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A fully automated method for computerized detection of pulmonary embolism in spiral computed tomography angiography was developed based on volumetric image analysis. The detection method is based on segmentation of pulmonary vessels to limit the search space, and analysis of several three-dimensional features inside segmented vessel volume. The features utilized are vascular size, local contrast based on mathematical morphology, degree of curvilinearity based on second derivatives, and geometric features such as volume and length. Detection results were obtained for 19 clinical data sets and the performance of the method was evaluated. Using the number and locations of thrombi diagnosed by radiologists as the gold standard, 100% sensitivity was achieved with 7.7 false positives per case, and 85 % sensitivity was obtained with 2.6 false positives. For identification of all the positive cases as positive, i.e., detection of at least one thrombus per positive case, 1.9 false positives per case were obtained. These preliminary results suggest that the method has potential for fully automated detection of pulmonary embolism. Index Terms--Computer-aided diagnosis, pulmonary embolism, segmentation, volumetric image analysis.

Magnetic resonance (MR) diffractive imaging is proposed as a new approach to MR angiography. The expression of the nuclear MR signal is similar to the equation for the Fresnel diffraction of a three-dimensional (3-D) object in light or sound waves. The proposed technique offers the possibility of fast angiographic imaging and the on-line reconstruction of 3-D volumetric images using the holographic technique. Static imaging experiments using an ultra-low-field MRI system are performed to verify the feasibility of the technique. It is shown that the images focused on an arbitrary plane can be reconstructed from data scanned in two dimensions, even though blurred image data is superimposed on the image. Moreover, the 3-D image can be observed in a coherent optical imaging system. This study demonstrates the possibility of the proposed method as a fast imaging technique for MR angiography. Index Terms--Angiography, Fresnel diffraction, holography, 3-D imaging.

Three-dimensional (3-D) angiographic methods are gaining acceptance for evaluation of atherosclerotic disease. However, measurement of vessel stenosis from 3-D angiographic methods can be problematic due to limited image resolution and contrast. We present a method for reconstructing vessel surfaces from 3-D angiographic methods that allows for objective measurement of vessel stenosis. The method is a deformable model that employs a tubular coordinate system. Vertex merging is incorporated into the coordinate system to maintain even vertex spacing and to avoid problems of self-intersection of the surface. The deformable model was evaluated on clinical magnetic resonance (MR) images of the carotid (n = 6) and renal (n = 2) arteries, on an MR image of a physical vascular phantom and on a digital vascular phantom. Only one gross error occurred for all clinical images. All reconstructed surfaces had a realistic, smooth appearance. For all segments of the physical vascular phantom, vessel radii from the surface reconstruction had an error of less than 0.2 of the average voxel dimension. Variability of manual initialization of the deformable model had negligible effect on the measurement of the degree of stenosis of the digital vascular phantom. Index Terms--Atherosclerosis, deformable model, magnetic resonance angiography, surface reconstruction.

Assigning anatomic labels to coronary arteries in X-ray angiograms is an important task in medical imaging, motivated by the desire to standardize the assessment of coronary artery disease and to facilitate the three-dimensional (3-D) reconstruction and visualization of the coronary vasculature. However, automatic labeling poses a number of significant challenges, including the presence of noise, artifacts, competing structures, misleading visual cues, and other difficulties associated with a dynamic and inherently complex structure. We have developed a model-guided approach that addresses these challenges and automatically labels the vascular structure in coronary angiographic images. The approach consists of two models: 1) a symbolic model, represented through a directed acyclic graph, that captures vascular tree hierarchies and branch interrelationships and 2) a generalized 3-D model that captures spatial and geometric relationships. Importantly, the approach detects ambiguities (such as vessel overlaps) that may be found in a frame of a cine sequence, and resolves these ambiguities by considering the information derived from other (unambiguous) frames in the temporal sequence, employing dynamic programming methods to match the image features found in the different (ambiguous and unambiguous) frames. This paper presents this model-guided labeling algorithm and discusses the experimental results obtained from implementing and applying the resulting labeling system to a variety of clinical images. The results indicate the feasibility of achieving robust and consistently accurate image labeling through this model-guided, temporal disambiguation method. Index Terms - Angiographic imaging, dynamic programming, image labeling, image understanding, medical imaging,

This paper describes the usefulness of computer assistance in the acquisition of 'good' images for stenosis diagnosis and quantification in coronary angiography. The system recommends the optimal viewpoints from which stenotic lesions can be observed clearly based on images obtained from initial viewpoints. First, the viewpoint dependency of the apparent severity of a stenotic lesion is experimentally analyzed using software phantoms in order to show the seriousness of the problem. The implementation of the viewpoint determination system is then described. The system provides good user-interactive tools for the semiautomated estimation of the orientation and diameter of stenotic segments and the three-dimensional (3-D) reconstruction of vessel structures. Using these tools, viewpoints that will not give rise to foreshortening and vessel overlap can be efficiently determined. Experiments using real coronary angiograms show the system to be capable of the reliable diagnosis and quantification of stenosis. Index Terms - Active vision, appearance analysis, computer-assisted diagnosis, coronary angiography, imaging strategy, stenosis diagnosis, stenosis quantification.

Reconstructing vasculature in three dimensions is a challenging problem. Early approaches concentrated on coronary vasculature in X-ray images, recent work uses magnetic resonance imagery of cerebral vasculature. In both cases a priori information has been used, and often the way this is represented has proven limiting to the scope of applications supported. For example, a particular representation may be useful only for X-ray images. This paper addresses two issues: 1) representing a collection of vasculature and 2) the reconstruction of individual vasculature from images. Our representation learns the variations in branching structures and vessel shapes that occur between individuals. It supports a vascular catalogue containing three-dimensional (3-D) anatomical models. The representation is task independent: here we use it to reconstruct vasculature from in, ages. Our algorithm has four features to which we draw attention: 1) it is not premised wholly upon X-ray images (though that is our focus here): 2) it produces several feasible solutions rather than one: 3) it can generalize from the catalogue to reconstruct instances not yet learned: 4) it exhibits polynomial time complexity, reasonable memory consumption, and is reliable. Both our representation and reconstruction algorithm are new and useful approaches. In support of these claims, we present results gathered from X-rays of both simulated and real vasculature. Index Terms - Cerebral vasculature. DSA, reconstruction, representation.

When performing hepatic digital subtraction angiography, misregistration of the mask image can cause artifacts that obscure pathology. For this article, researchers analyzed the breath expiration times of 59 patients and found that expiration times ranged from 1.8 seconds to 6 seconds. Thirty DSA series were obtained taking into account the patient's breath expiration time, and 35 series were obtained without taking the expiration time into consideration. Artifacts were detected in 20% of the DSA series where expiration time was taken into consideration and in 42.9% of series where it was not (p [is less than] 0.05). The research showed that misregistration of the mask image during hepatic DSA can be reduced by starting the exposure at the end of breath expiration., Three technical problems can adversely affect the outcome of hepatic digital subtraction angiography: saturation, diaphragmatic motion artifacts and misregistration artifacts in the diaphragmatic region of the liver. Research shows that [...]

Three-dimensional (3-D) high-resolution coronary angiograms offer a means for visualizing the entire coronary arterial tree from any orientation and for detecting and quantitating coronary arterial stenoses. Previously, a skilled operator had to perform several hours of tedious manual analysis using an interactive graphical user-interface (GUI) system (Tree Trace) to analyze a 3-D angiogram. We have devised an improved GUI system, consisting of three tools for analyzing 3-D angiograms. The Artery Extractor first performs automatic image-analysis operations to extract the central axes of the arterial tree. Next, using the Artery Display tool and results from the Artery Extractor, the operator can visualize structures in the angiogram and compute various measurements. Finally, the aforementioned Tree Trace tool can be used to manually correct irregularities in the automatically generated results of the Artery Extractor. The system greatly reduces operator analysis time, gives exactly reproducible results, uses true 3-D image-processing operations, and provides a comprehensive interface for visualizing and quantifying features of the 3-D coronary arteries.

We are developing an imaging system for three-dimensional (3-D) image analysis of vascular images. The purpose is to enhance the performance of clinicians in assessing anatomical information such as the orientation of blood vessels, cross-sectional area, surface shape and volume of vascular disease. The prototype system takes measurements in a short period of time by an X-ray rotational angiographic system using a CT gantry equipped with a cone-beam X-ray source and an image intensifier. The 3-D image reconstruction from two-dimensional (2-D) projections is performed by a short scan cone-beam filtered backprojection algorithm. The preprocessing of the 3-D image reconstruction includes the correction of the geometrical distortions and the shading artifact introduced by the image intensifier. In this paper, we present the short scan cone-beam filtered backprojection with scatter and veiling glare correction to improve the contrast sensitivity of 3-D reconstructed images. From results of the application to a patient's abdominal blood vessels, we show the effectiveness of these methods.

Visual estimation of coronary obstruction severity from angiograms suffers from poor inter- and intraobserver reproducibility and is often inaccurate. In spite of the widely recognized limitations of visual analysis, automated methods have not found widespread clinical use, in part because they too frequently fail to accurately identify vessel borders. We have developed a robust method for simultaneous detection of left and right coronary borders that is suitable for analysis of complex images with poor contrast, nearby or overlapping structures, or branching vessels. The reliability of the simultaneous border detection method and that of our previously reported conventional border detection method were tested in 130 complex images, selected because conventional automated border detection might be expected to fail. Conventional analysis failed to yield acceptable borders in 65/130 or 50% of images. Simultaneous border detection was much more robust (p < .001) and failed in only 15/130 or 12% of complex images. Simultaneous border detection identified stenosis diameters that correlated significantly better with observer-derived stenosis diameters than did diameters obtained with conventional border detection (p < 0.001). Simultaneous detection of left and right coronary borders is highly robust and has substantial promise for enhancing the utility of quantitative coronary angiography in the clinical setting.

Accurate and fully automatic assessment of vessel (stenoses) dimensions in angiographic images has been sought as a diagnostic tool, in particular for coronary heart disease. In this paper, we propose a new technique to estimate vessel borders in angiographic images, a necessary first step of any automatic analysis system. Unlike in previous approaches, the obtained edge estimates are not artificially smoothed; this is extremely important since quantitative analysis is the goal. Another important feature of the proposed technique is that no constant background is assumed, this making it well suited for nonsubtracted angiograms. The key aspect of our approach is that continuity/smoothness constraints are not used to modify the estimates directly derived from the image (which would introduce distortion) but rather to elect (without modifying) candidate estimates. Robustness against unknown background is provided by the use a morphological edge operator, instead of some linear operator (such as a matched filter) which has to assume known background and known vessel shape.

An algorithm is presented for the analysis and quantification of the vascular structures of the human retina. Information about retinal blood vessel morphology is used in grading the severity and progression of a number of diseases. These disease processes are typically followed over relatively long time courses, and subjective analysis of the sequential images dictates the appropriate therapy for these patients. In this research, retinal fluorescein angiograms are acquired digitally in a 1024 X 1024 16-b image format and are processed using an automated vessel tracking program to identify and quantitate stenotic and/or tortuous vessel segments. The algorithm relies on a matched filtering approach coupled with a priori knowledge about retinal vessel properties to automatically detect the vessel boundaries, track the midline of the vessel, and extract useful parameters of clinical interest. By modeling the vessel profile using Gaussian functions, improved estimates of vessel diameters are obtained over previous algorithms. An adaptive densitometric tracking technique based on local neighborhood information is also used to improve computational performance in regions where the vessel is relatively straight.