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      An eye tracking based virtual reality system for use inside magnetic resonance imaging systems

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          Abstract

          Patients undergoing Magnetic Resonance Imaging (MRI) often experience anxiety and sometimes distress prior to and during scanning. Here a full MRI compatible virtual reality (VR) system is described and tested with the aim of creating a radically different experience. Potential benefits could accrue from the strong sense of immersion that can be created with VR, which could create sense experiences designed to avoid the perception of being enclosed and could also provide new modes of diversion and interaction that could make even lengthy MRI examinations much less challenging. Most current VR systems rely on head mounted displays combined with head motion tracking to achieve and maintain a visceral sense of a tangible virtual world, but this technology and approach encourages physical motion, which would be unacceptable and could be physically incompatible for MRI. The proposed VR system uses gaze tracking to control and interact with a virtual world. MRI compatible cameras are used to allow real time eye tracking and robust gaze tracking is achieved through an adaptive calibration strategy in which each successive VR interaction initiated by the subject updates the gaze estimation model. A dedicated VR framework has been developed including a rich virtual world and gaze-controlled game content. To aid in achieving immersive experiences physical sensations, including noise, vibration and proprioception associated with patient table movements, have been made congruent with the presented virtual scene. A live video link allows subject-carer interaction, projecting a supportive presence into the virtual world.

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          Most cited references33

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          High-Speed Tracking with Kernelized Correlation Filters

          The core component of most modern trackers is a discriminative classifier, tasked with distinguishing between the target and the surrounding environment. To cope with natural image changes, this classifier is typically trained with translated and scaled sample patches. Such sets of samples are riddled with redundancies-any overlapping pixels are constrained to be the same. Based on this simple observation, we propose an analytic model for datasets of thousands of translated patches. By showing that the resulting data matrix is circulant, we can diagonalize it with the discrete Fourier transform, reducing both storage and computation by several orders of magnitude. Interestingly, for linear regression our formulation is equivalent to a correlation filter, used by some of the fastest competitive trackers. For kernel regression, however, we derive a new kernelized correlation filter (KCF), that unlike other kernel algorithms has the exact same complexity as its linear counterpart. Building on it, we also propose a fast multi-channel extension of linear correlation filters, via a linear kernel, which we call dual correlation filter (DCF). Both KCF and DCF outperform top-ranking trackers such as Struck or TLD on a 50 videos benchmark, despite running at hundreds of frames-per-second, and being implemented in a few lines of code (Algorithm 1). To encourage further developments, our tracking framework was made open-source.
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            Can virtual reality exposure therapy gains be generalized to real-life? A meta-analysis of studies applying behavioral assessments.

            In virtual reality exposure therapy (VRET), patients are exposed to virtual environments that resemble feared real-life situations. The aim of the current study was to assess the extent to which VRET gains can be observed in real-life situations. We conducted a meta-analysis of clinical trials applying VRET to specific phobias and measuring treatment outcome by means of behavioral laboratory tests or recordings of behavioral activities in real-life. Data sources were searches of databases (Medline, PsycInfo, and Cochrane). We included in total 14 clinical trials on specific phobias. Results revealed that patients undergoing VRET did significantly better on behavioral assessments following treatment than before treatment, with an aggregated uncontrolled effect size of g = 1.23. Furthermore, patients undergoing VRET performed better on behavioral assessments at post-treatment than patients on wait-list (g = 1.41). Additionally, results of behavioral assessment at post-treatment and at follow-up revealed no significant differences between VRET and exposure in vivo (g = -0.09 and 0.53, respectively). Finally, behavioral measurement effect sizes were similar to those calculated from self-report measures. The findings demonstrate that VRET can produce significant behavior change in real-life situations and support its application in treating specific phobias.
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              Recent Progress in Virtual Reality Exposure Therapy for Phobias: A Systematic Review.

              This review is designed to systematically examine the available evidence about virtual reality exposure therapy's (VRET) efficacy for phobias, critically describe some of the most important challenges in the field and discuss possible directions. Evidence reveals that virtual reality (VR) is an effective treatment for phobias and useful for studying specific issues, such as pharmacological compounds and behavioral manipulations, that can enhance treatment outcomes. In addition, some variables, such as sense of presence in virtual environments, have a significant influence on outcomes, but further research is needed to better understand their role in therapeutic outcomes. We conclude that VR is a useful tool to improve exposure therapy and it can be a good option to analyze the processes and mechanisms involved in exposure therapy and the ways this strategy can be enhanced. In the coming years, there will be a significant expansion of VR in routine practice in clinical contexts.
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                Author and article information

                Contributors
                kun.qian@kcl.ac.uk
                jo.hajnal@kcl.ac.uk
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                11 August 2021
                11 August 2021
                2021
                : 11
                : 16301
                Affiliations
                [1 ]GRID grid.13097.3c, ISNI 0000 0001 2322 6764, Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, , King’s College London, ; London, SE1 7EH UK
                [2 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, Department of Bioengineering, , Imperial College London, ; London, SW7 2AZ UK
                [3 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, Department of Physics, , Imperial College London, ; London, SW7 2AZ UK
                [4 ]GRID grid.5371.0, ISNI 0000 0001 0775 6028, Department of Electrical Engineering, , Chalmers University of Technology, ; 412 96 Gothenburg, Sweden
                [5 ]GRID grid.7728.a, ISNI 0000 0001 0724 6933, Department of Mechanical and Aerospace Engineering, , Brunel University London, ; London, UB8 3PN UK
                Article
                95634
                10.1038/s41598-021-95634-y
                8357830
                34381099
                dc499de7-fc6f-4041-89df-7956173eba5c
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 15 March 2021
                : 18 June 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000781, European Research Council;
                Award ID: 319456
                Funded by: Wellcome EPSRC Centre for Medical Engineering
                Award ID: WT 203148/Z/16/Z
                Funded by: FundRef http://dx.doi.org/10.13039/501100000266, Engineering and Physical Sciences Research Council;
                Award ID: EP/L016737/1
                Funded by: FundRef http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MR/P008712/1
                Funded by: FundRef http://dx.doi.org/10.13039/100010664, H2020 Future and Emerging Technologies;
                Award ID: FETOPEN 829186
                Funded by: FundRef http://dx.doi.org/10.13039/100010686, H2020 European Institute of Innovation and Technology;
                Award ID: ICT 644727
                Funded by: TRIMANUAL
                Award ID: MSCA 843408
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                biomedical engineering,magnetic resonance imaging,translational research
                Uncategorized
                biomedical engineering, magnetic resonance imaging, translational research

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