Precise 3D Deep Brain Stimulation Electrode Location based on

Multimodal Neuroimage Fusion

Nádia Moreira da Silva

, Verena E. Rozanski

, Sérgio Neves Tafula

and João Paulo Silva Cunha

1,3

INESC TEC (Instituto de Engengaria de Sistemas e Computadores do Porto), University of Porto,

Rua Roberto Frias 4200-465, Porto, Portugal

Department of Neurology, University of Munich at Marchioninistrasse 15, 81377 Munich, Germany

Faculty of Engineeiring, University of Porto, Rua Roberto Frias 4200-465, Porto, Portugal

Keywords: DBS, Parkinson, Dystonia, Co-registration, Electrodes Segmentation.

Abstract: The success of neurosurgery strongly depends on the pre-neurosurgical evaluation phase, in which the

delineation of the areas to be removed or to be stimulated must be very accurate. For patients undergoing

Deep Brain Stimulation (DBS) it is vital the delineation of the target area prior to surgery, and after the

implantation of the DBS lead to confirm the electrodes positioning. In this paper we present a system to

accurately determine the 3D position of DBS electrodes implanted within the brain of Parkinson and

Dystonia patients. The system was tested using a multimodal dataset from 16 patients (8 with Parkinson`s

disease and 8 with dystonia) and, on average, the differences between the detected electrodes positions and

the ones estimated manually by an experienced physician were less than a voxel in all cases.

1 INTRODUCTION

Movement disorders usually lead to a loss of

independence for the patients and high costs for the

health system (Stolze et al., 2005). Parkinson’s

disease is the most common movement disorder, a

hypokinetic syndrome due to neurodegeneration of

the substantia nigra. Dystonia, on the other hand, is a

hyperkinetic disorder characterised by tonic and

phasic involuntary muscle contractions. (Haegelen et

al., 2013); (Kupsch et al., 2003) The symptoms

associated to both diseases can be relieved or

suppressed by a continuous pharmaceutical

treatment. However, with long-term treatment some

patients develop resistance to drugs and a surgical

procedure may be required. In such cases, deep brain

stimulation (DBS) is the common clinical procedure,

promoting stimulation in target areas such as the

subthalamic nucleus (STN) or the globus pallidus

internus (GPi) (Haegelen et al., 2013). For the

stereotactic implantation, optimal target site is

chosen in relation to the midcommissural point, as

described by Guridi et al., (2000). The motor and

neuropsychological outcomes depend highly on the

precise location and trajectory of the electrodes,

which when stimulated will inhibit the activity in the

target structures for each disease (Haegelen et al.,

2013); (Lozano et al., 2004).

However, due to possible shifts of DBS leads

during surgical procedure, the final electrodes

locations can be misaligned from the planned

optimal target sites (Lalys et al., 2013); Videen T.O.

et al., 2008). Therefore, it is vital to accurately locate

the position of electrodes after DBS implantation, in

order to avoid an undesirable stimulation of non-

target areas.

In order to perform a detailed analysis of the

final anatomical position of each electrode, several

approaches were developed using multimodal

techniques, combining anatomical and functional

information (Hemm et al., 2009); (Lalys et al.,

2010); (Bardinet et al., 2009). Such methods are

indicated to confirm the implanted electrodes near

the target basal ganglia structures and avoid the

activation of the ones near others structures, which

are known to promote acute side effects (Lozano et

al., 2004). In addition, as described by Thani et al.,

(2011), an accurate anatomical location of each

electrode is fundamental in quality control and to

perform a more careful selection of the best

stimulation parameters for DBS.

Nevertheless, the localisation of each DBS

electrodes is still a challenging procedure. The

presence of metal implants in MRI or CT scans,

Moreira da Silva N., E. Rozanski V., Neves Tafula S. and Silva Cunha J..

Precise 3D Deep Brain Stimulation Electrode Location based on Multimodal Neuroimage Fusion.

DOI: 10.5220/0004727700480054

In Proceedings of the International Conference on Physiological Computing Systems (PhyCS-2014), pages 48-54

ISBN: 978-989-758-006-2

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

such as the electrodes and wires of the DBS lead,

can induce considerable image artefacts (Hebb et al.,

2010). These image distortions limit the capacity to

differentiate the electrodes from the DBS lead and

therefore obtain their precise location.

In the literature, several techniques are referred

to estimate the precise location of DBS electrodes.

Hebb et al., (2010) described an improvement in

the visualization of DBS’s electrodes by using a CT

imaging with an extended Hounsfield unit (EHU)

scale. Using EHU-CT each electrode can be clearly

visualized without any special image processing

techniques.

Since electrodes are indistinguishable from the

DBS lead in the standard CT or MRI scans, some

authors perform an electrodes artefact analysis,

based on semiautomatic algorithms, to gathered

information of the white and black artefacts

dimensions, and correlated it with the exact location

of the DBS electrodes. (Hemm et al., 2009); (Pollo

et al., 2004). Lalys et al., (2013) described an

automatic approach by focusing the electrode

artefact analysis within a specific region, restricted

by a brain mask. However, the accuracy of these

methods are highly dependent on the area of signal

artefact induced by the DBS lead (Thani et al.,

2011).

On the other hand, the exact coordinates of each

electrode can be estimated based on the detection of

the lowest tip of the DBS lead and its trajectory

(Rozanski et al., 2013); (Videen et al., 2008). These

estimations are manually performed in a

neuroimaging software by an experienced physician.

As result, commissure ROIs are manually drawn in

the coordinates previously estimated. However, this

process is very time consuming, complex and

requires an expert, with prior knowledge in

neuroimaging software tools.

In this study, we propose a user-friendly system

to automated segmentation and estimate the 3D

position of each electrode from the DBS lead,

implanted within the brain. Furthermore, a

multimodal pipeline is presented to provide

anatomical visualization of DBS electrodes, and

therefore assist the neurologists in the confirmation

of the implanted electrodes near the target areas.

2 MATERIALS

For this study 16 patients (eight with advanced

Parkinson disease and eight with focal Dystonia)

undergoing DBS were selected. All patients were

informed and gave their consent. Stereotactic

electrode insertion was carried out as previously

described by Singh et al., (2011). Parkinson patients

were operated in local anaesthesia to enable clinical

outcome; dystonia patients were operated in general

anaesthesia to avoid hyperkinetic movements during

operation.

All the patients had electrodes implanted

bilaterally. The DBS lead position was confirmed by

post-operative CT or T2 scans. A pre-operative T1

with 0.98 x 0.98 x 1mm voxel size was acquired to

guide the surgery and to visualize the target

structure. The T1-MRI images were gathered by a

3T-MRI unit (Sigma Exite MD GE). CT scans were

acquired by GE scanner, Brighspeed16 unit. For the

implantation a quadripolar electrode array was used

(model 3389, Medtronic Neurological Division,

Minnesota, USA) with 4 electrodes at the tip, spaced

0.5mm apart delivering stimulation individually or

in combination.

In Parkinson patients, DBS electrodes for

continuous activation were chosen after intense

testing for best clinical outcome according to

UPDRS scale. In dystonia patients, lowest DBS

electrodes were activated unless adverse effects

occurred.

3 METHODS

In this section, we describe the multimodal pipeline

for co-registration and the electrodes segmentation

algorithm. Furthermore, the functionalities of the

system are presented.

3.1 Multimodal Neuroimage Fusion

The skull-stripping tool BET (Brain extraction tool),

available in FSL, was used in all dataset to remove

the skull from the T1-MRI images.

Both CT and T2 images were co-registered using

a linear affine transformation with 6 degrees of

freedom. The geometric transformation was applied

using FLIRT (FMRIB’s linear image registration

tool), accessible in FSL 5.0, with T1-MRI as

reference image. Since the motion between images

was not significant, the angular range over which the

initial optimization search stage is performed was set

between -30 to 30 for all the axes. The cost function

chosen was the normalized mutual information, an

entropy-based cost function widely used for inter-

modal alignments, and described as the one with

more accurate results for this type of images (Cahill,

2010).

Precise3DDeepBrainStimulationElectrodeLocationbasedonMultimodalNeuroimageFusion

3.2 DBS Electrodes Segmentation

For the segmentation of each DBS electrode, an

algorithm was developed in MATLAB using the CT

and T2 data co-registered with T1-MRI.

The algorithm automatically determines the

bottom and upper tip of the DBS lead to deduce its

trajectory. The coordinates of each electrode are

estimated based on the DBS lead trajectory and the

standard distances of each electrode to the bottom

tip, provided in the datasheet of DBS lead.

In order to support the neurologists’ task in the

segmentation of the electrodes, a user interface was

built to implement this algorithm, making this

process more user-friendly.

The algorithm developed for the automatic

segmentation of DBS electrodes is described below

in more detail.

3.2.1 DBS Lead Segmentation

Initially, to determine the trajectory of the lead it is

necessary to extract it from the data and identify its

bottom and upper tips.

Since intensity-based segmentation approaches

were incapable to eliminate completely the skull in

the CT and T2 data, due to intensity similarities

between the DBS lead and the skull, a 3D labelling

connected components algorithm was designed. The

algorithm principle is based on the evidence that the

position of the lead varies slightly from slice to slice.

Thus, the algorithm estimates the centroid of

each label of each binary image, which is obtained

by a thresholding operation. The closest centroids in

successive slices are connected and a Euclidean

distance threshold is applied to eliminate the 3D

centroids combinations which are far apart and

therefore cannot represent the DBS lead. A

connectivity recursive function was designed to set

up 3D connect components, each one with a

different label assigned, based on the information of

the centroids connection between slices. Since each

centroid was constrained to be connected to only one

centroid in the following slice, no centroids were

repeated in different 3D connected components. The

recursive function ends the set-up of each connected

component when a centroid has no connection with

any centroids on the following slice. When all the

centroids have a 3D label assigned, the recursive

function cease.

The 3D labels of DBS leads are automatically

selected since they are the ones localized along a

trajectory from surface to deep brain. The bottom tip

is defined in the deepest slice in which the DBS

label is visible, and the upper tip in the last slice.

3.2.2 Electrodes Position Estimation

In order to determine each position of the electrode,

the x, y and z voxel coordinates of the bottom and

upper tips of the DBS lead were transformed to mm

space by equation 1, 2 and 3. For the transformation

between coordinate spaces, it was used a 4x4 affine

transformation matrix, M, saved in the header of the

NIfTI file.





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

  

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

 

,

(1)

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

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



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(2)

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

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 

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(3)

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

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(4)

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(5)

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(6)

Using the mm coordinates of DBS’s tips, it was

performed the following trigonometric equations, as

illustrated in Figure 1 (A).

 tan





δx

(7)





cos 

(8)

tan





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(9)

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cos 

(10)

cos







(11)

Knowing the values of these variables and the

distance of each electrode to the bottom tip, it was

possible to calculate the mm coordinates of the

centre of each electrode, δx1, δy1, h1, by

trigonometric relations as illustrated in Figure 1 (B).

The obtained positions were transformed to the

voxel space by equation 4, 5 and 6, to set up the 3D

electrodes mask.

Figure 1: (A) 3D representation of the relationship

between the lengths and angles for the different sides of

the DBS lead, where H represents its trajectory. (B) 3D

representation of the distances of a figurative electrode in

relation to the bottom tip.

PhyCS2014-InternationalConferenceonPhysiologicalComputingSystems

3.3 User-friendly Interface

The workflows of the algorithms described above

were implemented in an interface, developed in

MATLAB, which automatically estimates the

bottom and upper tips of the DBS lead and

calculates, using the trigonometric relations, each

position of the electrodes.

With the help of this system, the user can

automatically export a 3D mask with only electrodes

information, i.e., without brain, beam hardening and

skull information. Furthermore, the user can obtain

3D masks of the DBS leads as well as specify which

electrodes will be in the mask to be exported.

Since the CT or T2 datasets were aligned with

T1-MRI, before the segmentation, the interface

allows the overlapping of the T1-MRI with the

electrodes or DBS masks. This functionality is

useful to support the interpretation of data by the

neurologists.

Figure 2: Workflow Interface window for semiautomatic

segmentation, in which the user presses in the lateral

buttons to select the DBS’s tips, using the cursor.

An options panel was added to change default

parameters and therefore optimize the DBS

segmentation process or even change the

specifications of the DBS lead, such as the distances

of each electrode in relation to the bottom tip. This

latter feature is essential in case other DBS model,

different from the default, is used.

In case the user does not agree with the

suggestions given by the automatic approach, there

is the possibility of manual change (in the options

panel) of some segmentation parameters or even the

opportunity to perform a semiautomatic

segmentation, as depicted in Figure 2. This latter

only performs the pipeline using the trigonometric

relations, where the bottom and upper tips are

manually selected by the user.

In addition, the dataset can be analysed by

passing the slices, which can be helpful when the

automatic approach is selected to confirm the slices

where the DBS tips appear.

4 RESULTS AND DISCUSSION

4.1 Multimodal Neuroimage Fusion

Figure 3 revealed a fine alignment between T1 and

T2 or CT images. Therefore, the parameters chosen

for the co-registration are recommended for inter-

modal alignments, linear affine transformations and

datasets in which the motion is not significant.

According to Jenkinson et al., (2001), the use of

FLIRT in the presence of large ventricles, may result

in misalignments for others structures. In these

cases, higher order transformations or non-linear

warpings may be used to achieve fine alignments.

Figure 3: Co-registration of (A) CT with T1 datasets and

(B) T2 with T1 datasets. (Left) Original CT and T2

images; (Center) CT and T2 images aligned with T1 using

FLIRT; (Right) Fusion of CT or T2 images with T1,

which is displayed in blue.

Although the patients of this study were elderly

and therefore more susceptible to present large

ventricles, no failures were seen in FLIRT

performance, using the multimodal pipeline

described in this study.

4.2 DBS Electrodes Segmentation

As depicted in figure 4 (A), 3D labeling connected

components algorithm was very efficient to produce

a mask with only DBS lead data, for the CT datasets.

Nevertheless, the algorithm does appear to fail on

automatic mode for T2 datasets. Therefore to

generate the electrodes masks with T2 data, it is

recommended to use the semiautomatic

segmentation approach.

Precise3DDeepBrainStimulationElectrodeLocationbasedonMultimodalNeuroimageFusion

The electrodes position estimated by MATLAB

and the ones deduced manually by a physician were

compared in order to validate the MATLAB

interface and algorithm. The manually localization

of electrodes position was performed as described by

Rozanski et al., (2013).

Table 1: The mean (̅) and standard deviation (σ) of the

absolute differences, in mm, estimated from all datasets,

regardless of the segmentation method used for the

electrodes mask establishment.

mm x y z



0,507 0,592 0,444

0,337 0,426 0,366

Table 2: The mean (̅) and standard deviation (σ) of the

absolute differences, in mm, estimated with the automatic

and semiautomatic method.

Semiautomatic Automatic

x y z x y z



0,581 0,557 0,359 0,385 0,678 0,587

σ 0,362 0,358 0,316 0,258 0,530 0,413

Table 1 shows the average of the differences in

mm between the coordinates of each electrode

obtained by the interface and the ones manually

deduced, for the three axis. These differences are

very small, as suggested by Table 1.

Through the analysis of Table 2, it is concluded

that semiautomatic segmentation was less precise in

x direction compared with the automatic method,

probably due to random errors introduced by the

user in the selection of the tips and systematic errors

due to the voxel resolution, restricting the selection

of the center of the tips by the cursor. On the other

hand, automatic segmentation was less precise in y

and z directions, probably due to the presence of

some beam hardening in the upper slices that may

have affected the calculation of the centroid of the

upper tip and therefore the estimation of DBS’s

trajectory.

However, the trajectory imprecisions are not

significant, since either using the semiautomatic or

the automatic approach (Table 2), the differences

found were less than a voxel size.

Regarding computational time, the creation of

the electrodes mask using our tool lasts around 1

minute, which when compared with the normal

manual procedure performed by the physicians that

lasts around 40 min, is much faster and practical.

Figure 4: Fusion of CT images pre-aligned with: (A) the

DBS lead mask obtained after the 3D labelling connect

components algorithm; (B) electrodes mask obtained by

the automatic algorithm.

Normally, the physician needs to have prior

knowledge on neuroimaging software to manually

determine the bottom tip and specific angles in

relation to the DBS lead, in order to estimate its

trajectory and therefore determine each location of

the electrodes (Rozanski et al., 2013); (Guo et al.,

2013). Videen et al., (2008) uses a similar approach

by manually estimate the deepest tip and the center

of the lead in upper slices. On the other hand,

Zonenshayn et al., (2004) calculates the stereotactic

frame’s arc, collar angles and the most distal

electrode in stereotactic space by microelectrode

recordings, in order to estimate the trajectory of the

lead. In both methods, based on the trajectory and

the bottom tip, the coordinates of each electrode are

manually determined and used to create ROIs. This

entire procedure is very time consuming when

compared with the segmentation time of our tool,

which makes our system more pragmatic and

motivating for neurologists.

Therefore, our interface can be widely used by

physicians, since it is not time consuming and allows

an automatic and accurate estimation of electrodes

locations.

Furthermore, the user-friendly interface allows

the user to export a mask with DBS leads as well as

specify which electrodes will be in the mask to be

exported. These characteristics allow, for instance,

the selection of only the activated ones, which can

be useful in DBS investigation studies. As opposed,

the entire DBS lead mask can be useful in a clinical

environment to study the impact of microlesional

PhyCS2014-InternationalConferenceonPhysiologicalComputingSystems

damage caused by its implantation in the brain

(Horn et al., 2013).

The co-registration pipeline and the electrodes

masks exported by the interface allow the fusion of

T1-MRI with electrodes in a single view, as depicted

in Figure 5. This is important to visualize the

anatomical electrodes position and confirm their

implantation in the target areas, activating only the

ones which may lead to the better outcomes and

avoiding the ones which are near to structures such

as the substantia nigra responsible to promote acute

side effects (Ulla et al., 2011).

Figure 5: Multimodal neuroimage fusion: Electrodes mask

(red) overlaid with T1-MRI dataset where adjacent deep

brain structures can be related to the electrode positioning.

5 CONCLUSIONS

The purpose of this study was to develop a system

for the localization of electrodes from the DBS lead

and use multimodal techniques to confirm their

placement in the neurosurgery target areas.

Since the multimodal co-registration pipeline

results in fine alignments and an accurate brain

extraction (for all 16 cases), the parameters chosen

either for BET and FLIRT are recommended to

visualize the anatomic position of DBS electrodes,

in future studies.

Regarding the electrodes segmentation and

localization, it can be concluded that the user-

friendly interface can be useful in a clinical

environment and in future DBS studies to

automatically produce the electrodes masks, since

the differences between the detected electrodes

positions and the ones estimated manually by an

experienced physician were less than a voxel in all

cases.

In addition, the creation of the electrodes mask

using our tool lasts around 1 minute, much faster

and practical when compared with the common

procedures performed by physicians.

Therefore, using our system it is possible to

obtain an anatomic location of each electrode and

may contribute to the improvement of the efficacy of

DBS and consequently the patient’s outcome.

In the future, it would be interesting to use the

system to find any correlation of the exact anatomic

position of the electrodes with the clinical outcomes.

In additon, using DTI information, it would be also

possible to study the influence of the 3D electrode

localisation in the connectivity patterns of the

cortico-basal-ganglia-thalamo-cortical circuit. These

studies could be extended not only for Parkinson and

Dystonia patients, but for all the diseases treated by

DBS such as resistant depression, schizophrenia and

mood disorders.

ACKNOWLEDGEMENTS

This work is financed by the ERDF – European

Regional Development Fund through the

COMPETE Programme (operational programme for

competitiveness) and by National Funds through the

FCT – Fundação para a Ciência e a Tecnologia

(Portuguese Foundation for Science and

Technology) within project «FCOMP - 01-0124 -

FEDER-022701» and «PTDC/NEU-

SCC/0767/2012».

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PhyCS2014-InternationalConferenceonPhysiologicalComputingSystems