Exploring Crossing Fibers of the Brain's White Matter Using Directional Regions of Interest

Diffusion magnetic resonance imaging (dMRI) is a medical imaging method that can be used to acquire local information about the structure of white matter pathways within the human brain. By applying computational methods termed fiber tractography on dMRI data, it is possible to estimate the location and extent of respective nerve bundles (white matter pathways). Visualizing these complex white matter pathways for neuro applications is still an open issue. Hence, interactive visualization techniques to explore and better understand tractography data are required. In this paper, we propose a new interaction technique to support exploration and interpretation of white matter pathways. Our application empowers the user to interactively manipulate manually segmented, box- or ellipsoid-shaped regions of interest (ROIs) to selectively display pathways that pass through specific anatomical areas. To further support flexible ROI design, each ROI can be assigned a Boolean logic operator and a fiber direction. The latter is particularly relevant for kissing, crossing or fanning regions, as it allows the neuroscientists to filter fibers according to their direction within the ROI. By precomputing all white matter pathways in the whole brain, interactive ROI placement and adjustment are possible. The proposed fiber selection tool provides ultimate flexibility and is an excellent approach for fiber tract selection, as shown for some real-world examples.

[1]  Susanne Schnell,et al.  Global fiber reconstruction becomes practical , 2011, NeuroImage.

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

[3]  Gordon L. Kindlmann,et al.  Tensorlines: advection-diffusion based propagation through diffusion tensor fields , 1999, Proceedings Visualization '99 (Cat. No.99CB37067).

[4]  Carl-Fredrik Westin,et al.  A filtered approach to neural tractography using the Watson directional function , 2010, Medical Image Anal..

[5]  M. Raichle,et al.  Tracking neuronal fiber pathways in the living human brain. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

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

[7]  Derek K. Jones Diffusion MRI: Theory, methods, and applications , 2011 .

[8]  F. Yeh,et al.  Independent component analysis tractography combined with a ball–stick model to isolate intravoxel crossing fibers of the corticospinal tracts in clinical diffusion MRI , 2013, Magnetic resonance in medicine.

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

[10]  S. Wakana,et al.  Fiber tract-based atlas of human white matter anatomy. , 2004, Radiology.

[11]  Jan Sijbers,et al.  Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution , 2011, Human brain mapping.

[12]  Rachid Deriche,et al.  Deterministic and Probabilistic Tractography Based on Complex Fibre Orientation Distributions , 2009, IEEE Transactions on Medical Imaging.

[13]  Susumu Mori,et al.  Fiber tracking: principles and strategies – a technical review , 2002, NMR in biomedicine.

[14]  Christopher Nimsky,et al.  Fiber Selection from Diffusion Tensor Data based on Boolean Operators , 2010, Bildverarbeitung für die Medizin.

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

[16]  D. Tuch Q‐ball imaging , 2004, Magnetic resonance in medicine.

[17]  Daniel C. Alexander,et al.  Utilising measures of fiber dispersion in white matter tractography , 2012 .

[18]  Thomas Schultz,et al.  Feature Extraction for DW-MRI Visualization: The State of the Art and Beyond , 2011, Scientific Visualization: Interactions, Features, Metaphors.

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

[20]  David H. Laidlaw,et al.  InShape: In-Situ Shape-Based Interactive Multiple-View Exploration of Diffusion MRI Visualizations , 2012, ISVC.

[21]  Charl P. Botha,et al.  Fast and reproducible fiber bundle selection in DTI visualization , 2005 .

[22]  P. Basser,et al.  Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. , 1996, Journal of magnetic resonance. Series B.

[23]  A. Alexander,et al.  White matter tractography using diffusion tensor deflection , 2003, Human brain mapping.

[24]  Brian A. Wandell,et al.  800Exploration of the brain's white matter pathways with dynamic queries , 2004, IEEE Visualization 2004.

[25]  Horst K. Hahn,et al.  Real-time fiber selection using the Wii remote , 2010, Medical Imaging.

[26]  Carlos Alberola-Lopez,et al.  Tractography clustering for fiber selection in ROI-based diffusion tensor studies , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[27]  Carl-Fredrik Westin,et al.  Image Processing for Diffusion Tensor Magnetic Resonance Imaging , 1999, MICCAI.

[28]  Josie Wernecke,et al.  The inventor mentor - programming object-oriented 3D graphics with Open Inventor, release 2 , 1993 .