HandNavigator

Kry, Paul G.; Pihuit, Adeline; Bernhardt, Adrien; Cani, Marie-Paule

doi:10.1145/1450579.1450591

Cited by 20 publications

(7 citation statements)

References 21 publications

(18 reference statements)

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“…All distributions were normalized to the maximum dose depending on the particular field size. The calculated PDDs and lateral dose profiles were then compared to measurement data produced by the machine's manufacturer as the de facto 'gold standard' for this particular accelerator model (Kry 2008).…”

Section: In-field Validation Of the Accelerator Modelmentioning

confidence: 99%

“…The 10 cm × 10 cm field was scaled by 80% and the 40 cm × 40 cm field was scaled by 60% for clarity. Measurement data were produced by the machine's manufacturer as the 'gold standard' for this particular accelerator model (Kry 2008). For each tally the relative error was less than 2%.…”

Section: Neutronsmentioning

confidence: 99%

“…This paper describes a study to develop and validate detailed Monte Carlo computational models of a Varian Clinac medical linear accelerator, containing the necessary shielding surrounding the beam-line components, operating at 6 and 18 MV beam energies . The validation process involved tuning the initial electron beam parameters in order to match calculated in-field dose distributions with de facto 'gold standard' measurement data from the manufacturer (Kry 2008). The out-of-field photon dose distributions were compared with published measurements (Kry et al 2006(Kry et al , 2007.…”

Section: Introductionmentioning

confidence: 99%

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Monte Carlo modeling of a 6 and 18 MV Varian Clinac medical accelerator for in-field and out-of-field dose calculations: development and validation

Bednarz

2009

Phys. Med. Biol.

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There is a serious and growing concern about the increased risk of radiation-induced second cancers and late tissue injuries associated with radiation treatment. To better understand and to more accurately quantify non-target organ doses due to scatter and leakage radiation from medical accelerators, a detailed Monte Carlo model of the medical linear accelerator is needed. This paper describes the development and validation of a detailed accelerator model of the Varian Clinac operating at 6 and 18 MV beam energies. Over 100 accelerator components have been defined and integrated using the Monte Carlo code MCNPX. A series of in-field and out-of-field dose validation studies were performed. In-field dose distributions calculated using the accelerator models were tuned to match measurement data that are considered the de facto ‘gold standard’ for the Varian Clinac accelerator provided by the manufacturer. Field sizes of 4 cm × 4 cm, 10 cm × 10 cm, 20 cm × 20 cm and 40 cm × 40 cm were considered. The local difference between calculated and measured dose on the percent depth dose curve was less than 2% for all locations. The local difference between calculated and measured dose on the dose profile curve was less than 2% in the plateau region and less than 2 mm in the penumbra region for all locations. Out-of-field dose profiles were calculated and compared to measurement data for both beam energies for field sizes of 4 cm × 4 cm, 10 cm × 10 cm and 20 cm × 20 cm. For all field sizes considered in this study, the average local difference between calculated and measured dose for the 6 and 18 MV beams was 14 and 16%, respectively. In addition, a method for determining neutron contamination in the 18 MV operating model was validated by comparing calculated in-air neutron fluence with reported calculations and measurements. The average difference between calculated and measured neutron fluence was 20%. As one of the most detailed accelerator models for both in-field and out-of-field dose calculations, the model will be combined with anatomically realistic computational patient phantoms into a computational framework to calculate non-target organ doses to patients from various radiation treatment plans.

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Section: In-field Validation Of the Accelerator Modelmentioning

confidence: 99%

Section: Neutronsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Monte Carlo modeling of a 6 and 18 MV Varian Clinac medical accelerator for in-field and out-of-field dose calculations: development and validation

Bednarz

2009

Phys. Med. Biol.

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“…Among these studies include HandNavigator device [10] and Piivert device [3], with [10] focusing on kinesthetic virtual interaction, [3] combining the Ray-Casting technique with a finger gesture UI. The implementation of the hybrid UI concepts is a relatively common methodology in immersive VR development, as illustrated by EyeMotion interface [8], which integrated gaze tracking technology with a gesture recognition and mouse input interface.…”

Section: Related Workmentioning

confidence: 99%

Enhancing User Immersion and Virtual Presence in Interactive Multiuser Virtual Environments through the Development and Integration of a Gesture-Centric Natural User Interface Developed from Existing Virtual Reality Technologies

Emma-Ogbangwo

Cope

Behringer

et al. 2014

HCI International 2014 - Posters’ Extended Abstracts

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Abstract.Immersion, referring to the level of physical or psychological submergence of a user within a virtual space relative to that user's consciousness of the realworld environment, has predominantly been established as an indispensable part of interactive media designs. This is most prevalent in Virtual Reality (VR) platforms, as their applications are typically reliant on user believability. With a wide variation of possible methodologies for the enhancement of this feature, the collectively recognised paradigm lies on the emphasis of naturalism in the design of the virtual system [7]. Though widely used by some specialised VR applications [4] such concepts are yet to be fully explored in the more contemporary virtual systems such as Social Immersive Virtual Environment (SIVE).The focus of the study described in this paper are the techniques being developed to enhance user immersion, virtual presence and co-presence in a SIVE application, through the design and integration of a VR-based Natural User Interface (NUI) that allows users to naturally and intuitively interact with the virtual environment and other networked users through the utilisation of full body gesture controls. These gestural controls prioritise the emulation of the alternate equivalent of such real-wold interactions, whilst also providing an interface for the seamless and unobtrusive translation of the user's realworld physical state into the virtual environment through intuitive user to virtual avatar proprioceptive coordination.

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“…Hand is one of the cleverest control tools for human, so hand has been widely applied in HCI, especially in virtual reality and augmented reality fields [2] [3]. Based on the input devices that are used for feeding the model, these gesture interaction systems can be classified into four classes: data glove, marker [4] [5], hand image [6][7] [8], and drawing [9] [10]. The system presented in this paper belongs to the third class.…”

Section: Introductionmentioning

confidence: 99%

Wrist Recognition and the Center of the Palm Estimation Based on Depth Camera

Yao

Pan

2013

2013 International Conference on Virtual Reality and Visualization

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When the hand and the forearm enter the available depth range of the depth camera, the data of the hand and the forearm will be extracted together. If process these data as a whole, which maybe affect some important algorithms such as the center of the palm estimation, the orientation of the hand estimation and hand tracing. The paper analyzes the motion features of the wrist and the contour features of the hand, takes advantage of the geometric characteristics of an inscribed rectangle and proposes a wrist recognition algorithm. In order to reduce the computing time of estimating the center of the palm, the paper analyzes the geometric characteristics of an acute triangle and an inscribed circle, combines the features of the hand interaction and proposes a new algorithm of estimating the center of the palm. Proved by the experiments, the wrist recognition algorithm can separate the hand from the forearm well, and the new algorithm of estimating the center of the palm has a distinct advantage over the original algorithms in the performance. Keywords-Wrist recognition; the center of the palm estimation; depth camera; hand segment; gesture interaction I. 2013 International Conference on Virtual Reality and Visualization 978-0-7695-5150-0/13 $26.00

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HandNavigator

Cited by 20 publications

References 21 publications

Monte Carlo modeling of a 6 and 18 MV Varian Clinac medical accelerator for in-field and out-of-field dose calculations: development and validation

Monte Carlo modeling of a 6 and 18 MV Varian Clinac medical accelerator for in-field and out-of-field dose calculations: development and validation

Enhancing User Immersion and Virtual Presence in Interactive Multiuser Virtual Environments through the Development and Integration of a Gesture-Centric Natural User Interface Developed from Existing Virtual Reality Technologies

Wrist Recognition and the Center of the Palm Estimation Based on Depth Camera

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