X3D IN RADIATION THERAPY PROCEDURE PLANNING

Felix G. Hamza-Lup, Ivan Sopin, Dan Lipsa

School of Computing, Armstrong Atlantic State University, Savannah, Georgia, USA

Omar Zeidan

M.D. Anderson Cancer Center Orlando, Florida, USA

Keywords: X3D, Web-based Simulation, 3D Modeling, Radiation Therapy.

Abstract: Radiation therapy, an increasingly available and effective cancer treatment solution, involves sophisticated

machinery as well as careful planning. Interactive 3D simulations combined with accurate 3D patient

specific data can improve the planning process saving time and resources in generating the optimal

treatment plan. We illustrate the potential of X3D in radiation therapy, specifically radiation treatment

planning. Embedding patient specific data (2D CT scans) in the interactive virtual setup improves the

radiation therapy planning by early detection of collision cases. The X3D system may be used to support the

decision making process as well as innovations in medical planning and training.

1 INTRODUCTION

While 3D simulation is just emerging as an accepted

scientific discipline for medicine, the majority of

applications are in the area of inter-operative

navigation and training. We present the potential of

X3D in radiation therapy, specifically radiation

treatment planning.

Radiation therapy, an increasingly available and

effective cancer treatment solution, involves

sophisticated machinery as well as careful planning.

An interactive X3D-based simulation combined with

accurate 3D patient data obtained from CT scans and

MRI will improve the planning process, saving time

and resources in generating the optimal treatment

plan. We illustrate the potential of X3D in radiation

therapy, specifically radiation treatment planning.

Embedding patient-specific data in the virtual setup

improves the radiation therapy planning by early

detection of collision cases.

A preliminary assessment leads us to believe that

an accurate 3D virtual representation of the radiation

therapy/surgery procedure supports the decision

making process (e.g. specific configurations in the

hardware for complex medical procedures) as well

as innovation in medical planning and training.

In Section 2 we provide a brief introduction to

the field of radiation therapy and associated

hardware components. We also identify significant

related work in radiation therapy simulation and

training. Section 3 describes our solution and

provides details of implementation. In Section 4 we

focus on a very important component that allows us

to bring patient-specific information inside the

simulator to improve its realism and efficiency. In

Section 5 we present the preliminary assessment

implemented in collaboration with M. D. Anderson

Cancer Center, Orlando.

2 BACKGROUND AND RELATED

WORK

Radiation therapy is the careful use of high-energy

radiation to treat cancer. A radiation oncologist may

use radiation to cure cancer or for palliative

purposes. About 50 to 60 percent of cancer patients

are treated with radiation at some time during their

disease (

RadiologyInfo, 2006). Radiation destroys the

ability of cancerous cells to reproduce, and the body

naturally gets rid of these cells in time. A cancer

patient may be treated with radiation alone (e.g.

prostate cancer); however, sometimes radiation

therapy is only a part of a patient's treatment.

Patients can be treated with radiation therapy before

359

G. Hamza-Lup F., Sopin I., Lipsa D. and Zeidan O. (2007).

X3D IN RADIATION THERAPY PROCEDURE PLANNING.

In Proceedings of the Third International Conference on Web Information Systems and Technologies - Web Interfaces and Applications, pages 359-364

DOI: 10.5220/0001292003590364

 SciTePress

surgery, enabling a less radical surgery than would

otherwise be required. For example, some bladder

cancer patients can keep their bladder if radiation

therapy is effective.

A radiation oncologist may use the radiation

generated by a machine outside the patient's body.

This procedure is called External Beam Radiation

Therapy. Radiation also may be given with

radioactive sources that are injected in the patient;

procedure called brachytherapy. Our contribution to

treatment planning focuses on the External Beam

Radiation Therapy (EBRT).

2.1 Time and Safety in the Treatment

Process

EBRT involves advanced machinery that rotates

around the patient, following a carefully planned

schedule. The radiation delivery unit, called a linear

accelerator (LINAC), consists of three main moving

components: the gantry, the collimator, and the

table/couch, as illustrated in Figure 1. The center of

rotation of all these components is a virtual point in

space called isocenter.

Figure 1: Linear accelerator with on-board imaging.

The principle of operation for such devices is

fairly simple. Based upon the patient’s tumor

location, the hardware components change position

and orientation while delivering pre-planned

radiation doses to the patient on the table. However,

in some cases, the complex relative orientations of

all these components may cause a collision with the

components themselves or between the components

and the patient. In addition, LINAC head

attachments and patient immobilization devices may

also be a source of collision concerns. Therefore, a

radiation treatment planner may generate a

seemingly optimal plan, only to result in a collision

when a “dry run” (i.e. an execution of the plan

without radiation for collision checking) is

performed on the LINAC. This results in a delay to

the patient treatment, since the plan has to be revised

to account for these unforeseen collisions.

Additional time and resources must be invested to

adjust the existing or create an alternative treatment

plan.

Analytical methods for collision detection for

LINAC-based radiation surgery have been proposed

as a means to improve the EBRT planning process

(Beange and Nisbet, 2006; Hua, Chang, and Yenice,

2004; Humm, Pizzuto, and Fleischman, 1995;

Purdy, Harms, and Matthews, 1993). Analytical

methods, even though accurate, are based on the

hardware rotational and translational numerical

values disregarding patient-specific geometry.

Investigating previous work concerning graphical

simulations of the LINAC system, we have

identified the following limitations:

(1) sophisticated setups and additional software

and hardware components;

(2) The simulations involve only generic patient

body representations (Tsiakalos, Scherebmann, and

Theodorou, 2001); hence collisions with patients are

not accurately modelled or predicted;

(3) The simulations are local and can not be

deployed via the web for potential collaboration with

remote experts during treatment planning.

Researchers at the Hull Immersive Visualization

Environment proposed a VR environment for

training purposes (Beavis et al, 2006), using a virtual

patient based on the visible human female dataset.

Their prototype is targeted mainly at medical

training. Our efforts are directed towards a

distributed X3D-based system that will allow easy

access from a web browser to the virtual room and

will improve the actual planning process of the

EBRT by providing a high-resolution model of all

treatment components, including patient-specific

geometry.

2.2 Web-based Systems in Medical

Planning

Simulation-based Medical Planning has been

recently investigated for cardiovascular disease

(Steele et al, 2003). The Virtual Reality Modeling

Language (VRML) has been employed to provide

the visual web-based interface in the past. The

European Institute of Telesurgery has proposed a 3D

anatomical structure visualization and surgical

planning system that allows manipulation and

interaction on virtual organs extracted from CT-scan

or MRI data (Chirstophe, Luc, and Jacques, 2002).

With the advent of the X3D standard and its

extended functionality, the Internet-based systems

for simulation gained momentum. Currently, the

Medical Working Group, part of the Web3D

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consortium, is taking steps in developing an open

interoperable standard for the representation of

human anatomy based on input from a wide variety

of imaging modalities. Our research and

development efforts presented in the next section

take into consideration and build on the existing

technology, available in the public domain, allowing

us to offer medical personnel free of charge access

to the X3D simulation and associated paradigms.

3 AN X3D TOOL FOR

COLLISION DETECTION

As we pointed out in Section 2.1, time-consuming

“dry runs” are necessary to avoid collisions during

the EBRT process. The “dry runs” do not take into

consideration the patient’s geometry. Depending on

the size of the patient, unforeseen collisions might

occur.

Providing a virtual 3D environment that will let

the medical personnel view the execution of the

treatment plan allows them to avoid potential

collision cases and even experiment with new

configurations. Our preliminary efforts in designing

such an environment (Hamza-Lup, Davis, and

Zeidan, 2006) provided a set of useful observations

and guidelines for the current implementation.

The simulator implementation takes advantage of

two technologies, Java and X3D. X3D (Web3D

Consortium, 2006) is an ISO standard with an open

architecture and a rich range of capabilities for real-

time graphics processing that is employed in a wide

array of domains and user applications. A successor

to VRML, X3D is being developed by the Web3D

Consortium as a refined standard (Lau et al, 2003).

Figure 2: X3D Environment (3DRTT).

Besides X3D, in the development process we

employ several software tools for 3D modeling.

Figure 2 illustrates a snapshot of the virtual room

(denoted 3D Radiation Therapy Treatment —

3DRTT) which models the real environment,

depicted in Figure 1.

3.1 User Interaction

The simulator provides an intuitive floating

graphical user interface (GUI) for controlling the

angles and locations of the machine’s parts

(presented in Figure 2). The user may rearrange the

GUI components to avoid important objects

occlusion.

Volumetric slides and scrolls keep controlling

operations simple and naturally fit in the 3D scene.

The user can also show/hide a patient or the

radiation beam generated by the collimator by

turning designated switches on/off.

Besides the mouse manipulation capability, the

user’s viewpoint can be easily changed on the

orthogonal axes. As a part of X3D, preset

viewpoints provide handy control and fast overview

potential and can be switched with a single

keystroke.

An important component of the simulator is the

ECMAScript (ECMA International, 1999), a

JavaScript-like language that introduces additional

functionality into X3D. The ECMAScript functions

provide the GUI’s interactivity. For instance, a user

shows or hides the patient or the beam by clicking

the appropriate buttons, or the values change when

the user rotates the gantry either by dragging it or

scrolling the associated control element. The

position of the object on the screen is synchronized

with the position of the corresponding scroll wheel.

Figure 3: Client-Server Interaction.

The X3D model containing the ECMAScript and

the optimized polygonal objects is embedded in a

web page that the user accesses from a local

machine. When the user submits new values on the

web page, a Java servlet processes them. The server

does not only generate a new web page, but also

deploys a new X3D model, as described in Figure 3.

Every user of the simulator is assigned a unique

session ID. Thus, clients using the same model avoid

conflicts in controlling the simulator. So, 3DRTT

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361

has the distributed functionality of a 3D distributed

visual system on the web and can easily be shared

between researchers and remotely located medical

personnel.

3.2 3D Models Acquisition and

Processing

A 3D laser scanner from Faro™ Technologies is

employed to collect point clouds from several

viewpoints. Once the point clouds are collected, they

are merged into one cloud based on a set of specially

designated markers. We filter the noise and wrap the

valid points into a polygonal model.

To improve the rendering process, we decimate

the model by removing redundant polygons in flat

areas while securing a sufficient number of polygons

in the regions with complex geometry. The

polygonal model is exported into an X3D object and

employed as a part of the scene. All filtering,

wrapping, smoothing, and exporting operations are

performed using the built-in algorithms in the

commercially available Raindrop GeoMagic™

software.

Considering the geometrical complexity and the

high-polygonal resolution of the model, we have to

optimize the polygonal model such that adequate

frame-rates (25 FPS or more) are obtained on

machines with less rendering power. To improve the

rendering speed and reduce the file size, we are

using textures, simulating the geometry of complex

areas. Textures also save development time because

instead of processing a complex region we rather

substitute it with a texture that does not require a

long time to generate. Another positive side effect is

the network traffic reduction.

3.3 Collision Detection - Special

Collision Situations

Currently, the X3D standard supports only avatar

collision detection, meaning that users may “collide”

with virtual objects as they travel through the scene.

Only a few research groups attempted to

implement the object-to-object collision detection

directly in a VRML or X3D environment. For

instance, the V-COLLIDE library (Hudson et al,

1997) provides fast collision detection for arbitrary

polygonal objects based on the Robust and Accurate

Polygon Interference Detection (RAPID) algorithm

(Gottschalk, Lin, and Manocha, 1996) and is

potentially capable of computing collisions in

VRML. However, to the best of our knowledge,

none of these collision methods have been fully

implemented and tested in a VRML/X3D player.

Besides the visual check for collisions in

3DRTT, we are currently developing an automatic

collision detection system via bounding boxes that

approximates the shapes of virtual objects.

4 PATIENT 3D MODELS FROM

CT DATA

An important issue we address with this work is

real-patient data embedded in the X3D simulation.

We have to efficiently convert a set of CT scans of a

patient to a polygonal model of the patient’s body.

The set of CT scans used is stored using Digital

Imaging and Communication in Medicine (DICOM)

standard.

We process CT scans using the Visualization

Toolkit (VTK) (Schroeder, Martin, and Lorensen,

1996), a software system for 3D computer graphics,

image processing, and visualization. The first step in

our processing is to select from CT scans a volume

that eliminates the table on which the patient is

positioned. This operation reduces the number of

polygons obtained in our 3D patient model.

We apply the marching cubes algorithm

(Lorensen and Cline, 1987) with a value that selects

the isosurface of the patient’s skin. We used the

same value for the skin as it was done in (Lorensen,

2006). The marching cubes algorithm checks if the

corner values of a cube are above or below the

isosurface value. If some corner values are above

and some are below, then the isosurface obviously

intersects the cube, and we can compute the

intersection polygon by using a lookup table. The

lookup table provides a fast way to find the edges

intersected by the isosurface based on what corners

values are above and below the isosurface.

The application of the marching cubes algorithm

produces a polygonal model that includes the skin of

the patient as well as internal organs. We have

applied this algorithm on a set of 133 CT scans of a

patient torso. The number of polygons obtained

(approximately 500,000) significantly slowed down

the interaction with our web-based application. To

reduce the number of polygons, we apply a

decimation operation (Schroeder, Zarge, and

Lorensen, 1992) which reduces the number of

polygons to approximately 100,000. Decimation

works by evaluating the distance between each point

and the average plane of the triangles using the

point. If that distance is small, the point and all

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362

triangles using it can be deleted. Obviously, the

“hole” left has to be re-triangulated. Once re-

triangulation is accomplished, we apply a Laplace

smoothing operation (Field, 1988) that modifies the

position of each vertex to be the average of the

neighbouring vertices.

To obtain a “clean” polygonal model that

contains only the skin of the patient (remember, we

are interested in visual collision checks between the

hardware and the patient), we apply a polygon

connectivity filter that further reduces the number of

triangle strips to about 3,000. A challenge in

applying this algorithm is specifying the connected

surface. Choosing the maximum connected surface

isolates the skin of a patient in most cases. The result

is illustrated in Figure 4.

Figure 4: 3D Torso Model from CT Scans.

A final step in the process is the conversion of

the 3D model into an X3D object. The X3D object is

embedded in the scene on the LINAC table in the

same position and orientation as the real patient

(Figure 5).

Figure 5: Patient-specific 3D model on the Web.

Now, the medical personnel can virtually execute

the treatment plan and check for collisions with the

patient body. Of course, the CT data is limited to a

specific body part (in this case the torso). We are

planning an improvement to this approach, i.e. to

generate a full body patient model from incomplete

CT scan data. We think that such an improvement

will help detect potential collisions with other

patient body parts, e.g. hands, legs, etc.

5 PRELIMINARY ASSESSMENT

We have deployed the system on a secure web site

and allowed medical personnel from M.D. Anderson

Cancer Center, Orlando, to remotely access the web-

based simulation environment. Their first subjective

reaction was that the X3D world is very realistic and

that it improves their confidence in running the

LINAC in a real scenario, since they can now

visualize the relative position of all the components

involved, including the patient.

To objectively test collision scenarios, we asked

a radiation therapy technician and a therapist to

simulate a plan that contains collisions among the

system components (illustrated in Figure 6). The

result was that the X3D simulation provides an

accurate representation of the LINAC (specifically,

the Varian 23iX LINAC) that can predict any

collision scenarios with centimeter accuracy.

Figure 6: Visual Collision Validation.

The preliminary assessment using visual

inspection from different angles, illustrated in Figure

6, provides an early validation for the accuracy of

the simulator. The tool can be used off-line by

planners to inspect their patient-specific beam

arrangements before simulation runs. We are in the

process of measuring the impact of the X3D

simulator on the EBRT planning and execution

efficiency.

6 CONCLUSIONS AND NEAR

FUTURE

We have presented the development of an X3D

Web-based simulator for External Beam Radiation

Therapy treatment simulation. The ability to

detect/predict a possible collision between all

LINAC components for a given patient eliminates

the need for backup plans and saves planning time.

In addition, it enables the planner to explore

X3D IN RADIATION THERAPY PROCEDURE PLANNING

363

different and unconventional gantry-couch-

collimator combinations for treatment that may give

rise to better quality plans.

We are developing a database, containing X3D

objects corresponding to various EBRT equipment

models, to meet the demands of a broader range of

medical facilities and applications.

We hope that our research and development

efforts will advance the quality of the radiation

therapy process and consequently will improve

cancer patients’ treatment and recovery.

ACKNOWLEDGEMENTS

Support for this work came from M. D. Anderson

Cancer Foundation. We thank Anne Ocheltree and

Chris Morabito from FARO™ Technologies

Orlando for their support in obtaining the high

resolution 3D models for the EBRT room.

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