Interactive Visual Intervention Planning

Interactive Visualization for Intervention Planning in Particle Accelerator

Environments with Ionizing Radiation

Thomas Fabry

, Christian Braesch

and Bruno Feral

CERN, Geneva, Switzerland

SYMME, Universit

e de Savoie, Polytech Annecy-Chamb

ery, Chamb

ery, France

Keywords:

Interactive Data Visualization, Volume Rendering, Ionizing Radiation, Intervention Planning.

Abstract:

Intervention planning is crucial for maintenance operations in particle accelerator environments with ionizing

radiation, during which the radiation dose contracted by maintenance workers should be reduced to a mini-

mum. In this context, we discuss the visualization aspects of a new software tool, which integrates interactive

exploration of a scene depicting an accelerator facility augmented with residual radiation level simulations,

with the visualization of intervention data such as the followed trajectory and maintenance tasks. The visual-

ization of each of these aspects has its effect on the ﬁnal predicted contracted radiation dose. In this context,

we explore the possible beneﬁts of a user study, with the goal of enhancing the visual conditions in which the

intervention planner using the software tool is minimizing the radiation dose.

1 INTRODUCTION

Particle physics is a branch of modern physics that

studies the smallest known constituents of matter.

Particle physics research necessitates large and com-

plex scientiﬁc instruments: particle accelerators and

detectors (Wille, 2001; Myers, 2012). Accelerators

boost beams of particles to high energies before they

are made to collide with each other or with station-

ary targets. Detectors observe and record the results

of these collisions. The circulation and collisions of

high energy beams in the accelerators and detectors

have an undesirable consequence, namely the radio-

logical activation of some of the components of these

facilities.

The complexity of particle accelerators and de-

tectors lead to the frequent necessity of maintenance

operations. To protect maintenance personnel from

ionizing radiation during interventions in its parti-

cle accelerators and detectors, the so-called ALARP

or ALARA approach (As Low As Reasonably Pos-

sible or Achievable (UKA, 2012; Grupen, 2010)) is

mostly used, which consists of justifying, optimizing

and limiting the dose received by all those who need

to work on activated components.

A core issue during the planning of a maintenance

intervention in a facility with ionizing radiation is the

minimization of the equivalent dose contracted by the

maintenance workers during the intervention. This

optimization cannot easily be automated, since the

practical feasibility of working conditions during the

intervention tasks requires human assessment based

on experience. The visual conditions in which the in-

tervention planner can perform the optimization are

important, and the several layers of data involved in

the planning process, i.e. the facility geometry, the

radiation levels and the intervention trajectory, there-

fore need to be appropriately visualized.

In this context, we develop a tool that brings state-

of-the-art visualization techniques, similar to the ones

found in medical imaging for example, to intervention

planning to optimize human interventions in infras-

tructures in emitting ionizing radiation. We also sub-

mit the visualization method used in the developed

tool to a user test to evaluate the effectiveness of the

visualization and its perception by the user.

In the remainder of this article, ﬁrst, we describe

the important concepts of the planning of an interven-

tion in an environment with ionizing radiation in sec-

tion 2. Second, we discuss how visualization plays

an important role in this application and what the re-

quirements are for the application, and propose a soft-

ware tool for this purpose in section 3. Next, we dis-

cuss and explore the beneﬁts of a user test for the

interactive visualization and intervention planning in

section 4, and ﬁnally conclude the article in section 5.

585

Fabry T., Braesch C. and Feral B..

Interactive Visual Intervention Planning - Interactive Visualization for Intervention Planning in Particle Accelerator Environments with Ionizing Radiation.

DOI: 10.5220/0004212505850592

In Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Information

Visualization Theory and Applications (IVAPP-2013), pages 585-592

ISBN: 978-989-8565-46-4

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

2 PLANNING OF

AN INTERVENTION IN

AN ENVIRONMENT WITH

IONIZING RADIATION

2.1 Quantifying Ionizing Radiation

Ionizing radiation is radiation that has the power to

liberate an electron from an atom or molecule, thus

producing ions (atoms or molecules with an electric

charge). Because ions are chemically reactive, they

can cause biological damage when produced in living

tissue. Ionizing radiation can result in e.g. radiation

sickness and cancer, but also has practical uses in re-

search, medicine, and other areas. Sources of ionizing

radiation are ubiquitous, such as cosmic rays and nat-

urally occurring radioactive materials, but ionizing ra-

diation can also be created, with artiﬁcially created ra-

dioisotopes, X-ray tubes or particle accelerators, etc.

Ionizing radiation exists in various forms. The

particles of which ionizing radiation consists must

have a sufﬁciently high energy and interact with

the atoms of a target. These particles can be pho-

tons (electromagnetic radiation), electrons, positrons,

muons, protons, alpha particles, heavy atomic nuclei

or neutrons.

Workers that perform maintenance in an envi-

ronment with ionizing radiation contract a radiation

dose D. The unit of radiation dose is the gray (Gy),

which quantiﬁes the amount of radiation energy ab-

sorbed by a kilogram of matter and is equivalent to

J/kg.

Different types of radiation cause different levels

of damage to living tissue. This issue is addressed by

the equivalent dose H, which is calculated by multi-

plying the radiation dose D with a weighting factor Q

that is deﬁned based on the radiation type. The unit

of equivalent dose is the sievert (Sv). The effective

dose equivalent H

eff

is the equivalent of H for partial

body irradiation (Cossairt, 1999; Grupen, 2010).

2.2 Intervention Planning Concepts

An intervention I is a set of tasks T

that need to

be completed by the maintenance worker, each with a

speciﬁc description and an estimated duration τ

I = {T

; k = 0,1,. ..,K}. (1)

Task T

corresponds to the entrance of the facility by

the worker; task T

corresponds to the exit of the fa-

cility.

A trajectory T consists of a series of locations

, with i = 0,1,.. .,N. At each location m

, a main-

tenance worker will spend an amount of time denoted

by t

. The path between two consecutive locations

and m

i+1

is denoted by S

, with i = 0, 1, ... ,N − 1.

Each path S

is taken by the maintenance worker at a

velocity v

The planner of an intervention will decide on a

trajectory T with an intervention I in mind, thus con-

structing a map between I and T . As a result:

∀ T

∈ I : T

is assigned to a location m

and t

= τ

∀ m

∈ T and 6 ∃ T

assigned to location m

: t

= 0,

with K ≤ N.

The equivalent dose H contracted by the mainte-

nance worker performing an intervention I mapped

on a trajectory T is calculated as the sum of the ra-

diation received at the locations m

and the radiation

received over the paths S

between the locations:

H(I ,T ) =

∑

i=0

H(m

) +

N−1

∑

i=0

i+1

−1

H(s)ds, (2)

where s is a point on the path S

. The radiation rates

H are available from simulations of the activation of

the facility equipment or from manual measurements

performed previously in the irradiated facility.

A trajectory is optimal when the equivalent dose

H is minimal, respecting the constraints that all tasks

require a minimal execution time and that the veloc-

ities v

have to stay within the abilities of the main-

tenance worker. Some aspects of the trajectory op-

timization could be automated: the locations m

can

be placed such that the total amount of received ra-

diation is minimized. However, other aspects require

human assessment based on experience, such as prac-

tical considerations on the location from where a task

is executed, or the velocity with which a speciﬁc part

of the trajectory can be taken.

3 VISUALIZATION

FOR INTERACTIVE

INTERVENTION PLANNING

3.1 Approach

To make sure that the optimization of the radiation

dose coming with the human intervention in infras-

tructures emitting ionizing radiation can be performed

in the best possible conditions, the visualization of the

particle accelerator and detector facilities, the radia-

tion levels and the intervention data plays an impor-

tant role.

Radiation level simulations are a tool that are

much used to assess the radiological conditions of ac-

celerator facilities where maintenance interventions

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will be necessary. The FLUKA package is a tool

that can be used for the simulation of radiation lev-

els and other radiological quantities after operation

of accelerator and detector infrastructure (FLU, 2012;

Battistoni et al., 2007; Fass

o et al., 2005). The re-

sults of these simulations are most often visualized

using the FLUKA Advanced Interface (FLAIR) (Vla-

choudis, 2009) in a 2D way (see ﬁgure 1 for an exam-

ple of a FLAIR visualization of a radiation level simu-

lation), and this is also what is typically used for com-

munication between Radiation Protection experts and

other persons involved in maintenance operation plan-

ning at accelerator facilities (Vollaire and Widorski,

2011). Other software programs have been developed

to allow visualization of FLUKA simulation results,

such as SimpleGeo (Theis, 2012; Theis et al., 2006).

SimpleGeo is an interactive solid modeler, which is

made for implementing geometries for particle trans-

port problems based on Constructive Solid Geome-

try (CSG). Together with the DaVis3D plugin (DaV,

2012), SimpleGeo allows interactive visualization of

two-dimensional cuts of FLUKA voxel geometries.

Figure 1: A typical FLAIR visualization of radiation levels.

There is however no tool that can enhance the in-

tervention that is advanced enough to help interven-

tion planners and, intuitive enough to be interesting

for the maintenance workers, has 3D display possi-

blilities for both the models of the facilities and the

FLUKA simulation data, offers the possibility to per-

form interactive visual inspection of the radiation lev-

els, construct trajectories, and can calculate the result-

ing radiation dose contracted during a planned inter-

vention.

We opted to develop intervention planning soft-

ware in Python (pyt, 2012), and to use The Visualiza-

tion Toolkit (Schroeder et al., 2006) for the visualiza-

tion aspects of the intervention planning. For the de-

velopment of the graphical user interface (GUI), we

chose to make use of wxPython (Rappin and Dunn,

2006). Because major attention has to be paid to the

requirement of an intuitive graphical user interface al-

lowing fast and ﬂexible visualization, trajectory cre-

Figure 2: A screenshot of the software’s Graphical User

Interface, showing the volume rendering of a radiation dose

simulation in the TNC tunnel at CERN. The geometry of

the cavern is shown as well and is partly translucent to be

able to have an overall view on the situation.

ation, and reporting, the user interface (UI) is as much

as possible decoupled from the back-end of the soft-

ware.

Figure 2 shows a screenshot of the GUI of the tool

that was developed, illustrating the volume rendering

of the example radiation dose rates in the TNC tunnel

at CERN (Charitonidis et al., 2011).

In summary, the intervention planning software

requires appropriate visualization of the facility ge-

ometry, the radiation levels and the trajectory infor-

mation, as well as tools for interaction with them.

In addition, the software has to provide an interface

where the total radiation dose can be assessed and the

resulting trajectory is summarized. We thus devel-

oped a new software tool to be in line with the needs

that are detailed in the following subsections.

3.2 Interactive Visualization of

the Facility Geometry and

the Radiation Levels

The intervention planner software offers an interface

for the user where he can select the ﬁle containing the

geometry of the facility as well as the ﬁle containing

the applicable radiation levels. To get a good insight

into the work conditions in the facility, several tools

are available to interact with the geometries and to

assess the radiation levels at speciﬁc points.

Due to the nature of FLUKA simulation data, fre-

quently used at CERN, and the requirement of a clear

visualization of the working conditions, volume ren-

dering is the natural choice to visualize the facility ge-

ometry and the radiation levels. We consider volume

rendering to be a very intuitive volume visualization

technique, compared to e.g. volume slicing. Volume

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587

rendering has been around for many years (Levoy,

1988; Drebin et al., 1988). Recently, the development

and improvement of off-the-shelve GPUs has led to

the proposition of several interactive advanced volu-

metric illumination models (Ritschel, 2007).

3.3 Interactive Visualization of

the Trajectory Information

The trajectory is represented by a three-dimensional

cardinal spline. Splines are an ideal mathematical

representation of the trajectory, since they are piece-

wise deﬁned and possess a high degree of smoothness

at the points where their polynomial pieces connect,

i.e. at the locations m

. In addition, splines are very

intuitive to work with and allow to design and control

complex curves.

The number of locations is easily adjustable by the

user through the GUI, and the locations can be dis-

placed by the user to shape the spline into the trajec-

tory that the maintenance worker will follow. To illus-

trate the importance of trajectory optimization, Fig-

ure 3 shows two possible trajectories through the TNC

tunnel. The dose contracted during the trajectory vi-

sualized in Fig. 3(a) proves to be 25% higher than

the dose contracted when following the trajectory in

Fig. 3(b). These hypothetical trajectories are merely

an illustration of the principle but indicate that small

changes in an intervention can lead to much smaller

exposure: in this case passing at the right side of an

activated piece of equipment leads to a considirable

reduction in dose. The volume rendered data is com-

ing from a realistic simulation of the dose levels in an

existing facility.

3.4 Calculation of the Equivalent Dose

and Trajectory Report

The core of the application is the calculation of the

equivalent dose H received by a worker over a user-

deﬁned trajectory T through the simulation volume,

as deﬁned in Section 2.2. To calculate H, the tra-

jectory spline is discretized into n segments, creating

a series of consecutive points s

on the spline, with

j = 0,1,. .., n. The number of discretization steps

can be controlled by the user; the default value is

n = 1000. Using the trapezoidal rule, equation (2)

can now be calculated as:

H =

∑

i=0

H(m

) +

∑

j=0

H(s

) +

H(s

j+1

)

j+1

(3)

Increasing the value of n has a positive inﬂuence

on the accuracy of

h. However, one should keep in

(a)

(b)

Figure 3: Two possible trajectories for a maintenance inter-

vention in the TNC tunnel at CERN. The equivalent dose

contracted during the trajectory depicted in (a) proves to be

25% higher than the dose contracted during the trajectory

depicted in (b).

mind that the overall accuracy of equation (3) also de-

pends on the accuracy of the radiation rates

h, which

are obtained from simulations or from (sparse) man-

ual measurements, both with limited accuracy.

At the end of an intervention planning, the soft-

ware offers the possibility to generate a trajectory re-

port. This report lists the sources of the trajectory

planning (input ﬁles, units, . . . ); the trajectory infor-

mation and the results of the planning (computed re-

ceived dose, trajectory length, . . . ).

4 USER TEST

Since the use of a 3D visualization tool for the plan-

ning of interventions in facilities emitting ionizing ra-

diation is not implemented yet in the facilities it is

designed for, a user test is needed to prove that the

application is useful to the intervention planner. The

main goals of the user test we are proposing are, ﬁrst,

to qualitatively prove the usefulness of the 3D visu-

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588

alization for the user, and second to make way for a

larger user test, using more quantitative variables, in

order to discover the optimal settings for the 3D vi-

sualization. We also set a secondary goal, namely the

quantitative comparison of two different color maps

for the volume rendering visualization of the radiation

level simulations. We evaluate this using the quantita-

tive measures that are subject of the optimization dur-

ing the planning of the maintenance operations. The

appropriate use of color visualization is seen as a very

important, and one of the most fundamental subjects

in visualization (Silva et al., 2007). We thus want to

test whether the choice of the colormap has indeed an

important effect on the user experience, and if it has

an effect on the optimization process that the user is

performing in this application.

In scientiﬁc visualization literature, many publica-

tions of user tests and user test designs can be found.

However, these user tests deal almost exclusively with

the effectiveness of one visualization method on the

user perception, without incorporating the context of

a concrete application. For instance, many user stud-

ies can be found on the inﬂuence of different illumi-

nation models on 3D visualization on user perception

of static computer-generated images (Wanger, 1992;

Gribble and Parker, 2005; Ropinski et al., 2010; Lin-

demann and Ropinski, 2011;

Solt

eszov

a et al., 2011).

In contrast to this, our user test design is conceived to

take the interactive context of the trajectory planning

application into account. Furthermore, the user test

will also contribute to abate the relative scarceness of

volume rendering applications user studies. Indeed,

perceptual studies are scarcer in volume rendering ap-

plications than in surface rendering applications (Lin-

demann and Ropinski, 2011).

4.1 Material and Methods

As the user test is mainly a feasibility test for the de-

veloped software concept, the most important vari-

able that was recorded was the qualitative apprecia-

tion of the user on the usability of the tool. This was

done by asking for comments after the user test in-

stance was performed. The other recorded variables

were:

• H

rec

: the computed expected integrated equivalent

radiation dose received by the radiation worker

when he would run this trajectory at a constant

speed,

• l

rec

: the length of the trajectory that was con-

structed by the user and

• n

rec

: the number of control points the user used to

construct the trajectory.

In addition to this, the full session information is

recorded: all of the variables that are needed to re-

produce the view the user had at the end of his ses-

sion, including visualization, camera and interaction

parameters.

These parameters are recorded as the result of the

user test: a controlled possible real-life scenario of

a planning of an intervention. The user was shown

real-life simulation of example radiation dose rates in

the TNC tunnel at CERN. The TNC tunnel is part

of the infrastructure where the HiRadMat facility is

located (Efthymiopoulos et al., 2011). The HiRad-

Mat facility will be used to investigate the impact of

high energy particle beams on different materials.The

residual radiation dose rates originate from a FLUKA

simulation of beam impact on beam equipment for

the Large Hadron Collider (LHC) (Charitonidis et al.,

2011). The radiation doses were shown using the

GPU ray casting volume rendering algorithm as im-

plemented in VTK (Schroeder et al., 2006; VTK,

2011). This volume rendering was overlayed on a

transparent visualization of the geometry of the tun-

nel, as conceived and used for the FLUKA simula-

tion.

For the secondary goal of the user test, two color

maps were shown to the user: the standard, much-

used and much-contested rainbow color map (Bor-

land and Taylor II, 2007) and a continuous diverging

color map claimed to be well suited to scientiﬁc vi-

sualization (Moreland, 2009). The order of the color

maps in the user tests was randomized to mitigate the

effect of familiarity the user might get the second time

he perfoms the manual trajectory optimization. The

qualitative of the colormap is measured according to

the three recorded variables discussed above.

The scenario of the test is a scheme where a main-

tenance worker has to enter the facility through a

given entrance location, go to a given location to per-

forme a maintenance operation on a particular piece

of equipment, and leave the facility through a given

exit location. To let the user simulate this, the lo-

cations of the entrance, maintenance and exit points

where given as ﬁxed points on a dummy trajectory.

This dummy trajectory had a number of control points

that the user can move in order to alter the trajectory.

In addition, the user is given the possibility to sup-

press or add control points in order to be able to make

a more detailed trajectory (see ﬁgure 4). In every user

session, the user had to perform these actions twice,

using a visualization with different color maps.

In this context, the user was asked to construct a

trajectory that he thinks is optimal, in terms of radi-

ation the maintenance worker would undergo, given

the constraints and the visualization of the simulated

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Figure 4: The interface shown to the user performing the user test. The begin and end points and the control point marked

”Maintenance Activity” are ﬁxed. The user can alter the given trajectory by displacing the remaining control points, as well

as any additional control points that he can add.

radiation dose rates. In order for the test to be as

controlled as possible, most of the software user con-

trolled settings/features were disabled. The user was

given no real-time feedback in terms of resulting dose

of the planned trajectory.

The user test was performed 10 times, by 7 dif-

ferent subjects. All of the subjects were more or less

familiar with the type of facilities that our research

is being done for, but only one of them was famil-

iar with the particular facility used for the user test.

3 subjects did the user test twice. None of the sub-

jects is professionally involved in maintenance plan-

ning, what gives us the possibity to assess if it will

be feasible to use this tool not only in the interven-

tion planning but also to give the maintenance work-

ers an idea of the tasks they will have to perform and

the relation of these tasks to the relative radiation lev-

els they lead to. Furthermore, the fact that the user

test subjects are not professionally involved in main-

tenance planning will allow us to have stronger indi-

cations on the user-friendlyness of the software. The

interval between two tests performed by the same user

was always more than 2 weeks.

4.2 Results

As for the qualitative feasibility test, all of the sub-

jects were convinced of the potential of the given

tool. None of them had comments on the visualiza-

tion. There were some comments on the controls of

the 3D navigation. These comments are very interest-

ing and will be dealt with for the next version of the

user test. They are however directly not relevant to

the results of this paper.

As for the quantitative discriminatory test between

the two color map, box plots of the measured vari-

ables, per color map, can be found in ﬁgure 5. We

performed paired two-tailed t-tests on the three mea-

sured variables. The results found are:

• a t-value of p(9) = 0.821, p = 0.43 for the

computed expected integrated equivalent radia-

tion dose received by the radiation worker when

he would run this trajectory at a constant speed,

meaning that the computed expected integrated

equivalent radiation doses are not signiﬁcantly

different for the trajectory plannings with the dif-

ferent color maps;

• a t-value of p(9) = 0.609, p = 0.56 for the length

of the trajectory that was constructed by the user,

meaning that the trajectory lengths are not signif-

icantly different for the trajectory plannings with

the different color maps and

• a t-value of p(9) = 0.137, p = 0.89 for the number

of control points the user used to construct the tra-

jectory, meaning that the number of control points

the user preferred to make the trajectory are not

signiﬁcantly different for the different color maps.

4.3 Discussion

From the results of the user tests, we cannot con-

clude that there is a signiﬁcant difference between the

two color maps. We can thus conclude that for this

particular test, the color map is not of large impor-

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590

(a) (b)

(c)

Figure 5: Boxplot of the measured variables: 5(a) the com-

puted expected integrated equivalent dose (normalized),

5(b) the length of the constructed trajectory (normalized)

and 5(c) the number of control points used in the trajectory.

On the left the results of the user test, the data visualised

using the continuous diverging color map, for the data on

the right the rainbow color map was used.

tance for obtaining good results in operation planning

in facilities emitting ionizing radiation. The result is

however of limited strength, because of the relatively

small sample of users and the very small number of

scenarios. While we could have obtained more con-

clusive result with more users, we think that the main

improvement in the user test can be made with letting

the user optimize operation in more different facilities

and scenarios.

The Student t-test for the number of control points

is however leading to a p-value of 0.89, so that we can

almost, with a signiﬁcance level of α = 0.1, exclude

that the user prefers more or less control points with

one or the other color map. This could have been a

sign that the user is better able to minimize the ra-

diation dose over the trajectory with one color map.

This means that we can almost reject the hypothe-

sis that the user uses more control points to specify

the trajectory in the case of the continuous diverging

color map, because he considers to be better able to

constuct a detailed trajectory in this case. This con-

clusion is however also not evident when we plot the

normalized computed expected integrated equivalent

dose against the number of control points used per

user test instance (see ﬁgure 6). More user tests are

needed to conﬁrm this hypothesis.

Figure 6: The number of control points plotted against the

normalized computed expected integrated equivalent dose

per user test instance.

In general, relying on the qualitative results of the

user test, we can conclude from this test that the de-

veloped tool is well-suited for the intended purpose.

The user comments are very positive and make the

way for an extensive user test. Every user acknowl-

edged the possibility to better plan maintenance inter-

ventions using this tool.

For the color map part of the user test, we can-

not conclude that the continuous diverging color map

is outperforming the rainbow color map, which was

expected before the test. This can be caused by the

relatively small number of user tests performed, or it

can mean that the color map is not a critical factor in

this application. Both outcomes are potentially inter-

esting, but will have to be conﬁrmed in a future, more

extensive, test.

5 CONCLUSIONS

AND OUTLOOK

Particle accelerators and detectors used in particle

physics research are subject to ionizing radiation and

their components can become activated. To protect

the maintenance personnel from ionizing radiation

during interventions, the radiation dose received by

the workers during an intervention has to be mini-

mized. Our goal is to provide interactive visualization

means to plan an intervention which enables mini-

mization of the contracted radiation dose, taking prac-

tical conditions concerning maintenance tasks into ac-

count. This optimization cannot easily be automated

and therefore requires human assessment. The visu-

alization of the several layers of data involved in the

planning process, i.e. the facility geometry, the radi-

ation levels and the trajectory, therefore needs to be

clear, intuitive and interactive.

In this work, we started with a description of the

important concepts of the planning of an intervention

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in an environment with ionizing radiation, and dis-

cussed how visualization plays an important role in

this application. We proposed a software tool for this

purpose in section 3, and discussed and explored the

possibilities of a user test for the interactive visualiza-

tion and intervention planning tool.

ACKNOWLEDGEMENTS

This research project has been supported by a Marie

Curie Early Initial Training Network Fellowship of

the European Community’s Seventh Framework Pro-

gramme under contract number (PITN-GA-2010-

264336-PURESAFE).

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(2012). Visualization of FLUKA Voxel

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