiber and the connective tissues. Studies of the myocardial activation have been carried out at both cell and tissue levels. Previous work from our research group has successfully estimated the in-vivo motion and deformation of both the left and the right ventricles. In this paper, we present an iterative model to estimate the in-vivo myocardium material properties, the active forces generated along fiber orientation, and strain and stress distribution in both ventricles. Compared to the strain energy function approach, our model is more intuitively understandable. Using the model, we have simulated the mechanical events of a few different heart diseases. Noticeable strain and stress differences are found between normal and diseased hearts

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

We report a case of a large saphenous vein graft (SVG) aneurysm masquerading as a right atrial mass on transesophageal echocardiogram. Cardiac magnetic resonance angiography reliably made a diagnosis of SVG aneurysm extrinsically compressing right atrium. This case illustrates the importance of using combined imaging modalities for the diagnosis and management of cardiac masses

Heart disease is the leading cause of death in the Western world and consequently the study of normal and pathological heart behavior is an active research area. In particular, the study of the shape and motion of the heart is important because many heart diseases are strongly correlated to these two factors. The human heart is composed of two separate pumps: a right heart that pumps the blood through the lungs and a left heart that pumps the blood through the peripheral organs. In turn, each of these "hearts" is a two-chamber pump composed of an atrium and a ventricle. Special mechanisms in the heart provide cardiac rhythm and transmit action potentials throughout the heart muscle to cause the heart's rhythmic relaxation (diastole) and contraction (systole).

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

Computed Tomography (CT) is one of the most sensitive medical imaging modalities for detecting pulmonary nodules. Its high contrast resolution allows the detection of small nodules and thus lung cancer at a very early stage. In this paper, we propose a method for automating nodule detection from high-resolution chest CT images. Our method focuses on the detection of discrete types of granulomatous nodules less than 5 mm in size using a series of 3D filters, Pulmonary nodules can be anywhere inside the lung, e.g., on lung walls near vessels, or they may even be penetrated by vessels,. For this reason,, we first develop a new cylinder filter to suppress vessels, and noise. Although nodules usually have higher intensity values than surrounding regions, many malignant nodules are of low contrast. In order not to ignore low contrast nodules, we develop a spherical filter to, further enhance nodule intensity values, which is a novel 3D extension of Variable N-Quoit filter. As with most automatic nodule detection methods, our method generates false positive nodules. To address this, we also develop a filter for false positive elimination. Finally, we present promising results of applying our method to various clinical chest CT datasets with over 90% detection rate

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