Data quality in fMRI and simultaneous EEG–fMRI

RESEARCH ARTICLE

Data quality in fMRI and simultaneous EEG–fMRI

Toni Ihalainen • Linda Kuusela • Sampsa Turunen •

Sami Heikkinen • Sauli Savolainen • Outi Sipila

Received: 30 April 2013 / Revised: 24 March 2014 / Accepted: 27 March 2014

� ESMRMB 2014

Abstract

Object To evaluate functional magnetic resonance

imaging (fMRI) and simultaneous electroencephalography

(EEG)–fMRI data quality in an organization using several

magnetic resonance imaging (MRI) systems.

Materials and methods Functional magnetic resonance

imaging measurements were carried out twice with a uni-

form gel phantom on five different MRI systems with field

strengths of 1.5 and 3.0 T. Several image quality parame-

ters were measured with automatic analysis software. For

simultaneous EEG–fMRI, data quality was evaluated on

3.0 T systems, and the phantom results were compared to

data on human volunteers.

Results The fMRI quality parameters measured with

different MRI systems were on an acceptable level. The

presence of the EEG equipment caused superficial artifacts

on the phantom image. The typical artifact depth was

15 mm, and no artifacts were observed in the brain area in

the images of volunteers. Average signal-to-noise ratio

(SNR) reduction in the phantom measurements was 15 %,

a reduction of SNR similar to that observed in the human

data. We also detected minor changes in the noise of the

EEG signal during the phantom measurement.

Conclusion The phantom proved valuable in the suc-

cessful evaluation of the data quality of fMRI and EEG–

fMRI. The results fell within acceptable limits. This study

demonstrated a repeatable method to measure and follow

up on the data quality of simultaneous EEG–fMRI.

Keywords Magnetic resonance imaging � Phantoms �Electroencephalography

Introduction

Functional magnetic resonance imaging (fMRI) measures

hemodynamic response in the brain during a predetermined

stimulus or task, or a resting state [1, 2]. Differences of

only a few percent in the blood oxygen level-dependent

(BOLD) signal are measured. From a technical standpoint,

an fMRI study consists of an echo-planar imaging (EPI)

sequence run over an extended period of time. EPI

sequences usually require the use of rapidly switching

gradients of maximum amplitude. Some system and

physiological noise are always present [3]. Even if some of

the noise can be reduced by data preprocessing, the quality

of the original data is essential. When the signal-to-noise

ratio (SNR) is low or the MRI signal stability is compro-

mised, the BOLD signal may be indiscernible or an

unexpected non-BOLD-related signal may correlate falsely

with the paradigm used [4]. Statistical averaging is limited

in the clinical setting, and sufficient SNR is crucial. EPI

sequences are also prone to several types of artifacts. When

fMRI is clinically used for presurgical evaluation, spatial

accuracy is essential [5, 6].

The demand for high stability together with maximum

instrumentation loading for a long period of time makes

fMRI one of the most demanding applications of MRI

T. Ihalainen (&) � L. Kuusela � S. Turunen � S. Savolainen �O. Sipila

HUS Medical Imaging Center, P.O. Box 340,

00029 HUS, Helsinki, Finland

e-mail: [email protected]

T. Ihalainen � L. Kuusela � S. Turunen � S. Savolainen

Department of Physics, University of Helsinki, P.O. Box 64,

00014 Helsinki, Finland

S. Heikkinen

Department of Chemistry, University of Helsinki, P.O. Box 55,

00014 Helsinki, Finland

123

Magn Reson Mater Phy

DOI 10.1007/s10334-014-0443-6

technology. Even more challenging is the simultaneous

acquisition of electroencephalography (EEG) and fMRI [7,

8], currently the subject of active research. Simultaneous

EEG–fMRI may help with localizing the foci of epileptic

seizures [9, 10]. Small signals are being measured with the

two techniques, which tend to interfere with each other’s

performance. The EEG equipment may introduce promi-

nent susceptibility-based image artifacts, as well as

reduction of image SNR [11, 12]. Krakow et al. [13] and

Mullinger et al. [14] have conducted phantom studies of

the effects of various EEG equipment on fMRI image

quality in simultaneous EEG–fMRI. They have reported

artifacts caused by EEG electrodes, as well as lower SNR

caused by increased coil loading. Most of the literature

discussing data quality issues of simultaneous EEG–fMRI

has focused on the quality of and artifacts in the EEG

signal (e.g., [15–17]).

Quality assurance tools are needed to ensure that MRI

systems meet the high performance demands of fMRI at all

times. MRI image quality can be assessed with test objects,

or phantoms, filled with a liquid or gel that is visible in

MRI. Several internationally acknowledged phantom sets

and protocols, as well as manufacturer-specific phantoms

are available for regular quality assurance [e.g., 18–20].

Friedman and Glover [21] have published a protocol for

fMRI quality assurance, focusing on signal stability

parameters and their automated analysis. This protocol has

served to ensure uniform measurements in the research

consortium Biomedical Informatics Research Network

(BIRN). Other studies of fMRI quality assurance protocols

that emphasize the role of automated data analysis have

also been published [22, 23].

In our organization, both clinical and research fMRI

studies were ongoing with five MRI systems of three dif-

ferent manufacturers; EEG–fMRI studies were ongoing

with two systems. The aims of this study were (1) to

evaluate fMRI data quality with a suitable phantom and

software, (2) to evaluate the data quality of EEG–fMRI as

much as possible using the same tools, and (3) to compare

the data quality measurements with EEG–fMRI data on

human volunteers.

Materials and methods

Phantom preparation

The desired properties of the phantom and analysis soft-

ware included (1) their suitability for several MRI systems

of different manufacturers and field strengths, (2) easy

measurement and data analysis, (3) an internationally val-

idated method, and (4) the possibility to include simulta-

neous EEG–fMRI. Because the method published by

Friedman and Glover seemed to fulfil these criteria, we

selected it for this study and constructed a uniform gel

phantom according to their instructions [21]. The gel was

prepared in a glass ball with an inside diameter of 18 cm.

We acquired high-resolution T1-weighted images of the

phantom to verify the absence of significant air bubbles.

The fMRI image quality measurements

We measured fMRI image quality using two 3.0 T systems

(from two different manufacturers) and three 1.5 T systems

(from three different manufacturers). The MRI systems

(A–E) used for the measurements as well as the number of

head coil elements in each system appear in Table 1. The

measurements were carried out with an axial gradient echo-

based single-shot EPI sequence: TR = 2,000 ms,

TE = 30/40 ms (3.0/1.5 T), field of view = 22 cm, num-

ber of slices = 30, slice thickness = 4 mm, slice

gap = 1 mm, flip angle = 77�, matrix size = 64 9 64,

receiver bandwidth = 2,298 Hz/pixel, and number of

acquired time frames = 200 [21]. If the number of slices

and the receiver bandwidth could not be set precisely on

some 1.5 T systems, we set the values as close to the ref-

erence values as possible. On MRI system B, we used

parallel imaging (Sensitivity Encoding, SENSE) with a

factor of 1.8.

We carried out two rounds of measurements with the

phantom approximately one year apart (later referred to as

measurements 1 and 2). The measurements using MRI

systems A and B were carried out with the image intensity

correction (IC) on and off, since use of the multi-element

head receiver coils permits normalization of the signal

intensity throughout the imaging volume with the help of

the sensitivity information of the coil elements. In addition,

control measurements were carried out using MRI system

B without parallel imaging to evaluate its effects on the

data.

For the phantom image analysis, we used the software

released by BIRN (BXH/XCEDE tools version 1.9.8.6,

downloaded at http://www.nitrc.org/projects/bxh_xcede_

tools/). The software automatically measured signal fluc-

tuation and drift, the SNR and the signal-to-fluctuation

Table 1 The MRI systems, their field strengths, and the number of

elements in the receiver head coils used in this study

MRI

system

Manufacturer and

model name

Field

strength (T)

Number of head coil

elements

A Siemens Verio 3.0 12

B Philips Achieva 3.0 8

C Siemens Avanto 1.5 12

D Philips Achieva 1.5 8

E GE Signa Hdxt 1.5 8


123

http://www.nitrc.org/projects/bxh_xcede_tools/

http://www.nitrc.org/projects/bxh_xcede_tools/

noise ratio, the radius of decorrelation (the Weisskoff test),

the center of mass maximum displacement and drift in

three directions, as well as smoothness and ghosting. The

analysis software calculated two distinct values for drift;

we used the one that measured the value based on the

second-order polynomial fit of the signal curve. The radius

of decorrelation defined the size of the region of interest

(ROI) at which the voxels lost their statistical indepen-

dence [21]. The smoothness values reflected the smooth-

ness of the image noise expressed as the full width at half

maximum in three directions. In addition to measured

values, the software produced several parametric images of

the measurement series.

The EEG–fMRI data quality measurements

The measurements were carried out in the presence of the

EEG equipment using the 3.0 T MRI systems (A and B), as

these MRI systems were also being used in simultaneous

EEG–fMRI studies. A 64-channel EEG cap (BrainProducts,

Inc., Munich, Germany) was positioned on the phantom

surface as it would have been positioned on a human head

(Fig. 1). The electrodes were electrically connected with

ECI electrogel (Electro-Cap International, Inc., Eaton, OH,

USA) or EC2 Genuine Grass Electrode Cream (Grass

Technologies, Inc., Warwick, RI, USA) to prevent damage to

the EEG equipment. The electrocardiography (ECG) and

electrooculography (EOG) electrodes included in the cap

were also attached to the phantom. The EEG cap was con-

nected to two MRPlus amplifiers (BrainProducts, Inc.,

Munich, Germany) that were also in the magnet bore. The

EEG signal was also recorded with the same measurement

set-up on the 3.0 T systems. Recording began before the EPI

sequence was turned on and continued throughout the

acquisition. A separate recording was performed outside the

MRI room. We measured the effects of the helium pump as

well as in-bore lighting and ventilation on the EEG signal.

Because all the EEG channels were electrically connected

with gel, the analysis was limited to the evaluation of noise

and the detection of faulty channels. Two rounds of EEG–

fMRI measurements were carried out in connection with

fMRI measurements 1 and 2 described earlier, and the image

quality was also analyzed in a similar way. The EEG signal

was analyzed separately with BrainVision Analyzer 2.0

(BrainProducts, Inc., Munich, Germany). When necessary,

the recorded signal was corrected for artifacts related to

gradient switching using a well-known average artifact

subtraction method [24].

Based on the experience of the first measurements, we

conducted two further measurement sessions. Within each

session, we acquired four series with the EEG cap, and four

series without the EEG cap. Between each acquisition, the

phantom was removed from the magnet bore to introduce

some variation to the MRI system self-adjustments. The

position of the EEG cap was marked on the phantom surface

to make the positioning more repeatable. The ECG and EOG

electrodes suspected of having caused strong artifacts in the

previous measurements were now attached to the phantom

outside the field of view, as they would be in a study of a

human subject. The maximum penetration depth of the sur-

face artifacts caused by the electrodes was measured man-

ually slice by slice with a distance tool of ImageJ version

1.44p (Rasband, W., US National Institutes of Health, USA).

The SNR calculation [21] included the generation of a

signal image, which was a voxel-by-voxel average of the

images from the time series, and a difference image, which

was computed by summing all odd images and all even

images and then calculating a voxel-by-voxel difference of

these sum images. The SNR was then calculated as

SNR ¼ meanffiffiffiffiffiffiffiffi

r=np ;

where n was the number of time points, mean was the mean

voxel value of a signal image inside a selected ROI, and rwas the variance of voxel values from a difference image

inside the same ROI. To test whether the size of the ROI

affected the SNR measurement, we repeated the analysis

with ten different ROI sizes between 9 9 9 and 27 9 27

voxels in the four acquisitions of measurement 1 on MRI

system A. We calculated Pearson’s correlation coefficients

(r) for ROI size and SNR. This information served to assess

the validity of the SNR measurement in the presence of the

EEG equipment.

Comparison of the EEG–fMRI measurements to human

data

We retrospectively examined human fMRI data from three

imaging sessions (two volunteers, one participated in two

Fig. 1 fMRI phantom with the EEG cap


123

sessions) conducted using MRI system B to investigate

whether the image quality findings from the phantom

measurements were comparable to the effects in studies of

human subjects. The ethics committee of the Hospital

District of Helsinki and Uusimaa approved the volunteer

studies, and the volunteers provided their written informed

consent. Each of these imaging sessions included four

identical gradient echo-based EPI sequences (TR =

3,000 ms, TE = 35 ms, number of signal acquisitions = 1,

voxel size = 2.8 9 2.8 9 4.0 mm, EPI factor = 79, and

number of time frames = 115) both with and without the

EEG equipment. No stimuli were delivered during these

acquisitions.

We used the ImageJ software to estimate the SNR of the

images of the volunteers. We first selected two slices, one

at the level of the centrum semiovale and the other, 24 mm

above it. We calculated the signal and difference images as

well as the SNR values in a manner similar to the one

previously described for the phantom case. Medial and

lateral square ROIs (30 9 30 mm) were placed on each

slice (Fig. 2). We used the same ROI coordinates for each

respective slice. We then calculated the SNR values for

each ROI. Because each voluntary session consisted of four

identical acquisitions both with and without the EEG

equipment, we calculated the average SNR values for these

four acquisitions and, subsequently, calculated for each

ROI the SNR difference caused by the EEG equipment.

Results

fMRI image quality

The results of the signal fluctuation (i.e., temporal noise)

and drift appear in Figs. 3 and 4, respectively. Apart from

four measurements, the percentage of the fluctuation value

was\0.2 % in all the measurements. In all cases, the signal

drift during the measurement of 200 time frames was

\1 %. The minimum and maximum SNR were 245 and

345 on 3.0 T systems, and 82 and 174 on 1.5 T systems,

respectively. The SNR results appear in Table 2. Pairwise

comparison of the SNR values of the respective measure-

ments with the signal intensity correction on and off yiel-

ded an average SNR reduction, with correction, of 8 % on

MRI system A and 0 % on MRI system B. The mean ghost

signal percentage was B2 % with MRI systems B and D,

and between 2 % and 5 % with other systems.

The radius of decorrelation, which describes the statis-

tical independence of the voxels during the fMRI time

series, varied substantially, with values ranging from 1.7 to

12.4 voxels; in general, the 1.5 T systems achieved higher

values than did the 3.0 T systems. The mean smoothness

measurements demonstrated up to two-fold greater differ-

ences despite the same nominal acquisition voxel size,

which was 3.4 9 3.4 9 4.0 mm in all measurements.

Fig. 2 Placement of the regions of interest in SNR determination of

the data on human volunteers. The 30 9 30 mm regions were placed

on two slices: at the level of centrum semiovale (left) and on the slice

24 mm above it

Fig. 3 The results of the signal

fluctuation (%) for two rounds

of measurements taken during

the fMRI time series on

different MRI systems. This

figure also presents the results

with EEG cap or intensity

correction (IC) on MRI systems

A and B, as well as the results

both with and without

Sensitivity Encoding (SENSE)

on MRI system B


123

Maximum center of mass displacement values varied

from 0 to 0.4 mm (mean = 0.09 mm). On MRI systems B

and D, several abrupt but small (*0.2 mm) changes in the

phantom image center-of-mass were detected in the y

direction. We were also able to detect these changes in the

images when using careful window adjustment. Calculated

for the same MRI systems, the mean of all maximum

displacement measurements in the y direction was

0.20 mm, while it was 0.09 mm when calculated for the

other systems. The maximum absolute value for center-of-

mass drift was 0.36 mm (mean = 0.06 mm).

Except for a small temporal stability reduction in MRI

system D, we found no differences between measurements

1 and 2. Because the measurement of other MRI systems

produced equal results, the difference probably originated

from a small change in the stability of that system. When

control measurements were carried out without SENSE on

MRI system B, we observed no other effects than the 10 %

increase in SNR.

EEG–fMRI data quality

The image quality measurements carried out with the EEG

equipment resulted in superficial artifacts in the mean

image (Fig. 5). During the first two rounds of measure-

ments, the total signal dropout extended to 25 mm from the

Fig. 4 The results of the signal

drift (%) across the fMRI time

series for two rounds of

measurements on different MRI

systems. This figure also

presents the results with the

EEG cap or intensity correction

(IC) on MRI systems A and B,

as well as the results both with

and without Sensitivity

Encoding (SENSE) on MRI

system B

Table 2 Signal-to-noise ratios as measured in the two rounds of measurements

MRI system A MRI system B, with SENSE MRI system B, without SENSE

fMRI, 3.0 T

Measurement 1 263 294

Measurement 1, with IC 245 301

Measurement 2 270 288 345

Measurement 2, with IC 253 305 321

MRI system C MRI system D MRI system E

fMRI, 1.5 T



MRI system A MRI system B, with SENSE MRI system B, without SENSE

EEG–fMRI

Measurement 1 234 221

Measurement 1, with IC 208 189


Measurement 2, with IC 170 272 284

IC intensity correction, SENSE sensitivity encoding


123

phantom surface, and the effect on the signal level was

clearly visible as far as 50 mm from the surface. In the

additional measurements with improved electrode posi-

tioning, we measured the artifact depth slice by slice; the

key results appear in Table 3. The typical artifact depths

were 15 mm or less; the artifacts were deepest (as deep as

23 mm) in the two to three uppermost slices. The artifacts

showed no substantial differences in nature or strength

between the two 3.0 T systems. When the EEG equipment

was present in the first measurements, the SNR was

approximately 25 % lower (see Table 2). In the additional

measurements, the average SNR reduction was 15 %, with

an 11 % reduction on MRI system A and a 19 % reduction

on MRI system B (Table 3). The ROI size showed

no statistically significant correlation with SNR

(-0.52 B r B 0.31 in the four datasets tested). When the

EEG equipment was present, we found no effects on

temporal stability or other quality parameters.

The background noise in the EEG signal was approxi-

mately 2 lV peak-to-peak outside the MRI room and

10 lV inside the magnet. On MRI system B, we detected a

2-Hz interference signal with a maximal peak-to-peak of

50 lV that disappeared completely when the helium pump

was switched off; the general background noise level also

dropped to 5–8 lV peak-to-peak. In-bore lighting or ven-

tilation had no effect on the signal. The noise spectra of the

two amplifiers differed in some measurements even though

both amplifiers shared identical technical properties. The

amplifiers were placed on top of each other inside the

magnet, but the difference was independent of the order of

the amplifiers, and the interference did not depend on the

spatial distribution of the measurement electrodes on the

EEG cap. The interference did, however, correlate cycli-

cally with the channel number.

Comparison of the EEG–fMRI measurements

to the human data

Visual evaluation of three volunteer fMRI examinations

both with and without the EEG equipment revealed no

artifacts extending to the brain area. However, we did

observe a signal reduction in all three examinations with

the EEG cap, especially in the posterior brain. The average

SNR reduction in the presence of the EEG equipment was

24 % (18 % for medial and 30 % for lateral ROIs).

Discussion

In the first part of this study, we evaluated the image

quality of the fMRI protocol of several MRI systems. In

general, the results agreed well with previously reported

values and observations. Because the key parameters (i.e.,

signal drift and fluctuation values during an fMRI time

series) were in accordance with typical values reported by

Friedman and Glover [21], we could conclude that the

systems measured were operating at an acceptable stability

level. The differences in mean smoothness values for dif-

ferent MRI systems were notable despite the same nominal

acquisition and reconstruction voxel size. Taking into

account the differences in SNR and radius of decorrelation,

the probable explanation is the different filtering or other

manufacturer-dependent preprocessing of the fMRI data.

However, knowledge of the effects of data processing on

the resultant image quality for each MRI system would be

important in designing an fMRI study and interpreting the

data. The use of multi-element coils enables the use of

techniques such as parallel imaging or image intensity

correction. This may reduce SNR, which in some cases in

human studies may be critical. The use of parallel imaging

may, however, prove useful when optimizing the level of

SNR, artifacts, and speed [25].

The manufacturer did not confirm the reason for the

small changes in the phantom image center-of-mass on

MRI systems B and D. These changes likely have no effect

on the interpretation of data from an fMRI study of a

human subject, as the changes were small in comparison to

physiological movement or typical voxel size. The result,

Table 3 Signal-to-noise ratios and artifacts in EEG–fMRI

MRI

system

Session Series Relative

SNR (%)

Max artifact

depth in mm

(slice

number)

Average

artifact depth

(mm), slices

19–28

A 1 1 90 23 (29) 12

2 86 20 (29) 12

3 90 20 (29) 13

4 81 23 (30) 14

2 1 94 13 (24–30) 12

2 92 13 (25–30) 12

3 90 15 (26, 30) 12

4 89 14 (27–30) 12

B 1 1 83 18 (26) 14

2 75 19 (27) 15

3 86 19 (27) 15

4 90 18 (27, 28) 14

2 1 71 18 (27, 29) 13

2 80 19 (29) 14

3 84 18 (29) 14

4 76 20 (29) 14

The average artifact depth is the average value of the maximum

artifact depth of each slice 19–28

Relative SNR (%) is calculated by comparing the SNR of each series

to the average SNR of the four series in the respective session

acquired without the EEG cap


123

however, indicated that the automated analysis is capable

of detecting occurrences that will likely not be observed if

the image data will undergo only visual inspection.

The superficial artifacts typically observed in the EEG–

fMRI measurement were comparable to those measured by

Bonmassar et al. [26] and Mullinger et al. [14]. The

obvious explanation for the signal dropout around the

electrodes was the B0 inhomogeneity. The strength of the

artifacts may depend on the type of electrodes and gel used.

During the first two rounds of measurements, we observed

effects extending as far as 50 mm from the phantom sur-

face. However, the strongest artifacts were absent from the

subsequent measurements when the ECG and EOG elec-

trodes were consistently positioned outside the field of

view. The largest observed artifacts in these measurements

were in the uppermost slices, where the number of elec-

trodes affecting the respective slice was greatest. The SNR

reduction probably resulted from higher coil impedance

when the EEG cap is inside the coil [11]. The effect of

EEG electrode-induced artifacts on the SNR measurement

can be ignored, because the SNR did not significantly

correlate with the analysis of ROI size. However, the SNR

reduction was somewhat smaller when the ECG and EOG

electrodes were positioned further from the imaging area,

which may explain the difference in observations.

In the EEG data, the intermittent noise differences

between the two amplifiers probably resulted from inter-

ference induced within leads or connectors between the

EEG cap and the amplifiers. The variation in the difference

is likely due to slight differences in the cable and amplifier

positions. Our previous experiences in human studies

support this supposition. The amplifier difference, as well

as other observed interferences were considered correct-

able with the help of conventional methods for correcting

EEG artifacts, as they generally perform well with regular

small-amplitude artifacts. The findings also supported the

recommendation to switch off the helium pump during

EEG–fMRI [27].

The artifacts detected in the phantom measurements were

not visible in the brain area in the images of the volunteers.

Thus, if the depth of the artifact in the phantom does not

exceed the depth in our measurements, the margin between

the electrode and the brain should be sufficient. In their study,

Mullinger et al. [14] showed that the artifacts that were

pronounced in the phantom image did not extend into the

brain due to the localized nature of the magnetic field inho-

mogeneities. Bonmassar et al. [26] reported a maximum

artifact depth of 15 mm from the surface, which should not

affect the clinical results. The SNR loss measured in the

images of volunteers, acquired with the EEG cap, was in line

with the phantom measurements. In a respective study,

however, Luo and Glover [28] demonstrated an SNR loss of

only 8 %. The difference may be attributed to the smaller

contribution of physiological noise (no stimuli delivered)

and the smaller voxel size in our scans. The lower SNR

resulting from the presence of the EEG equipment may have

some practical consequences, such as limiting the use of

diffusion tensor imaging or other methods where the SNR is

critical in the same imaging session. The type of EEG cap or

gel content may also affect the results [26, 29].

Because fMRI is one of the most demanding applica-

tions of MRI, an organization performing clinical and

scientific fMRI studies obviously requires a regular and

consistent quality assurance protocol. The measurements

and analyses carried out with the protocol suggested by

Friedman and Glover [21] proved straightforward. Suc-

cessful construction of the phantom required expertise in

chemistry, but phantom positioning, measurement and data

analysis were quick and simple procedures. The method

was also appropriate for the repeatable assessment of

Fig. 5 Superficial artifacts visible in the images representing mean pixel values across the fMRI time series. No EEG cap (left); with EEG cap

(right)


123

image quality in the presence of EEG equipment. Com-

parison to the volunteer data supported the relevance of the

results of the phantom measurements. If simultaneous

EEG–fMRI is performed in a clinical or research envi-

ronment, such a test should be run with the EEG equip-

ment, at least whenever new pieces of equipment are

introduced, to ensure that artifacts and SNR remain within

acceptable limits. Recoding EEG data during the phantom

measurement can also reveal valuable information, such as

the EEG amplifier difference in our study. The results of

this study can serve in establishing a quality assurance

protocol and its reference values. Our results support the

typical values reported by Friedman and Glover for use as

reference values in assessing basic fMRI image quality. In

simultaneous EEG–fMRI, the acceptance limits for SNR

reduction and artifact depth could be set to 20 % and

20 mm, respectively. The ECG or EOG electrodes should

remain outside the imaging field of view. Because many

factors (e.g., cap and gel types) can influence the results,

baseline values may be site-specific.

An important limitation of many MRI quality assurance

protocols is that they fail to assess contrast parameters,

whereas the contrast is an essential property that sets MRI

above other modalities in many diagnostic areas. The fMRI

quality assurance method investigated in this study shares

this same limitation. Although some solutions have been

suggested [30, 31], the BOLD contrast is difficult to mimic

with phantoms. Possible future directions would incorpo-

rate the assessment of the contrast mechanism into the

quality assurance procedure discussed in this study.

Conclusion

We successfully evaluated the data quality of fMRI and

EEG–fMRI using a phantom in a multi-scanner clinical and

research environment. The phantom measurements showed

that the five evaluated MRI systems operated acceptably for

fMRI; nevertheless, we detected some differences in image

reconstruction and center-of-mass changes. We detected

superficial artifacts in the phantom data of EEG-fMRI, but

not in the brain area in the images of human volunteers. We

also observed an SNR reduction of comparable magnitude

in both the phantom and human data. This study demon-

strated a repeatable method for measuring and following up

on the data quality of simultaneous EEG–fMRI.

References

1. Ogawa S, Tank DW, Menon R, Ellermann JM, Kim SG, Merkle

H, Ugurbil K (1992) Intrinsic signal changes accompanying

sensory stimulation: functional braing mapping with magnetic

resonance imaging. Proc Natl Acad Sci USA 89:5951–5955

2. Biswal B, Zerrin Yetkin F, Haughton VM, Hyde JS (1995)

Functional connectivity in the motor cortex of resting human

brain using echo-planar MRI. Magn Reson Med 34:537–541

3. Jezzard P, Matthews PM, Smith SM (2001) Functional MRI: an

introduction to methods. Oxford University Press, New York

4. Purdon PL, Weisskoff RM (1998) Effect of temporal autocorre-

lation due to physiological noise and stimulus paradigm on voxel-

level false-positive rates in fMRI. Hum Brain Mapp 6:239–249

5. Zijlmans M, Huiskamp G, Hersevoort M, Seppenwoolde JH, van

Huffelen AC, Leijten FSS (2007) EEG–fMRI in the preoperative

work-up for epilepsy surgery. Brain 130:2343–2353

6. Tieleman A, Deblaere K, Van Roost D, Van Damme O, Achten E

(2009) Preoperative fMRI in tumour surgery. Eur Radiol

19:2523–2534

7. Goldman RI, Stern JM, Engel J Jr, Cohen MS (2000) Acquiring

simultaneous EEG and functional MRI. Clin Neurophysiol

111:1974–1980

8. Carmichael DW, Vulliemoz S, Rodionov R, Thornton JS, McE-

voy AW, Lemieux L (2012) Simultaneous intracranial EEG–

fMRI in humans: protocol considerations and data quality. Neu-

roImage 63:301–309

9. Gotman J, Kobayashi E, Bagshaw AP, Benar CG, Dubeau F

(2006) Combining EEG and fMRI: a multimodal tool for epilepsy

research. J Magn Reson Imaging 23:906–920

10. Lemieux L, Krakow K, Fish DR (2001) Comparison of spike-

triggered functional MRI BOLD activation and EEG dipole

model localization. NeuroImage 14:1097–1104

11. Carmichael D (2010) Image quality issues. In: Mulert C, Lemieux

L (eds) EEG–fMRI. Springer, Berlin, pp 173–199

12. Scarff CJ, Reynolds A, Goodyear BG, Ponton CW, Dort JC,

Eggermont JJ (2004) Simultaneous 3-T fMRI and high-density

recording of human auditory evoked potentials. NeuroImage

23:1129–1142

13. Krakow K, Allen PJ, Symms MR, Lemieux L, Josephs O, Fish

DR (2000) EEG recording during fMRI experiments: image

quality. Hum Brain Mapp 10:10–15

14. Mullinger K, Debener S, Coxon R, Bowtell R (2008) Effects of

simultaneous EEG recording on MRI data quality at 1.5, 3 and 7

tesla. Int J Psychophysiol 67:178–188

15. Ritter P, Becker R, Freyer F, Villringer A (2010) EEG quality:

the image acquisition artefact. In: Mulert C, Lemieux L (eds)

EEG–fMRI. Springer, Berlin, pp 153–171

16. Mullinger KJ, Castellone P, Bowtell R (2013) Best current

practice for obtaining high quality EEG data during simultaneous

fMRI. J Vis Exp 76:e50283

17. Ritter P, Becker R, Graefe C, Villringer A (2007) Evaluating

gradient artifact correction of EEG data acquired simultaneously

with fMRI. Magn Reson Imaging 25:923–932

18. Lerski RA, De Certaines JD (1993) Performance assessment and

quality control in MRI by Eurospin test objects and protocols.

Magn Reson Imaging 11:817–833

19. De Wilde J, Price D, Curran J, Williams J, Kitney R (2002)

Standardization of performance evaluation in MRI: 13 years’

experience of intersystem comparison. Concepts Magn Reson

15:111–116

20. Weinreb J, Wilcox PA, Hayden J, Lewis R, Froelich J (2005)

ACR MRI accreditation: yesterday, today, and tomorrow. J Am

Coll Radiol 2:494–503

21. Friedman L, Glover GH (2006) Report on a multicenter fMRI

quality assurance protocol. J Magn Reson Imaging 23:827–839

22. Simmons A, Moore E, Williams SCR (1999) Quality control for

functional magnetic resonance imaging using automated data

analysis and Shewhart charting. Magn Reson Med 41:1274–1278


123

23. Stocker T, Schneider F, Klein M, Habel U, Kellermann T, Zilles

K, Shah NJ (2005) Automated quality assurance routines for

fMRI data applied to a multicenter study. Hum Brain Mapp

25:237–246

24. Allen PJ, Josephs O, Turner R (2000) A method for removing

imaging artifact from continuous EEG recorded during functional

MRI. NeuroImage 12:230–239

25. Preibisch C, Pilatus U, Bunke J, Hoogenraad F, Zanella F, Lan-

fermann H (2003) Functional MRI using sensitivity-encoded

echo planar imaging (SENSE-EPI). NeuroImage 19:412–421

26. Bonmassar G, Hadjikhani N, Ives JR, Hinton D, Belliveau JW

(2001) Influence of EEG electrodes on the BOLD fMRI signal.

Hum Brain Mapp 14:108–115

27. Nierhaus T, Gundlach C, Goltz D, Thiel SD, Pleger B, Villringer

A (2013) Internal ventilation system of MR scanners induces

specific EEG artifact during simultaneous EEG–fMRI. Neuro-

Image 74:70–76

28. Luo Q, Glover GH (2012) Influence of dense-array EEG cap on

fMRI signal. Magn Reson Med 68:807–815

29. Negishi M, Abildgaard M, Laufer I, Nixon T, Constable RT

(2008) An EEG (electroencephalogram) recording system with

carbon wire electrodes for simultaneous EEG–fMRI (functional

magnetic resonance imaging) recording. J Neurosci Methods

173:99–107

30. Renvall V, Joensuu R, Hari R (2006) Functional phantom for

fMRI: a feasibility study. Magn Reson Imaging 24:315–320

31. Olsrud J, Nilsson A, Mannfolk P, Waites A, Stahlberg F (2008) A

two-compartment gel phantom for optimization and quality

assurance in clinical BOLD fMRI. Magn Reson Imaging

26:279–286


123

Documents

Data quality in fMRI and simultaneous EEG–fMRI