We present the design and prototype implementation of a scientific visualization language called Zifazah for composing 3D visualizations of diffusion tensor magnetic resonance imaging (DT-MRI or DTI) data. Unlike existing tools allowing flexible customization of data visualizations that are programmer-oriented, we focus on domain scientists as end users in order to enable them to freely compose visualizations of their scientific data set. We analyzed end-user descriptions extracted from interviews with neurologists and physicians conducting clinical practices using DTI about how they would build and use DTI visualizations to collect syntax and semantics for the language design, and have discovered the elements and structure of the proposed language. Zifazah makes use of the initial set of lexical terms and semantics to provide a declarative language in the spirit of intuitive syntax and usage. This work contributes three, among others, main design principles for scientific visualization language design as well as a practice of such language for DTI visualization with Zifazah. First, Zifazah incorporated visual symbolic mapping based on color, size and shape, which is a sub-set of Bertin's taxonomy migrated to scientific visualizations. Second, Zifazah is defined as a spatial language whereby lexical representation of spatial relationship for 3D object visualization and manipulations, which is characteristic of scientific data, can be programmed. Third, built on top of Bertin's semiology, flexible data encoding specifically for scientific visualizations is integrated in our language in order to allow end users to achieve optimal visual composition at their best. Along with sample scripts representative of our language design features, some new DTI visualizations as the running results created by end users using the novel visualization language have also been presented.
Many other powerful tools have also been developed for exploring DTI visualizations @cite_12 @cite_1 @cite_28 @cite_3 @cite_20 . However, due to the data complexity, domain users' needs for performing their various tasks in daily practice have not yet been fully satisfied by using those tools. To give users more flexibility, some of the visualization tools are made highly configurable by allowing a wide range of settings @cite_25 @cite_11 . Nevertheless, it is still challenging to design a thoroughly effective visualization tool to meet all the needs of users. For instance, although sometimes able to meet specific requirements, higher flexibility of a visualization tool may even make the tool more complex to use for domain users @cite_10 .
@cite_25 @cite_28 @cite_1 @cite_3 @cite_10 @cite_12 @cite_20 @cite_11
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Processing and visualization of 3D medical data is nowadays a common problem. However, it remains challenging because the diversification and complexification of the available sources of information, as well as the specific requirements of clinicians, make it difficult to solve in a computer science point of view. Indeed, clinicians need ergonomic, efficient, intuitive and reactive softwares. Moreover, they need new solutions to fully exploit their data, but they often cannot access state-of-the-art methods as those are mostly available in complicated softwares. The MedINRIA software was born to fill this lack and consists of a collection of tools that optimally exploit various types of data (e.g., 3D images, diffusion tensor fields, neural fibers as obtained in DT-MRI). It provides state-of-the-art algorithms while keeping a user-friendly graph-ical interface. For each of these tools, we first introduce its dedicated application and the processing methods it contains. Then, we focus on the features that make interactions with data even more intuitive. Med-INRIA is a free software, available on Windows, Linux and MacOSX. Other MedINRIA tools are underway to make cutting edge research in medical imaging rapidly available to clinicians. The interest clinicians have shown in MedINRIA so far indicates that the need of such simple, yet powerful softwares is real and increasing. This paper describes a participatory design process employed to invent an interface for 3D selection of neural pathways estimated from MRI imaging of human brains. Existing pathway selection interfaces are frustratingly difficult to use, since they require the 3D placement of regions-of-interest within the brain data using only a mouse and keyboard. The proposed system addresses these usability problems by providing an interface that is potentially more intuitive and powerful: converting 2D mouse gestures into 3D path selections. The contributions of this work are twofold: 1) we introduce a participatory design process in which users invent and test their own gestural selection interfaces using a Wizard of Oz prototype, and 2) this process has helped to yield the design of an interface for 3D pathway selection, a problem that is known to be difficult. Aspects of both the design process and the interface may generalize to other interface design problems. Navigating through large-scale virtual environments such as simulations of the astrophysical Universe is difficult. The huge spatial range of astronomical models and the dominance of empty space make it hard for users to travel across cosmological scales effectively, and the problem of wayfinding further impedes the user's ability to acquire reliable spatial knowledge of astronomical contexts. We introduce a new technique called the scalable world-in-miniature (WIM) map as a unifying interface to facilitate travel and wayfinding in a virtual environment spanning gigantic spatial scales: power-law spatial seating enables rapid and accurate transitions among widely separated regions; logarithmically mapped miniature spaces offer a global overview mode when the full context is too large; 3D landmarks represented in the WIM are enhanced by scale, positional, and directional cues to augment spatial context awareness; a series of navigation models are incorporated into the scalable WIM to improve the performance of travel tasks posed by the unique characteristics of virtual cosmic exploration. The scalable WIM user interface supports an improved physical navigation experience and assists pragmatic cognitive understanding of a visualization context that incorporates the features of large-scale astronomy To disentangle and analyze neural pathways estimated from magnetic resonance imaging data, scientists need an interface to select 3D pathways. Broad adoption of such an interface requires the use of commodity input devices such as mice and pens, but these devices offer only two degrees of freedom. CINCH solves this problem by providing a marking interface for 3D pathway selection. CINCH interprets pen strokes as pathway selections in 3D using a marking language designed together with scientists. Its bimanual interface employs a pen and a trackball (see Figure 1), allowing alternating selections and scene rotations without changes of mode. CINCH was evaluated by observing four scientists using the tool over a period of three weeks as part of their normal work activity. Event logs and interviews revealed dramatic improvements in both the speed and quality of scientists' everyday work, and a set of principles that should inform the design of future 3D marking interfaces. More broadly, CINCH demonstrates the value of the iterative, participatory design process that catalyzed its evolution. Diffusion tensor imaging (DTI) is an MRI-based technique for quantifying water diffusion in living tissue. In the white matter of the brain, water diffuses more rapidly along the neuronal axons than in the perpendicular direction. By exploiting this phenomenon, DTI can be used to determine trajectories of fiber bundles, or neuronal connections between regions, in the brain. The resulting bundles can be visualized. However, the resulting visualizations can be complex and difficult to interpret. An effective approach is to pre-determine trajectories from a large number of positions throughout the white matter (full brain fiber tracking) and to offer facilities to aid the user in selecting fiber bundles of interest. Two factors are crucial for the use and acceptance of this technique in clinical studies: firstly, the selection of the bundles by brain experts should be interactive, supported by real-time visualization of the trajectories registered with anatomical MRI scans. Secondly, the fiber selections should be reproducible, so that different experts will achieve the same results. In this paper we present a practical technique for the interactive selection of fiber-bundles using multiple convex objects that is an order of magnitude faster than similar techniques published earlier. We also present the results of a clinical study with ten subjects that show that our selection approach is highly reproducible for fractional anisotropy (FA) calculated over the selected fiber bundles. Diffusion tensor imaging (DTI) is a magnetic resonance imaging method that can be used to measure local information about the structure of white matter within the human brain. Combining DTI data with the computational methods of MR tractography, neuroscientists can estimate the locations and sizes of nerve bundles (white matter pathways) that course through the human brain. Neuroscientists have used visualization techniques to better understand tractography data, but they often struggle with the abundance and complexity of the pathways. In this paper, we describe a novel set of interaction techniques that make it easier to explore and interpret such pathways. Specifically, our application allows neuroscientists to place and interactively manipulate box or ellipsoid-shaped regions to selectively display pathways that pass through specific anatomical areas. These regions can be used in coordination with a simple and flexible query language which allows for arbitrary combinations of these queries using Boolean logic operators. A representation of the cortical surface is provided for specifying queries of pathways that may be relevant to gray matter structures and for displaying activation information obtained from functional magnetic resonance imaging. By precomputing the pathways and their statistical properties, we obtain the speed necessary for interactive question-and-answer sessions with brain researchers. We survey some questions that researchers have been asking about tractography data and show how our system can be used to answer these questions efficiently.