Nicole Wake1, Steven H. Baete1, Ying-Chia Lin1, Fernando E. Boada1, and Daniel K. Sodickson1
1Bernard and Irene Schwartz Center for Biomedical Imaging, Center for Advanced Imaging Innovation and Research, Department of Radiology, New York University School of Medicine, New York, NY, United States
Synopsis
The objective of this study was to create a
workflow to view 3D fiber tracts derived from diffusion spectrum imaging (DSI)
in augmented reality (AR) and to test the application of visualizing
tractography models in AR. Visualizing
tractography in AR may allow for enhanced comprehension of the connectivity in
the brain which could impact patient care and management.
Introduction
DSI is a useful magnetic resonance imaging (MRI)
technique that can be used to characterize the three-dimensional (3D) diffusional
displacement of water molecules in a model-free approach.1,2 3D fiber tracts may be generated using DSI or
other diffusion MRI methods. The resulting fiber tracts are typically presented
on a two-dimensional (2D) display that cannot effectively show depth
information. Augmented Reality (AR) is
an exciting technology that may help to overcome these limitations by allowing
the user to visualize 3D data in the real world. The purpose of this study was 1) to create a
workflow to visualize fiber tracts derived from DSI in the Microsoft HoloLens,
an AR headset capable of rendering 3D holograms in the real world and 2) to
perform an initial evaluation of the added value of visualizing the 3D fiber
tracts in AR to the clinical workflow. Methods
DSI was performed in one healthy brain on a clinical
3T scanner (Skyra, Siemens, Erlangen) with a 32-channel head coil using a
radially symmetric q-space sampling scheme (RDSI).3 A single-shot twice refocused spin echo
sequence4 was used with the following sequence parameters: bmax -4000s/mm2,
TE/TR=114/2600ms, 2.2mm3 resolution, field of view 220 x200mm, 60
slices, parallel imaging (2x GRAPPA) and
multiband acceleration of two5, one b0 acquisition, one average, 10:49min. Fiber tracts were
generated with a modified streamline tracking algorithm as implemented in DSI
Studio (http://dsi-studio.labsolver.org,
1000 tracts, uniformly distributed seed points). A 3D surface mesh with embedded color maps was
generated using a custom made script (Python) in Blender
(http://www.blender.org). The mesh was then exported in .obj format so that it
could be imported directly into the Unity game engine, version 5.4 (Unity
Technologies, San Francisco, CA, USA). The model was finally deployed into the
Microsoft HoloLens headset for viewing in AR.
An outline of the process used to produce the virtual fiber tracts is
shown in Figure 1. Fiber tracts were evaluated by two
experienced radiologists and three magnetic resonance physicists with expertise
in diffusion imaging. Questions
regarding which method of viewing was preferred and whether the HoloLens model
would be helpful for surgical planning were asked.Results
Our developments have permitted stereoscopic 3D
exploration of white matter fiber tracts in AR using the Microsoft HoloLens (Figure 2). The AR model permitted a good visual match to
the original 3D surface mesh (Figure 3). A short video of the AR fiber tracts is shown
in Figure 4. For the model
evaluation, four out of five (80%) of the reviewers preferred the AR model over
the computer model. In addition, all believed
that the AR model would be helpful for surgical planning due to the advanced 3D
spatial visualization and ability to view from different angles.Discussion
We have developed a method to visualize DSI
tractography in AR using the Microsoft HoloLens. Our results suggest that visualization of
white matter fiber tracts in AR facilitates navigation through the complex
fiber tract models, thus helping to gain better insight in the connectivity in
the brain.Conclusion
This study demonstrates that it is feasible to
create 3D models of fiber tracts that can be implemented and viewed in AR. Visualization in AR provides physicians and
surgeons with a more realistic depiction of the anatomy and may have great
impact in surgical planning and navigation. Acknowledgements
This work was supported by the Center for Advanced Imaging Innovation
and Research (www.cai2r.net), a NIBIB Biomedical Technology Resource Center
(NIH P41 EB017183).References
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