Abstract Radiological investigations are common medical practice for the diagnosis of peripheral vascular diseases. Existing visualization methods such as Curved Planar Reformation (CPR) depict calcifications on vessel walls to determine if blood is still able to flow. While it is possible with conventional CPR methods to examine the whole vessel lumen by rotating around the centerline of a vessel, we propose Curvicircular Feature Aggregation (CFA), which aggregates these rotated images into a single view. By eliminating the need for rotation, vessels can be investigated by inspecting only one image. This method can be used as a guidance and visual analysis tool for treatment planning. We present applications of this technique in the medical domain and give feedback from radiologists.

It is a difficult task to design transfer functions for noisy data. In traditional transfer-function spaces, data values of different materials overlap. In this paper we introduce a novel statistical transfer-function space which in the presence of noise, separates different materials in volume data sets. Our method adaptively estimates statistical properties, i.e. the mean value and the standard deviation, of the data values in the neighborhood of each sample point. These properties are used to define a transfer-function space which enables the distinction of different materials. Additionally, we present a novel approach for interacting with our new transfer-function space which enables the design of transfer functions based on statistical properties. Furthermore, we demonstrate that statistical information can be applied to enhance visual appearance in the rendering process. We compare the new method with 1D, 2D, and LH transfer functions to demonstrate its usefulness.

This paper presents a novel method for the interactive identification of contextual interest points within volumetric data by picking on a direct volume rendered image. In clinical diagnostics the points of interest are often located in the center of anatomical structures. In order to derive the volumetric position which allows a convenient examination of the intended structure, the system automatically extracts contextual meta information from the DICOM (Digital Imaging and Communications in Medicine) images and the setup of the medical workstation. Along a viewing ray for a volumetric picking, the ray profile is analyzed for structures which are similar to predefined templates from a knowledge base. We demonstrate with our results that the obtained position in 3D can be utilized to highlight a structure in 2D slice views, to interactively calculate centerlines of tubular objects, or to place labels at contextually-defined volumetric positions.

Although real-time interactive volume rendering is available even for very large data sets, this visualization method is used quite rarely in the clinical practice. We suspect this is because it is very complicated and time consuming to adjust the parameters to achieve meaningful results. The clinician has to take care of the appropriate viewpoint, zooming, transfer function setup, clipping planes and other parameters. Because of this, most often only 2D slices of the data set are examined. Our work introduces LiveSync, a new concept to synchronize 2D slice views and volumetric views of medical data sets. Through intuitive picking actions on the slice, the users define the anatomical structures they are interested in. The 3D volumetric view is updated automatically with the goal that the users are provided with expressive result images. To achieve this live synchronization we use a minimal set of derived information without the need for segmented data sets or data-specific pre-computations. The components we consider are the picked point, slice view zoom, patient orientation, viewpoint history, local object shape and visibility. We introduce deformed viewing spheres which encode the viewpoint quality for the components. A combination of these deformed viewing spheres is used to estimate a good viewpoint. Our system provides the physician with synchronized views which help to gain deeper insight into the medical data with minimal user interaction.

In volume rendering, it is very difficult to simultaneously visualize interior and exterior structures while preserving clear shape cues. Highly transparent transfer functions produce cluttered images with many overlapping structures, while clipping techniques completely remove possibly important context information. In this paper, we present a new model for volume rendering, inspired by from illustration. It provides a means of interactively inspecting the interior of a volumetric data set in a feature-driven way which retains context information. The context-preserving volume rendering model uses a function of shading intensity, gradient magnitude, distance to the eye point, and previously accumulated opacity to selectively reduce the opacity in less important data regions. It is controlled by two user-specified parameters. This new method represents an alternative to conventional clipping techniques, sharing their easy and intuitive user control, but does not suffer from the drawback of missing context information.

In volume rendering it is very difficult to simultaneously visualize interior and exterior structures while preserving clear shape cues. Very transparent transfer functions produce cluttered images with many overlapping structures, while clipping techniques completely remove possibly important context information. In this paper we present a new model for volume rendering, inspired by techniques from illustration that provides a means of interactively inspecting the interior of a volumetric data set in a feature-driven way which retains context information. The context-preserving volume rendering model uses a function of shading intensity, gradient magnitude, distance to the eye point, and previously accumulated opacity to selectively reduce the opacity in less important data regions. It is controlled by two user-specified parameters. This new method represents an alternative to conventional clipping techniques, shares their easy and intuitive user control, but does not suffer from the drawback of missing context information.

This paper presents importance-driven feature enhancement as a technique for the automatic generation of cut-away and ghosted views out of volumetric data. The presented focus+context approach removes or suppresses less important parts of a scene to reveal more important underlying information. However, less important parts are fully visible in those regions, where important visual information is not lost, i.e., more relevant features are not occluded. Features within the volumetric data are first classified according to a new dimension, denoted as object importance. This property determines which structures should be readily discernible and which structures are less important. Next, for each feature, various representations (levels of sparseness) from a dense to a sparse depiction are defined. Levels of sparseness define a spectrum of optical properties or rendering styles. The resulting image is generated by ray-casting and combining the intersected features proportional to their importance (importance compositing). The paper includes an extended discussion on several possible schemes for levels of sparseness specification. Furthermore, different approaches to importance compositing are treated.

This work introduces importance-driven volume rendering as a novel technique for automatic focus and context display of volumetric data. Our technique is a generalization of cut-away views, which - depending on the viewpoint - remove or suppress less important parts of a scene to reveal more important underlying information. We automatize and apply this idea to volumetric data. Each part of the volumetric data is assigned an object importance, which encodes visibility priority. This property determines which structures should be readily discernible and which structures are less important. In those image regions, where an object occludes more important structures it is displayed more sparsely than in those areas where no occlusion occurs. Thus the objects of interest are clearly visible. For each object several representations, i.e., levels of sparseness, are specified. The display of an individual object may incorporate different levels of sparseness. The goal is to emphasize important structures and to maximize the information content in the final image. This work also discusses several possible schemes for level of sparseness specification and different ways how object importance can be composited to determine the final appearance of a particular object.

Abstract We present Volume dots (Vots), a new primitive for volumetric data modelling, processing, and rendering. Vots are a point‐based representation of volumetric data. An individual Vot is specified by the coefficients of a Taylor series expansion, i.e. the function value and higher order derivatives at a specific point. A Vot does not only represent a single sample point, it represents the underlying function within a region. With the Vots representation we have a more intuitive and high‐level description of the volume data. This allows direct analytical examination and manipulation of volumetric datasets. Vots enable the representation of the underlying scalar function with specified precision. User‐centric importance sampling is also possible, i.e., unimportant volume parts are still present but represented with just very few Vots. As proof of concept, we show Maximum Intensity Projection based on Vots. Categories and Subject Descriptors (according to ACM CCS): I.3.6 [Computer Graphics]: Graphics Data Structures and Data Types

Traditional volume visualization techniques may provide incomplete clinical information needed for applications in medical visualization. In the area of vascular visualization important features such as the lumen of a diseased vessel segment may not be visible. Curved planar reformation (CPR) has proven to be an acceptable practical solution. Existing CPR techniques, however, still have diagnostically relevant limitations. In this paper, we introduce two advances methods for efficient vessel visualization, based on the concept of CPR. Both methods benefit from relaxation of spatial coherence in favor of improved feature perception. We present a new technique to visualize the interior of a vessel in a single image. A vessel is resampled along a spiral around its central axis. The helical spiral depicts the vessel volume. Furthermore, a method to display an entire vascular tree without mutually occluding vessels is presented. Minimal rotations at the bifurcations avoid occlusions. For each viewing direction the entire vessel structure is visible.

Non-linear filtering is an important task for volume analysis. This paper presents hardware-based implementations of various non-linear filters for volume smoothing with edge preservation. The Cg high-level shading language is used in combination with latest PC consumer graphics hardware. Filtering is divided into pervertex and per-fragment stages. In both stages we propose techniques to increase the filtering performance. The vertex program pre-computes texture coordinates in order to address all contributing input samples of the operator mask. Thus additional computations are avoided in the fragment program. The presented fragment programs preserve cache coherence, exploit 4D vector arithmetic, and internal fixed point arithmetic to increase performance. We show the applicability of non-linear filters as part of a GPU-based segmentation pipeline. The resulting binary mask is compressed and decompressed in the graphics memory on-the-fly.

Visualization of tubular structures such as blood vessels is an important topic in medical imaging. One way to display tubular structures for diagnostic purposes is to generate longitudinal cross-sections in order to show their lumen, wall, and surrounding tissue in a curved plane. This process is called curved planar reformation (CPR). We present three different methods to generate CPR images. A tube-phantom was scanned with computed tomography (CT) to illustrate the properties of the different CPR methods. Furthermore we introduce enhancements to these methods: thick-CPR, rotating-CPR and multi-path-CPR.

We report on using computed tomography (CT) as a model acquisition tool for complex objects in computer graphics. Unlike other modeling and scanning techniques the complexity of the object is irrelevant in CT, which naturally enables to model objects with, for example, concavities, holes, twists or fine surface details. Once the data is scanned, one can apply post-processing techniques for data enhancement, modification or presentation. For demonstration purposes we chose to scan a Christmas tree which exhibits high complexity which is difficult or even impossible to handle with other techniques. However, care has to be taken to achieve good scanning results with CT. Further, we illustrate post-processing by means of data segmentation and photorealistic as well as non-photorealistic surface and volume rendering techniques.

This paper deals with vessel exploration based on computed tomography angiography. Large image sequences of the lower extremities are investigated in a clinical environment. Two different approaches for peripheral vessel diagnosis dealing with stenosis and calcification detection are introduced. The paper presents an automated vessel-tracking tool for curved planar reformation. An interactive segmentation tool for bone removal is proposed.