Minutes, Arts & Sciences Student Life Committee Meeting, Thursday, February 11, 2010

Major white matter tracts are bundles of neuronal fibers connecting the cortical brain areas to deep seated regions and periphery. An example is the pyramidal tract, which is responsible for motor function, or the corpus callosum connecting both brain hemispheres. Their preservation during brain surgery is of major importance, in order to avoid postoperative new neurological deficits, such as impairment of motor function

Cost-effectiveness of cognitive-behavioral group therapy for dysfunctional fear of progression in chronic arthritis patients

Background Anxiety disorders are widespread in patients with chronic diseases such as rheumatoid arthritis (RA). This paper targets the cost-effectiveness analysis of a cognitive-behavioral group therapy (CBT) in comparison to a client-centered, supportive-experiential group therapy (SET) in arthritis patients with dysfunctional fear of progression. Methods From the societal perspective, direct costs were compared with the reduction of fear of progression over time. Means, their 95% confidence intervals (95% CI), the incremental cost-effectiveness graphic and the acceptability curve were obtained using 1000 non-parametric bootstrap replications. Results A total of 174 RA patients were included in the economic evaluation. The estimated means (95% CI) of direct costs and reduction of fear of progression were, respectively, €7945.34 (5075.59; 11335.08) and 0.25 (−0.48; 0.99) for patients in the SET and 5619.25 € (3950.67; 7708.52) and 0.94 (0.29; 1.62) for patients in the CBT. As the majority of the cost-effect pairs after bootstrap analysis were located in the southeast quadrant of the cost-effectiveness plane, the CBT can be considered a dominant intervention. Conclusion The main result of our study is the higher cost-effectiveness of CBT in comparison to SET in RA patients with dysfunctional fear of progressio

Fast and Interactive 3D-Segmentation of Medical Volume Data

The segmentation of tomographic image data is an important prerequisite for a meaningful visualization in medicine. To circumvent difficulties related to automatic approaches, we suggest interactive segmentation which includes the knowledge of a user more efficiently. Using intelligent scissors, as presented in [1] for the 2D case, we suggest 3D filters for the calculation of the cost matrix, an automatic procedure which propagates contours to adjacent slices and three communicating 2D displays for convenient delineation of contours in volume data. Additionally, we provide volume growing based on a statistical approach, as presented in [2, 3], allowing to select coherent subvolumes interactively. For the immediate evaluation we introduce additional 3D displays for the visualization of polygonal and volumetric representations of the segmentation results. 1 Introduction For the spatial orientation within complex medical volume data various techniques for 3D visualization have been deve..

Integrated Registration and Visualization of Medical Image Data

Different imaging modalities give insight to vascular, anatomical and functional information which assist diagnosis and therapy planning in medicine. Registration and consecutive visualization allow to combine the image data and thereby convey more meaningful images to the clinician. Applying a voxel based approach based on mutual information, accurate and retrospective registration is provided. However, optimization and consecutive visualization procedures require a huge amount of trilinear interpolation operations to re--sample the data. Ensuring fast performance which is fundamental for medical routine, we suggest an integrated approach which takes advantage of the imaging and texture mapping subsystem of graphics computers. All trilinear interpolation is completely performed with hardware assisted 3D texture mapping. The 1D and 2D histograms of the datasets which are necessary for the calculation of mutual information are obtained with different hardware accelerated imaging operati..

Semi-Automatic Registration Of 3D-Multi-Modality Brain Images Based On An Information Theoretic Approach

For the retrospective, rigid body registration of two 3D datasets from different modalities (MR, CT and PET) an automatic, voxel based approach, as suggested by Collignon et al. [3], was implemented using mutual information of grey-value pairs as a measure of similarity. The applied information theoretic approach combines the segmentation of features and the consecutive registration in one process. By introducing a resolution pyramid and a threshold we accelerated the registration process and made it more robust with respect to the partial volume effect. In order to obtain good starting values we used a least square point matching approach based on approximately corresponding anatomical landmarks. Our results show that good registration can be achieved for MRI-MRA, MRI-CT and MRI-PET even in case of big partial volume effect. 1. INTRODUCTION In many clinical situations in neuroradiology the fusion of images from different modalities taken at different times represents an important pr..

Efficient Representation of Cortical Convolutions for the Analysis of Brain Surface Topology

Various time e#cient procedures were developed allowing to calculate planar representations of the brain in MR and CT clearly conveying the whole surface topology. For the comparison of the provided techniques we present additional complex functionality for the transformation of cortical convolutions between di#erent representations after extracting and marking them manually or automatically. This includes re--projection to the original volume data in order to compare our approach to results obtained with direct volume rendering. Considering brain information exclusively, and ensuring a standardized orientation for the inter--patient comparison di#erent segmentation and registration procedures are provided for the pre--processing. All implementation was integrated in a flexible and modular extensible platform allowing for convenient manipulation and visualization. Keywords: cortical convolutions, visualization, segmentation, registration

Non-linear integration of DTI-based fiber tracts into standard 3D MR data

Diffusion tensor imaging (DTI) provides information about the location of white matter tracts within the human brain. This information is essential for preoperative neurosurgical planning to achieve maximal tumor resection while avoiding postoperative neurological deficits. Due to the anatomical distortion of echo planar imaging, DT images- and as a result the fiber tracts computed from them-are distorted. In this paper, we present a novel approach to account for those distortions. All voxels containing fibers within the distorted DT dataset were marked. Subsequently, a non-linear registration with standard 3D MR data was performed. The marked voxels were re-extracted from the registered DT dataset and displayed within the 3D MR dataset. The strategy introduced in this paper is an essential prerequisite for the integration of fiber tract data into 3D MR datasets. The fused data is of high value for neuronavigation and thereby a benefit for neurosurgery.

Enhanced visualization of diffusion tensor data for neurosurgery

Diffusion tensor imaging is a recent magnetic resonance technique that provides comprehensive information about water diffusivity. Since diffusivity correlates with tissue structure at the microscopic level diffusion tensor imaging reveals fibrous structures such as white matter pathways in the field of neurosurgery. White matter tracts are thereby of major interest and the ability to distinguish between different white matter patterns is subject of growing research in neurosurgery. Different approaches for the examination of the data such as streamline tracking or direct volume rendering have been developed. In this work an integrated method for visualizing this kind of data is presented utilizing glyph- and streamline-based visualization in combination with magnetic resonance data showing anatomy to obtain a maximum of information

Interactive Animation of Volume Line Integral Convolution Based on 3D Texture Mapping

. Line integral convolution (LIC) is an effective technique for visualizing vector fields. The application of LIC to 3D flow fields has yet been limited by difficulties to efficiently display and animate the resulting 3D images. Texture-based volume rendering allows interactive visualization and manipulation of 3D LIC textures. In this context, we suggest two related approaches for animating static 3D flow fields without the computational expense and the immense memory requirements for pre-computed 3D textures and without loss of interactivity. This is achieved by using a single 3D LIC texture and a set of time surfaces as clipping geometries. In our first approach we use the clipping geometry to pre-compute a special 3D LIC texture that can be animated by time-dependent color tables. Our second approach uses time volumes to actually clip the 3D LIC volume interactively during rasterization. 1 Introduction 3D flow visualization is an important topic of research. A number of different ..