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      Multiuser virtual reality environment for visualising neuroimaging data

      research-article
      Healthcare Technology Letters
      The Institution of Engineering and Technology
      brain, biomedical MRI, medical image processing, virtual reality, rendering (computer graphics), neurophysiology, data visualisation, neuroimaging data, high-performance consumer virtual reality systems, MRI volumes, neuroanatomical surface models, text-based annotations, OpenVR software development kit, virtual space, multiuser virtual reality environment, medical imaging, diffusion tensors, streamline tractography, HTC Vive, OpenGL, fibre track selection, automated brain MRI analysis packages, Vive controllers, A8730, Biophysics of neurophysiological processes, A8760I, Medical magnetic resonance imaging and spectroscopy, A8770E, Patient diagnostic methods and instrumentation, B6135, Optical, image and video signal processing, B7510N, Biomedical magnetic resonance imaging and spectroscopy, C5260B, Computer vision and image processing techniques, C6130V, Virtual reality, C7330, Biology and medical computing

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          Abstract

          The recent advent of high-performance consumer virtual reality (VR) systems has opened new possibilities for immersive visualisation of numerous types of data. Medical imaging has long made use of advanced visualisation techniques, and VR offers exciting new opportunities for data exploration. The author presents a new framework for interacting with neuroimaging data, including MRI volumes, neuroanatomical surface models, diffusion tensors, and streamline tractography, as well as text-based annotations. The system was developed for the HTC Vive using C++, OpenGL, and the OpenVR software development kit. The author developed custom GLSL shaders for each type of data to provide high-performance real-time rendering suitable for use in a VR environment. These are integrated with an interface that enables the user to manipulate the scene through the Vive controllers and perform operations such as volume slicing, fibre track selection, and structural queries. The software can read data generated by existing automated brain MRI analysis packages, enabling the rapid development of subject-specific visualisations of multimodal data or annotated atlases. The system can also support multiple simultaneous users, placing them in the same virtual space to interact with each other while visualising the same datasets, opening new possibilities for teaching and for collaborative exploration of neuroimaging data.

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          Most cited references11

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          Construction of a 3D probabilistic atlas of human cortical structures.

          We describe the construction of a digital brain atlas composed of data from manually delineated MRI data. A total of 56 structures were labeled in MRI of 40 healthy, normal volunteers. This labeling was performed according to a set of protocols developed for this project. Pairs of raters were assigned to each structure and trained on the protocol for that structure. Each rater pair was tested for concordance on 6 of the 40 brains; once they had achieved reliability standards, they divided the task of delineating the remaining 34 brains. The data were then spatially normalized to well-known templates using 3 popular algorithms: AIR5.2.5's nonlinear warp (Woods et al., 1998) paired with the ICBM452 Warp 5 atlas (Rex et al., 2003), FSL's FLIRT (Smith et al., 2004) was paired with its own template, a skull-stripped version of the ICBM152 T1 average; and SPM5's unified segmentation method (Ashburner and Friston, 2005) was paired with its canonical brain, the whole head ICBM152 T1 average. We thus produced 3 variants of our atlas, where each was constructed from 40 representative samples of a data processing stream that one might use for analysis. For each normalization algorithm, the individual structure delineations were then resampled according to the computed transformations. We next computed averages at each voxel location to estimate the probability of that voxel belonging to each of the 56 structures. Each version of the atlas contains, for every voxel, probability densities for each region, thus providing a resource for automated probabilistic labeling of external data types registered into standard spaces; we also computed average intensity images and tissue density maps based on the three methods and target spaces. These atlases will serve as a resource for diverse applications including meta-analysis of functional and structural imaging data and other bioinformatics applications where display of arbitrary labels in probabilistically defined anatomic space will facilitate both knowledge-based development and visualization of findings from multiple disciplines.
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            BrainSuite: an automated cortical surface identification tool.

            We describe a new magnetic resonance (MR) image analysis tool that produces cortical surface representations with spherical topology from MR images of the human brain. The tool provides a sequence of low-level operations in a single package that can produce accurate brain segmentations in clinical time. The tools include skull and scalp removal, image nonuniformity compensation, voxel-based tissue classification, topological correction, rendering, and editing functions. The collection of tools is designed to require minimal user interaction to produce cortical representations. In this paper we describe the theory of each stage of the cortical surface identification process. We then present classification validation results using real and phantom data. We also present a study of interoperator variability.
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              A multimodal, multidimensional atlas of the C57BL/6J mouse brain.

              Strains of mice, through breeding or the disruption of normal genetic pathways, are widely used to model human diseases. Atlases are an invaluable aid in understanding the impact of such manipulations by providing a standard for comparison. We have developed a digital atlas of the adult C57BL/6J mouse brain as a comprehensive framework for storing and accessing the myriad types of information about the mouse brain. Our implementation was constructed using several different imaging techniques: magnetic resonance microscopy, blockface imaging, classical histology and immunohistochemistry. Along with raw and annotated images, it contains database management systems and a set of tools for comparing information from different techniques. The framework allows facile correlation of results from different animals, investigators or laboratories by establishing a canonical representation of the mouse brain and providing the tools for the insertion of independent data into the same space as the atlas. This tool will aid in managing the increasingly complex and voluminous amounts of information about the mammalian brain. It provides a framework that encompasses genetic information in the context of anatomical imaging and holds tremendous promise for producing new insights into the relationship between genotype and phenotype. We describe a suite of tools that enables the independent entry of other types of data, facile retrieval of information and straightforward display of images. Thus, the atlas becomes a framework for managing complex genetic and epigenetic information about the mouse brain. The atlas and associated tools may be accessed at http://www.loni.ucla.edu/MAP.
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                Author and article information

                Contributors
                Journal
                Healthc Technol Lett
                Healthc Technol Lett
                HTL
                Healthcare Technology Letters
                The Institution of Engineering and Technology
                2053-3713
                19 October 2018
                October 2018
                19 October 2018
                : 5
                : 5
                : 183-188
                Affiliations
                Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, David Geffen School of Medicine at UCLA , Los Angeles, California, USA
                Article
                HTL.2018.5077 HTL.2018.5077
                10.1049/htl.2018.5077
                6222246
                56dc96c2-6888-41f8-9092-858e3fcc46d0

                This is an open access article published by the IET under the Creative Commons Attribution-NonCommercial-NoDerivs License ( http://creativecommons.org/licenses/by-nc-nd/3.0/)

                History
                : 13 August 2018
                : 20 August 2018
                Funding
                Funded by: National Institutes of Health
                Award ID: R01NS074980
                Categories
                Special Issue: Papers from the 12th Workshop on Augmented Environments for Computer-Assisted Interventions

                brain,biomedical mri,medical image processing,virtual reality,rendering (computer graphics),neurophysiology,data visualisation,neuroimaging data,high-performance consumer virtual reality systems,mri volumes,neuroanatomical surface models,text-based annotations,openvr software development kit,virtual space,multiuser virtual reality environment,medical imaging,diffusion tensors,streamline tractography,htc vive,opengl,fibre track selection,automated brain mri analysis packages,vive controllers

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