The Diffusion Visualization Explorer (DiVE) Tool
Poster No:
2188
Submission Type:
Abstract Submission
Authors:
Siddharth Narula1, Iyad Ba Gari2, Shruti Gadewar3, Sunanda Somu1, Neda Jahanshad4
Institutions:
1University of Southern California, Los Angeles, CA, 2University of Southern California, Marina Del Rey, CA, 3USC, Marina Del Rey, CA, 4Imaging Genetics Center, Keck School of Medicine of University of Southern California, Los Angeles, California
First Author:
Co-Author(s):
Neda Jahanshad, PhD
Imaging Genetics Center, Keck School of Medicine of University of Southern California
Los Angeles, California
Imaging Genetics Center, Keck School of Medicine of University of Southern California
Los Angeles, California
Introduction:
Diffusion MRI based tractography reconstructs the white matter (WM) pathways in the brain, allowing for a 3D assessment of structural brain connectivity. Visualization tools for tractography are important for displaying complexities of WM pathways. These tools help researchers and clinicians understand the individual brain's structural connectivity and display population based findings related to neurological and psychiatric conditions. While classical tools like Trackvis [1], MRView [2], MI-Brain [3], DSI-studio [4] analyze and visualize dMRI data to different degrees there is a need for a tool with a seamless integration of tracts, masks, and mesh structures for displaying individual tracts and population level statistical findings. Our tool, Diffusion Visualization and Explorer (DiVE) was developed out of necessity for our novel Medial Tractography Analysis (MeTA) toolbox to complement existing toolboxes, offering enhancements such as statistics-based visualization, creating and saving high-quality images, and simultaneous visualization of bundle specific meshes, volumes or masks, and streamlines or tracts. We distribute DiVE as a stand alone package for wider use.
Methods:
DiVE uses the Free Unified Rendering library in pYthon (FURY) a high-performance scientific visualization library [5]. FURY can visualize diverse components, including streamlines, brain masks, and meshes, coexisting within the same spatial context. We leverage OpenGL, a versatile cross-platform application that renders both 2D and 3D surfaces. DiVE takes a 3D region of interest label image in NIFTI format and renders it as a set of 3D contours. It applies either the color specified by the user or a random color for single labels and chooses a set of distinct colors for multi-labeled masks using "distinctipy" [6] if a colormap is not provided. Tract rendering can be conducted across all common formats (trk, tck, trx, vtk), with user defined coloring options, as well as available defaults. A mesh (vtk) is rendered as a surface mesh using pyVista [7] polydata inherited from Python VTK representing the geometry of 3D objects using a combination of points, vertices, lines, and polygons. DiVE's 3D visualization feature allows users to render complex fiber structures in 3D space, enabling viewing of fiber bundles from different angles and perspectives, providing a comprehensive understanding of their spatial distribution. Each fiber is displayed as tubes with a user-defined width. DiVE also allows for the overlay of NIFTI masks and surface meshes on the fiber tracts, which can map scalar values to color or opacity, providing insights into tissue microstructure. Users can also add as a background 3D or 2D slices from the full brain NIFTI images to understand how fibers interact with specific brain regions. The GUI and examples of different visualization options can be found in Figure 1.
Results:
DiVE is a dynamic open-source initiative, operating across multiple platforms, and we anticipate continuous development and active community engagement. Python integration allows for easy scripting, a high degree of flexibility and automation. The software provides an extensive array of visualization capabilities encompassing both 2D and 3D rendering for streamline and bundle tractography visualization, and more. Visualizations are enhanced by the inclusion of user-defined statistical metrics, along the trajectories of white matter bundles. We showcase results from [8], wherein the t-value derived from MeTA_25% core volume is mapped in association with the corresponding p-value Figure 1. DiVE is compatible with various neuroimaging file formats, ensuring seamless integration of existing data.
Conclusions:
DiVE is available for the wider diffusion tractography and visualization community https://github.com/USC-LoBeS/DiVE to complement existing toolboxes with a range of customization options to fine tune the visualization of tracts, meshes and masks and create custom visualizations.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s)
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity 1
Novel Imaging Acquisition Methods:
Diffusion MRI
Keywords:
Open-Source Code
Tractography
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Diffusion Visualization Explorer (DiVE) Tool
1|2Indicates the priority used for review
Provide references using author date format
[2] J.-D. Tournier et al., “MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation,” Neuroimage, vol. 202, p. 116137, Nov. 2019.
[3] F. Rheault, J.-C. Houde, N. Goyette, F. Morency, and M. Descoteaux, “Mi‐brain, a software to handle tractograms and perform interactive virtual dissection,” In Proceedings of the ISMRM Diffusion study group workshop, Lisbon, 2016.
[4] F.-C. Yeh et al., “Population-averaged atlas of the macroscale human structural connectome and its network topology,” Neuroimage, vol. 178, pp. 57–68, Sep. 2018.
[5] E. Garyfallidis et al., “FURY: advanced scientific visualization,” J. Open Source Softw., vol. 6, no. 64, p. 3384, Aug. 2021.
[6] J. Roberts, J. Crall, K.-M. Ang, and Y. Brandt, alan-turing-institute/distinctipy: v1.2.3. 2023. doi: 10.5281/zenodo.8355862.
[7] C. Sullivan and A. Kaszynski, “PyVista: 3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK),” J. Open Source Softw., vol. 4, no. 37, p. 1450, May 2019.
[8] I. Ba Gari et al., “Along-Tract Parameterization of White Matter Microstructure using Medial Tractography Analysis (MeTA),” in The 19th International Symposium on Medical Information Processing and Analysis, 2023.
Acknowledgements:
This work is supported in part by NIH grants: R01MH134004, P41EB015922 and RF1AG057892. Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.