MR COMPATIBLE OPTICAL MOTION TRACKING

Building an Optical Tracking System for Head Motion Compensation in MRI

Martin Hoßbach

Department Cognitive Computing and Medical Imaging

Fraunhofer Institute for Computer Graphics Research (IGD), Darmstadt, Germany

Keywords:

Optical tracking, Head motion tracking, MRI-compatible.

Abstract:

Magnetic Resonance Imaging (MRI), in spite of its potential in medical diagnosis, has one major drawback:

Image acquisition is a slow process, requiring the patient to not move for several minutes. This renders MRI

useless in a number of cases. In the case of MR Imaging of the Head, optical motion tracking can be used for

motion compensation, thereby greatly improving image quality. In this paper, an MR-compatible approach of

tracking the patient’s head is presented which does not require his or her cooperation, based on stereo-optical

marker tracking. It is adapted to work in the MRI scanner, does not inﬂuence the MR image acquisition and is

easily integrated into clinical routine.

1 INTRODUCTION

Quite similar to the way a photograph gets blurred

when the person in front of the camera moves, many

medical imaging modalities suffer from motion arti-

facts when the patient is moving during image acqui-

sition. This is normally dealt with by asking the pa-

tient not to move or by sedating him or her. In some

cases, however, the patient will move, and sedating

is impossible. When dealing with medical imaging of

the human head (which is the focus of this paper), one

solution is to track the patient’s head motion, enabling

compensation of the head motion for these cases.

Among the imaging modalities suffering from pa-

tient head movement, positron emission tomography

(PET) is probably easiest to cope with: standard mo-

tion tracking equipment can be used since neither

spatial nor environmental restrictions (like magnetic

ﬁelds in MRI) apply. Langner tracked infrared mark-

ers mounted to a pair of ski goggles to track the head

motion (Langner, 2008). Ma et al. reconstructed the

patients head motion using a stereo camera system

outside a PET scanner using SIFT feature tracking

(Ma et al., 2008).

Dold et al. proved the concept of MRI head mo-

tion compensation by head motion tracking with stan-

dard IR tracking of a marker, held by the patient with

his or her teeth (Dold et al., 2006). This marker is a

major drawback of Dolds approach: Non-cooperative

patients will not keep it between their teeth. However,

Dold et al. showed that image quality can be greatly

improved by motion compensation, even in the case

of a cooperative patient.

This paper concentrates on head motion tracking

for magnetic resonance imaging (MRI) and shows

how to build a motion tracking system that is fully

MR compatible, requiring no patient cooperation

whatsoever. Particularly, the patient does not have

to hold a marker between his or her teeth or wear

marker-goggles. In section 2 the hardware setup is ex-

plained, as well as the difﬁculties resulting from that

choice and how they were solved. In section 3 the sys-

tem’s performance is analyzed. Section 4 concludes

the paper.

2 SYSTEM DESIGN

2.1 Hardware

The basic idea is to build an optical tracking system

with two cameras that ﬁts into the MR tomographs

bore. To achieve MR compatibility, the system and its

453

Hoßbach M. (2010).

MR COMPATIBLE OPTICAL MOTION TRACKING - Building an Optical Tracking System for Head Motion Compensation in MRI.

In Proceedings of the International Conference on Computer Vision Theory and Applications, pages 453-456

DOI: 10.5220/0002830004530456

 SciTePress

Figure 1: MR compatible camera built by MRC Systems,

Heidelberg. Coin as size reference.

Figure 2: Camera mounting on top of Siemens Trio Head

Coil.

components have to be designed such that they do not

affect the MR image acquisition and that the tracking

is not inﬂuenced by the MRI scanner. Spatial condi-

tions in the bore and of the head coil need to be taken

into consideration.

As a result, MR compatible cameras like those

built by MRC Systems (Figure 1) are used, which

achieve a decent image quality even under poor light-

ing conditions. The cameras provide a standard PAL

video signal of 768 ×576 pixels at 25 fps. With a spe-

cial mounting, two of these cameras are attached to

the head coil with the patient’s forehead in their ﬁeld

of view.

The camera mounting is designed so that it can be

removed from the head coil and later be reattached

without changing the position and orientation of the

cameras in relation to the head coil, thus preserving

camera and stereo camera system calibration (Figure

2).

The two cameras are then calibrated using a

Figure 3: SURF-Features in an image acquired by the

MR compatible cameras described in section 2.1. SURF-

Features are found mainly in areas that can move indepen-

dent from the skull, thus rendering them useless for head

motion tracking.

checkerboard calibration pattern and the stereo cam-

era calibration functions found in the OpenCV li-

brary

2.2 Tracking Approach

Motion tracking can be performed using a marker-

less or a markerbased approach. Although integrat-

ing markerless tracking methods into the MRI scan-

ning procedure is much easier, since only little opera-

tor intervention is necessary, a markerbased approach

has been chosen: Feature tracking methods like SIFT,

SURF or the “good features to track” work ﬁne in var-

ious environments for the inverse problem of ego mo-

tion estimation (Se et al., 2001). However, these fea-

tures performed poorly with the images provided by

the MR compatible cameras mentioned in section 2.1

(Figure 3). Features are found mainly in facial areas

that can move independently from the skull, thereby

rendering the estimated motion unusable for head mo-

tion tracking.

In this scenario, it is essential that only the mo-

tion of the patients skull is tracked. For this reason,

a different approach using ﬁducial markers has been

chosen. However, a marker like the one used by Dold

et al. can obviously not be used, because the patient

has to hold it with his or her teeth. A non-cooperative

patient will not do this, but nonetheless may be mov-

ing, thus prohibiting the acquisition of MR images.

Standard marker-based tracking techniques can

still be used if markers of a special color are attached

to the patients forehead. This color should normally

not be found in the human face. Blue circular paper

The OpenCV library: http://opencv.

willowgarage.com

VISAPP 2010 - International Conference on Computer Vision Theory and Applications

454

Figure 4: Image of volunteer with blue markers on his fore-

head (left), and the result of transforming the image to the

HSV color model and thresholding the H-channel (right).

stickers, similar to those used in (Ohayon and Rivlin,

2006), turned out to work nicely: After transform-

ing the image to the HSV color model, they can be

segmented with simple image processing operations

(see Figure 4). Segments are checked for plausibil-

ity. Then, the center of gravity is calculated for each

segment.

2.3 Stereo Matching

Extracting the feature points (the centers of the blue

markers) in each image results in a 2D feature list for

each image. Now the corresponding feature points

have to be found. Features are matched using two

separate approaches, both relying only on the epipo-

lar constraint: Let F be the fundamental matrix of

the camera system, such that x

= 0 for one point

in space X with image positions x

and x

. Let f



l,0

, x

l,1

, x

l,2

, . . . , x

l,n



be a set of feature points in the

left camera image and f

{

r,0

, x

r,1

, x

r,2

, . . . , x

r,m

}

set of feature points in the right camera image. A

matching M ⊂ f

× f

needs to be found that contains

each feature point once or not at all and that has a

minimal epipolar error

c(M) =

∑

(l,r)∈M

Matching is basically a minimization problem.

The Kuhn-Munkres-Algorithm (Munkres, 1957) can

be used to accomplish this. It does, however, match

all feature points, and therefore also matches points,

like misdetected markers, that clearly should not be

matched. This can be resolved by removing all

matches with an epipolar distance exceeding a certain

threshold.

During image acquisition (when the MRI scan-

ner is actually working), mismatches must not occur.

Therefore, a second, more conservative and therefore

more robust matching approach has been developed.

It requires knowledge of the feature points and the

matches from the last set of images:

First, from all 2D feature points all points are

matched that should obviously be matched because

there are no other feature points on the same epipo-

lar plane. Using the Kuhn-Munkres algorithm, the

2D feature points from the current set of images are

matched to the 2D feature points from the previous set

of images. Then we can tell if a certain pair of 2D fea-

ture points in the current set of images was matched in

the last set of images. From the remaining 2D feature

points, those that were matched in the previous set of

images are also matched in the current set of images.

All remaining 2D feature points are ignored.

Having matched the 2D feature points, the 3D po-

sition of the feature points are reconstructed in rela-

tion to the cameras by triangulation. An appropri-

ate method can be found in (Hartley and Zisserman,

2000).

2.4 Model Tracking

The two matching strategies are used in different

phases of tracking: The Kuhn-Munkres-Approach is

used in preparation, when the operator is still able to

change the marker setup, and ask the patient to move

his head if mismatches occur. Once the matching

looks ﬁne, a click on a button ends preparation phase,

and two things happen: First, the matching strategy

is changed to the conservative strategy and secondly

the current set of 2D feature points is triangulated.

The resulting 3D positions are stored as reference for

the tracking. From that point on, after matching of

the feature points, the 3D positions of the respective

markers is calculated.

Now the rigid transformation needs to be found

that transforms the initial marker positions into the

current ones. This is complicated assuming that both

point clouds may be incomplete, noisy and contain

outliers. A two-step-approach is used:

1. for each three element subset of the reference

point cloud and each three element subset of the

current point cloud, calculate the euclidean dis-

tances between the three points. If distances are

similar enough (below a certain threshold), use

these three correspondences to calculate a trans-

formation. How many points of the two point

clouds match if this transformation is applied?

Find the transformation maximizing this number

and therefore minimizing the average displace-

ment.

2. Using the best transformation found in step 1,

transform the reference point cloud, and calculate

MR COMPATIBLE OPTICAL MOTION TRACKING - Building an Optical Tracking System for Head Motion

Compensation in MRI

455

a reﬁned transformation, this time using not only

three points but all matching points.

This approach is similar to the RANSAC algo-

rithm. However, considering the small size of the

point sets, an exhaustive search can be performed

without any signiﬁcant performance drawback.

The transformations are smoothed with a Kalman-

ﬁlter: The head is considered a rigid body. The

current position and rotation (using versor represen-

tation) are both 3D vectors x, r ∈ R

. The kalman

state vector considers two derivatives, being therefore

(x, r, ˙x, ˙r, ¨x, ¨r) ∈ R

Then, the transformations are passed on to the

MRI scanner via ethernet.

3 RESULTS

The system described in section 2 has been imple-

mented in C

. All image processing was done on

the graphics board using the CUDA API. An overall

frame rate of approximately 40 fps has been achieved

(which is enough to analyze all frames coming from

the cameras) and a latency of 0.05 s. The follow-

ing experiments were made to illustrate the systems

performance with MR compatible cameras, but un-

der better lighting conditions than usually found in-

side the MR scanner.

3.1 Accuracy

One major problem in feature detection is that pro-

jective transformations transform circles into shapes

similar to ellipses. In most cases, the circle center

are not projected onto the center of gravity of the el-

lipse. A blue circular marker with a small black dot

in its center was used to measure the distance. Under

extreme angles the displacement can be as much as

10% of the circle radius.

To evaluate the accuracy of the center of gravity,

a static scene has been constructed containing only

a single circular marker. The experiment shows that

under good lighting conditions, the center of gravity

of a marker is detected with an accuracy of roughly

0.5 pixels.

To evaluate the accuracy of the triangulated 3D

position of a single marker, the same setup has been

used. Thus, stereo matching always works correctly

and the triangulated 3D position should be exact. The

reconstructed 3D positions have a standard deviation

of 0.015 mm for the x- and y-axis, and 0.08 mm for

the z-axis.

4 CONCLUSIONS

A method to build an optical tracking system that can

be used for head motion compensation in MRI has

been presented. To achieve MRI compatibility, cer-

tain drawbacks had to be accepted: MR compatible

cameras are used that provide images with standard

TV resolution. As a result, markerless tracking ap-

proaches cannot be used. Instead, an approach track-

ing circular blue markers sticking to the patients fore-

head has been chosen.

Because of the image resolution provided by the

cameras, tracking accuracy of this approach is not

as good as it would have been with standard indus-

trial cameras with a much higher resolution and frame

rate. Furthermore, the stereo feature matching and the

model tracking algorithms had to be able to cope with

noisy feature positions.

Thus, an MR compatible tracking system has been

built that can be used with non-cooperative patients.

In contrast to other approaches, the system is com-

pletely MR compatible. It uses markers that do not

require the patients cooperation

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