A STEREOPHOTOGRAMMIC SYSTEM TO POSITION PATIENTS

FOR PROTON THERAPY

Neil Muller

Department of Mathematical Sciences, University of Stellenbosch, Stellenbosch, South Africa

Evan de Kock, Ruby van Rooyen and Chris Trauernicht

Medical Radiation Group, iThemba Labs, Somerset West, South Africa

Keywords:

Stereo vision, proton therapy, visually guided robotics.

Abstract:

Proton therapy is a successful treatment for lesions that are hard to treat using conventional radiotherapy, as

the radiation dose to nearby critical structures can be tightly controlled. To realise these advantages, the patient

needs to be accurately positioned, and monitored during treatment to ensure that no motion occurs. iThemba

LABS uses a ﬁxed beam-line, and moves the patient using a suitable positioning device. In this paper, we

discuss several aspects the stereo vision based system used to both determine the position of the patient in the

room, and to monitor the patient during treatment.

1 INTRODUCTION

Proton therapy is a useful treatment method as the

dose distribution properties of the proton beam al-

low higher doses to be delivered to the target volume

with lower doses to the surrounding tissue. Due to the

high cost associated of treatment, it is often reserved

for lesions that are difﬁcult to treat with conventional

radiotherapy techniques, such as inter-cranial lesions

or lesions close to critical structures (see for example

(Webb, 1993)).

iThemba LABS

has been involved with proton

therapy for over ten years. Due to cost restrictions,

iThemba LABS uses a ﬁxed beam-line to deliver the

proton dose, and a motorised chair with 5 degrees

of freedom to position the patient (see (Jones et al.,

1995)). A second treatment vault is being built that

uses a robot-controlled manipulator to position the pa-

tient. The position of the patient during setup and

treatment is monitored by a number of cameras and

stereo techniques are used to calculate the patient’s

position. A critical issue is the high accuracy re-

quired.

Laboratory for Accelerator Based Science

2 PROTON VAULTS AT ITHEMBA

LABS

The treatment environment (see Figure 1), shows the

treatment layout. We have 9 cameras observing the

patient during treatment. During a treatment session,

at least 3 cameras will be used to calculate the position

of the markers on the mask (see Figure 2(a)). The

cameras are positioned around the beam isocenter at

an average distance of 2m from the isocenter. The

positions were chosen to maximise the volume visible

to the cameras while not obstructing activities in the

vault.

The position of the target volume is determined

from the CT scan used to plan the treatment. So that

the position of the target volume can be related to the

mask, the patient is scanned wearing the mask. When

the patient is in the reference position for the scan, a

stereo system, calibrated to CT scanners coordinate

system (see (de Kock et al., 2002)), determines the

position of the markers on the mask. Thus the rela-

tionship between the target volume and the markers

on the mask is known.

The stereophotogrammetric (SPG) system is de-

signed to be used in both vaults, and the control

systems for the chair and the robot are designed to

present a common interface to the SPG system. The

538

Muller N., de Kock E., van Rooyen R. and Trauernicht C. (2007).

A STEREOPHOTOGRAMMIC SYSTEM TO POSITION PATIENTS FOR PROTON THERAPY.

In Proceedings of the Second International Conference on Computer Vision Theory and Applications - IU/MTSV, pages 538-541

 SciTePress

Figure 1: The treatment vaults..

(a) Treatment

Mask.

(b) The SPG System’s

View of the Treatment

Mask.

Figure 2: The treatment mask.

SPG system is responsible both for positioning the

patient correctly, and monitoring the patient during

treatment to ensure that no excessive movement oc-

curs.

3 THE

STEREOPHOTOGRAMMETRIC

SYSTEM

A central design aim of the current project is to

achieve the required level of accuracy using commod-

ity hardware. Thus we choose to use conventional

PAL CCD cameras, and use frame-grabbers based off

the venerable BT-878 chipset.

Since we need to monitor the patient position in

real time and react to patient motion, we restrict our-

selves to a subset of the cameras during a treatment

session to minimise the computational load. We also

try to ensure that the marker detection problem is as

simple as possible, by using retro-reﬂective markers,

illuminated by green light-emitting diodes (LEDs)

mounted next to the cameras, and narrow band-pass

ﬁlters on the cameras, to minimise the effect of the

background on the image, and to ensure we have good

contrast between the markers and the background.

The markers are 10mm in diameter. A typical image

of the mask is shown in Figure 2(b).

The markers used are circular, and, because of the

lighting conditions, the contrast between the markers

and the background is high. Thus the markers can be

found by searching for bright regions, and the marker

centroid can be found using ellipse ﬁtting techniques.

Each candidate marker is only accepted if the size is

within acceptable limits, and the deviation of the ob-

served shape of the marker from an ideal ellipse is

sufﬁciently small (see (van Rooyen, 2003)).

4 CALIBRATION

Calibration of the SPG system needs to achieve two

goals. We need to calibrate the parameters of the cam-

eras, and their relative positions, so we can obtain ac-

curate results from the stereo reconstruction. Since

the SPG system must position the patient with respect

to the beam, which is ﬁxed in the room, calibration

needs to establish the relationship between the room

coordinate system and the coordinate system of the

cameras.

4.1 Distortion

As we are using conventional zoom lenses, radial dis-

tortion is a factor. We use a two-stage approach to

estimate the distortion. We determine an accurate

base distortion model from observing a suitable pat-

tern (shown in Figure 3(a)) from a known position.

(a) Distortion Correction

Pattern.

(b) The Calibration

Cube.

Figure 3: Calibration patterns.

This is designed to be easy for our software to pro-

cess. By observing the extent to which the lines of

dots curve, the parameters of the distortion model can

be obtained. We use a standard model for radial dis-

tortion, namely

, y

) = (x

, y

) + (

, y

)δ(κ) (1)

where

= x

+ y

= (x

− c

)

+ (y

− cyr)

(2)

and

δ(κ) = κ

+ κ

+ ... (3)

Since the relationship between the camera and the

pattern is known, and the pattern is known to a high

degree of accuracy, the only unknown parameters af-

fecting the image are the distortion coefﬁcients and

the focal length. These can be estimated accurately

using standard non-linear optimisation techniques.

We restrict the model to a fairly low-dimensional

polynomial for numerical stability. While we do not

directly model tangential distortion, we allow the cen-

tre of distortion to differ from the centre of the image,

as this is a good approximation for the decentring dis-

tortion (see (Stein, 1997)).

This base distortion model is expected to change

over time, as many events will require the cameras to

be re-adjusted in the vault. Since such changes to the

model should be small, and as removing the camera to

recalculate the model in the ﬁxed rig is inconvenient,

we wish to update the model. Thus we mount the

pattern in Figure 3(a) on a portable planar object. This

is held so that it roughly ﬁlls the ﬁeld on vision of the

camera.

By extracting the lines from the pattern, we can

test if the model is still valid. If the model is no

longer valid, we can use our knowledge of the pat-

tern to calculate an updated distortion model, (similar

to (Tamaki et al., 2002)). Since the pattern is held so

that it is approximately face-on to the camera, we can

use that and current distortion model as a reasonable

initial estimate, and the optimiser converges to the up-

dated solution quickly.

From this updated distortion model, we can then

decide whether the drift from the base model has be-

come too large, in which case we remove the camera

and recalculate the base distortion model, or else we

simply use the updated model (see (van Rooyen and

Muller, 2004) for more details).

4.2 Camera Calibration

Since we need to calibrate the cameras to the surveyed

coordinate system in the room, we use a single cube

to calibrate all the cameras. This cube is mounted on

a specially constructed jig which can be reliably posi-

tioned at the reference position. The correspondence

between the cube position and the room was obtained

by surveying the room with the cube in position, and

is known to a high degree of accuracy. The relation-

ship between the cube and the beam-line is regularly

checked using a theodolite mounted along the beam-

line. We use different cubes for each vault, and the

jig design is slightly different between each vault so

there is no possibility of using the incorrect cube.

Each face of has a large number of circular mark-

ers, whose positions are known from the surveying re-

sults. Each face also as a distinctive pattern of squares

and triangles which is used to automatically label all

the points detected on the cube (see ﬁgure 3(b)). From

the number of markers visible, we can determine if it

has an adequate view of the volume around the beam

isocenter.

The current distortion model is used to correct for

distortion before calibration, and then calibration pro-

ceeds using Tsai’s method (see (Tsai, 1987) for de-

tails).

5 POSITIONING THE PATIENT

For each target volume, several beams, each with dif-

ferent entry points are planned. To treat a beam, the

patient is positioned so that the target volume is posi-

tioned at the beam isocenter, and the entry point lies

along the beam axis, between the beam source and

the target volume. A collimator is inserted into the

beam line to shape the beam to the target volume,

and, once the patient is in position, the collimator is

rotated to match the proﬁle of the target volume. In

a single treatment session, the patient will be treated

using multiple beams.

Before treatment, the patient is secured to the po-

sitioning system and moved to a standard reference

position. In this position, most of the markers on the

mask can be observed, and stereo vision techniques

are used to determine the 3D position of a subset of

the markers on the mask.

These markers are then registered to the marker

positions in the reference position of the CT Scan.

This determines the transformation between the CT

scanner reference position and the current patient po-

sition. From this, the current position of the target

volume and entry point can be determined. The re-

quired movement to position the patient is calculated

and sent to the positioning system.

This procedure is repeated until the patient is in

position. This ﬁnal position is veriﬁed by comparing

an X-ray taken along the beam path with the predicted

X-ray view (see (van der Bijl, 2006)).

Positioning the patient for the next beam in a ses-

sion follows the same procedure, except that, since

the current position of the patient is known, there is

no need to return to the reference position.

6 MONITORING THE PATIENT

Monitoring the patient is a subset of the positioning

loop. Since the patient is monitored continually after

being positioned, and the frame-rate is high in com-

parison to the speed of the movements of interest, we

can a simple nearest neighbour comparison to track

markers.

On each iteration, the current position of the target

volume and the entry point are calculated and com-

pared to the required positions. If the differences ex-

ceed the speciﬁed tolerances, the patient is declared

to be out of position and the beam is interrupted.

Monitoring continues for some time after this to

determine if this is a transient event. If the patient

moves back into position after a short delay and re-

mains in position for a suitable period, the treatment

is resumed, otherwise the beam is aborted, and the

patient is repositioned.

7 ACCURACY OF THE SYSTEM

To assess the accuracy of the SPG system in reason-

ably realistic circumstances, we ran a series of ex-

periments using a dummy mask and using the mo-

torised chair. We constructed 8 treatment beams for

this mask, with the target point of each beam coinci-

dent with one of the markers on the mask. The accu-

racy was measured using a theodolite mounted along

the beam-line.

The mask was positioned for each beam using the

SPG system, and then the chair was moved until the

marker chosen as the target point was centred in the

view of the theodolite. The displacement required to

centre the marker was recorded for each beam, and

this was repeated for all the beams, using several dif-

ferent camera combinations for each beam. These ex-

periments were repeated daily over 5 successive days.

Because the of position of the theodolite, the dis-

placement along the beam-line, could not be ob-

served, so we only measured the error orthogonal to

the beam-line.

The average error was found to be 0.305mm with a

standard deviation of 0.219mm. Since the slice thick-

ness typically used for planning patients at iThemba

LABS is 1mm, this is well within the required toler-

ances.

The results for each beam are listed in Table 1.

Table 1: Accuracy Test Results.

Beam Avg ∆d Std Dev ∆d

1 0.436 0.116

2 0.187 0.088

3 0.175 0.105

4 0.416 0.185

5 0.158 0.074

6 0.182 0.086

7 0.247 0.119

8 0.265 0.160

All 0.305 0.219

8 CONCLUSIONS

In this paper, we describe several aspects of the

stereophotogrammetric system used for patient po-

sitioning at iThemba LABS. By exploiting the con-

trolled natured of the environment, we can obtain

good results using simple approaches. In addition to

positioning the patient, we can monitor the patient and

respond if the patient moves during treatment. The

system achieves acceptable accuracy using commod-

ity hardware.

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