Computed tomography (CT) has a trend towards higher resolution and higher noise. This development has increased the interest in anisotropic smoothing techniques for CT, which aim to reduce noise while preserving structures of interest. However, existing smoothing techniques are slow, which makes clinical application difficult. Furthermore, the published methods have limitations with respect to preserving small details in CT data. This paper presents a widely applicable speed optimized framework for anisotropic smoothing techniques. A second contribution of this paper is an extension to an existing smoothing technique aimed at better preserving small structures of interest in CT data. Based on second-order image structure, the method first determines an importance map, which indicates potentially relevant structures that should be preserved. Subsequently an anisotropic diffusion process is started. The diffused data is used in most parts of the images, while structures with significant second-order information are preserved. The method is qualitatively evaluated against an anisotropic diffusion method without structure preservation in an observer study to assess the improvement of 3-D visualizations of CT series and quantitatively by determining the reduction of the difference between low and high dose CT scans of in vitro carotid plaques.

In digital subtraction angiography (DSA), patient motion is the primary cause of image quality degradation. The motion correction algorithms developed so far were not sufficiently fast so as to be suitable for integration in a clinical setting. In this paper we describe a new image registration technique for motion artifact reduction in DSA which is fully automatic, effective, and computationally very efficient. Using an image content driven control point selection mechanism and modern graphics hardware for image warping, the algorithm requires less than one second per DSA image (on average). Preliminary experiments on cerebral DSA images illustrate the applicability of the technique.

The reconstruction of images is an important operation in many applications. From sampling theory, it is well known that the sine-function is the ideal interpolation kernel which, however, cannot be used in practice. In order to be able to obtain an acceptable reconstruction, both in terms of computational speed and mathematical precision, it is required to design a kernel that is of finite extent and resembles the sinc-function as much as possible. In this paper, the applicability of the sine-approximating symmetrical piecewise nth-order polynomial kernels is investigated in satisfying these requirements. After the presentation of the general concept, kernels of first, third, fifth and seventh order are derived. An objective, quantitative evaluation of the reconstruction capabilities of these kernels is obtained by analyzing the spatial and spectral behavior using different measures, and by using them to translate, rotate, and magnify a number of real-life test images. From the experiments, it is concluded that while the improvement of cubic convolution over linear interpolation is significant, the use of higher order polynomials only yields marginal improvement.

Magnetic resonance imaging (MRI) offers potential advantages over conventional X-ray techniques for guiding and evaluating vascular interventions. Image guidance of such interventions via passive catheter tracking requires real-time image processing. Commercially available MR scanners currently do not provide this functionality. This paper describes an image processing environment that allows near-real-time MR-guided vascular interventions. It demonstrates 1) that flexibility can be achieved by separating the scanner and the image processing/display system, thereby preserving the stability of the scanner and 2) that sufficiently rapid visualization can be achieved by low-cost workstations equipped with graphics hardware. The setup of the hardware and the software is described in detail. Furthermore, image processing techniques are presented for guiding the interventionalist through simple vascular anatomy. Finally, results of a phantom balloon angioplasty experiment are presented.

Coil embolization is a safe treatment for cerebral aneurysms only if the width of the neck in relation to the fundus of the aneurysm is small. Therefore, accurate visualization of the aneurysmal neck is required both in the diagnostic process and during the intervention. Conventional digital subtraction angiography (DSA) is still the preferred modality for the examination of cerebrovascular abnormalities like aneurysms, but it often does not provide the required morphological characteristics due to the suboptimal selection of projection angles and resulting overprojections of surrounding vasculature. This paper presents a method for performing a computer-assisted calculation of the optimal projection angles for DSA by post-processing computed tomographic angiography (CTA) volume data using ray-casting techniques and a combination of image processing algorithms. By means of phantom studies, retrospective simulations of angiograms, and in vivo applications of calculated optimal viewing angles, it is demonstrated that the proposed method results in better angiographic projections of the neck of saccular aneurysms with small neck-fundus ratio than those acquired at standard angles prescribed by clinical protocols.

In most cases nonlinear diffusion filtering is implemented by means of explicit finite difference schemes. These algorithms are not very efficient, since they are only stable for small time steps. We address this problem by presenting unconditionally stable semi-implicit schemes which are based on an additive operator splitting (AOS). They are very efficient since they can be implemented by recursive filtering, and their separability allows a straightforward implementation in any dimension. We analyse their behaviour on a parallel computer and demonstrate that parallel AOS schemes on a modern shared-memory multiprocessor system with 8 processors allow a speed-up of two orders of magnitude in comparison to the widely-used explicit scheme on a single processor.

The case study describes a system that allows the use of interactive volume rendering for routine clinical diagnosis. In this setup, a SGI RealityStation acts like a remote rendering system which is controlled by a user interface that was added to an existing clinical system. The paper describes some implementation aspects, including several system optimizations that were carried out in order to optimize rendering speed. Initial results are very promising; the authors present three examples of clinical findings that were made using this system. Because of the setup, clinicians are now much more aware of the possibilities that modern hardware offers for interactive volume visualization.

This paper presents three classes of speed-up techniques for calculating high-quality perspective Maximum Intensity Projection (MIP) images from Magnetic Resonance Angiography (MRA) data. The method of choice is a combination of Ray Acceleration by Distance Coding (RADC) with Closest Vessel Projection (CVP); this technique combines a five- to ten-fold speed improvement with improved visualization of blood vessels.