Composing DTI Visualizations with End-user Programming

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.

[1]  P. Basser,et al.  In vivo fiber tractography using DT‐MRI data , 2000, Magnetic resonance in medicine.

[2]  Matthias Hauswirth,et al.  Trevis: a context tree visualization & analysis framework and its use for classifying performance failure reports , 2010, SOFTVIS '10.

[3]  Jacques Bertin,et al.  Semiology of Graphics - Diagrams, Networks, Maps , 2010 .

[4]  Pat Hanrahan,et al.  Polaris: a system for query, analysis and visualization of multi-dimensional relational databases , 2000, IEEE Symposium on Information Visualization 2000. INFOVIS 2000. Proceedings.

[5]  Pat Hanrahan,et al.  Show Me: Automatic Presentation for Visual Analysis , 2007, IEEE Transactions on Visualization and Computer Graphics.

[6]  Jeffrey Heer,et al.  Protovis: A Graphical Toolkit for Visualization , 2009, IEEE Transactions on Visualization and Computer Graphics.

[7]  Mary Czerwinski,et al.  Understanding the verbal language and structure of end-user descriptions of data visualizations , 2012, CHI.

[8]  David H. Laidlaw,et al.  Exploring 3D DTI Fiber Tracts with Linked 2D Representations , 2009, IEEE Transactions on Visualization and Computer Graphics.

[9]  Jeffrey Heer,et al.  SpanningAspectRatioBank Easing FunctionS ArrayIn ColorIn Date Interpolator MatrixInterpola NumObjecPointI Rectang ISchedu Parallel Pause Scheduler Sequen Transition Transitioner Transiti Tween Co DelimGraphMLCon IData JSONCon DataField DataSc Dat DataSource Data DataUtil DirtySprite LineS RectSprite , 2011 .

[10]  David Akers,et al.  CINCH: a cooperatively designed marking interface for 3D pathway selection , 2006, UIST.

[11]  Jock D. Mackinlay,et al.  Automating the design of graphical presentations of relational information , 1986, TOGS.

[12]  David H. Laidlaw,et al.  Quantitative tractography metrics of white matter integrity in diffusion-tensor MRI , 2008, NeuroImage.

[13]  Brian A. Wandell,et al.  Exploring connectivity of the brain's white matter with dynamic queries , 2005, IEEE Transactions on Visualization and Computer Graphics.

[14]  Chi-Wing Fu,et al.  Scalable WIM: Effective Exploration in Large-scale Astrophysical Environments , 2006, IEEE Transactions on Visualization and Computer Graphics.

[15]  Richard Sproat,et al.  WordsEye: an automatic text-to-scene conversion system , 2001, SIGGRAPH.

[16]  Niklas Elmqvist,et al.  A Taxonomy of 3D Occlusion Management for Visualization , 2008, IEEE Transactions on Visualization and Computer Graphics.

[17]  Gordon L. Kindlmann,et al.  Strategies for Direct Volume Rendering of Diffusion Tensor Fields , 2000, IEEE Trans. Vis. Comput. Graph..

[18]  David F. Tate,et al.  A Novel Interface for Interactive Exploration of DTI Fibers , 2009, IEEE Transactions on Visualization and Computer Graphics.

[19]  Pat Hanrahan,et al.  Polaris: A System for Query, Analysis, and Visualization of Multidimensional Relational Databases , 2002, IEEE Trans. Vis. Comput. Graph..

[21]  Wendy T. Lucas,et al.  A Simple Language for Novel Visualizations of Information , 2007, ICSOFT/ENASE.

[22]  Jeffrey Heer,et al.  Declarative Language Design for Interactive Visualization , 2010, IEEE Transactions on Visualization and Computer Graphics.

[23]  Gordon Kindlmann,et al.  Visualization and Analysis of Diffusion Tensor Fields , 2004 .

[24]  Frans Vos,et al.  Fast and reproducible fiber bundle selection in DTI visualization , 2005, VIS 05. IEEE Visualization, 2005..

[25]  Ravi Kumar,et al.  Pig latin: a not-so-foreign language for data processing , 2008, SIGMOD Conference.

[26]  David H. Laidlaw,et al.  Visualizing Diffusion Tensor MR Images Using Streamtubes and Streamsurfaces , 2003, IEEE Trans. Vis. Comput. Graph..

[27]  Nicolas Toussaint,et al.  MedINRIA: Medical Image Navigation and Research Tool by INRIA , 2007 .

[28]  John Maeda,et al.  Computational information design , 2004 .

[29]  David Akers,et al.  Wizard of Oz for participatory design: inventing a gestural interface for 3D selection of neural pathway estimates , 2006, CHI Extended Abstracts.