Diffusion MRI visualization

\u3cp\u3eModern diffusion magnetic resonance imaging (dMRI) acquires intricate volume datasets and biological meaning can only be found in the relationship between its different measurements. Suitable strategies for visualizing these complicated data have been key to interpretation by physicians and neuroscientists, for drawing conclusions on brain connectivity and for quality control. This article provides an overview of visualization solutions that have been proposed to date, ranging from basic grayscale and color encodings to glyph representations and renderings of fiber tractography. A particular focus is on ongoing and possible future developments in dMRI visualization, including comparative, uncertainty, interactive and dense visualizations.\u3c/p\u3

Geometric modelling for virtual colon unfolding

A virtual endoscopie view is not necessarily the best way to examine a hollow organ, such us, the colon. The inner surface of the colon is where polyps are located, and therefore what is examined by the physicians. A flight through the colon using a common endoscopie view shows a smal! percentage of the inner surface. Virtually unfolding of the colon can be a more etficient way to look at the inner surface. We propose two methods to unfold the colon : a method that unfolds the colon locally using local projections, and a method that obtains global unfolding of the colon by achieving a suitable parameterization of its surface

Visualization and processing of tensors and higher order descriptors for multi-valued data

Arising from the fourth Dagstuhl conference entitled Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data (2011), this book offers a broad and vivid view of current work in this emerging field. Topics covered range from applications of the analysis of tensor fields to research on their mathematical and analytical properties

Fuzzy fibers:uncertainty in dMRI tractography

\u3cp\u3eFiber tracking based on diffusion weighted Magnetic Resonance Imaging (dMRI) allows for noninvasive reconstruction of fiber bundles in the human brain. In this chapter, we discuss sources of error and uncertainty in this technique, and review strategies that afford a more reliable interpretation of the results. This includes methods for computing and rendering probabilistic tractograms, which estimate precision in the face of measurement noise and artifacts. However, we also address aspects that have received less attention so far, such as model selection, partial voluming, and the impact of parameters, both in preprocessing and in fiber tracking itself. We conclude by giving impulses for future research.\u3c/p\u3

Illustrative white matter fiber bundles

Diffusion Tensor Imaging (DTI) has made feasible the visualization of the fibrous structure of the brain whitematter. In the last decades, several fiber-tracking methods have been developed to reconstruct the fiber tracts fromDTI data. Usually these fiber tracts are shown individually based on some selection criteria like region of interest.However, if the white matter as a whole is being visualized clutter is generated by directly rendering the individualfiber tracts. Often users are actually interested in fiber bundles, anatomically meaningful entities that abstractfrom the fibers they contain. Several clustering techniques have been developed that try to group the fiber tractsin fiber bundles. However, even if clustering succeeds, the complex nature of white matter still makes it difficultto investigate. In this paper, we propose the use of illustration techniques to ease the exploration of white matterclusters. We create a technique to visualize an individual cluster as a whole. The amount of fibers visualized forthe cluster is reduced to just a few hint lines, and silhouette and contours are used to improve the definition of thecluster borders. Multiple clusters can be easily visualized by a combination of the single cluster visualizations.Focus+context concepts are used to extend the multiple-cluster renderings. Exploded views ease the explorationof the focus cluster while keeping the context clusters in an abstract form. Real-time results are achieved by theGPU implementation of the presented techniques. Keywords: I.3.3 [Computer Graphics]: Picture/Image Generation—Bitmap and framebuffer operations, Display algorithms, Viewing algorithms

Glyph-based comparative visualization for diffusion tensor fields

\u3cp\u3eDiffusion Tensor Imaging (DTI) is a magnetic resonance imaging modality that enables the in-vivo reconstruction and visualization of fibrous structures. To inspect the local and individual diffusion tensors, glyph-based visualizations are commonly used since they are able to effectively convey full aspects of the diffusion tensor. For several applications it is necessary to compare tensor fields, e.g., to study the effects of acquisition parameters, or to investigate the influence of pathologies on white matter structures. This comparison is commonly done by extracting scalar information out of the tensor fields and then comparing these scalar fields, which leads to a loss of information. If the glyph representation is kept, simple juxtaposition or superposition can be used. However, neither facilitates the identification and interpretation of the differences between the tensor fields. Inspired by the checkerboard style visualization and the superquadric tensor glyph, we design a new glyph to locally visualize differences between two diffusion tensors by combining juxtaposition and explicit encoding. Because tensor scale, anisotropy type, and orientation are related to anatomical information relevant for DTI applications, we focus on visualizing tensor differences in these three aspects. As demonstrated in a user study, our new glyph design allows users to efficiently and effectively identify the tensor differences. We also apply our new glyphs to investigate the differences between DTI datasets of the human brain in two different contexts using different b-values, and to compare datasets from a healthy and HIV-infected subject.\u3c/p\u3

Overview + detail visualization for ensembles of diffusion tensors

\u3cp\u3eA Diffusion Tensor Imaging (DTI) group study consists of a collection of volumetric diffusion tensor datasets (i.e., an ensemble) acquired from a group of subjects. The multivariate nature of the diffusion tensor imposes challenges on the analysis and the visualization. These challenges are commonly tackled by reducing the diffusion tensors to scalar-valued quantities that can be analyzed with common statistical tools. However, reducing tensors to scalars poses the risk of losing intrinsic information about the tensor. Visualization of tensor ensemble data without loss of information is still a largely unsolved problem. In this work, we propose an overview + detail visualization to facilitate the tensor ensemble exploration. We define an ensemble representative tensor and variations in terms of the three intrinsic tensor properties (i.e., scale, shape, and orientation) separately. The ensemble summary information is visually encoded into the newly designed aggregate tensor glyph which, in a spatial layout, functions as the overview. The aggregate tensor glyph guides the analyst to interesting areas that would need further detailed inspection. The detail views reveal the original information that is lost during aggregation. It helps the analyst to further understand the sources of variation and formulate hypotheses. To illustrate the applicability of our prototype, we compare with most relevant previous work through a user study and we present a case study on the analysis of a brain diffusion tensor dataset ensemble from healthy volunteers.\u3c/p\u3