MULTIVOXEL MR SPECTROSCOPY TOOL

FOR BRAIN CANCER DETECTION IN NEURONAVIGATION

Performance

Juan José Fuertes

, Valery Naranjo

, Pablo González

, Ángela Bernabeu

, Mariano Alcañiz

1,3

and

Javier Sanchez

Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano (I3BH),

Universitat Politècnica de València, I3BH/LabHuman, Camino de Vera s/n, 46022, Valencia, Spain

Inscanner S.L., Unidad de Resonancia Magnética, Alicante, Spain

Ciber, Fisiopatología de Obesidad y Nutrición, CB06/03 Instituto de Salud Carlos III, Madrid, Spain

Philips Healthcare España, Maria de Portugal 1, 28050, Madrid, Spain

Keywords: Magnetic Resonance Spectroscopy, Brain Cancer Detection, Multivoxel, Magnetic Resonance Imaging,

Neuronavigator.

Abstract: This work presents a simple and interactive spectroscopic tool to help clinicians for brain cancer detection.

Firstly, Magnetic Resonance Spectroscopy (MRS) and Magnetic Resonance Imaging (MRI) are registered

to perform brain analysis. After processing the spectroscopic signals with HLSVD method for water

suppression, zero-filling and phase-correction algorithms, and apodization functions to improve the signal-

to-noise ratio (SNR), the metabolite brain maps are generated in order to analyze brain composition. A 3D-

spatial distribution of the anatomical and spectroscopic images and how they are registered are presented to

facilitate surgery planning. The goal is to generate metabolite brain maps which can be merged with

anatomical images in the neuronavigator to provide the surgeon with the exact point where performing the

biopsy.

1 INTRODUCTION

Many spectroscopic tools have been developed to

solve the problem of quantifying signals in

H MRS

data in the last 20 years (Castillo et al., 1996;

Stoyanova et al., 1995). The most popular tools are

the jMRUI software package (Stefan et al., 2009),

the LCModel

(Gruber et al., 2004) and the

AQSES software (Simonetti et al., 2006) among a

large series of software applications. All of them

incorporate the main algorithms to process the

spectroscopic signals in order to correct distortions

and imperfections, perform water suppression and

generate metabolite maps (Poullet et al., 2007).

They offer black box quantitation algorithms based

on singular value decomposition (SVD) such as

HSVD (De Beer and Van Ormondt, 1992), HLSVD

(Pijnappel et al., 1992) and HTLS (Van Huffel et al.,

1994), which are efficient to quantify signals with

good signal-to-noise ratio (SNR); in addition, they

are used to suppress the dominant signals, for

example the water coin. Moreover, these tools also

offer quantitation algorithms with available prior

knowledge such as AMARES (Vanhamme and Van

Huffel, 1997) or QUEST (Ratiney et al., 2005),

which let us impose conditions on the model-

function parameters. There are much more methods

for spectroscopic analysis, but in summary, the main

steps included in most of software tools (Poullet et

al., 2007) in order to quantify the signals and

generate the metabolite maps are:

1. Phase correction.

2. Dominant signals removal.

3. Signal enhancement with noise reduction.

4. Estimation of spectral parameters.

5. Quantitation with model functions or signal

basis sets.

There are many frameworks which already exist,

therefore there are no need to re-invent the wheel but

understand processes, appreciate limitations and

167

Fuertes J., Naranjo V., González P., Bernabeu Á., Alcañiz M. and Sanchez J..

MULTIVOXEL MR SPECTROSCOPY TOOL FOR BRAIN CANCER DETECTION IN NEURONAVIGATION - Performance.

DOI: 10.5220/0003770501670172

In Proceedings of the International Conference on Biomedical Electronics and Devices (BIODEVICES-2012), pages 167-172

ISBN: 978-989-8425-91-1

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

provide clinicians with the needs they have. The

software we present incorporates the main

algorithms to process the data, performs the

registration between spectroscopic data and MR

images, and generates the metabolite maps.

Additionally, it introduces a 3D view to merge the

3D anatomical information reconstructed from the

MR images with the metabolite information from the

spectroscopic signals in order to satisfy clinicians. It

combines the effect of molecular structure on the

magnetic field experienced by an atomic nucleus,

the Chemical Shift, with the effects of the magnetic

field gradients used in MRI. In addition, the MRS

has enormous potential because it allows direct

observation of the chemical basis of disease.

In this work we are focusing on multivoxel

spectroscopy analysis instead of single voxel

spectroscopy in order to study the features of

multiple voxels at the same time. This is significant

when the area to study is not well defined, such as

tumour monitoring or biopsy surgery, where the

spatial distribution has notable importance. It

provides the clinicians with information about the

physical constitution of each disease.

In short, the aim of this work is to provide the

surgeon with a 3D view about the area where the

patient has the tumour and to generate a DICOM

metabolite image which will be used in the

neuronavigator when the surgeon will perform the

biopsy. In this way, it will be able to detect clearly

the tumour area.

This paper is set up as follows: section 2 shows

the software structure, explaining briefly each of the

four blocks of the tool: multivoxel selection, signal

processing, metabolite brain maps and 3D image

reconstruction view. In section 3 the features of the

algorithms to process the signals are detailed and

section 4 shows the image registration to provide the

3D brain view; in section 5, an acceptability test of

the software is shown. Finally, a brief conclusion is

given in section 6 and the future work is introduced

in section 7.

2 TOOL WINDOW STRUCTURE

This section explains how the application for brain

cancer detection is organized. It allows the surgeon

to see “the inside” of the anatomical patient’s brain

together with the metabolite maps after the signals

are analyzed. The tool is divided into four blocks

(Figure 1). In the first block, anatomical images are

shown in order to select the voxels of the signals

which will be processed. In the second block, the

spectroscopic signals are processed in order to

generate the metabolite maps which are presented

with the MR images in the third block. Finally, the

fourth block shows a 3D-view of the anatomical

structure of the patient’s brain and the field of view

(FOV) of the spectroscopy imaging (also the VOI

that stands for the volume of interest excited inside

the FOV) which let the surgeon see the surgery

planning to perform the biopsy.

Figure 1: Block diagram of the software tool.

2.1 Anatomical Images and Multivoxel

Selection

The first step once the application is running is to

load the anatomical images of the patient, providing

the user with the axial, coronal and sagittal images

so as to select those slices which are going to be

analyzed with spectroscopy imaging. Next, the

spectroscopic data are loaded and we are able to

select those voxels which belong to the tumour area.

Figure 2: Main window of the tool.

Multi-voxel selection Signal processing

Metabolite maps 3D view

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The spectroscopy imaging has a size of H*W*P

millimetres, where H stands for height, W stands for

width and P stands for depth. In this way, the size of

each voxel can be calculated. Figure 2 shows the

main window where we can see the anatomical

images with the spectroscopic voxels. The red box is

the field of view of the spectroscopy imaging and

the red box belongs to the area of those voxels

which are excited. Both boxes are registered with the

anatomical images as we will show in section 4.

2.2 Signal Processing

After the voxels of spectroscopic images have been

selected, the signals are processed to quantify and

generate the metabolite maps. In Figure 3 it is

possible to see the processing block interface, where

water suppression, signal to noise improvement and

quantifying algorithms are applied.

Figure 3: Signal processing window.

This block is divided into two windows: in the

first one, the original signals in time and frequency

domain are shown. In x-axis, milliseconds, hertz,

parts per millon (ppm) and points are the existing

options; real, imaginary and module options are

available in y-axis. In the second block, the result

after processing the signals is given, calculating the

metabolite amplitudes to create the colour maps.

There are much more tools such as zoom, inverse

ilustration, etc. and it is also possible to analyze each

signal independently clicking on it.

2.3 Metabolite Brain Maps

Once the original signals are processed and

quantified, the metabolite maps are shown as a blue-

red scale in order to emphasize the areas where there

is carcinogenic tissue. In Figure 4 the multivoxel

selection window and the result-window are shown

where the metabolite maps are registered with

anatomical images. There is also the option to

generate a medical report automatically in pdf

format with the patient information, the disease

features, the anatomical images, the metabolite

maps, and the information that the clinicians wish.

Figure 4: Creation of metabolite maps.

2.4 3D Anatomical View - Surgery

Planning

In order to know the planning of the MR

spectroscopy, the 3D model of patient’s head

(Figure 5) has been reconstructed and the

registration with the volume of spectroscopy

imaging (FOV and VOI) performed. In this way, the

surgeon knows exactly the area to study with

spectroscopic technique and where the tumour is

located.

Figure 5: 3D brain reconstruction with spectroscopic

volume.

In addition, once the signals are processed and

the colour maps generated, it is also possible to

recover the 3D colour maps to see the dangerous

regions together with anatomical information (see

Figure 6).

Figure 6: 3D metabolite map reconstruction.

MULTIVOXEL MR SPECTROSCOPY TOOL FOR BRAIN CANCER DETECTION IN NEURONAVIGATION -

Performance

169

If all metabolite maps corresponding to

anatomical images are generated, the 3D colour

reconstruction of the full spectroscopic imaging is

performed. In this way the surgeon knows as a

colour scale the aggressiveness of the tumour.

3 SIGNAL PROCESSING

METHODS

The purposes of signal processing methods

incorporated in this application are water removal,

baseline and phased correction, signal to noise

improvement and signal quantitation. With this aim,

many algorithms have been proposed since 1990 in

the time and frequency domain (Vanhamme et al.,

(2011), but nowadays only some of them are really

used in multivoxel spectroscopy imaging. Ideally,

the FID signal obtained with the MRS machine is

noiseless and results exactly from the addition of K

exponentially damped sinusoids which are

characterized by frequencies f

, amplitudes A

phases φ

, damping factors α

, length of the FID N, i

the square root of -1 and Δt the sampling interval, as:



=

∑

























n= 0,...,N-1. (1)

Before applying the water removal algorithm, it

is also possible to correct the phase of each signal in

order to know the sign of the peaks; moreover, it has

to take into account because phase is the main factor

that affects resolution. In order to remove the water

signal, the exponentially damped sinusoids whose

frequencies appear in the water region are selected

and subtracted from the original FID through the

Hankel Lanczos Singular Value Decomposition

(HLSVD-PRO) algorithm (Laudadio et al., 2002),

which estimates the whole set of model parameters

making full use of mathematical model functions.

Optimal values of number of sinusoids (K) and

Hankel size (N and M) must be determined;

therefore, values obtained by computer simulations

are established by default (K=25, N=1024 and

M=512), allowing the clinicians to change these

values for each signal (Cabanes et al., 2001). The

user can select the water region with two cursors as well.

After the water signal is suppressed, Lorentzian

and Gaussian functions to improve the signal to

noise ratio are applied. The exponentially function

implemented to improve de SNR is:

















∗∗

(2)

where factor is the apodization coefficient, sign is

the sign of the factor, and t is the time which is t

when the function is Gaussian.

At this time, the quantitation method is

performed in order to know the concentration of the

metabolites in each voxel. AMARES and HLSVD

are the algorithms which can be used in our

application. In Figure 7 the signal fitting with

HLSVD method is shown. As the concentration of

each metabolite is known, it is possible to determine

the relationship among them. In this way, the colour

maps of Naa/Cr, Cho/Cr, Cho/Naa, etc. are

calculated (N-acetylaspartate (Naa), creatine (Cr),

cholina (Cho)).

Figure 7: Signal quantitation with HLSVD method.

The new metabolite maps are saved in DICOM

format images to be used in the neuronavigator. In

this way, the red areas where the tumour is

aggressive will be highlight to let the surgeon

perform the biopsy accurately.

4 IMAGE REGISTRATION

The 3D anatomical view was introduced in section

2.4 to show the new functionality included in this

software tool. in this manner, the surgeon is able to

see the sagittal, coronal and axial views and the 3D

view of the spectroscopic planning (Figure 8).

Figure 8: Sagittal, axial, coronal and 3D view.

NAA

Cr Cho

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The first step for image registration is to locate

both anatomical and spectroscopic images in the 3D

space with their own rotation matrix. At this point, it

is possible to know which anatomical images belong

to each spectroscopic slice, through the expression:





, , 





x, y, z



+t∗M’ (3)

where the absolute position of the each

spectroscopic slice is calculated. e(x,y,z) is the

absolute position of the edges of the spectrum,

c(x,y,z) is the center of the FOV, t is the thickness of

each slice and M is the rotation matrix. Thus, the

surgeon knows exactly the anatomical areas which

have been analyzed with MR spectroscopy.

5 ACCEPTABILITY TEST

One of the most important features when we deal

with medical software is the clinicians’ acceptability

besides the required accuracy. In order to make

easier the use of this application, we have been

working in the design of a user-friendly interface,

asking doctors for ease of use (green), noticed

usability (blue), human-machine interaction (yellow)

and satisfaction level (orange). After creating the

first version of the software, the obtained results in a

10 point scale after asking 5 radiologists and

medical engineers are:

Table 1: Results of the acceptability test.

Clarity when using methods 8

Software learning (easy) 9.4

Methodology to process data 9

Direct methods route 10

Signal processing methods (number and

suitable)

8.4

Image processing methods (number and

suitable)

Generate a PDF report 10

Time-work relation 8.6

Human-machine interaction 10

User friendly interface 10

I would like to use this tool in my work 9

Graphical results 9.4

Easy 9

Intuitive 8.6

Useful 9

Handy 9

The table 1 shows the evaluation results of the

software in terms of acceptability: the user

satisfaction and the human interaction prove the

good performance of the tool. We have to improve

the noticed usability in order to make the tool more

suitable.

6 CONCLUSIONS

In this paper, a software tool to help surgeons detect

brain cancer through the use of magnetic resonance

spectroscopy has been presented.

Many applications and algorithms have been

proposed in the last years, but only some of them are

really used. The application presented in this paper

integrates whose algorithms that are daily used

providing the clinician with a user-friendly tool.

Furthermore, our goal is not to re-invent the

algorithms but understand processes and provide the

clinicians with the needs they have. This is the

reason why a novel 3D planning view which shows

the area analyzed with MRS is incorporated. It

consists of anatomical and spectroscopic images,

and the 3D reconstruction of patient’s brain.

Another significant feature of the tool is the

creation of metabolite brain maps. They must be

compatible with neuronavigator, so the surgeon

knows exactly the point where pointing with a

needle when it is performing the surgery. For this

reason the images are saved in DICOM format.

7 FUTURE WORK

Coming soon experiments are focused on making a

comparison with other software to verify the

functionality of the algorithms in order to generate

reliable metabolite maps. As far as new tool methods

are concerned, the fusion with functional images

such as Positron Emission Tomography (PET) is

being studied. The goal is to perform a 3D PET

reconstruction and register anatomical, metabolite

and functional images.

Another area of study is the creation of DICOM

images which are compatible with neuronavigator.

In this way, we are working on the enhancement of

the tumour areas to provide the surgeon with a

friendly interface to perform the biopsy accurately.

Once the DICOM images are generated, the surgeon

will perform the biopsy in the operation theatre,

taking samples of the emphasized brain tissue and

corroborating if the patient has or not tumour. PET

MULTIVOXEL MR SPECTROSCOPY TOOL FOR BRAIN CANCER DETECTION IN NEURONAVIGATION -

Performance

171

will be also fused with the rest of images in the

neuronavigator.

New quantification and water removal methods

based on non-linear filters (1D mathematical

morphology) are also being studied for its

application to MR spectroscopy.

ACKNOWLEDGEMENTS

This work has been supported by Centro para el

Desarrollo Tecnológico Industrial (CDTI) under the

project ONCOTIC (IDI-20101153), and partially by

projects Consolider-C (SEJ2006-14301/PSIC),

“CIBER of Physiopathology of Obesity and

Nutrition, an initiative of ISCIII” and Excellence

Research Program PROMETEO (Generalitat

Valenciana. Conselleria de Educación, 2008-157).

We would like to express our deep gratitude to the

Hospital Clínica Benidorm for its participation in

this project. The work of Juan José Fuertes has been

supported by a FPI grant from “Programa de Ayudas

de Investigación y Desarrollo (PAID)” of UPV.

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