Expert Systems with Applications 40 (2013) 44–51

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Expert Systems with Applications

journal homepage: www.elsevier .com/locate /eswa

Component-based technique for determining the effects of acupuncturefor fighting migraine using SPECT images

M.M. López a,⇑, J.M. Górriz a, J. Ramírez a, M. Gómez-Río b, J. Verdejo c, J. Vas d

a Dept. of Signal Theory, Networking and Communications, 18071, University of Granada, Spainb Nuclear Medicine Service, Virgen de las Nieves Hospital, Granada, Spainc Dept. of Clinical Psychology, 18071, University of Granada, Spaind Pain Treatment Unit, Primary Care Center, Dos Hermanas, Spain

a r t i c l e i n f o

Keywords:Support Vector Machine (SVM)SPECTAcupunctureRegions of interestMigraine

0957-4174/$ - see front matter � 2012 Elsevier Ltd. Ahttp://dx.doi.org/10.1016/j.eswa.2012.07.004

⇑ Corresponding author. Fax: +34 958243271.E-mail address: miriamlp@ugr.es (M.M. López).

a b s t r a c t

In this work, SPECT brain images are analyzed automatically in order to determine the effects of acupunc-ture applied for fighting migraine. For this purpose, two different groups of patients are randomly col-lected and received verum and sham acupuncture, respectively. Changes in the brain perfusionpatterns can be measured quantitatively by dealing with the images in a classification context. A classi-fication scheme consisting of a component-based feature extraction technique in combination with Sup-port Vector Machines allows us to accurately determine the regions of interest (ROIs) where acupunctureproduced more intense effects, and whether these effects are correlated with a decrease or an increase ofthe brain activity. Effects produced by verum and sham acupuncture are studied, and the best method forintensity normalization is discussed. The result is a complete, objective system which can be used forgeneral purposes in the visual assessment of perfusion images.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

Acupuncture is an important part of health care in Asian culturethat can be traced back almost 3000 years. This ancient Chineseintervention consists of applying pressure, needling, heat, and elec-trical stimulation to specific acupuncture points to restore patientsto good health (Liu & Akira, 1994; Pomeranz & Stux, 1989). Basedupon the results of well-designed and appropriately controlledclinical trials, the National Institutes of Health (NIH), in November1997, issued a statement that supported the efficacy of acupunc-ture for specific conditions, such as pain, nausea, and vomiting(Ankarali, Sumbuloglu, Yazici, Yalug, & Selekler, 1998). Severalstudies have demonstrated specific effects of acupuncture on cere-bral blood flow (Hsieh et al., 2001), functional magnetic resonanceimaging (Cho et al., 1998) or other techniques such as synchrotronX-ray fluorescence (Yan et al., 2009). Although pain alleviation isone of the most common applications of acupuncture, the mecha-nism of acupuncture-induced analgesia remains unclear.

Migraine and tension-type headache give rise to notable health(Solomon, 1997; Stewart & Lipton, 1994) economic (Stewart & Lip-ton, 1994) and social costs (Ankarali, Sumbuloglu, Yazici, Yalug, &Selekler, 2009; Lipton et al., 2003). Despite the undoubted benefitsof medication (Goadsby, Lipton, & Ferrari, 2002) many patients

ll rights reserved.

continue to experience distress and social disruption. This leadspatients to try, and health professionals to recommend, non-phar-macological approaches to headache care. One of the most popularapproaches seems to be acupuncture.

Scintigraphic images using single photon emission computer-ized tomography (SPECT) and/or positron emission tomography(PET) which demonstrate the physiological processes of a tissue,allow measurement of physiologic processes as well as alterationsrelated to various diseases (Saha, 2004). The availability of sophis-ticated imaging methods such as SPECT and PET, as well as in vitroexperimental assays have prompted their use to study techniquesused in the Traditional Chinese Medicine (TCM), as acupuncture(Campbell, 2006; Newberg et al., 2005). Indeed, some of thesestudies have particularly focused on the effects of acupuncturefor fighting migraine (Biella et al., 2001; Vas et al., 2008), where re-gions of interest are determined by using informatic programssuch as ‘‘Brain Ratios’’ or the well-known approach in neuroscienceStatistical Parametric Mapping (SPM) (Friston, Ashburner, Kiebel,Nichols, & Penny, 2007). However, this latter technique requiresthe number of observations (i.e. scans or samples forming thegroup) to be greater than the number of voxels forming each scan(often named variables) and furthermore, final interpretations onthe selected voxels are given by experts in an subjective way.

In this work we use computational methods such as supervisedmachine learning algorithms to carry out a quantitative, objectivestudy on the effects of acupuncture for fighting migraine. SPECTimages are used as input to feed an automatic system able to

Table 1Demographic details of the dataset. Number of samples, percentage of males (M) andfemales (F), and age (mean/standard deviation) is given for each group.

# Samples Sex (M/F) (%) Age (years)

A 18 16.67/83.33 44.44/9.65B 18 38.89/61.11 43.94/11.98

M.M. López et al. / Expert Systems with Applications 40 (2013) 44–51 45

analyze the images and detect the regions where acupuncture hadmore significant effects. This is achieved by using images proceed-ing from a randomized clinical trial in which real and sham acu-puncture has been applied to patients suffering from migrainecombined with classification methods.

2. Material and methods

2.1. Image acquisition and database categorization

Our database consists of 36 migraine patients, 18 subjected toindividualized active or verum acupuncture and 18 with minimalor sham acupuncture. Other details about the samples formingthe database are given in Table 1. Pre- and post-acupuncutre acqui-sitions are taken under very different conditions. For the first up-take the patient is injected with 260 MBq (7 mCi) of 99mTc-ECD,after what the patient is given the acupuncture session (verumor sham). Five minutes before ending the acupuncture session,925 MBq (25 mCi) of 99mTc-ECD are administered intravenously.SPECT images were taken with a PRISM 3000 machine.

For a later categorization of the images, all pre-acupunctureacquisitions are considered as belonging to class x1 whereas labelx2 is assigned to post-acupuncture acquisitions. On the otherhand, migraine images are separated in group A or B if the acu-puncture modality was verum or sham, respectively. Thus, eachvolume is included in one of the following groups: XA

x1, XA

x2, XB

x1

and XBx2

.

2.2. Image preprocessing

All images were first co-registered to the SPM template (Fristonet al., 2007) in order to accomplish the following assumption: thesame position in the volume coordinate system within differentvolumes corresponds to the same anatomical position. This makesit possible to do meaningful voxel wise comparisons betweenSPECT images. In addition, SPECT imaging generates volumes thatonly give a relative measure of the blood flow. The blood flow mea-sure is relative to the blood flow in other regions of the brain. Di-rect comparison of the voxel intensities, between images, evendifferent acquisitions of the same subject, is thus not possiblewithout normalization of the intensities. In this work, the intensitynormalization is an issue of great importance due to different con-ditions under which images are taken. This fact results in large dif-ferences between mean values of images belonging to x1 and x2

classes, which certainly will cause problems in the automatic anal-ysis of the images. Furthermore, the patient’s state during the firstacquisition (before the application of acupuncture) varies to a largeextent. Some patients are imaged during a migraine attack, suffer-ing in the moment of the acquisition severe pain that affects oneside of the head and sometimes both sides. Other patients are trea-ted between seizures, that is, not suffering pain at the moment ofthe acquisition. In order to deal with this factor carefully, three dif-ferent normalization methods are applied to the images:

� Method 1. Normalization to the mean. The mean values l1 andl2 of the pre- and post-acupuncture acquisitions correspondingto the same patient are obtained individually, respectively. Afterthat, pre- and post-acupuncture (xx1 and xx2 ) volumes are nor-malized by scaling them as follows:

xjx1NORMALIZED ¼ xj

x1� ðl1 þ l2Þ=2

l1

xjx2NORMALIZED ¼ xj

x2� ðl1 þ l2Þ=2

l2

j ¼ 1; . . . ;18

ð1Þ

� Method 2. Normalization to the maximum. The maximum val-ues max1 and max2 of the pre- and post-acupuncture acquisi-tions are obtained individually for each patient, respectively.After that, pre- and post-acupuncture (xx1 and xx2 ) volumesare normalized by scaling them as follows:

xjx1NORMALIZED ¼ xj

x1� ðmax1 þmax2Þ=2

max1

xjx2NORMALIZED ¼ xj

x2� ðmax1 þmax2Þ=2

max2

j ¼ 1; . . . ;18

ð2Þ

� Method 3. Normalization to the individual maximum. Each vol-ume is individually normalized by scaling it to its maximum,computed as the 3% of the voxels with highest intensity values.Thus, images are treated independently regardless the pre-assigned label xxi

. This method ensures that the intensity levelsof each sample range from 0 to 1.

Methods 1 and 2 seek to homogenize each patient’s pre- andpost-acupuncture volumes, overcoming the difficulties yielded bythe different conditions under which both acquisitions are taken;method 3 is aimed at providing a global homogenization of thewhole dataset for automatic processing purposes. A discussion onwhich method is the most suitable for the issue under study ismade in Section 4.

2.3. Voxel selection

There are clear motivations for reducing the dimensionality ofthe feature space to a reasonable minimum: (i) reduction of thecomputational cost of the training and testing algorithms, (ii) elim-ination of correlation between features, and (iii) selection of themost discriminant set of features. Several methods for featureselection can be found in the literature: methods using thet-student Test (Friston et al., 2007; Salas-Gonzalez et al., 2010;Saxena, Pavel, Quintana, & Horwitz, 1998) as well as other methodsbased on compression techniques such as principal componentanalysis (López et al., 2009; Spetsieris, Ma, Dhawan, & Eidelberg,2009), independent component analysis (Illán et al., 2011), kernelmethods (Polat & Gnnes, 2009) or factor analysis (Lin & Chien,2009) have been used to perform the feature reduction task.

In this work, a first approach for voxel selection is to discard vox-els corresponding to regions out of the brain, as well as those voxelswith lower intensity values both in pre- and post-acupunctureacquisitions. These voxels are automatically detected by computingan activation mask as done in Górriz et al. (2008), setting in thiswork the mask threshold to 25%. After voxels of no interest arediscarded by means of the mask application, the resulting volumeis divided into 3 � 3 � 3-sized cubic, overlapping blocks or compo-nents, each one of which will be individually studied until the wholevolume is covered. This component factorization of the brain willovercome a major problem associated with pattern recognitionsystems, which is the so-called curse of dimensionality, that is, thenumber of available features for designing the classifier can be verylarge compared with the number of available training examples.Fig. 1 shows the resulting volume after applying the mask to asample of the database.

0

50

100

150

200

250

Fig. 1. Selected voxels from the whole volume after applying a mask, which discards voxels with intensity values under the 25% of the maximum of the mean image.

46 M.M. López et al. / Expert Systems with Applications 40 (2013) 44–51

2.4. Regions of interest

The perfusion changes in the brain activity produced by acu-puncture may be determined by means of different methods. Themost direct and straightforward one consists of subtracting thepost-acupuncture brain pattern from the one produced before acu-puncture is applied. The resulting volume will represent the re-gions where activation has decreased with negatives values andthe ones where activation has increased as positive. Images needto have been previously normalized by one of the previous meth-ods – for the shake of clarity, from now on the NORMALIZED nota-tion will be omitted. Fig. 2 represents single views of the activationpatterns obtained after subtracting the post-acupuncture averageimage from the pre-acupuncture acquisition, i. e.,

M ¼ 1N1

X

xj2x1

xj �1

N2

X

xj2x2

xj ð3Þ

where N1 = N2 = 18 are the number of samples in groups Xx1 andXx2 , respectively. The images in this figure correspond to group A,and has been obtained by using the three different normalizationmethods described in Section 2.2. In order to show the most signif-icant regions, only the 75% percentage of positive and negative val-ues are represented. For a better visualization, these regions areplotted on the ch2bet anatomical template provided by MRIcro Soft-ware. An initial result can be seen at a glance: normalization to themaximum (method 2) and normalization to the individual maxi-mum (method 3) yielded almost identical activation patterns,whereas normalization to the mean (method 1) turned out to bethe most sensitive to an increase of brain activity, detecting moreclearly positive activation regions than negative ones. Although

Fig. 2. A single view of the average images computed on the individual subtractions postfor group A.

images are not shown for group B, the same effects related to thenormalization method are visible.

2.5. Automatic detection of ROIs

A set of different algorithms can be found in the literature forallocating ROIs (Chaves et al., 2011; Segovia et al., 2010). In thiswork, support vector machines (SVMs, see Section 3.1) are usedfor detecting ROIs in an automatic way (Illán et al., 2011). In aclassification framework, ROIs are defined as those groups of vox-els – to which we will refer as components, where a classificationmethod such as SVM provides a satisfactory performance in thetask of distinguishing two classes. In terms of class categorization,those groups of voxels on which SVM yields high accuracy separa-tion rates can be considered as a meaningful component. That is, ifpre- and post-acupuncture brain perfusion patterns represented bySPECT images are considered as two differentiated classes, thenthose blocks on which the SVM yields higher accuracy rates corre-spond to the regions where class differences were more noticeable,and therefore it can be assumed that the acupuncture effects inthose regions were more intense.

A single SVM will be trained and tested on each component ofthe brain. The feature vectors will contain 27 values correspondingto the intensity levels of the voxels in the block. The selected com-ponent is extracted from all the samples in the dataset, so that wecount on N feature vectors, xi = [x1,x2, . . . ,x27], i = 1,. . . ,36 to whicha binary label is associated: ‘‘0’’ and ‘‘1’’ for pre- and post-acupunc-ture acquisitions, respectively. Besides the motivations describedin Section 2.3, this voxel selection method allows us to allocatethe ROIs throughout the volume accurately. Fig. 3 shows graphi-cally a scheme of the feature extraction and classification methodsapplied for a single component.

-acupuncture � pre-acupuncture for three different intensity normalization methods

Fig. 3. Feature extraction and classification scheme. A single classification task is defined for each component, considering the components of pre- and post-acupunctureacquisitions as features of different classes. A Leave-One-Out cross validation strategy is carried out on the features to assess the accuracy of the component under study.

M.M. López et al. / Expert Systems with Applications 40 (2013) 44–51 47

The importance of each component is set according to the accu-racy value given as the output of the SVM for this component. Thissignificance value is computed after the training stage by means ofthe Leave-One-Out (LOO) cross validation strategy. It consists oftraining the learning algorithm with the selected component ofall the samples in the dataset but one, which is used to test theclassifier. This procedure is repeated N times, leaving out a differ-ent sample -patient in each iteration until all samples have beenused as test. After the N iterations, an average value of accuracyis computed for the component under study.

The complete volume is evaluated component by componentfollowing the procedure described above. The obtained accuracyvalue for each component is assigned to the central voxel of theblock. The next component is made up by shifting the last evalu-ated component by one voxel, that is, components are overlappingso that a single voxel is included in a feature vector as many timesas it takes part of a component. In other words, there are so manycomponents as voxels in the volume, and each one acts as the

center of the cubic component only once. Once all the componentshave been evaluated, a component-based map of accuracy isobtained. Regions where perfusion of pre- and post-acupuncturepatterns differ in a greater measure are represented by highervalues of accuracy, whereas regions with lower accuracy valuescorrespond to components with similar gray level distribution forboth classes, since the classifier fails to identify them as belongingto different classes.

3. Theory

3.1. Support vector machines

SVMs (Burges, 1998) have attracted recent attention from thepattern recognition community due to a number of theoreticaland computational merits derived from the Statistical LearningTheory (Vapnik, 1995) developed by Vladimir Vapnik at AT& T.

48 M.M. López et al. / Expert Systems with Applications 40 (2013) 44–51

These techniques have been successfully used in a number ofapplications including voice activity detection (Ramírez, YTlamos,Górriz, & Segura, 2006), content-based image retrieval (Müller,Michoux, Bandon, & Geissbuhler, 2004), text classification (Leroy& Rindflesch, 2005) and medical imaging diagnosis (Chaves et al.,2009; Martínez-Murcia, Górriz, Ramírez, Puntonet, & Salas-Gonz-alez, 2012).

SMVs separate binary labeled training data by the hyperplane

f ðxÞ ¼ wT xþw0 ð4Þ

where w is known as the weight vector and w0 as the threshold.This hyperplane is maximally distant from the two classes (knownas the maximal margin hyperplane). The objective is to build a func-tion f : Rp ! f�1g using training data that is, p-dimensional pat-terns xi obtained in the feature extraction step, and class labels yi:

ðx1; y1Þ; ðx2; y2Þ; . . . ; ðxN; yNÞ 2 Rp � f�1g; ð5Þ

so that f will correctly classify new examples (x,y).In a binary classification task, the classifier may make either of

the two mistakes: considering like positive a test sample that wasinitially labeled as negative (i.e., the associated real label is y = �1)which is called False Positive (FP), or the contrary, which is calledFalse Negative (FN). When positive and negative samples are

Fig. 4. Accuracy map obtained after individual evaluation of components for groups (

Table 2Classification results using intensity level of ROIs as features, for different thresholds thatacupuncture acquisitions for groups A/B.

� 75% 80%

Method 1 Acc. 94.44/91.67 97.22/94.44Sen. 90/89.47 94.74/94.44Spe. 100/94.12 100/94.44

Method 2 Acc. 97.22/97.22 97.22/97.22Sen. 94.74/94.74 94.74/94.74Spe. 100/100 100/100

Method 3 Acc. 97.22/97.22 97.22/94.44Sen. 94.74/100 94.74/100Spe. 100/94.74 100/90

correctly classified they are marked as True Positive (TP) and TrueNegative (TN), respectively. The performance of the classifier canbe assessed by computing accuracy, sensitivity (ability of the clas-sifier to detect true positive samples) and specificity (ability of theclassifier to detect true negative samples) rates, which are definedas:

Accuracy ¼ TPþ TNTPþ TNþ FPþ FN

� 100 ð6Þ

Sensitivity ¼ TPTPþ FN

� 100; Specificity ¼ TNTNþ FP

� 100 ð7Þ

4. Results and discussion

Fig. 4 shows the accuracy map obtained when images were pre-viously normalized according to method 2. The definition of ROIwill depend on how low or high we set an accuracy-based thresh-old �. Higher values of � correspond to higher accuracy regions, andtherefore a lower number of voxels comes out, and vice versa.

We now define a classification task using all voxels in ROIs asfeatures to train a SVM-based classifier, in order to determine theclassification power of the system in distinguishing pre- and post-acupuncture acquisitions for different normalization methods.

50

60

70

80

90

100

50

60

70

80

90

100

up) A and (down) B. Regions with higher accuracy values are considered as ROIs.

set the definition of ROI. The classification task consists of separating pre- from post-

85% 90% 95%

97.22/97.22 94.44/88.89 94.44/094.74/100 90/93.75 94.44/0100/94.74 100/85 94.44/0

100/94.44 97.22/97.22 88.89/0100/94.44 94.74/100 88.89/0100/94.44 100/94.74 88.89/0

97.22/94.44 100/80.55 77.78/094.74/100 100/86.67 75/0100/90 100/76.19 81.25/0

M.M. López et al. / Expert Systems with Applications 40 (2013) 44–51 49

Feature vectors are made up from the intensity level of voxels con-tained in ROIs, and the associated labels are assigned according tothe acquisition (‘‘0’’ or ‘‘1’’ if the voxels correspond to pre- or post-acupuncture acquisition, respectively) as done in the previous stage.

Fig. 5. Intensity of the ROIs when subtracting post- from pre-acupuncture samples andacupuncture produced brain activation and vice versa.

Experiments are carried out separately for both groups A and B anddifferent values of the threshold �. Results are shown in Table 2.

We consider interesting to find out whether the activation ofthe perfusion pattern increased or decreased after the acupuncture

averaging for groups (a) A and (b) B. Positive values correspond to regions where

Table 3Statistical significance of mean values of voxels included in the selected ROIs (� = 80%). p values greater than 0.05 are not considered as statistically significant.

Method 1 Method 2 Method 3

Group A F(1,17) = 3.211; p = 0.091 F(1,17) = 6.796; p = 0.018 F(1,17) = 7.113; p = 0.016Group B F(1,17) = 1.591; p = 0.224 F(1,17) = 1.750; p = 0.203 F(1,17) = 1.594; p = 0.224

50 M.M. López et al. / Expert Systems with Applications 40 (2013) 44–51

session. To this aim, post-acupuncture samples are subtractedfrom the pre-acupuncture ones and averaged, keeping only the val-ues of voxels within ROIs. The value of these voxels is shown uponthe anatomical template in Fig. 5 for groups A and B. Two findingsarise from these images. On one hand, it can be clearly set that pla-cebo acupuncture did not activate the same regions that verummodality. Furthermore, real acupuncture produced, in generalterms, a decrease in the activation pattern of the patient, since inmost cases ROIs appear in cold tones. On the other hand, fromthe images it can be assumed that each acupuncture modality al-tered different ROIs and in a different way. For instance, the righttransverse temporal gyri increases its activity only with placeboacupuncture, not even being identified as a ROI for real modality.In the same way, sham acupuncture (Fig. 5(a)) had a more acute ef-fect on left caudate, whereas real acupuncture response in this re-gion was more moderate and symmetric, affecting as well the rightside. This finding coincides with those published in Newberg et al.(2005), where a normalization of the asymmetry in the brain activ-ity was observed after the application of acupuncture.

4.1. Statistical significance of selected voxels

A study on the statistical significance of the selected voxels isperformed in order to determine both the most suitable normaliza-tion method and the differences of the effects produced by real andsham acupuncture. For each group A and B independently, an anal-ysis of variance (ANOVA) on the mean values of the obtained ROIsis carried out considering two factors: (i) the acquisition time, withpre- and post-acupuncture levels and (ii) the activation change,with positive and negative levels. Thus, for each subject fourparameters are given: (1) mean value of voxels included in ROIsthat increased their value after acupuncture computed on thepre-acupuncture acquisition, (2) mean value of voxels includedin ROIs that decreased their value after acupuncture computedon the pre-acupuncture acquisition, (3) mean value of voxels in-cluded in ROIs that increased their value after acupuncture com-puted on the post-acupuncture acquisition and (4) mean value ofvoxels included in ROIs that decreased their value after acupunc-ture computed on the post-acupuncture acquisition. The resultsof the statistical significance are shown in Table 3.

Two statements can be done from the results of Tables 2 and 3.Firstly, in terms of accuracy rates (Table 2) the classifier performsbetter when the training data is extracted from group A than forgroup B. In other words, real acupuncture yielded greater changesin the perfusion patterns in comparison with sham modality, beingclearer to the classifier the distinction of the pre- and post-acu-puncture acquisitions for group A than for group B. This statementis statistically reinforced by the results shown in Table 3 that provethat, for the same threshold (i.e. � = 80%), sham acupuncture pro-duced changes in the activation patterns that are not statisticallysignificant (p > 0.05) regardless the intensity normalization meth-od previously applied to the images.

A second important result we obtain from these tables of resultsconcerns the suitability of each normalization method for theproblem under study. Focusing on group A where differences be-tween classes are more meaningful and statistically significant, itcan also be stated that in terms of accuracy, methods 2 and 3 out-performed or equaled method 1. Furthermore, ROIs found on the

images normalized by method 1 are not statistically significantfor group A nor B. All these results lead us to discard method 1as a suitable normalization method. Methods 2 and 3 yielded sim-ilar statistical significance values as well as accuracy results mak-ing both suitable for assuming a correct normalization method.However, we suggest method 2 as the most suitable normalizationmethod since it takes into account the intensity distribution differ-ences due to the different conditions under which pre- and post-acupuncture acquisitions are taken, as explained in Section 2.1,performing that way a personalized normalization that intrinsi-cally keeps intra subject information.

5. Conclusions

This work presents a novel method to detect changes in thebrain activity patterns of two populations by means of machinelearning-based algorithms. In particular, a dataset consisting inSPECT images of patients, to whom acupuncture is applied in orderto mitigate pain produced by migraine, is used as income to train aSVM-based system, labeling pre- and post-acupuncture acquisi-tions as different classes. A meticulous feature extraction tech-nique which covers the whole volume by breaking it down intosmall, cubic-shaped components is used to solve the small samplesize problem and locate accurately the regions where occurred themost significant activity changes. A study on the most suitableintensity normalization method is discussed as well. The experi-ments are carried out on two different subsets of patients, depend-ing on whether the acupuncture modality applied was sham orverum, and results point at real acupuncture as an effective wayof producing real changes in the brain activation pattern. The clas-sification results are reinforced by means of an analysis of variancemade on the mean values of the voxels included in the detectedROIs. With this machine learning-based technique we show a pow-erful method for detecting ROIs which can be used in a wide rangeof application, not only acupuncture for fighting migraine, provid-ing clinicians a real, helpful tool for assisting the visual assessmentof brain perfusion images.

Acknowledgments

This work was partly supported by the MICINN under theTEC2008-02113 project and the Consejerı́a de Innovación, Cienciay Empresa (Junta de Andalucı́a, Spain) under the Excellence Pro-jects P07-TIC-2566, P09-TIC-4530 and P11-TIC-7103. The authorswould also like to thank Mr. Antonio Rafael Hidalgo Muñoz for con-tributing useful advice for improving this work.

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