Search

High angular resolution diffusion imaging (HARDI) is a MRI imaging technique that is able to better capture the intra-voxel diffusion pattern compared to its simpler predecessor diffusion tensor imaging (DTI). However, HARDI in general produces very noisy diffusion patterns due to the low SNR from the scanners at high b-values. Furthermore, it still exhibits limitations in areas where the diffusion pattern is asymmetrical (bifurcations, splaying fibers, etc.). To overcome these limitations, enhancement and denoising of the data based on context information is a crucial step. In order to achieve it, convolutions are performed in the coupled spatial and angular domain. Therefore the kernels applied become also HARDI data. However, these approaches have high computational complexity of an already complex HARDI data processing. In this work, we present an accelerated framework for HARDI data regularizaton and enhancement. The convolution operators are optimized by: pre-calculating the kernels, analysing kernels shape and utilizing look-up-tables. We provide an increase of speed, compared to previous brute force approaches of simpler kernels. These methods can be used as a preprocessing for tractography and lead to new ways for investigation of brain white matter.

Illustrationen werden erfolgreich in den Ingenieurwissenschaften, den Naturwissenschaften und der Medizin zur abstrahierten Darstellung von Objekten und Situationen verwendet. Typischerweise sind diese Illustrationen Zeichnungen, bei denen der Illustrator künstlerische Techniken zur Betonung relevanter Aspekte der Objekte einsetzt. Im Gegensatz dazu erzeugen Visualisierungen eine direkte, nicht abstrahierte visuelle Darstellung von Simulationen, gescannten Objekten oder modellierten Daten. Durch die inhärente Komplexität dieser Datensätze stellt sich die Interpretation dieser Daten jedoch oft als schwierig dar. Die illustrative Visualisierung hingegen versucht beide Ansätze zur einer abstrahierten Darstellung eines Datensatzes zu verbinden, in der die wesentlichen Charakteristika betont werden. Dieser Ansatz bekommt eine besondere Bedeutung bei sehr komplexen Modellen, die u.U. aus vielen einzelnen Objekten bestehen, wie z.B. einzelne Bauteile einer Maschine, oder segmentierten Organen aus einem CT- oder MRT-Datensatz eines Menschen. Während im Allgemeinen die illustrative Visualisierung einer bessere Betonung ausgewählter und relevanter Informationen als die traditionelle Visualisierung erreicht, so stellen viele nah beieinander gelegene Objekte eine Herausforderung dar, da sie klar von einander getrennt werden müssen.

Traditionally, computer graphics strived to achieve the technically best representation of the scenario or scene. For rendering, this lead to the preeminence of representations based on the physics of light interacting with different media and materials. Research in virtual reality has focused on interactivity and therefore on real-time rendering techniques that improve the immersion of users in the virtual environments. In contrast, visualization has focused on representations that that maximizes the information content. In most cases, such representations are not physically-based, requiring instead more abstract approaches. Recently, the increasing integration of the extensive knowledge and methods from perception research into computer graphics has fundamentally altered both fields, offering not only new research questions, but also new ways of solving existing issues. In rendering, for example, the integration can lead to the targeted allocation of computing resources to aspects of a scene that matter mos t for human observers. In visualization, the manner in which information is presented is now often driven by knowledge of low-level cues (e.g., pre-attentive features). Assumptions about how to best present information are evaluated by a psychophysical experiment. This same trend towards perceptually driven research has perhaps had the longest tradition in virtual reality, where the useramp;8217;s response to specific interaction and rendering techniques is examined using a variety of methods. Against this backdrop of an increasing importance of perceptual research in all areas related to computer generated imagery, we provide a state of the art report on the current state of perception in computer graphics.

Advanced algorithms and efficient visualization techniques are of major importance in intra-operative imaging and image-guided surgery. The surgical environment is characterized by a high information flow and fast decisions, requiring efficient and intuitive presentation of complex medical data and precision in the visualization results. Regions or organs that are classified as risk structures are in this context of particular interest. This paper summarizes advanced algorithms for medical visualization with special focus on risk structures such as tumors, vascular systems and white matter fiber tracts. algorithms and techniques employed in intra-operative situations or virtual and mixed reality simulations are discussed. Finally, the prototyping and software development process of medical visualization algorithms is addressed.

The art and profession of medical illustration depend not only on the talent and skills of an illustrator, but also on the complimentary knowledge of human anatomy. Therefore, anatomical illustrations were mostly based on comprehensive dissections and observations during surgery. Recently, illustrative visualization techniques have been utilized to illustrate features and shapes of anatomical objects. Thereby, illustrative visualization provides representations that highlight relevant features, while de-emphasizing irrelevant details.However, anatomical illustrations usually include multiple neighboring structures, which increases the complexity of the illustration problem, as the relationship between multiple organs still remains difficult to convey. In this paper, we present an approach for the combined illustrative visualization of multiple anatomical structures based on scanned patient data. To enhance the expressiveness, we incorporate multiple stylized rendering and shading techniques, and propose strategies for tuning object attributes (such as color, opacity, and silhouette), which allows to better perceive the spatial relationship between objects as well as to draw the focus to targeted structures.

A proper classification is a key factor for the successful visualization of 3D medical data. In direct volume rendering applications, the data is classified by means of a transfer function mapping each voxel characteristics to optical properties such as color and opacity, thus highlighting certain regions over others. In a medical scenario, the structures of interest that must be highlighted may vary strongly depending on the application. Therefore, easy and intuitive user interaction is crucial to achieve a meaningful classification for a given patient's scan. In this paper, we focus on a novel approach that combines direct visualization and interaction with 3D data in a medical augmented reality (AR) environment. The proposed method takes into account regions of interest directly defined by the physician in the patient's anatomy and employs this information to automatically generate an adequate transfer function. Illustrative results demonstrate the utility of this interactive visualization paradigm for medical applications.

In augmented reality, virtual graphical objects are overlaid over the real environment of the observer. Conventional augmented reality systems normally use standard real-time rendering methods for generating the graphical representations of virtual objects. These renderings contain the typical artifacts of computer generated graphics, e.g., aliasing caused by the rasterization process and unrealistic, manually configured illumination models. Due to these artifacts, virtual objects look artifical and can easily be distinguished from the real environment. A different approach to generating augmented reality images is the basis of stylized augmented reality [FBS05c]. Here, similar types of artistic or illustrative stylization are applied to the virtual objects and the camera image of the real enviroment. Therefore, real and virtual image elements look significantly more similar and are less distinguishable from each other. In this paper, we present the results of a psychophysical study on the effectiveness of stylized augmented reality. In this study, a number of participants were asked to decide whether objects shown in images of augmented reality scenes are virtual or real. Conventionally rendered as well as stylized augmented reality images and short video clips were presented to the participants. The correctness of the participants' responses and their reaction times were recorded. The results of our study show that an equalized level of realism is achieved by using stylized augmented reality, i.e., that it is significantly more difficult to distinguish virtual objects from real objects.