NIVR: Neuro Imaging in Virtual Reality (bibtex)
by Tyler Ard, David M. Krum, Thai Phan, Dominique Duncan, Ryan Essex, Mark Bolas, Arthur Toga
Abstract:
Visualization is a critical component of neuroimaging, and how to best view data that is naturally three dimensional is a long standing question in neuroscience. Many approaches, programs, and techniques have been developed specifically for neuroimaging. However, exploration of 3D information through a 2D screen is inherently limited. Many neuroscientific researchers hope that with the recent commercialization and popularization of VR, it can offer the next-step in data visualization and exploration. Neuro Imaging in Virtual Reality (NIVR), is a visualization suite that employs various immersive visualizations to represent neuroimaging information in VR. Some established techniques, such as raymarching volume visualization, are paired with newer techniques, such as near-field rendering, to provide a broad basis of how we can leverage VR to improve visualization and navigation of neuroimaging data. Several of the neuroscientific visualization approaches presented are, to our knowledge, the first of their kind. NIVR offers not only an exploration of neuroscientific data visualization, but also a tool to expose and educate the public regarding recent advancements in the field of neuroimaging. By providing an engaging experience to explore new techniques and discoveries in neuroimaging, we hope to spark scientific interest through a broad audience. Furthermore, neuroimaging offers deep and expansive datasets; a single scan can involve several gigabytes of information. Visualization and exploration of this type of information can be challenging, and real-time exploration of this information in VR even more so. NIVR explores pathways which make this possible, and offers preliminary stereo visualizations of these types of massive data.
Reference:
NIVR: Neuro Imaging in Virtual Reality (Tyler Ard, David M. Krum, Thai Phan, Dominique Duncan, Ryan Essex, Mark Bolas, Arthur Toga), In Proceedings of Virtual Reality (VR), 2017 IEEE, IEEE, 2017.
Bibtex Entry:
@inproceedings{ard_nivr:_2017,
	address = {Los Angeles, CA},
	title = {{NIVR}: {Neuro} {Imaging} in {Virtual} {Reality}},
	isbn = {978-1-5090-6647-6},
	url = {http://ieeexplore.ieee.org/abstract/document/7892381/},
	doi = {10.1109/VR.2017.7892381},
	abstract = {Visualization is a critical component of neuroimaging, and how to best view data that is naturally three dimensional is a long standing question in neuroscience. Many approaches, programs, and techniques have been developed specifically for neuroimaging. However, exploration of 3D information through a 2D screen is inherently limited. Many neuroscientific researchers hope that with the recent commercialization and popularization of VR, it can offer the next-step in data visualization and exploration.
Neuro Imaging in Virtual Reality (NIVR), is a visualization suite that employs various immersive visualizations to represent neuroimaging information in VR. Some established techniques, such as raymarching volume visualization, are paired with newer techniques, such as near-field rendering, to provide a broad basis of how we can leverage VR to improve visualization and navigation of neuroimaging data. Several of the neuroscientific visualization approaches presented are, to our knowledge, the first of their kind.
NIVR offers not only an exploration of neuroscientific data visualization, but also a tool to expose and educate the public regarding recent advancements in the field of neuroimaging. By providing an engaging experience to explore new techniques and discoveries in neuroimaging, we hope to spark scientific interest through a broad audience.
Furthermore, neuroimaging offers deep and expansive datasets; a single scan can involve several gigabytes of information. Visualization and exploration of this type of information can be challenging, and real-time exploration of this information in VR even more so. NIVR explores pathways which make this possible, and offers preliminary stereo visualizations of these types of massive data.},
	booktitle = {Proceedings of {Virtual} {Reality} ({VR}), 2017 {IEEE}},
	publisher = {IEEE},
	author = {Ard, Tyler and Krum, David M. and Phan, Thai and Duncan, Dominique and Essex, Ryan and Bolas, Mark and Toga, Arthur},
	month = mar,
	year = {2017},
	keywords = {MxR},
	pages = {465--466}
}
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