Acoustic Noise Concerns in Functional
Magnetic Resonance Imaging
Adriaan Moelker*and Peter M.T. Pattynama
Department of Radiology, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands
Abstract: Magnetic resonance (MR) acoustic scanner noise may negatively affect the performance of
functional magnetic resonance imaging (fMRI), a problem that worsens at the higher field strengths
proposed to enhance fMRI. We present an overview of the current knowledge on the effects of confound-
ing acoustic MR noise in fMRI experiments. The principles and effectiveness of various methods to reduce
acoustic noise in fMRI are discussed, practical considerations are addressed and recommendations are
made. Hum. Brain Mapp. 20:123–141, 2003.
© 2003 Wiley-Liss, Inc.
Keywords: MRI; fMRI; EPI; acoustic noise; SPL; acoustic noise reduction
Within the last decade, functional magnetic resonance
imaging (fMRI) has evolved into a widely used technique for
functional brain imaging [Belliveau et al., 1991] that pro-
vides valuable insights into sensory, motor, and cognitive
brain processing [Cacace et al., 2000]. Briefly, fMRI is based
on quantifying the increase in regional cerebral blood flow
as a response to activation of brain regions [Ogawa et al.,
1993], which can be made visible by proportionate changes
in the blood oxygen level dependent (BOLD) MR contrast
[Ogawa et al., 1990]. The MR signal changes are generally
small, in the range of 5–7% at 1.5 T [Bernal and Altman,
2001], and careful composition of stimuli and tasks for evok-
ing brain activation and their presentation in complex par-
adigms is essential for inducing distinct BOLD responses.
The statistical inferences to be drawn (relating stimulus
presentation to BOLD response) are, therefore, vulnerable to
various sources of errors [Josephs et al., 1999].
An important confounding factor caused by the MR im-
ager itself is acoustic noise [Cacace et al., 2000; Cho et al.,
1997]. Intense sound levels are generated during imaging
that may interfere with the mapping of brain functions [Cho
et al., 1997]. A particular problem in the auditory system is
that the MR-generated acoustic noise evokes undesirable
BOLD signals [Shah et al., 1999]. In other brain regions,
acoustic noise may spoil fMRI experiments, primarily by
way of other mechanisms such as distraction [Cho et al.,
The acoustic issue in fMRI is likely to expand with increas-
ing use of high performance MR systems, most of them at 1.5
T, that are suitable for demanding echo planar imaging (EPI)
with high spatial resolution [de Zwart et al., 2002]. More-
over, the current trend to higher field strength systems of ?7
T for human fMRI [Yacoub et al., 2001a], illustrated by the
recent FDA approval for clinical fMRI at 4.0 T [Campeau et
al., 2001], makes the acoustic problem even more important,
as acoustic noise levels increase with the magnetic field
strength [Moelker et al., 2003a].
This study presents a systematic overview of the various
aspects of MR-related acoustic noise with regard to fMRI
experiments: (1) a description of the multiple sources of
acoustic noise in the MR environment, (2) the mechanisms
and (3) extent of the interference with regard to both the
stimulus and cortical activation, (4) methods for sound re-
duction that are currently used or are under investigation,
and (5) practical considerations and recommendations to
minimize the effects of MR-generated acoustic noise on
functional brain mapping.
*Correspondence to: Dr. Adriaan Moelker, Erasmus Medical Center
Rotterdam, Department of Radiology, Dr. Molewaterplein 50, PO
Box 1738, 3000 DR Rotterdam, The Netherlands.
Received for publication 2 June 2003; Accepted 31 July 2003
? Human Brain Mapping 20:123–141(2003) ?
© 2003 Wiley-Liss, Inc.
SOUND GENERATION IN THE
There are multiple different sources of acoustic noise in
the MR imager. In descending order of relative contribution
to the overall sound pressure level (SPL), these are: (1) the
gradient currents, (2) eddy currents, (3) radio frequency (RF)
and slice-selection pulses and, as a non-imaging related
entity, (4) ambient or background noise.
The interactions between the fluctuating readout and
phase encoding currents in the gradient coils and the main
static magnetic field of the MR scanner evoke Lorentz forces
that act on the gradient coils and their connecting wires
[Edelstein et al., 2002; Mansfield et al., 1994, 1995, 1998;
Mansfield and Haywood, 2000]. As a result, the coils and
wires buckle and bend inducing compressional waves in the
surrounding gradient supports. Subsequently, these com-
pressional waves are conducted toward the peripheral struc-
tures of the MR system, such as the main magnet, and
launched into air as acoustic sound. The Lorentz forces
increase logarithmically with the magnetic field strength
and the applied gradient current [Mansfield et al., 1998].
Noise levels, therefore, also increase with both stronger
magnetic field strengths and gradient currents [Moelker et
al., 2003a; Price et al., 2001]. During EPI, the most exten-
sively employed pulse sequence in fMRI [Parrish, 1999],
equivalent-continuous SPLs range from 90–117 dB at 1.5 T
and from 105–133 dB at 3.0 T. Peak levels are even higher: up
to 130 and 140 dB at 1.5 T and 3 T, respectively [Cho et al.,
1997; Foster et al., 2000; Miyati et al., 1999, 2001; Moelker et
al., 2003a; Price et al., 2000; Prieto et al., 1998, 1999; Shellock
et al., 1998].
The frequency distribution of the gradient sounds is rele-
vant to the confounding effects of acoustic noise on fMRI
and the efficacy of noise reduction techniques (see below).
The range of frequencies that are present in the imager noise
is dictated by both the spectral shape of the gradient current
(or pulse sequence) and the mechanical construction of the
MR imager [Hedeen and Edelstein, 1997]. Pulse sequences
are periodic with a fundamental frequency (reciprocal of the
period) and harmonics, the latter being multiples of the
fundamental frequency. The fundamental frequency can be
extracted from the gradient current by means of a Fourier
transform [Hedeen and Edelstein, 1997; Hennel et al., 1999;
Ravicz et al., 2000]. In EPI the fast succession of alternating
readout and phase encoding gradient currents results in the
appearance of a relatively high, audible, fundamental fre-
quency of (in one reported instance) 1.9 kHz with softer but
still audible harmonics at 3.8, 5.8, 7.7, and 9.6 kHz [Foster et
al., 2000]. Most MR imagers do not generate these pure tones
in isolation, rather a complex, broadband acoustic noise
spectrum [Ravicz et al., 2000]. This is because of a modula-
tion of the acoustic noise by MR system-specific structural
resonances in the gradient coils and supportive materials,
and by additional volume resonances in the system and MR
room [Bowtell and Peters, 1999; Cho et al., 1997; Mansfield et
al., 1998; Miyati et al., 1999, 2001; Moelker et al., 2003b;
Ravicz and Melcher, 2001]. The entire transition from gradi-
ent current to acoustic noise, including these resonances, is
referred to as the ?frequency response function? [Hedeen
and Edelstein, 1997].
The large fluctuating electromagnetic gradient fields in
the MR system induce eddy currents in the electrically con-
ducting parts of the imager [Hedeen et al., 2001]. The eddy
currents themselves give rise to additional mechanical
movements, in particular in the warm (inner) magnet bore,
the RF coil and the RF shield [Edelstein et al., 2002; Kat-
sunuma et al., 2002]. In single-shot EPI, eddy current leakage
seems to cause an acoustic noise that is soft (?3 dB less)
compared to that produced by the gradient coils [Edelstein
et al., 2002]. This is in contrast with gradient echo based
pulse sequences in which avoiding eddy current induced
noise might reduce the sound intensity by as much as 10 dB
[Edelstein et al., 2002].
RF and Slice Selection Pulses
RF and slice selection pulses represent a third, small
source of noise in fMRI. Slice selection currents are in fact
similar to the readout and phase encoding currents, but as
slice selection is generally done simultaneously with RF
excitation, both are discussed here. In single-shot EPI acqui-
sitions the incidence of these pulses is low when playing out
the imaging sequence. As a result, the time-averaged acous-
tic noise level of RF and slice-selective pulses is small com-
pared to gradient noise (for RF alone at least 5 dB less)
[Edelstein et al., 2002]. This may be different in spin echo
and purely RF-based pulse sequences that encompass mul-
tiple RF excitations per image acquisition [Counter et al.,
1997]. However, these pulse sequences are less frequently
used in fMRI studies. In a fast spin echo sequence, for
example, RF produced only slightly less sound compared to
the gradient currents (2 dB) [Edelstein et al., 2002].
The in-room air-conditioning, the MR ventilation system,
and the cryogen pumping in (most) MR systems are percep-
tible sources of acoustic noise in the MR environment not
related to the imaging procedure. These background noises
are of low magnitude with intensities ranging from 45–71 dB
on an A-weighted scale. The frequencies are ?100 Hz (en-
vironmental equipment) and range from 100 to 500 Hz
(cryogen cooling installation) [Cho et al., 1997; Counter et al.,
1997; Foster et al., 2000; Hurwitz et al., 1989; Loenneker et al.,
2001; McJury et al., 1994; McJury, 1995; Miyati et al., 1999,
2001; Moelker et al., 2003b; Oesterle et al., 2001; Ravicz et al.,
2000; Shellock et al., 1994]. During conventional MR imag-
ing, ambient noises are usually negligible because their
sound intensity is much lower than those generated by the
gradient coils [American National Standard S1.13-1995;
?Moelker and Pattynama?
? 124 ?
Miyati et al., 2001]. One can assume, however, that ambient
noise may have a (small) negative effect on fMRI experi-
ments (to our knowledge, there are no publications on this
subject). First, ambient noise levels are relatively low, but
still clearly audible in the absence of gradient noise. There-
fore, general assumption that the absence of image acquisi-
tion sounds equals a period of silence in which stimuli can
be presented uninterrupted and uncontaminated [Belin et
al., 1999] is not valid. Second, low frequencies in the range of
ambient noise are likely to induce activation in a larger area
of the auditory cortex compared to higher frequencies (with
otherwise identical sound intensity) [Bilecen et al., 1998a].
This has been tentatively explained by the increased sensi-
tivity of the auditory system to higher frequencies.
It should be mentioned that, although the equivalent-
continuous SPL has been predominantly used as a measure
for assessing the effects of acoustic scanner noise in fMRI
[Cho et al., 1998; Elliott et al., 1999; Shah et al., 1999, 2000;
Talavage et al., 1999; Ulmer et al., 1998a], it is probably the
peak sound pressure level that is the more appropriate
measure. Peak levels do not take into account the silent
periods that occur during the functional experiment. Conse-
quently, peak levels better represent the sound intensity
when the acoustic noise generation is primarily condensed
in the image acquisition window (between concurrent fMRI
acquisitions) [Brechmann et al., 2002; Jakob et al., 1998].
PATHWAYS OF ACOUSTIC NOISE
INTERFERENCE WITH FMRI EXPERIMENTS
MR-related acoustic noise may interfere with functional
MR acquisitions both through direct and indirect pathways.
Direct interference occurs because the acoustic noise in itself
induces an increase in regional cerebral blood flow, interact-
ing with the BOLD response of the brain activation of inter-
est. Indirect interference implies that acoustic noise may
affect the perception and processing of the stimulus of in-
terest by a distracting effect. This section discusses these
mechanisms of interference (summarized in Table I), fol-
lowed by mechanisms that are specific for fMRI experiments
of the auditory, motor, and visual senses.
MR-related acoustic noise induces a BOLD response in the
auditory cortex. It has been shown that, similar to other
auditory stimuli [Di Salle et al., 2001; Hall et al., 1999],
acoustic noise induces a hemodynamic response within 2 to
3 seconds after the onset of acoustic noise [Talavage et al.,
1998a, 1999] that peaks after 3 to 8 seconds (hemodynamic
delay) [Bandettini et al., 1998; Belin et al., 1999; Edmister et
al., 1999; Hall et al., 1999, 2000a; Le et al., 2001] and returns
to baseline in ?8 sec [Hall et al., 1999, 2000a; Le et al., 2001;
Robson et al., 1998]. The variation in the hemodynamic
response is due to the fMRI methodology used to identify
these response times [Bandettini and Cox, 2000; Hall et al.,
1999] and also due to intersubject variability. A BOLD re-
sponse induced by the scanner sounds in areas other than
the auditory cortex is, to our knowledge, unknown. Acoustic
confounding in these cortices is primarily thought to occur
through indirect effects.
The adverse hemodynamic response to acoustic scanner
noise results in an elevation of the BOLD response to be
measured, in particular the baseline level (OFF condition)
[Hall et al., 1999]. Thus, the dynamic range (ON vs. OFF
condition) of the BOLD response decreases (also called clip-
ping; Bandettini and Cox, 2000], making the stimulus-in-
duced cortical activation more difficult to detect statistically
[Edmister et al., 1999; Hall et al., 1999, 2000a; Robson et al.,
1998; Talavage et al., 1999; Yang et al., 2000]. The hemody-
namic responses to MR-related acoustic noise and stimulus-
induced brain activation do not add up linearly [Talavage et
al., 1998b]; this implies that a simple subtraction is not
possible [Bandettini et al., 1998; Edmister et al., 1999; Hall et
al., 1999; Mazard et al., 2002; Robson et al., 1998]. It is
assumed that BOLD responses do not add up linearly be-
cause of saturation, illustrated by a reduced BOLD response
to acoustic scanner noise when preceded by another acoustic
stimulation (Fig. 1) [Di Salle et al., 2001]. Furthermore, it has
been suggested that imager noise influences the spatial dis-
TABLE I. Mechanisms of acoustic noise interference
Activation by scanner noise within
same volume acquisition;
primarily interfering with auditory
Activation by scanner noise of
preceding volume acquisition;
primarily interfering with auditory
Attention Increased activation in attention-
related cortical areas
Decreased activation in cortical areas
by (inter-modal) distraction
Slowly developing adaptational loss
of attention; might be
advantageous in noisy
Not substantially related to scanner
Overlap of spectral components of
scanner noise and auditory
stimuli; confined to auditory fMRI
Changes in cochlear perception of
auditory stimuli (intensity and
frequency); confined to auditory
Changes in cochlear perception
auditory stimuli (intensity and
frequency); confined to auditory
Stapedial muscle reflex
Temporary hearing loss
?Acoustic Noise Concerns in fMRI?
? 125 ?
tribution of the stimulus-induced fMRI responses in audi-
tory cortex [Edmister et al., 1999]. Through what mechanism
and to what extent has not yet been investigated.
Based on the BOLD response time course of the scanner
noise in the fMRI experiment, two mechanisms of acoustic
confounding in fMRI can be distinguished, i.e., intra-acqui-
sition and inter-acquisition responses (black areas in Fig. 2A
and 2B, respectively). The intra-acquisition response refers
to an imager noise-induced BOLD response that interferes
with the functional data to be acquired later on within the
same multislice (or volume) acquisition [Talavage et al.,
1998]. As the BOLD response starts 2 sec after onset of noise,
the intra-acquisition response occurs when the image acqui-
sition window is ?2 sec. The inter-acquisition response, on
the other hand, is generated when the acoustic noise BOLD
response persists during the next volume acquisition [Tala-
vage et al., 1998a]. The parameter determining whether the
inter-acquisition response applies is the time between two
successive slices or volume acquisitions, i.e., the sequence
repetition time (TR) minus the acquisition window [Shah et
al., 2000]. For short acquisition times, therefore, the inter-
acquisition response occurs when the TR is shorter than the
time required for the BOLD response to noise to return to
baseline level (Fig. 2B).
Direct confounding in auditory cortex
The auditory cortex encompasses primary, secondary and
adjacent regions. The primary auditory cortex is a relatively
small (1–4 cm3) region bilaterally located on the superior
temporal gyrus (Heschl’s gyrus), including Brodmann’s area
BA41 (Fig. 3). This auditory field is characterized by a strong
connection with the peripheral auditory system. Sounds
elicit robust responses in the primary auditory cortex reflect-
ing both the intensity and tonotopy of the sound as per-
ceived by the cochlea [Ehret, 1997]. The adverse effects of
imager noise are most apparent in the primary auditory
cortex and have been described in detail elsewhere [Bandet-
tini et al., 1998; Bilecen et al., 1998a; Cho et al., 1998; Elliott
et al., 1999; Hall et al., 1999, 2000a; Jakob et al., 1998; Loen-
neker et al., 2001; Shah et al., 1999; Talavage et al., 1999;
Ulmer et al., 1998b]. The secondary auditory cortex is the
surrounding area and includes among others BA22 (in-
cludes Wernicke’s area) and BA42. This region is relevant in
early auditory processing intimately involved in phonolog-
ical and nonword auditory decoding and attention-related
enhancement of responses [Shapleske et al., 1999]. Cortical
activation of the secondary auditory cortex by imager noise
is less conclusive than for the primary region. This is due to
both ambiguous activation below significant threshold lev-
els [Talavage et al., 1999] and omitted classification of the
auditory cortex into primary and secondary cortex in some
studies [Bilecen et al., 1998a; Cho et al., 1998; Elliott et al.,
1999]. Only one investigation failed in the attempt to iden-
tify activation in the secondary auditory cortex using MR-
noise [Bandettini et al., 1998]. Recent investigations have
demonstrated activation (changes) in the middle temporal
gyrus and superior temporal sulcus, pertaining to the sec-
ondary auditory cortex [Jakob et al., 1998; Loenneker et al.,
2001; Ulmer et al., 1998b], and the associated auditory cor-
tices [Hall et al., 1999, 2000a; Mazard et al., 2002; Shah et al.,
1999]. The BOLD signal changes in both primary and sec-
ondary auditory cortex vary considerably ranging from
0.32–9%. This large variability probably reflects differences
in the MR noise duration (prolonged stimulation causes
BOLD-to-stimulus response (left grey
curve) and BOLD-to-scanner-noise re-
sponse (right grey curve) do not linearly
add up because of saturation of the
BOLD-response (A). Therefore, simple
subtraction of baseline (BOLD re-
sponse without preceding stimulation)
results in a net negative response (B).
The checked boxes are dummy image
acquisitions to allow longitudinal mag-
netization to reach steady state.
?Moelker and Pattynama?
? 126 ?
stronger activation) [Robson et al., 1998] and intensity (the
more intense the MR noise, the greater the signal changes)
[Brechmann et al., 2002; Hall et al., 2001; Jancke et al., 1998].
It has been hypothesized that the dissemination of the
acoustic noise-induced activation into the secondary regions
is a response to the complex periodic properties of scanner
noise, similar to those present in conversational speech [Be-
lin et al., 1999; Mazard et al., 2002; Ulmer et al., 1998a]. As
human speech is predominantly processed in the left hemi-
sphere [Binder et al., 1997], it is not surprising that imager
noise activation favors the left over the right secondary
auditory cortex [Bilecen et al., 1998b; Ulmer et al., 1998b].
Recent studies found small negative BOLD responses in
visual and motor cortices [Hu et al., 1997], i.e., within the
initial 2–3 sec after stimulus presentation [Yacoub et al.,
1999]. This initial dip, caused by an oxygen depletion at
microvascular level [Yacoub and Hu, 2001b], allows for im-
aging with higher spatial specificity suitable for cortical
columnar functional imaging [Duong et al., 2000]. Unfortu-
nately, in auditory cortex the ensuing intra-acquisition re-
sponse is likely to impede the exploration of this initial dip.
Although an initial dip has been described recently by Ban-
dettini and Cox , this was explained by the negative
overshoot of a previous time series.
Imager noise can also confound functional experiments
through indirect pathways that are predominantly atten-
tion-related. Basically, attention is a mechanism enabling the
processing of a stimulus or task of a specific sense (modality)
[Woldorff et al., 1993]. Neuroregulative dysfunctions, such
as schizophrenia, are known to exhibit disruptions in atten-
tion. Therefore, it has been hypothesized that psychiatric
disorders are more vulnerable to indirect confounding by
MR-generated noise [Mathiak et al., 2002]. Focusing atten-
BOLD-to-stimulus responses confounded
by intra-acquisition response (A) and in-
ter-acquisition response (B) to acoustic
scanner noise (black areas). The intra-
acquisition occurs when the volume ac-
quisition takes ?2 sec; the inter-acquisi-
tion response occurs when the time
between the volume acquisitions is
shorter than the time the BOLD re-
sponse (to scanner noise) takes to return
Human cortex (lateral view) according to Brodmann’s cytoarchi-
?Acoustic Noise Concerns in fMRI?
? 127 ?
tion on a specific modality implies that the perception and
cortical response related to that modality are positively in-
fluenced and modulated [Berman and Colby, 2002]. On the
other hand, involuntary loss of focus (distraction) and ne-
glect may reduce the cortical response to the stimulus [Es-
cera et al., 1998; Jancke et al., 1999]. Distracting stimuli might
be perceived from either the same modality (intra-modal
interaction), e.g., acoustic scanner noise in auditory fMRI or
from other modalities (inter-modal interaction), e.g., scanner
noise in visual fMRI [Mazard et al., 2002]. Accordingly, the
changes in attention as a result of MR-related acoustic noise
may lead to both an increase in activity in attention-related
brain areas and to a drop in cortical activity in the brain
areas of interest (distraction). The location of these effects
can be appreciated at both cortical and subcortical levels
[Maeder et al., 2001; Opitz et al., 2002].
It has been shown that the left auditory cortex (T1a, ante-
rior part of Heschl’s gyrus) is involved in foreground–back-
ground decomposition, i.e., the capability of the auditory
system to monitor targets in a background [Scheich et al.,
1998]. This cortical area shows more activation in response
to low intensity tones (36 dB) in a background of MR-related
acoustic noise (40 dB) compared to louder tones (?48 dB)
[Brechmann et al., 2002]. This increased activation in T1a
was assigned to the required attentional effort for detecting
the softer sounds [Brechmann et al., 2002]. In an analogous
experiment, increased activation has been demonstrated in
the posterior part of the calcarine cortex during visual im-
agery tasks in noisy MR conditions (compared to less noisy
conditions) [Mazard et al., 2002]. The activation in the cal-
carine cortex reflected the greater visual attentional load
required for maintaining vivid images [Mazard et al., 2002].
In a similar manner, secondary motor areas, involved in
motor activity planning, showed inconsistent activation
among subjects that were exposed to loud MR imager noise.
Again, activation has been attributed to attention responses
in this area [Elliott et al., 1999].
Distraction by scanner noise may potentially modulate
BOLD responses both intra- and inter-modally [Mazard et
al., 2002]. Its modulations are in the same order of magni-
tude as the BOLD response of interest [Vouloumanos et al.,
2001] but not in the time course of the BOLD response [Hall
et al., 2000]. In a recent fMRI study, fMRI signal changes by
visual stimulation decreased by approximately 50% in the
presence of acoustic MR noise, attributed to exhaustion and
a loss of attention [Cho et al., 1998; Loenneker et al., 2001].
By contrast, congruent, simultaneous stimulation in differ-
ent modalities might enhance their cortical response [Cal-
vert et al., 1999]. As an example, the presentation of speech
employing both auditory and visual stimuli can amplify the
cortical responses of both these senses [Calvert et al., 1999].
Difficult tasks require increased attentional efforts and may,
therefore, be more affected by distracting scanner noise [El-
liott et al., 1999]. Parallel to this, a close relationship has been
demonstrated between the intensity of scanner noise and
performance, reaction time and BOLD signal changes (ex-
periments carried out for auditory cortex) [Edmister et al.,
1999; Shah et al., 1999, 2000; Ulmer et al., 1998b].
Functional MRI studies are vulnerable to motion artifacts,
because of the (generally) long experiment times and the
small BOLD signal changes [Seto et al., 2001]. It has been
hypothesized that motion artifacts are related to anxiety,
and anxiety itself to acoustic scanner noise [Quirk et al.,
1989]. Artifactual brain activations might, therefore, be tem-
porally correlated with the stimulus but are in fact corre-
lated with the imager noise [Sunaert and Yousry, 2001]. One
study reported that anxiety was not significantly associated
with motion artifacts, making the above hypothesis less
likely [Dantendorfer et al., 1997]. This outcome has been
supported by an fMRI experiment that found a similar inci-
dence of motion artifacts in silent and loud acoustic scanner
noise conditions [Elliott et al., 1999].
Indirect confounding in auditory cortex
Auditory fMRI may be further impaired by the (louder)
imager noise due to its screening effects on stimuli
[Scheich et al., 1998; Shah et al., 2000]. Extracting the
stimuli in an obscuring acoustic background encompasses
the interplay of the auditory system at both the cochlear
(masking) and at the cortical levels (foreground–back-
ground decomposition), discussed earlier. Cochlear mask-
ing, at the inner ear, is frequency selective whereby hear-
ing thresholds peak around the masker frequency (Fig. 4)
[Oxenham and Plack, 1998]. In auditory fMRI the compet-
itive effect between stimulus and scanner noise depends
on the overlap of the spectral components [Belin et al.,
1999; Di Salle et al., 2001]. Recent experiments have suf-
fered from acoustic scanner noise with frequencies from
800 to 1,200 Hz that largely hindered the perception of a
1,000 Hz pure tone stimulus [Le et al., 2001]. In contrast,
stimulation with pure sinusoidal tones at 200 and 3,000
Hz were clearly perceived, as evidenced by large BOLD
signal changes [Le et al., 2001].
Masking continues even after the MR scanner noise has
stopped (forward masking) [Backes and van Dijk, 2002].
The reason for this is that the cochlear nerve fibers have a
recuperation phase of up to 400 msec during which they
show a reduced response to novel stimulation [Frisina,
2001]. The louder the background noise the longer this
effect will last, and this phenomenon has been implied as
the cause of highly variable BOLD responses (by a factor
of 2) between different fMRI studies of otherwise similar
set-up [Backes and van Dijk, 2002; Belin et al., 1999]. In
cases where the hemodynamic response to scanner noise
takes longer to subside than forward masking, however,
forward masking is expected to be a trivial confounding
Both cochlear and cortical processing are required for
stimulus extraction in noisy environments. Their relative
contributions depend on among others the physical proper-
ties of the presented auditory stimulus. There is an empiri-
cally found difference between pure tones and complex
sounds in that the obscuring effects of pure tones occur at a
?Moelker and Pattynama?
? 128 ?
cochlear level, whereas complex amplitude and frequency
modulated stimuli have an effect at primarily a cortical level
[Hari and Makela, 1988]. As a consequence, MR-related
acoustic noise may have a different effect on fMRI process-
ing of simple versus complex acoustic stimulation. To min-
imize concurrent processing at cortical and cochlear levels,
pure tones can be used, e.g., as standard stimuli within the
broad-band (complex) imager noise.
Acoustic scanner noise might further interfere with au-
ditory fMRI by means of habituation. Habituation is an
adaptational phenomenon of the auditory cortex, charac-
terized by a reduced BOLD response after prolonged
exposure of several minutes [Bandettini et al., 1998; Bernal
and Altman, 2001; Pfleiderer et al., 2002]. The fMRI map-
ping of the tonotopic organization by Bilecen et al. [1998b]
suffered from habituation effects in several of their sub-
jects. During continuous scanner background noise, this
effect might in fact be advantageous; the confounding
effect of imager noise vanishes during the functional ex-
periment. For noncontinuous acoustic noise, as in sparse
temporal sampling, this phenomenon is obviously less
likely to occur.
Intense scanner noise may also potentially alter the sound
levels and spectral characteristics of the presented auditory
stimulus by inducing a stapedial muscle reflex [Hall et al.,
2001]. This reflex, occurring when the sound intensity is ?80
dB, lowers the perceived loudness for frequencies ?1 kHz
(?10 to ?20 dB) and amplifies frequencies between 1–3 kHz
[Counter and Borg, 1993; Pascal et al., 1998]. Whether the
stapedial muscle reflex modulates stimuli in auditory fMRI,
and to what extent this alters brain activation, has not yet
A final confounding factor relevant to specifically audi-
tory fMRI is in the temporary loss of hearing that ensues in
the presence of intense MR noise [Brummett et al., 1988]. The
extent of hearing loss is correlated with the scanner noise
intensity with dominant effects in the frequency range of the
acoustic noise [Ulmer et al., 1998a]. For example, an EPI
pulse sequence with a TR of 1 second has been reported to
induce a convex-shaped reduction of minimum hearing
thresholds over the audible frequency range [Ulmer et al.,
1998a]. This caused auditory stimuli, such as speech and
syllables, to be perceived as relatively flat sounds.
Indirect confounding in visual and motor cortices
MR scanner noise may also cause measurable artifacts in
fMRI of non-auditory cortices, although the effects are less
pronounced than in the auditory cortex. In this respect, few
investigators have evaluated visual and motor cortices [Cho
et al., 1998; Elliott et al., 1999; Loenneker et al., 2001; Ludwig
et al., 1999; Mazard et al., 2002].
The visual cortex is located in the occipital lobe of the
cerebrum (Fig. 3), encompassing the primary visual cortex
(V1, BA17) receiving afferent bundles from the thalamus
and projecting to the associated visual cortices (anterior
temporal and the parietal lobes). Investigators mentioned
decreases of up to 50% less significantly activated pixels
in primary [Cho et al., 1998; Loenneker et al., 2001; Lud-
wig et al., 1999] and associated [Loenneker et al., 2001]
visual cortex in imager noise backgrounds. Only one in-
vestigator did not observe signal changes in noisy com-
pared to silent experiment conditions, which might have
been caused by the relatively small sample size used
[Elliott et al., 1999].
To our knowledge, only two investigators tested for im-
ager noise interference with functional experiments of the
human motor cortex [Cho et al., 1998; Elliott et al., 1999],
which comprises primary motor cortex (BA4), premotor
(BA6) and supplementary motor areas (Fig. 3) [Mattay and
Weienberger, 1999]. In an experiment that made use of ad-
ditional scanner noise, Cho et al. reported a 30% increase in
the extent (number of activated pixels) of motor cortex acti-
vation [Cho et al., 1998]. The larger cortical activation was
attributed to a facilitated processing of the motor stimulus
by simultaneous acoustic stimulation [Burke et al., 2000; Cho
et al., 1998]. In the experiments of Elliot et al. , a higher
variability among subjects in the supplementary motor areas
was detected; an area that is involved in the planning of
motor activities. Their findings indicated that MR-related
acoustic noise predominantly effects through an indirect
Principle of cochlear masking by a pure sinusoidal tone at 1.2 kHz
at various SPLs (shown within curves) demonstrating broad-band
increments in minimum hearing thresholds with increasing pure
tone intensities. Hearing thresholds are on a linear scale (vertical
axis). The dotted line represents the sensitivity of human hearing
over frequency (horizontal axis), being significantly reduced at
lower and higher frequencies. Note the asymmetry (upward
spread) of masking, i.e., higher frequencies are masked to a greater
extent than lower frequencies.
?Acoustic Noise Concerns in fMRI?
? 129 ?
METHODS TO REDUCE
ACOUSTIC IMAGER NOISE
Various methods exist to lower the adverse effects of
acoustic noise. First, the functional experiment paradigm
can be optimized by taking into account the temporal char-
acteristics of the cortical responses to both the MR noise and
the stimulus. Such an acoustic artifact-free experiment is
also referred to as a silent paradigm design. A second, more
fundamental approach is the elimination of the actual
sources of acoustic noise. This can be achieved by differently
exciting the gradient system (silent pulse sequence design)
and by engineering improvements in MR hardware. Finally,
pragmatic methods of passive and active noise canceling can
be used. Table II lists the various noise reduction techniques
and their current status of application in fMRI.
Silent Functional Paradigm Designs
MR-related acoustic noise precludes a completely con-
trolled functional environment and poses restrictions on the
usable functional paradigm designs. The most widely used
paradigm in fMRI is the conventional block design (Fig. 5A),
composed of alternating ON and OFF conditions of each
several tens of seconds [Parrish, 1999]. This simple design is
sensitive to MR noise because of interference along the inter-
and intra-acquisition time scales and the simultaneous pre-
sentation of stimulus and scanner noise [Le et al., 2001]. A
more careful construction of the functional paradigm can
minimize the effects of scanner noise, as discussed below in
Avoiding inter-acquisition interference
Edmister et al.  and Shah et al.  have experi-
mented with silent paradigms that eliminated the inter-
acquisition interference making use of continuous music
and speech-related sounds, respectively. By using long TRs,
such that the next volume acquisition occurs only after the
acoustic noise BOLD response has subsided, these investi-
gators could completely avoid the inter-acquisition response
(Fig. 5B). They independently found an optimal TR (which
included an acquisition window of ?2 sec), in terms of
magnitude and extent of activation, of at least 7 sec [Edmis-
ter et al., 1999; Shah et al., 2000]. The use of such long TRs is
referred to as sparse temporal sampling [Hall et al., 1999].
From the imaging perspective, an advantage of sparse
temporal sampling is in the complete recovery of the MR
TABLE II. Noise reduction techniques in functional MRI
Functional paradigm design
Sparse temporal sampling
Clustered volume acquisition
Magnetization subtraction method
Silent pulse sequence design
Avoiding inter-acquisition BOLD response to MR noise by increasing TR to ?7 sec
Avoiding intra-acquisition BOLD response by clustering image acquisitions
Subtraction of intra-acquisition response
Train of RF pulses under constant phase and readout gradients; SPL reduction ?15 dB;
scarce resolution and SNRs
Avoiding higher harmonics in the acoustic noise spectrum; attenuation up to 40 dB;
limited to slow sequences
Addition of SIMEX pulses for better volume coverage within same acquisition window;
FLASH imaging at 43 dB
Addition of multiple array detectors for compensating smaller gradient current
amplitudes and undersampling; reduction 14 dB(A)
Intrinsically low-pass filtered due to sinusoidal gradient currents; further reduction of
acoustic noise at the expense of imaging time; BOLD contrast images acquired at 67
dB with good coverage
Low-pass filtering gradient pulses
Low-pass filtering with SIMEX pulses
Low-pass filtering with SENSE
Interleaved spiral-k imaging
MR-imager configuration changes
Increasing mass gradient system
Mounting gradient system to floor
Vacuum enclosing gradient coils
Increases inertia to mechanical vibrations
Absorption of acoustic vibrations; reduction ?10 dB during EPI
Reduction of up to 10 dB during EPI at 1.5 Tesla; currently applied in Toshiba’s Excelart
and General Electric’s Twinspeed systems
Acoustic noise reduction up to 20 dB, particularly at higher frequencies
Canceling of opposite forces in coil assemblies; not implemented in commercial MR
imagers; reductions of up to 35 dB
Reduction of eddy currents in RF-coil and main magnet
Passive absorption of acoustic noise by earplug, earmuff, helmet, vacuum cushions or
total body encapsulation; subjective reductions up to 60 dB when combining earplug,
earmuff and helmet
Destructive interposition of anti-noise; objective reductions up to 40 dB; subjective
reduction 5 dB at 2 kHz; not commonly used in fMRI
Lorentz force balancing
Eddy current reduction
Passive noise reduction
Active noise reduction
?Moelker and Pattynama?
? 130 ?
magnetization during the lengthy imaging intervals, result-
ing in better signal-to-noise ratios (SNR) in the next volume
acquisition (increased T2*-weighting) [Elliott et al., 1999;
Hall et al., 1999]. This largely compensates for the data
reduction per unit time of the sparse temporal sampling
technique [Hall et al., 2001]. Also, stimuli are presented and
subject responses evaluated in virtual silence (except from
ambient noise). This is particularly suitable in psycho-acous-
tic experiments, in which the relation between human per-
ception of sound and its characteristics is under investiga-
tion [Belin et al., 1999]. An additional gain in the dynamic
range of the BOLD response can be attained by taking ad-
vantage of the BOLD overshoot phase in the OFF condition
[Bandettini et al., 2000; Hall et al., 1999]. This overshoot is a
temporary negative level of the BOLD response before it
returns to the baseline level. By acquiring the OFF condition
during the overshoot phase, the signal difference between
stimulus and non-stimulus conditions is optimally enhanced
[Hall et al., 1999].
Avoiding intra-acquisition interference
As fMRI is moving toward whole brain imaging with
larger brain coverage, thinner slices and better in-plane res-
olution, the intra-acquisition response becomes more rele-
vant [Shah et al., 2000]. Intra-acquisition interference has
been found to decrease when using very rapid volume ac-
quisitions, such as clustered volume acquisitions (CVA) [Ta-
lavage et al., 1998a]. In CVA, a series of volumes is rapidly
imaged making use of tailored RF pulses and rescaled gra-
dient amplitudes for reducing cross-talk between adjacent
slices [Edmister et al., 1999]. Its temporal location within the
functional paradigm ideally coincides with the peak of the
BOLD response to the stimulus of interest (Fig. 5C) [Eden et
al., 1999; Hall et al., 1999]. Because of intersubject variability
of the BOLD response [Amaro et al., 2002], prolonged stim-
ulation may be preferred to reach the plateau in most sub-
jects. An optimal time span of a CVA acquisition of ?3 sec
has been empirically found making use of short CVA image
acquisitions preceded with additional MR noise (readout
pulses) [Talavage et al., 1999]. By contrast, a distributed
volume acquisition (Fig. 5B), in which imaging was done
equally throughout the TR period, resulted in remarkably
lower, insignificant signal changes [Talavage et al., 1999].
Hence, a scanning time of a volume of ?2 sec will com-
pletely avoid the intra-acquisition effects of the scanner
noise within that volume (Fig. 5C) [Shah et al., 2000; Tala-
vage et al., 1998a, 1999].
An advantage of the CVA method over the distributed
volume acquisition method is the improvement of motion
registration methods, because motion is relatively more
likely to occur between rather than within CVA acquisitions
[Edmister et al., 1999]. As a relative drawback, compressing
the image acquisition to within 2 sec evidently restricts slice
coverage. Also, the burst of imaging noise produces louder
Silent functional paradigms (B–G) vs. conventional block design
(A), including event-related paradigms (E–G). Grey lines repre-
sent BOLD responses to scanner noise during imaging (white and
grey scaled bars), and the dotted lines the measured BOLD
response. B: The intra-acquisition response. C: BOLD responses
are spoiled by MR scanner noise of preceding acquisitions (inter-
acquisition response). Note that the acquisition time prolongs
when avoiding the inter-acquisition response (not scaled). The size
of the arrows indicates the dynamic range of that particular
imaging paradigm (ON vs. OFF conditions) and is smallest in the
conventional imaging paradigm. G: Stroboscopic event-related im-
aging paradigm illustrated by the random time shifts of the various
events. Dummy acquisitions are taken and discarded to allow time
for the longitudinal magnetization to reach steady state.
?Acoustic Noise Concerns in fMRI?
? 131 ?
imager sounds (peak sound level) than when the imaging
noise is distributed equally over the TR period.
Ideally, both the intra-and inter-acquisition responses to
MR scanner noise are to be avoided. To this end, it is advised
that the functional paradigm should comprise a combination
of short CVA-like data acquisitions and sparse temporal
sampling with long TR. This provides a virtually silent
functional experiment (Fig. 5D,E), with the quiet periods
shifted to one end of TR and the whole imaging procedure
to the other end. Currently, this combined functional para-
digm design is most widely employed for silent auditory
fMRI studies [e.g., Scheffler et al., 1998; Tanaka et al., 2000].
Another approach to quiet functional paradigm design
that makes use of compensation for the intra-acquisition
response rather than avoiding it is the magnetization sub-
traction method [Bandettini et al., 1998; Di Salle et al., 2001].
As depicted in Figure 6, the decay of the (plateau-ed) BOLD
response to the stimulus can be corrected for the imager
noise-induced BOLD response by a voxel wise subtraction.
Magnetization subtraction proved successful in several ex-
periments showing relatively large signal changes of up to
9% in the auditory cortex [Bandettini et al., 1998; Di Salle et
al., 2001]. In contrast to sparse temporal sampling, this decay
sampling procedure has a high temporal resolution suitable
for detailed sampling of the decaying BOLD course [Di Salle
et al., 2001]. This method assumes that the BOLD responses
to both the stimulus of interest and the MR scanner noise
add up in a linear manner [Di Salle et al., 2001]. Subse-
quently, subtraction might be justifiable when the longitu-
dinal magnetization changes during initial imaging are
equal in both ON and OFF conditions [Bandettini et al.,
1998]. In one magnetization subtraction experiment, nega-
tive signal changes were measured that might have been
caused by such nonlinearities [Di Salle et al., 2001]. With this
and the above-mentioned studies in mind, the validity of the
procedure is in our opinion debatable.
Event-related sparse temporal sampling
A relative drawback of the above-described silent func-
tional paradigm designs is in their long duration (propor-
tionate with the increase in TR). More rapid event-related
based designs have therefore been proposed for silent par-
adigms [Belin et al., 1999; Yang et al., 2000]. Event-related
functional experiment set-ups are characterized by short,
discrete stimulation (Fig. 5E) of ?2 sec, rather than pro-
longed stimulation (Fig. 5A–D). Such short stimuli have
been found to be enough to induce significant brain activa-
tion [Rosen et al., 1998]. General advantages of event-related
paradigms are among others the randomization of stimulus
presentations (trials), the application of different stimuli
(Fig. 5F) and the post hoc sorting of data based on arbitrary
parameters or stimuli [Cacace et al., 2000]. Analogous to this,
event-related designs are less susceptible to image motion
artifacts because the actual behavioral responses can be tem-
porally resolved from the undisturbed important functional
images [Birn et al., 1999].
In silent event-related fMRI, stimuli are basically pre-
sented in the scanner noise-free intervals (between volume
acquisitions) and attempts are made to measure the BOLD
response at its peak hemodynamic response [Le et al., 2001].
Despite the high inter-subject variability of the BOLD re-
sponse in auditory cortex [Josephs et al., 1999], consistent
results with silent event-related paradigms have been found.
Using pure tone bursts of 900 msec, Yang et al.  found
a 54% increase (to 2.17%) in BOLD signal changes when
comparing a silent event-related acquisition with a conven-
tional event-related acquisition [Yang et al., 2000]. As an-
Principle of the magnetization subtraction technique. The three main components that contribute to the
functional acquisition, i.e., measured BOLD curve, the response to MR scanner noise (upper panels)
and the longitudinal magnetization (lower panels) are depicted. The similar magnetization decay curves
in both ON and OFF conditions are assumed to be similar allowing (voxel-wise) subtraction. Note that
the scale of the BOLD-signals is small compared to the scale of the longitudinal magnetization.
?Moelker and Pattynama?
? 132 ?
other example, this method has been successfully employed
in tonotopic mapping experiments of the auditory cortex
[Engelien et al., 2002; Le et al., 2001]. Functional acquisitions
with stimulus durations as short as 50 msec were only
minimally hampered by the MR imager noise [Engelien et
The silent event-related technique may be further en-
hanced by randomly shifting the temporal location of stim-
uli within the scanner noise-free period (Fig. 5G) [Backes et
al., 2002; Belin et al., 1999; Amaro et al., 2001]. With this
so-called stroboscopic BOLD imaging, the temporal shifts of
stimuli within the functional paradigm represent different
trials that provide sampling of the complete BOLD response.
Stroboscopic BOLD imaging is beneficial in terms of its
temporal resolution (resolution equal to minimum time shift
between trials) and reduced habituation effects [Belin et al.,
1999]. As a potential drawback, BOLD responses from stim-
uli that were shifted to the end of TR might interfere with
BOLD response of preceding (early) stimuli. This might be
avoided by adapting TR congruent with the stimulus time
shift that in addition benefits from shorter experiment du-
rations. Subsequent differences in longitudinal magnetiza-
tion should, however, be dealt with during image post-
processing [Talavage et al., 1999].
Several fMRI paradigm designs have been discussed with
the aim to provide an acoustically controlled fMRI experi-
ment with increases in signal amplitude and extent. It is
obvious that the sparse temporal sampling technique in-
creases the total imaging time compared to conventional
block designs [Bilecen et al., 1998a,b; Hall et al., 1999, 2000a;
Robson et al., 1998]. Parallel to this, longer TRs (?9 sec) may
result in attention loss and subsequently lower BOLD re-
sponses [Shah et al., 2000] and are, therefore, better avoided.
TRs of several seconds allow more time for the longitudinal
magnetization to recover, which intrinsically results in
larger relative signal changes [Amaro et al., 2002]. Several
studies have reported recently the successful use of TRs of
?9 sec [Formisano et al., 2002; Liebenthal et al., 2003]. Be-
sides its extended experiment time, the sparse temporal
sampling technique is also unsuitable to acquire a fine tem-
poral resolution of the BOLD response [Belin et al., 1999;
Hall et al., 1999]. Both the magnetization subtraction method
and the stroboscopic event-related paradigms provide better
temporal sampling of the BOLD response. The magnetiza-
changes compared to the sparse temporal sampling tech-
niques (up to 9% using magnetization subtraction [Di Salle
et al., 2001] vs. up to 2.2% using sparse temporal sampling
[Hall et al., 1999]). Its validity is, however, in debate because
of expected non-linearities when adding up BOLD re-
sponses to the stimulus of interest and the imager noise.
Also, the sampling of the BOLD response is partial, i.e.,
restricted to its plateau phase and return to baseline level.
The stroboscopic event-related method is a promising tech-
nique in terms of its good temporal sampling of the BOLD
response (signal changes reported up to 1.5%) [Backes et al.,
2002]. Although the image acquisition time tends to increase
as compared to sparse temporal sampling (because of the
larger number of different trials with otherwise identical TR)
adequate results with experiment times of only 10 min have
been reported (temporal resolution of 2 sec) [Backes et al.,
Silent Pulse Sequence Design
As mentioned previously, the most dominant noise source
in the MR environment originates from the gradient system.
Modulating the motion behavior of the gradient system, i.e.,
by redesigning the pulse sequence, may therefore decrease
scanner noise. Such silent pulse sequences can broadly be
differentiated into (1) the sequences based on RF Burst im-
aging, and (2) those based on re-shaping the readout and
phase encoding gradient currents.
Burst imaging pulse sequences
In the area of silent Burst imaging relatively little work
has been done. Burst is the generic name given to a class of
pulse sequences that employ multiple low flip-angle pulses
under a constant readout and phase encoding gradient (and
the subsequent refocusing of a set of echoes equal to the
number of RF bursts) [Hennig and Hodapp, 1993]. This
results in barely audible acoustic clicks due to the low num-
ber of gradient switching steps [Jakob et al., 1998]. Jakob et
al.  assessed a single-shot technique that showed well
localized activity in visual and auditory cortex within only
12 gradient ramps in 105 msec. Peak sound levels at 2 meter
distance from the MR system ranged from 52–55 dB(A)
[Jakob et al., 1998]. From the imaging perspective, functional
Burst imaging proved successful in sleep staging and in
visual and auditory experiments with signal changes of up
to 6% and 3%, respectively [Jakob et al., 1998; Lovblad et al.,
1999]. Additional attractive properties besides its quietness
include the geometric fidelity, small power deposition, low
demands on gradient strength (high amplitudes but low
switching rates) and, therefore, suitability for systems with-
out EPI hardware [Cremillieux et al., 1997; Jakob et al., 1998].
Nevertheless, in our opinion, the major drawbacks limit
further development of the Burst sequences in fMRI, which
are: poor SNRs trading off with resolution, limited multi-
slice capabilities (due to rapid T2* signal loss) and consid-
erable motion sensitivity [Cremillieux et al., 1997; Jakob et
al., 1998]. The application of Burst imaging is, therefore,
primarily restricted to functional experiments in which ab-
sence of scanner noise throughout the entire examination is
a prerequisite (as in sleep staging, although many successful
studies have also been carried out with EPI).
Silent pulse sequences based on redesigning gradient
The acoustic scanner noise spectrum primarily comprises
a fundamental frequency and harmonics, both deducible
from the gradient current spectrum by means of Fourier
transform (Fig. 7) [Hedeen and Edelstein, 1997]. For rela-
tively slow pulse sequences (fundamental frequency below
?Acoustic Noise Concerns in fMRI?
? 133 ?
?100 Hz), the higher order harmonics are predominantly
audible. Avoidance of these by band-pass filtering the gra-
dient current (pulse sequence design) would, therefore, re-
sult in a significant reduction of the loudness [Hennel et al.,
1999]. Based on this, Hennel et al.  formulated three
principles for silent modeling of gradient pulses: (1) use
sinusoidal gradient slopes, (2) maximize slope durations
(that lowers the fundamental frequency), and (3) minimize
the number of slopes by merging gradient pulses [Ludwig et
al., 1999]. Applying these behavioral restrictions to both
spin-echo and gradient-echo based pulse sequences has re-
duced noise production by up to 40 dB(A) with acceptable
image quality [Girard et al., 2000; Hennel et al., 1999]. For
faster pulse sequences such as FLASH (fast low angle shot
gradient echo) and EPI, considerably less reduction could be
obtained [Girard et al., 2000; Hennel et al., 1999]. The reason
is that the fundamental frequency of these rapid imaging
protocols shifts toward the audible range of human hearing,
thereby making the harmonics relatively less influential in
the acoustic domain [Hennel et al., 1999].
Recently, modifications of silent gradient pulse modeling
have been proposed to allow for faster imaging while still
preserving significant noise reduction [Hennel, 2000; 2001;
Loenneker et al., 2001; Ludwig et al., 1999; Oesterle et al.,
First, simultaneous multislice excitation (SIMEX) pulses
have been implemented into a silent FLASH sequence pro-
viding larger volume coverage within the same image ac-
quisition window [Loenneker et al., 2001; Ludwig et al.,
1999]. SIMEX pulses are single multifrequency RF pulses
composed of linearly combined carrier frequencies that se-
lectively excite parallel slices [Loenneker et al., 2001]. While
maintaining high SNR and spatial resolution, it was possible
to measure up to eight slices simultaneously making use of
one SIMEX pulse [Loenneker et al., 2001]. Successful stimu-
lation of four parallel slices was appreciated in auditory and
visual cortices with increased extent of cerebral activation in
the silent pulse sequence condition (43.1 dB(A)) [Loenneker
et al., 2001].
Another silent pulse sequence design has been proposed
by de Zwart et al. . Sensitivity encoding (SENSE) with
multi-element detector arrays was incorporated into a silent
BOLD-contrast EPI pulse sequence, which performed excel-
lently from the acoustic perspective. With a two-fold under-
sampling and halving the gradient amplitude, the acoustic
load subsided about 14 dB(A) on a 1.5 T imager. From the
imaging perspective, image acquisition time, resolution and
the quality of the functional maps were similar in both
conventional and SENSE-prepared EPI sequences [de Zwart
et al., 2002]. However, these findings might be task-depen-
Relationship between gradient current and acoustic noise fre-
quency distribution (in octave bands) obtained for several sinusoi-
dal pulses by means of Fourier transform (A, black bars). With
flattening of the slew rate, higher order harmonics disappear from
the acoustic spectrum. For a pure sinusoidal gradient current, the
acoustic noise comprises one pure tone (B). Extending the gradi-
ent current over time causes the fundamental frequency to lower
(C). Grey bars represent the frequency distribution of a rectan-
gular gradient pulse.
?Moelker and Pattynama?
? 134 ?
dent and not applicable to other imaging paradigms and
pulse sequences [de Zwart et al., 2002].
A final way of improving the temporal resolution is by
making use of interleaved spiral trajectory k-space imaging.
Spiraled filling of k-space is more time-efficient than con-
ventional filling, because almost 100% of the image acquisi-
tion window is spent on data collection [Oesterle et al.,
1999]. Oesterle et al. [1999, 2001] adjusted a silent spiral-k
BOLD contrast pulse sequence by slow ramp times and
several spiral interleaves. They found that a minimum rise
time of 6 msec (20 times below the hardware limit) and 64
interleaves (24.3 msec/interleave) with a slow return lead-
ing off to zero generated only 72 dB(A) at a Bruker 2 Tesla
system [Oesterle et al., 2001]. Image contrast and SNR were
slightly reduced compared to a conventional silent gradient
echo sequence due to the long duration of the spiral readout
(T2* signal loss). Slightly lower resolution was a result of the
missing parts in the k-space corners. Despite that, volume
coverage was four times better.
In summary, the current silent pulse sequences allow for
fast BOLD contrast imaging suitable to measure the hemo-
dynamic response in a relatively low noise environment. In
particular the incorporation of parallel imaging techniques
in spiral MR imaging, eventually combined with other tech-
niques such as SIMEX pulses, might provide substantial
advances in acoustic scanner noise reduction. With such an
imaging protocol the sound intensity is expected to drop
below 55 dB(A).
MR Hardware Configuration Changes
Restricting the mobility of the gradient coil assembly is a
time-honored engineering approach to make quieter MR
scanners. This can be achieved by constructing heavier gra-
dient coils and mountings, thereby effectively limiting their
responsiveness to Lorentz forces [Katsunuma et al., 2002].
Mounting the gradient coils supports directly to the floor
(that forces immediate absorption of vibrational energy)
provides an additional reduction of about 10 dB during EPI
imaging [Katsunuma et al., 2002]. These methods reached a
maximum because of the currently used strong static mag-
netic fields and gradient strengths required for ultra-fast
imaging techniques. Evidently, the acceptable total mass of
the gradient system is limited [Mansfield et al., 1994, 1995].
An interesting MR hardware development is the incorpo-
ration of the gradient coils in vacuum enclosures that effec-
tively interrupt airborne acoustic noise propagation (reduc-
tion ?10 dB) [Katsunuma et al., 2002]. For additional
restriction of structure-borne noise, the gradient assembly is
(acoustically) released from its mountings by means of rub-
ber dampers [Katsunuma et al., 2002]. A research scanner
with such a configuration provided acoustic noise reduc-
tions of up to 30 dB [Edelstein et al., 2002; Katsunuma et al.,
2002]. So-called ?quiet? MR-systems have become commer-
cially available encompassing a vacuum enclosed gradient
system in addition to insulators (Excelart, Toshiba Corpora-
tion, Tochigi, Japan, and Signa Twinspeed, General Electric,
Milwaukee, WI) [Katsunuma et al., 2002; Price et al., 2001].
PVC-vinyl acoustic foam insulators, positioned between the
gradient coils and shimming coils, reduce acoustic noise
levels by 10 dB during common EPI imaging at 3 T [Foster et
al., 2000]. For similar pulse sequences, passive insulation
with a fiberglass cylinder mounted directly on the inner
warm bore provides approximately 20 dB noise reduction
[Mechefske et al., 2002; Moelker et al., 2003b].
An alternative but still experimental solution makes use of
the principle of Lorentz force balancing [Bowtell et al., 1995,
1999; Mansfield et al., 1994, 1995, 1998]. In a force balanced
coil arrangement, opposite Lorentz forces can mechanically
be coupled by embedding the coil in stiff, noncompressive
enclosures [Mansfield et al., 1994]. As a result, the opposite
forces in the gradient structure will null and quench [McJury
and Shellock, 2000]. Typical force balanced gradient coils
have proven good noise attenuation of up to 40 dB at par-
ticularly low excitation frequencies of 100 Hz, unfortunately
decreasing to 0 dB at 3.5 kHz [Mansfield et al., 1995; Mans-
field et al., 1994]. The disappointing results were due to the
natural resonance frequencies of the gradient structure that
were overlapping the excitation frequencies [Mansfield et
al., 1994]. In turn these resonances resulted in phase errors,
thus providing less cancellation and even unintended boost-
ing of acoustic amplitudes [Mansfield et al., 1994].
Two recent improvements in the acoustic screening prin-
ciple should be mentioned. First, the natural resonance fre-
quencies of the gradient set could be pushed up toward
higher frequencies (minimizes phase errors) [Mansfield et
al., 1998]. This can be accomplished by either reducing the
dimensions of the gradient supports (Fig. 8) or by choosing
stiffer materials [Mansfield et al., 1994, 1995, 1998]. Addi-
tional screening loops in the assembly may further quench
the natural resonance modes of the gradient structure (Fig.
8) [Mansfield et al., 1998]. For such coil arrangements cast in
epoxy glass-reinforced material, average noise attenuation
of about 35 dB at 3.26 kHz has been reported [Mansfield et
Other important scanner configuration changes are possi-
ble in the restriction of eddy currents in both the RF coil and
main magnet [Katsunuma et al., 2002]. A typical low acous-
tic noise RF coil is equipped with thinner copper plates for
less induction of eddy currents and independently mounted
to the patient bore [Edelstein et al., 2002]. Similarly, eddy
currents are sufficiently strong to cause substantial vibra-
tions in the main magnet [Edelstein et al., 2002]; less noise is
produced when making the shielding coils (secondary gra-
dient coils) longer than the magnet [Katsunuma et al., 2002].
This construction effectively counteracts eddy current leak-
age into the main magnet [Katsunuma et al., 2002].
Passive Noise Reduction
The most widely used approach to counter the effects of
scanner noise in fMRI is the simple and economical appli-
cation of earplugs and/or earmuffs [Dancer et al., 1992;
McJury and Shellock, 2000]. Characteristically, these protec-
tive devices attenuate proportionally with the frequency
[Ravicz et al., 2001]. For example, the extensively used com-
?Acoustic Noise Concerns in fMRI?
? 135 ?
pressional E-A-R foam earplug (Aearo Company, South-
bridge, UK) provides noise attenuation of 20 dB at 0.5 kHz
and 30 dB at the frequencies ?1 kHz [Berger et al., 1998;
Ravicz et al., 2001]. Earmuffs show a similar reduction pat-
tern, but with slightly less reduction at frequencies ?1 kHz
[Berger et al., 1998]. Obviously, the efficacy of passive acous-
tic screening is restricted to air-conductive hearing. With
respect to the close contact of the subject with the mechan-
ically vibrating MR table and imager, bone conduction
emerges as a relevant issue in fMRI. Although air conduc-
tion dominates in the absence of hearing protection, bone
conduction through head and body become significant
when wearing earplugs and earmuffs [Ravicz et al., 2001].
Therefore, combining earplug and earmuff results in a sub-
jective attenuation of only 39–49 dB during EPI noise at
?1.9 kHz. This is considerably less than an objective reduc-
tion, i.e., recordings made in the external ear canal that
excludes bone conducted noise, of over 60 dB [Foster et al.,
2000; Ravicz et al., 2001].
Incorporation of a passive noise attenuating system into a
head RF coil has been suggested as a measure to restrict
bone conduction [Talavage et al., 1999]. Ravicz et al. 
assessed the attenuation efficacy of combining earmuffs and
earplugs with a helmet, made of heavy barrier layers of
foam composites. Subjective reductions were up to 60 dB
with a residual acoustic load that was dominated by body
conduction. More commonly used is the application of vac-
uum-pumped cushions (inside the head coil) filled with, e.g.,
sand [Monroe et al., 1999] and eventually in combination
with earmuffs [Baumgart et al., 1998; Brechmann et al.,
2002]. For complete acoustic screening, total encapsulation
of the subject might be a viable option providing an addi-
tional reduction of 10 dB [McJury and Shellock, 2000].
An issue that has been raised with the use of passive
hearing protectors is the possible interference with speech
and syllable understanding relevant to auditory fMRI
[Brummett et al., 1988; Chambers et al., 2001; Hurwitz et al.,
1989; McJury et al., 1997]. Passive devices, however, im-
prove rather than impair speech intelligibility for normal-
hearing persons in noisy environments [Abel and Spencer,
1997]. The effects of combining passive aids with active
noise cancellation on speech are unclear, but evidence sug-
gests that active noise cancellation improves intelligibility
by about 10% [Abel and Spencer, 1997]. Another issue with
passive noise cancelling devices is the non-uniform attenu-
ation in the frequency domain. Specifically when perform-
ing tonotopic mapping studies of the auditory cortex, one
should be aware of the frequency distortions of auditory
stimuli. This issue might be circumvented by frequency-
specific compensation of the stimuli in the audio system.
Also, the integration of probe tubes in earplugs or earmuffs
for pneumatic-driven sound delivery is helpful. With such
A: Improved principle of acoustic screening by force balancing: small coil dimension and an
additional screening coil. The amplitude and phase of the screening currents should be adjusted for
optimal acoustic noise reduction. B: Four-sector gradient coil for generation of x-, y-, or z-gradient
(x-gradient in this figure). F, Lorentz forces acting on gradient coil wires induced the gradient
?Moelker and Pattynama?
? 136 ?
devices, auditory stimuli can be conducted to the subject’s
auditory pathway relatively unattenuated, thereby not im-
peding stimulus perception. Other concerns with hearing
protectors are discomfort and variations in individual fit-
ting. Deep insertion of an earplug, for example, reduces low
frequencies (?500 Hz) better than a “partially” inserted
earplug [Berger et al., 1998; Dancer et al., 1992; McJury et al.,
2000; Ravicz et al., 2001].
Active Noise Cancellation
Additional noise reduction can be achieved by the incor-
poration of Active Noise Cancellation (ANC) techniques
into passive hearing protectors. Active noise cancellation
reduces acoustic noise by the introduction of a sound that is
exactly the inverse of the original noise [Goldman et al.,
1989]. An ANC system makes use of either feedback or
feedforward mechanisms [Chen et al., 1999; McJury et al.,
The feedback ANC strategy is used in many commercial
headsets (although not yet commercially available for use in
MRI) and encompasses an error microphone for capturing
residual noise close to the subject’s ear, and a processing
unit that generates the antisound. Previously, moderate de-
creases in the perceived noise level (11.1 dB) during both
spin-echo and gradient-echo pulse sequences have been
measured for frequencies of ?500 Hz [Goldman et al., 1989].
Implementation of self-adapting neural networks for further
error minimization demonstrated extended noise extinction
to about 20 dB while clearly preserving added speech [Chen
et al., 1999]. A problem of feedback ANC in auditory fMRI is
a result of the short distance between the error microphone
and the subject’s ear, causing cancellation of both the MR-
related acoustic noise and, more importantly, acoustic stim-
uli [Chambers et al., 2001].
In feedforward ANC, the microphone is placed close to
the noise source and the anti-noise is injected into the noise
propagation path [Chen et al., 1999]. Because the timings
and amplitudes of the gradient noise during fMRI are very
predictable [Edelstein et al., 2002], a feedforward strategy
seems preferable to the feedback strategy [Ravicz et al.,
2000]. Noise reductions of up to 40 dB for frequencies be-
tween 0.5 and 3 kHz have been reported [Chambers et al.,
2001]. The subjective performance during EPI (that includes
bone conduction) was substantially worse with reductions
of only 12 and 5 dB at 0.6 and 1.9 kHz, respectively [Cham-
bers et al., 2001].
A technique analogous to ANC is active structural acous-
tic control (ASAC) that might be a novel solution to the
acoustic problem in fMRI. This method makes use of panels
with vibro-acoustic sensors and active actuators that intro-
duce antivibrations (similar to anti-noise but in materials
other than air) [Berry, 2001]. Such active panels, combined
with passive insulators, could theoretically replace the cur-
rently used inner and outer shroud materials of the MR
imager, thereby providing SPL reductions over a large fre-
In summary, ANC is a promising technique that substan-
tially lessens the imager noise levels, especially at lower
frequencies ?1 kHz. Considering the current trend toward
faster imaging techniques with consequently more intense
noise at higher frequencies, the application of ANC in fMRI
may become less effective. Combining ANC with passive
measures has proven beneficial in terms of the quality of
sound (timbre), because of their complementary frequency
characteristics [Abel and Spencer, 1999].
MR-related acoustic noise has demonstrable effects on
fMRI of the auditory cortex. Its interference with auditory
functional experiments is primarily a result of direct cortical
activation. With respect to the nonauditory cortices, the
empirical data to date are limited and sometimes contradic-
tory, therefore more experiments are necessary to better
elucidate the effects of MR-related acoustic noise. The an-
swer to this problem may be in the design of a silent func-
tional paradigm that controls the (psycho-) acoustic interfer-
ences that play a role in the non-auditory cortices.
The current trend toward clinical fMRI and cognitive re-
search makes the acoustic problem more relevant. In neuro-
degenerative diseases (e.g., Alzheimer’s disease) and psy-
chiatric diseases (e.g., schizophrenia), fixation and attention
to stimuli is complicated [Mathiak et al., 2002]. In such
patients, the confounding effects of MR-generated acoustic
noise on functional data acquisitions are, therefore, likely to
be of greater magnitude. In addition, fMRI plays an increas-
ing role in planning of surgical procedures in the brain, i.e.,
by delineating diseased from normal tissue [Sunaert and
Yousry, 2001]. Impaired statistical inferences from func-
tional images due to artifactual absence of activation might
potentially cause normal tissue to be considered diseased.
Besides the trend toward clinical imaging, technical devel-
opments in magnetic resonance imaging considerably boost
the acoustic noise levels and subsequently its effects in fMRI.
Until now, most of the fMRI research has been carried out on
1.5 T systems, but the current need for higher field strengths
and stronger gradients will lead to substantially more in-
tense scanner noise that may counteract the efforts to reduce
Various methods to reduce acoustic noise may help to
provide artifact-free fMRI. The efficacy of these measures is,
unfortunately, interdependent. For example, derating gradi-
ent currents through silent imaging designs lowers the fre-
quency distribution of scanner noise, but thereby also con-
fines the acoustic benefits of passive barrier materials (less
reduction at lower frequencies). Consequently, the simulta-
neous application of both, results in less reduction of the
sound intensity than one would expect based on the reduc-
tion that can be gained for each method separately. The use
of earplugs or earmuffs is currently the most widely used
approach to soften the MR-related acoustic noise to suffi-
ciently low levels. From the hardware engineering perspec-
tive, successful advances in noise reduction are primarily in
the application of vacuum enclosures with passive acoustic
?Acoustic Noise Concerns in fMRI?
? 137 ?
liners. These developments, specifically the hardware mod-
ifications, should allow for quieter MRI scanners that enable
fast (conventional) fMRI unhampered by MR-related acous-
Abel SM, Spencer DL (1999): Speech understanding in noise with
earplugs and muffs in combination. Appl Acoust 57:61–68.
Abel, SM, Spencer DL (1997): Active noise reduction versus conven-
tional hearing protection. Relative benefits for normal-hearing
and impaired listeners. Scand Audiol 26:155–167.
Amaro E, Brammer MJ, Cruz AC, Trezza PM, Leite CC, Cerri GG
(2001): Comparing silent event related fMRI to normal acquisi-
tions. Proceedings of the 87th Scientific Assembly and Annual
Meeting, Chicago, IL. p 137.
Amaro E, Williams SC, Shergill SS, Fu CH, MacSweeney M, Pic-
chioni MM, Brammer MJ, McGuire PK (2002): Acoustic noise
and functional magnetic resonance imaging: Current strategies
and future prospects. J Magn Reson Imaging 16:497-510.
American National Standard (1995): Measurement of sound pres-
sure levels in air. Melville, NY: Acoustical Society of America:
American National Standard S3.44-1996 (1996): Determination of
occupational noise exposure and estimation of noise-induced
hearing impairment. Melville, NY: Acoustical Society of Amer-
Backes WH, van Dijk P (20020: Simultaneous sampling of event-
related BOLD responses in auditory cortex and brainstem. Magn
Reson Med 47:90–96.
Bandettini PA, Cox RW (2000): Event-related fMRI contrast when
using constant interstimulus interval: theory and experiment.
Magn Reson Med 43:540–548.
Bandettini PA, Jesmanowicz A, Van Kylen J, Birn RM, Hyde JS
(1998): Functional MRI of brain activation induced by scanner
acoustic noise. Magn Reson Med 39:410–416.
Baumgart F, Kaulisch T, Tempelmann C, Gaschler-Markefski B,
Tegeler C, Schindler F, Stiller D, Scheich H (1998): Electrody-
namic headphones and woofers for application in magnetic res-
onance imaging scanners. Med Phys 25:2068–2070.
Belin P, Zatorre RJ, Hoge R, Evans AC, Pike B (1999): Event-related
fMRI of the auditory cortex. Neuroimage 10:417–429.
Belliveau JW, Kennedy DN, McKinstry RC, Buchbinder BR, Weiss-
koff RM, Cohen MS, Vevea JM, Brady TJ, Rosen BR (1991):
Functional mapping of the human visual cortex by magnetic
resonance imaging. Science 254:716–719.
Berger EH, Franks JR, Behar A, Casali JG, Dixon-Ernst C, Kieper
RW, Merry CJ, Mozo BT, Nixon CW, Ohlin D, Royster JD,
Royster LH (1998): Development of a new standard laboratory
protocol for estimating the field attenuation of hearing protec-
tion devices. Part III. The validity of using subject-fit data. J
Acoust Soc Am 103:665–672.
Berman RA, Colby CL (2002): Auditory and visual attention mod-
ulate motion processing in area MT?. Cogn Brain Res 14:64–74.
Bernal B, Altman NR (2001): Auditory functional MR imaging. Am J
Berry A (2001): Advanced sensing strategies for the active control of
vibration and structural radiation. Noise Control Eng J 49:54–65.
Bilecen D, Radu EW, Scheffler K (1998a): The MR tomograph as a
sound generator: fMRI tool for the investigation of the auditory
cortex. Magn Reson Med 40:934–937.
Bilecen D, Scheffler K, Schmid N, Tschopp K, Seelig J (1998b):
Tonotopic organization of the human auditory cortex as detected
by BOLD-fMRI. Hear Res 126:19–27.
Binder JR, Frost JA, Hammeke TA, Cox RW, Rao SM, Prieto T
(1997): Human brain language areas identified by functional
magnetic resonance imaging. J Neurosci 17:353–362.
Birn RM, Bandettini PA, Cox RW, Shaker R (1999): Event-related
fMRI of tasks involving brief motion. Hum Brain Mapp 7:106–
Bowtell R, Peters ( 1999]: Analytic approach to the design of trans-
verse gradient coils with co-axial return paths. Magn Reson Med
Bowtell RW, Mansfield P (1995): Quiet transverse gradient coils:
Lorentz force balanced designs using geometrical similitude.
Magn Reson Med 34:494–497.
Brechmann A, Baumgart F, Scheich H (2002): Sound-level-depen-
dent representation of frequency modulations in human audi-
tory cortex: a low-noise fMRI study. J Neurophysiol 87:423–433.
Brummett RE, Talbot JM, Charuhas P (1988): Potential hearing loss
resulting from MR imaging. Radiology 169:539–540.
Burke, M, Schwindt W, Ludwig U, Hennig J, Hoehn M (2000):
Facilitation of electric forepaw stimulation-induced somatosen-
sory activation in rats by additional acoustic stimulation: an
fMRI investigation. Magn Reson Med 44:317–321.
Cacace AT, Tasciyan T, Cousins JP (2000): Principles of functional
magnetic resonance imaging: application to auditory neuro-
science. J Am Acad Audiol 11:239–272.
Calvert GA, Brammer MJ, Bullmore ET, Campbell R, Iversen SD,
David AS (1999): Response amplification in sensory-specific cor-
tices during cross modal binding. Neuroreport 10:2619–2623.
Campeau NG, Huston J, Bernstein MA, Lin C, Gibbs GF (2001):
Magnetic resonance angiography at 3.0 Tesla: initial clinical
experience. Top Magn Reson Imaging 12:183–204.
Chambers J, Akeroyd MA, Summerfield AQ, Palmer AR (2001):
Active control of the volume acquisition noise in functional
magnetic resonance imaging: method and psychoacoustical
evaluation. J Acoust Soc Am 110:3041–3054.
Chen CK, Chiueh TD, Chen JH (1999): Active cancellation system of
acoustic noise in MR imaging. IEEE Trans Biomed Eng 46:186–
Cho ZH, Chung SC, Lim DW, Wong EK (1998): Effects of the
acoustic noise of the gradient systems on fMRI: a study on
auditory, motor, and visual cortices. Magn Reson Med 39:331–
Cho ZH, Park SH, Kim JH, Chung SC, Chung ST, Chung JY, Moon
CW, Yi JH, Sin CH, Wong EK (1997): Analysis of acoustic noise
in MRI. Magn Reson Imaging 15:815–822.
Counter SA, Borg E (1993): Acoustic middle ear muscle reflex pro-
tection against magnetic coil impulse noise. Acta Otolaryngol
Counter SA, Olofsson A, Grahn HF, Borg E (1997): MRI acoustic
noise: sound pressure and frequency analysis. J Magn Reson
Cremillieux, Y, Wheeler-Kingshott CA, Briguet A, Doran SJ (1997):
STEAM-Burst: a single-shot, multi-slice imaging sequence with-
out rapid gradient switching. Magn Reson Med 38:645–652.
Dancer A, Grateau P, Cabanis A, Barnabe G, Cagnin G, Vaillant T,
Lafont D (1992): Effectiveness of earplugs in high-intensity im-
pulse noise. J Acoust Soc Am 91:1677–1689.
Dantendorfer K, Amering M, Bankier A, Helbich T, Prayer D,
Youssefzadeh S, Alexandrowicz R, Imhof H, Katschnig H (1997):
A study of the effects of patient anxiety, perceptions and equip-
?Moelker and Pattynama?
? 138 ?
ment on motion artifacts in magnetic resonance imaging. Magn
Reson Imaging 15:301–306.
de Zwart JA, van Gelderen P, Kellman P, Duyn JH (2002): Reduction
of gradient acoustic noise in MRI using SENSE-EPI. Neuroimage
Di Salle F, Formisano E, Seifritz E, Linden DE, Scheffler K, Saulino
C, Tedeschi G, Zanella FE, Pepino A, Goebel R, Marciano E
(2001): Functional fields in human auditory cortex revealed by
time-resolved fMRI without interference of EPI noise. Neuroim-
Duong TQ, Kim DS, Ugurbil K, Kim SG (2000): Spatiotemporal
dynamics of the BOLD fMRI signals: toward mapping submil-
limeter cortical columns using the early negative response.
Magn Reson Med 44:231–242.
Edelstein WA, Hedeen RA, Mallozzi RP, El-Hamamsy SA, Acker-
mann RA, Havens TJ (2002): Making MRI quieter. Magn Reson
Eden GF, Joseph JE, Brown HE, Brown CP, Zeffiro TA (1999):
Utilizing hemodynamic delay and dispersion to detect fMRI
signal change without auditory interference: the behavior inter-
leaved gradients technique. Magn Reson Med 41:13–20.
Edmister WB, Talavage TM, Ledden PJ, Weisskoff RM (1999): Im-
proved auditory cortex imaging using clustered volume acqui-
sitions. Hum Brain Mapp 7:89–97.
Ehret G (1997): The auditory cortex. J Comp Physiol [A] 181:547–
Elliott MR, Bowtell RW, Morris PG (1999): The effect of scanner
sound in visual, motor, and auditory functional MRI. Magn
Reson Med 41:1230–1235.
Engelien A, Yang Y, Engelien W, Zonana J, Stern E, Silbersweig DA
(2002): Physiological mapping of human auditory cortices with a
silent event- related fMRI technique. Neuroimage 16:944–953.
Escera C, Alho K, Winkler I, Naatanen R (1998): Neural mechanisms
of involuntary attention to acoustic novelty and change. J Cogn
Formisano E, Linden DE, Di Salle F, Trojano L, Esposito F, Sack AT,
Grossi D, Zanella FE, Goebel R (2002): Tracking the mind’s
image in the brain I: time-resolved fMRI during visuospatial
mental imagery. Neuron 35:185–194.
Foster JR, Hall DA, Summerfield AQ, Palmer AR, Bowtell RW
(2000): Sound-level measurements and calculations of safe noise
dosage during EPI at 3 T. J Magn Reson Imaging 12:157–163.
Frisina RD (2001]: Subcortical neural coding mechanisms for audi-
tory temporal processing. Hear Res 158:1–27.
Girard F, Marcar VL, Hennel F, Martin E (2000): Anatomic MR
images obtained with silent sequences. Radiology 216:900–902.
Goldman AM, Gossman WE, Friedlander PC (1989): Reduction of
sound levels with anti-noise in MR imaging. Radiology 173:549–
Hall DA, Haggard MP, Akeroyd MA, Palmer RA, Summerfield AQ,
Elliott MR, Gurney EM, Bowtell RW (1999): “Sparse” temporal
sampling in auditory fMRI. Hum Brain Mapp 7:213–223.
Hall DA, Haggard MP, Summerfield AQ, Akeroyd MA, Palmer AR,
Bowtell RA (2001): Functional magnetic resonance imaging mea-
surements of sound-level encoding in the absence of background
scanner noise. J Acoust Soc Am 109:1559–1570.
Hall DA, Summerfield AQ, Goncalves MS, Foster JR, Palmer AR,
Bowtell RW (2000a): Time-course of the auditory BOLD re-
sponse to scanner noise. Magn Reson Med 43:601–606.
Hall WA, Liu H, Martin AJ, Pozza CH, Maxwell RE, Truwit CL
(2000b): Safety, efficacy, and functionality of high-field strength
interventional magnetic resonance imaging for neurosurgery.
Hari R, Makela JP (1988): Modification of neuromagnetic responses
of the human auditory cortex by masking sounds. Exp Brain Res
Hedeen RA, Mallozzi R, Edelstein WA, Havens T (2001): Vibro-
acoustic modeling of noise in magnetic resonance imagers. Pro-
ceedings of the 9th Annual Meeting of the International Society
of Magnetic Resonance in Medicine, Glasgow, UK. p 1751.
Hedeen RA, Edelstein WA (1997): Characterization and prediction
of gradient acoustic noise in MR imagers. Magn Reson Med
Hennel F (2000): Acoustic optimisation of rapid MRI. Proceedings of
the 8th Annual Meeting of the International Society of Magnetic
Resonance in Medicine, Denver, CO. p 2010.
Hennel F (2001): Fast spin echo and fast gradient echo MRI with low
acoustic noise. J Magn Reson Imaging 13:960–966.
Hennel F, Girard F, Loenneker T (1999): “Silent” MRI with soft
gradient pulses. Magn Reson Med 42:6–10.
Hennig J, Hodapp M (1993): Burst imaging. MAGMA 1:39–48.
Hu X, Le TH, Ugurbil K (1997): Evaluation of the early response in
fMRI in individual subjects using short stimulus duration. Magn
Reson Med 37:877–884.
Hurwitz R, Lane SR, Bell RA, Brant-Zawadzki MN (1989): Acoustic
analysis of gradient-coil noise in MR imaging. Radiology 173:
Jakob PM, Schlaug G, Griswold M, Lovblad KO, Thomas R, Ives JR,
Matheson JK, Edelman RR (1998): Functional burst imaging.
Magn Reson Med 40:614–621.
Jancke L, Mirzazade S, Shah NJ (1999): Attention modulates activity
in the primary and the secondary auditory cortex: a functional
magnetic resonance imaging study in human subjects. Neurosci
Jancke L, Shah NJ, Posse S, Grosse-Ryuken M, Muller-Gartner HW
(1998): Intensity coding of auditory stimuli: an fMRI study.
Josephs O, Henson RN (1999): Event-related functional magnetic
resonance imaging: modelling, inference and optimization. Phi-
los Trans R Soc Lond B Biol Sci 354:1215–1228.
Katsunuma A, Takamori H, Sakakura Y, Hamamura Y, Ogo Y,
Katayama R (2002): Quiet MRI with novel acoustic noise reduc-
tion. MAGMA 13:139–144.
Le TH, Patel S, Roberts TP (2001): Functional MRI of human audi-
tory cortex using block and event-related designs. Magn Reson
Liebenthal E, Binder JR, Piorkowski RL, Remez RE (2003): Short-
term reorganization of auditory analysis induced by phonetic
experience. J Cogn Neurosci 15:549–558.
Loenneker T, Hennel F, Ludwig U, Hennig J (2001): Silent BOLD
imaging. MAGMA 13:76–81.
Lovblad KO, Thomas R, Jakob PM, Scammell T, Bassetti C, Gris-
wold M, Ives J, Matheson J, Edelman RR, Warach S (1999): Silent
functional magnetic resonance imaging demonstrates focal acti-
vation in rapid eye movement sleep. Neurology 53:2193–2195.
Ludwig U, Loenneker T, Hennel F, Hennig J (1999): Getting rid of
acoustic noise: functional MRI with silent simultaneous multi-
slice excitation gradient-echo (SIMEX) sequences. Proceedings of
the 7th Annual Meeting of the International Society of Magnetic
Resonance in Medicine, Philadelphia, PA. p 1662.
Maeder PP, Meuli RA, Adriani M, Bellmann A, Fornari E, Thiran JP,
Pittet A, Clarke S (2001): Distinct pathways involved in sound
recognition and localization: a human fMRI study. Neuroimage
Mansfield P, Chapman BL, Bowtell R, Glover P, Coxon R, Harvey
PR (1995): Active acoustic screening: reduction of noise in gra-
?Acoustic Noise Concerns in fMRI?
? 139 ?
dient coils by Lorentz force balancing. Magn Reson Med 33:276–
Mansfield P, Glover P, Bowtell R (1994): Active rapid acoustic
screening: design principles for quiet gradient coils in MRI. Meas
Sci Technol 5:1021–1025.
Mansfield P, Glover PM, Beaumont J (1998): Sound generation in
gradient coil structures for MRI. Magn Reson Med 39:539–550.
Mansfield P, Haywood B (2000): Principles of active acoustic control
in gradient coil design. MAGMA 10:147–151.
Mathiak K, Rapp A, Kircher TT, Grodd W, Hertrich I, Weiskopf N,
Lutzenberger W, Ackermann H (2002): Mismatch responses to
randomized gradient switching noise as reflected by fMRI and
Mattay VS, Weinberger DR (1999): Organization of the human
motor system as studied by functional magnetic resonance im-
aging. Eur J Radiol 30:105–114.
Mazard A, Mazoyer B, Etard O, Tzourio-Mazoyer N, Kosslyn SM,
Mellet E (2002): Impact of fMRI acoustic noise on the functional
anatomy of visual mental imagery. J Cogn Neurosci 14:172–186.
McJury M, Blug A, Joerger C, Condon B, Wyper D (1994): Short
communication: acoustic noise levels during magnetic resonance
imaging scanning at 1.5 T. Br J Radiol 67:413–415.
McJury M, Shellock FG (2000): Auditory noise associated with MR
procedures: a review. J Magn Reson Imaging 12:37–45.
McJury M, Stewart RW, Crawford D, Toma E (1997): The use of
active noise control (ANC) to reduce acoustic noise generated
during MRI scanning: some initial results. Magn Reson Imaging
McJury M (1995): Acoustic noise levels generated during high field
MR imaging. Clin Radiol 50:331–334.
Mechefske CK, Geris R, Gati JS, Rutt BK (2002): Acoustic noise
reduction in a 4 T MRI scanner. MAGMA 13:172–176.
Miyati T, Banno T, Fujita H, Mase M, Narita H, Imazawa M, Ohba
S (1999): Acoustic noise analysis in echo planar imaging: multi-
center trial and comparison with other pulse sequences. IEEE
Trans Med Imaging 18:733–736.
Miyati T, Banno T, Fujita H, Mase M, Narita H, Imazawa M, Sanada
S, Koshida K, Kasuga T (2001): Characteristics of acoustic noise
in echo-planar imaging. Front Med Biol Eng 10:345–356.
Moelker A, Wielopolski PA, Pattynama PMT (2003a): Relationship
between magnetic field strength and MR-related acoustic noise
levels. MAGMA 16:52–55.
Moelker A, Vogel MW, Pattynama PMT (2003b): Efficacy of passive
acoustic screening in the MR-Environment: Implications for the
design of imager and MR-suite. J Magn Reson Imaging 17:270–
Monroe JW, Holtman R, Holtman K, Schmalbrock P, Clymer BD
(1999): Evaluation of various materials for acoustic noise atten-
uation in MRI. Proceedings of the 7th Annual Meeting of the
International Society of Magnetic Resonance in Medicine, Glas-
gow, UK. p 101.
Oesterle C, Hennel F, Hennig J (2001): Quiet imaging with inter-
leaved spiral read-out. Magn Reson Imaging 19:1333–1337.
Oesterle C, Hennel F, Kraemer F, Hennig J (1999): Silent spiral
imaging. MAGMA 8:S479.
Ogawa S., Lee TM, Kay AR, Tank DW (1990): Brain magnetic
resonance imaging with contrast dependent on blood oxygen-
ation. Proc Natl Acad Sci USA 87:9868–9872.
Ogawa S, Menon RS, Tank DW, Kim SG, Merkle H, Ellermann JM,
Ugurbil K (1993): Functional brain mapping by blood oxygen-
ation level-dependent contrast magnetic resonance imaging. A
comparison of signal characteristics with a biophysical model.
Biophys J 64:803–812.
Opitz B, Rinne T, Mecklinger A, von Cramon DY, Schroger E (2002):
Differential contribution of frontal and temporal cortices to au-
ditory change detection: fMRI and ERP results. Neuroimage
Oxenham AJ, Plack CJ (1998): Suppression and the upward spread
of masking. J Acoust Soc Am 104:3500–3510.
Parrish T (1999): Functional MR imaging. Magn Reson Imaging Clin
N Am 7:765–782.
Pascal J, Bourgeade A, Lagier M, Legros C (1998): Linear and
nonlinear model of the human middle ear. J Acoust Soc Am
Pfleiderer B, Michael N, Ostermann J, Soros P, Heindel W (2002):
Visualization of auditory habituation by means of fMRI. Pro-
ceedings of the 10th Annual Meeting of the International Society
of Magnetic Resonance in Medicine, Honolulu, HI. p 1470.
Price DL, De Wilde JP, Papadaki AM, Curran JS, Kitney RI (2000):
Frequency analysis of MRI acoustic noise. Proceedings of the 8th
Annual Meeting of the International Society of Magnetic Reso-
nance in Medicine, Denver, CO. p 2008.
Price DL, De Wilde JP, Papadaki AM, Curran JS, Kitney RI (2001):
Investigation of acoustic noise on 15 MRI scanners from 0.2 T to
3 T. J Magn Reson Imaging 13:288–293.
Prieto TE (1999): Acoustic noise levels in head gradient coils during
EPI as a function of frequency encoding direction. Proceedings
of the 7th Annual Meeting of the International Society of Mag-
netic Resonance in Medicine, Philadelphia, PA. p 105.
Prieto TE, Bennet K, Weyers D (1998): Acoustic noise levels in a
head gradient coil during echo planar imaging at 3T. Proceed-
ings of the 6th Annual Meeting of the International Society of
Magnetic Resonance in Medicine, Sydney, Australia. p 750.
Quirk ME, Letendre AJ, Ciottone RA, Lingley JF (1989): Anxiety in
patients undergoing MR imaging. Radiology 170:463–466.
Ravicz ME, Melcher JR (2001): Isolating the auditory system from
acoustic noise during functional magnetic resonance imaging:
examination of noise conduction through the ear canal, head,
and body. J Acoust Soc Am 109:216–231.
Ravicz ME, Melcher JR, Kiang NY (2000): Acoustic noise during
functional magnetic resonance imaging. J Acoust Soc Am 108:
Robson MD, Dorosz JL, Gore JC (1998): Measurements of the tem-
poral fMRI response of the human auditory cortex to trains of
tones. Neuroimage 7:185–198.
Rosen BR, Buckner RL, Dale AM (1998): Event-related functional
MRI: past, present, and future. Proc Natl Acad Sci USA 95:773–
Scheffler K, Bilecen D, Schmid N, Tschopp K, Seelig J (1998): Audi-
tory cortical responses in hearing subjects and unilateral deaf
patients as detected by functional magnetic resonance imaging.
Cereb Cortex 8:156–163.
Scheich H, Baumgart F, Gaschler-Markefski B, Tegeler C, Tempel-
mann C, Heinze HJ, Schindler F, Stiller D (1998): Functional
magnetic resonance imaging of a human auditory cortex area
involved in foreground-background decomposition. Eur J Neu-
Seto E, Sela G, McIlroy WE, Black SE, Staines WR, Bronskill MJ,
McIntosh AR, Graham SJ (2001): Quantifying head motion asso-
ciated with motor tasks used in fMRI. Neuroimage 14:284–297.
Shah NJ, Jancke L, Grosse-Ruyken ML, Muller-Gartner HW (1999):
Influence of acoustic masking noise in fMRI of the auditory
cortex during phonetic discrimination. J Magn Reson Imaging
?Moelker and Pattynama?
? 140 ?
Shah NJ, Steinhoff S, Mirzazade S, Zafiris O, Grosse-Ruyken ML,
Jancke L, Zilles K (2000): The effect of sequence repeat time on
auditory cortex stimulation during phonetic discrimination.
Shapleske J, Rossell SL, Woodruff PW, David AS (1999): The pla-
num temporale: a systematic, quantitative review of its struc-
tural, functional and clinical significance. Brain Res Rev 29:26–
Shellock FG, Morisoli SM, Ziarati M (1994): Measurement of acous-
tic noise during MR imaging: evaluation of six “worst-case”
pulse sequences. Radiology 191:91–93.
Shellock FG, Ziarati M, Atkinson D, Chen DY (1998): Determination
of gradient magnetic field-induced acoustic noise associated
with the use of echo planar and three-dimensional, fast spin echo
techniques. J Magn Reson Imaging 8:1154–1157.
Sunaert S, Yousry TA (2001): Clinical applications of functional
magnetic resonance imaging. Neuroimaging Clin N Am 11:221–
Talavage TM, Edmister WB, Ledden PJ, Weisskoff RM (1998a):
Quantification of the impact of fMRI scanner noise on auditory
cortex. Proceedings of the 6th Annual Meeting of the Interna-
tional Society of Magnetic Resonance in Medicine. p 1502.
Talavage TM, Edmister WB (1998b): Saturation and nonlinear fMRI
responses in auditory cortex. Neuroimage 7:S362.
Talavage TM, Edmister WB, Ledden PJ, Weisskoff RM (1999): Quan-
titative assessment of auditory cortex responses induced by im-
ager acoustic noise. Hum Brain Mapp 7:79–88.
Tanaka H, Fujita N, Watanabe Y, Hirabuki N, Takanashi M, Oshiro
Y, Nakamura H (2000): Effects of stimulus rate on the auditory
cortex using fMRI with ’sparse’ temporal sampling. Neuroreport
Ulmer JL, Biswal BB, Mark LP, Mathews VP, Prost RW, Millen SJ,
Garman JN, Horzewski D (1998a): Acoustic echoplanar scanner
noise and pure tone hearing thresholds: the effects of sequence
repetition times and acoustic noise rates. J Comput Assist To-
Ulmer JL, Biswal BB, Yetkin FZ, Mark LP, Mathews VP, Prost RW,
LD Estkowski, McAuliffe TL, Haughton VM, Daniels DL
(1998b): Cortical activation response to acoustic echo planar
scanner noise. J Comput Assist Tomogr 22:111–119.
Vouloumanos A, Kiehl KA, Werker JF, Liddle PF (2001): Detection
of sounds in the auditory stream: event-related fMRI evidence
for differential activation to speech and nonspeech. J Cogn Neu-
Woldorff MG, Gallen CC, Hampson SA, Hillyard SA, Pantev C,
Sobel D, Bloom FE (1993): Modulation of early sensory process-
ing in human auditory cortex during auditory selective atten-
tion. Proc Natl Acad Sci USA 90:8722–8726.
Yacoub E, Hu X (2001b): Detection of the early decrease in fMRI
signal in the motor area. Magn Reson Med 45:184–190.
Yacoub E, Le TH, Ugurbil K, Hu X (1999): Further evaluation of the
initial negative response in functional magnetic resonance im-
aging. Magn Reson Med 41:436–441.
Yacoub E, Shmuel A, Pfeuffer J, Van De Moortele PF, Adriany G,
Andersen P, Vaughan JT, Merkle H, Ugurbil K, Hu X (2001a):
Imaging brain function in humans at 7 Tesla. Magn Reson Med
Yang Y, Engelien A, Engelien W, Xu S, Stern E, Silbersweig DA
(2000): A silent event-related functional MRI technique for brain
activation studies without interference of scanner acoustic noise.
Magn Reson Med 43:185–190.
?Acoustic Noise Concerns in fMRI?
? 141 ?