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The determination of myocardial volume at risk distal to coronary stenosis provides important information for prognosis and treatment of coronary artery disease. In this paper, we present a novel computational framework for estimating the myocardial volume at risk in computed tomography angiography (CTA) imagery. Initially, epicardial and endocardial surfaces, and coronary arteries are extracted using an active contour method. Then, the extracted coronary arteries are projected onto the epicardial surface, and each point on this surface is associated with its closest coronary artery using the geodesic distance measurement. The likely myocardial region at risk on the epicardial surface caused by a stenosis is approximated by the region in which all its inner points are associated with the sub-branches distal to the stenosis on the coronary artery tree. Finally, the likely myocardial volume at risk is approximated by the volume in between the region at risk on the epicardial surface and its projection on the endocardial surface, which is expected to yield computational savings over risk volume estimation using the entire image volume. Furthermore, we expect increased accuracy since, as compared to prior work using the Euclidean distance, we employ the geodesic distance in this work. The experimental results demonstrate the effectiveness of the proposed approach on pig heart CTA datasets.

The spline-based Mathematical Cardiac Torso (MCAT) phantom is a realistic software simulation designed to simulate single photon emission computed tomographic (SPECT) data. It incorporates a heart model of known size and shape; thus, it is invaluable for measuring accuracy of acquisition, reconstruction, and post-processing routines. New functionality has been added by replacing the standard heart model with left ventricular (LV) epicaridal and endocardial surface points detected from actual patient SPECT perfusion studies. LV surfaces detected from standard post-processing quantitation programs are converted through interpolation in space and time into new B-spline models. Perfusion abnormalities are added to the model based on results of standard perfusion quantification. The new LV is translated and rotated to fit within existing atria and right ventricular models, which are scaled based on the size of the LV. Simulations were created for five different patients with myocardial infractions who had undergone SPECT perfusion imaging. Shape, size, and motion of the resulting activity map were compared visually to the original SPECT images. In all cases, size, shape and motion of simulated LVs matched well with the original images. Thus, realistic simulations with known physiologic and functional parameters can be created for evaluating efficacy of processing algorithms.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A new implementation of the iterative closest point method for automatic registration is introduced. It is designed to unify three-dimensional models of the coronary artery tree created from biplane angiograms with three-dimensional models of the left ventricular epicardial surface created from perfusion SPECT. The speed and efficacy of the technique is evaluated using simulations and five patient studies. The technique is shown to be fast and quantitatively accurate in the simulations; evaluations of the results in patients are also satisfactory.

We present a solution method for adaptively smoothing magnetic resonance (MR) images while preserving discontinuities. We assume that the spatial behavior of MR data can be captured by a first order polynomial defined at every pixel. The formulation itself is similar to Leclerc's work on piecewise-smooth image segmentation, but we use the graduated non- convexity (GNC) algorithm as an optimizing tool for obtaining the solution. This requires initial values for polynomial coefficients of order greater than zero. These values are obtained by using ideas similar to that found in robust statistics. This initial step is also useful in determining the variance of the noise present in the input image. The variance is related to an important parameter (alpha) required by the GNC algorithm. Firstly, this replaces the heuristic nature of (alpha) with a quantity that can be estimated. Secondly, it is useful especially in situations where the variance of the noise is not uniform across the image. We present results on synthetic and MR images. Though the results of this paper are given using first order polynomials, the formulation can handle higher order polynomials.

This work addresses the problem of finding surfaces in 3-D images. This problem involves segmentation and parameterization. The solution method we propose is designed to answer the following questions: (1) Is a point (x,y,z) in 3-space a surface point? (2) If so, what is the surface normal at that point, and (3) What are the neighboring voxels in the u and v directions on the surface? We use a labeling scheme to define a surface element, at a point, in (u, v) directions. By restricting the neighborhood volume at a point, we have a small set of labels in u and v directions. A cost functional is set up, in terms of surface normal and labels at a point, following the calculus of variations methodology. Estimation of surface normal at a point depends on normals at neighboring points and label connectivity criteria defined. This also facilitates handling of surfaces that are not smoothly varying. The final solution is found by finding the set of normal and labels at every point that minimize the cost functional. Finally, we apply our method to find surfaces in 3-D synthetic and medical image data.

Classification of tissue-types in Magnetic Resonance (MR) images has received considerable attention in the medical image processing literature. Interpretation of MR images is based on multiple images corresponding to the same anatomy. Relaxation Labeling (RL) is a commonly used low-level technique in computer vision. We present a solution method for the classification of tissue-types in brain MR images using RL. Information from multiple images is combined to form an initial classification. RL is used to resolve the ambiguity present in the initial classification by incorporating user specified compatibility coefficients. A problem with RL is the smoothing of borders between tissue-types. We include edge information from the original images to overcome this problem. This results in a marked improvement in performance. We present results from patient images.

A relaxation labelling model was implemented to detect 3-d endo- and epicardial surfaces in ECG gated single photon emission computed tomographic perfusion studies of the heart. The model was tested using studies from normal volunteers and from patients with coronary artery disease. LV volumes calculated from the abnormal patient studies correlated well with those from contrast ventriculography, r=0.82 and r=0.89 for end systole and end diastole, respectively. The ejection fractions correlated well with those from radionuclide ventriculograms (r=0.93.) For normal volunteers, left ventricular endocardial volumes calculated using relaxation labelling correlated to those computed from user-traced surfaces with r-0.98. The correlation between epicardial volumes computed using relaxation labelling and hand-traced edges was also 0.98.