Pulmonary embolism (PE) is a common life-threatening disorder for which an early diagnosis is desirable. We propose a new system for the automatic detection of PE in contrast-enhanced CT images. The system consists of candidate detection, feature computation and classification. Candidate detection focuses on the inclusion of PE--even complete occlusions--and the exclusion of false detections, such as tissue and parenchymal diseases. Feature computation does not only focus on the intensity, shape and size of an embolus, but also on locations and the shape of the pulmonary vascular tree. Several classifiers have been tested and the results show that the performance is optimized by using a bagged tree classifier with two features based on the shape of a blood vessel and the distance to the vessel boundary. The system was trained on 38 CT data sets. Evaluation on 19 other data sets showed that the system generalizes well. The sensitivity of our system on the evaluation data is 63% at 4.9 false positives per data set, which allowed the radiologist to improve the number of detected PE by 22%.

Visually assessing the effect of the coronary artery anatomy on the perfusion of the heart muscle in patients with coronary artery disease remains a challenging task. We explore the feasibility of visualizing this effect on perfusion using a numerical approach. We perform a computational simulation of the way blood is perfused throughout the myocardium purely based on information from a three-dimensional anatomical tomographic scan. The results are subsequently visualized using both three-dimensional visualizations and bull's eye plots, partially inspired by approaches currently common in medical practice. Our approach results in a comprehensive visualization of the coronary anatomy that compares well to visualizations commonly used for other scanning technologies. We demonstrate techniques giving detailed insight in blood supply, coronary territories and feeding coronary arteries of a selected region. We demonstrate the advantages of our approach through visualizations that show information which commonly cannot be directly observed in scanning data, such as a separate visualization of the supply from each coronary artery. We thus show that the results of a computational simulation can be effectively visualized and facilitate visually correlating these results to for example perfusion data.

We present novel, comprehensive visualization techniques for the diagnosis of patients with Coronary Artery Disease using segmented cardiac MRI data. We extent an accepted medical visualization technique called the bull's eye plot by removing discontinuities, preserving the volumetric nature of the left ventricular wall and adding anatomical context. The resulting volumetric bull's eye plot can be used for the assessment of transmurality. We link these visualizations to a 3D view that presents viability information in a detailed anatomical context. We combine multiple MRI scans (whole heart anatomical data, late enhancement data) and multiple segmentations (polygonal heart model, late enhancement contours, coronary artery tree). By selectively combining different rendering techniques we obtain comprehensive yet intuitive visualizations of the various data sources.

This paper discusses two applications of probing dense volumetric data for MR orthopedics and dynamic contrast enhanced MRI mammography. In order not to reduce the context information and to extract the essential part of the data, we apply Profile Flags. A Profile Flag is a 3D glyph for probing and annotating the volumetric data. The first application area deals with visualization of T2 profiles for interactive inspection of knee cartilage and detection of lesions. In the second application, we present the usability the Profile Flags for measuring of time-signal profiles for a set of time-dependent MR volumes. Several extensions of the basic Profile Flag concept are described in detail and discussed. These extensions include selection of a set of profiles based on spatial as well as curve differences, automatic positioning of the Profile Flags, and adaptation for probing of time-varying volumetric data. Additionally, we include the evaluation of the used methods by our medical partners.

Transfer function design plays a crucial role in direct volume rendering. Furthermore, it has a major influence on the efficiency of the visualization process. We have developed a framework that facilitates the semi-automatic design of transfer functions. Similarly to other approaches we generate clusters in the transfer function domain. We created a real-time interaction with a hierarchy of clusters. This interaction effectively substitutes cumbersome settings of clustering thresholds. Our framework is also able to easily combine different clustering criteria. We have developed two similarity measures for clustering of material boundaries. One is based on the similarity of the boundaries in the transfer function domain and the other on their spatial relation. We use the LH space as the transfer function domain. This space facilitates the clustering of material boundaries. We demonstrate our approach on several examples.

We have developed a method for automatic contour propagation in cine cardiac magnetic resonance images. The method consists of a new active contour model that tries to maintain a constant contour environment by matching gray values in profiles perpendicular to the contour. Consequently, the contours should maintain a constant position with respect to neighboring anatomical structures, such that the resulting contours reflect the preferences of the user. This is particularly important in cine cardiac magnetic resonance images because local image features do not describe the desired contours near the papillary muscle. The accuracy of the propagation result is influenced by several parameters. Because the optimal setting of these parameters is application dependent, we describe how to use full factorial experiments to optimize the parameter setting. We have applied our method to cine cardiac magnetic resonance image sequences from the long axis two-chamber view, the long axis four-chamber view, and the short axis view. We performed our optimization procedure for each contour in each view. Next, we performed an extensive clinical validation of our method on 69 short axis data sets and 38 long axis data sets. In the optimal parameter setting, our propagation method proved to be fast, robust, and accurate. The resulting cardiac contours are positioned within the interobserver ranges of manual segmentation. Consequently, the resulting contours can be used to accurately determine physiological parameters such as stroke volume and ejection fraction.

A crucial step in volume rendering is the design of transfer functions that will highlight those aspects of the volume data that are of interest to the user. For many applications, boundaries carry most of the relevant information. Reliable detection of boundaries is often hampered by limitations of the imaging process, such as blurring and noise. We present a method to identify the materials that form the boundaries. These materials are then used in a new domain that facilitates interactive and semiautomatic design of appropriate transfer functions. We also show how the obtained boundary information can be used in region-growing-based segmentation.

This paper describes a tool for the visualization of T/sub 2/ maps of knee cartilage. Given the anatomical scan, and the T/sub 2/ map of the cartilage, we combine the information on the shape and the quality of the cartilage in a single image. The Profile Flag is an intuitive 3D glyph for probing and annotating of the underlying data. It comprises a bulletin board pin-like shape with a small flag on top of it. While moving the glyph along the reconstructed surface of an object, the curve data measured along the pin's needle and in its neighborhood are shown on the flag. The application area of the Profile Flag is manifold, enabling the visualization of profile data of dense but in-homogeneous objects. Furthermore, it extracts the essential part of the data without removing or even reducing the context information. By sticking Profile Flags into the investigated structure, one or more significant locations can be annotated by showing the local characteristics of the data at that locations. In this paper we are demonstrating the properties of the tool by visualizing T/sub 2/ maps of knee cartilage.

Conventional edge-detection methods suffer from the dislocation of curved surfaces due to the PSF. We propose a new method that uses the isophote curvature to circumvent this. It is accurate for objects with locally constant curvature, even for small objects (like blood vessels) and in the presence of noise.

In recent years, advances in computer technology and a significant increase in the accuracy of medical imaging have made it possible to develop systems that can assist the clinician in diagnosis, planning, and treatment. This paper deals with an area that is generally referred to as computer-assisted surgery, image-directed surgery, or image-guided surgery. We report the research, development, and clinical validation performed since January 1996 in the European Applications in Surgical Interventions (EASI) project, which is funded by the European Commission in their "4th Framework Telematics Applications for Health" program. The goal of this project is the improvement of the effectiveness and quality of image-guided neurosurgery of the brain and image-guided vascular surgery of abdominal aortic aneurysms, while at the same time reducing patient risks and overall cost. We have developed advanced prototype systems for preoperative surgical planning and intraoperative surgical navigation, and we have extensively clinically validated these systems. The prototype systems and the clinical validation results are described in this paper.