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Comparison of Robarts’s 3T
and 7T MRI Machines for
obtaining fMRI Sequences Medical Biophysics 3970: General Laboratory
Jacob Matthews
4/13/2012
Supervisor: Rhodri Cusack, PhD
Assistance: Annika Linke, Postdoctoral Fellow
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Introduction
MRI has been a rapidly expanding and evolving imaging modality in recent years. This is
because it is one of few non-invasive, non-ionizing forms of gathering information about the structure
and behavior of our internal organ systems. This is doubly true for the brain, for which surgery carries
high risks, and ionizing radiation can be damaging in even lower quantities.
The use of fMRI scans to obtain time course data of an entire brain volume is one of the first
opportunities researchers have had to correlate physiological and psychological behavior with biological
processes in the brain. When the BOLD contrast imaging technique was shown to correlate with
activation of individual brain regions, research began into mapping the brain, a field which is still only
understood at its most basic levels. fMRI has become the sole modality used for brain mapping since it
was introduced in the early 1990s.
Initial hurdles faced with fMRI imaging included the low spatial resolution and signal to noise
ratio of the rapidly obtained images. Such problems were marginally improved by fine tuning sequence
parameters, but the biggest improvements lay with hardware innovation and the move to higher powered
MRI machines.
The Robarts research facility at the University of Western Ontario has, in addition to its more
common 3T Siemens whole body scanner, a recently developed and acquired 7T human head scanner.
This scanner was brought to the facility in 2009 and used for its first clinical study in 2011,
The purpose of this research was to obtain equivalent image sets from both machines given the
constraints of the hardware involved, and to compare, both qualitatively and quantitatively, the image
sets from each machine.
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Theory
The Physics of Magnetic Resonance Imaging
MRI is an imaging modality reliant on the nuclear resonance properties of tissues. Every atom
has a nuclear magnetic spin, an inherent property of atoms (as a consequence of it being a property of
fundamental particles), which dictates its rotation about an axis through its centre. These magnetic spins
give the atom a magnetic dipole, the fundamental principle exploited in MRI.
Any magnetic dipole will align itself with a larger external magnetic field. Without an external
magnetic field all the dipoles will be aligned randomly. This is where the primary B0 field comes into
play in MRI. The B0 field is the strongest field used in the MRI machine, and is the field strength given
to the name of the machine, in our case 3T and 7T. This field is aligned with the z-axis of the machine,
in line with the subject lying inside. The strength of this field causes all of the diploes in a subject to
align with the z-axis as seen in figure 1, a starting point from which we can manipulate the dipoles. Note
that the spins can align either parallel or anti-parallel to the external field. This becomes important when
looking at signal to noise ratio later.
The next step in MRI is to force the dipoles to precess around the z-axis they have been aligned
to. First, an understanding of precession is required. Precession occurs when an object rotating on an
axis experiences a torque in a direction other than that of the primary rotation axis. This causes the spin
Figure 1: Showing the magnetic dipoles
of hydrogen atoms (protons) randomly
aligned, and then aligned with an
external magnetic field along its axis.
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axis of the object to rotate itself around the primary rotation axis. In the case of the magnetic dipole of
the atom, we can cause the precession of the spin axis around the axis of the B0 field as seen in figure 2.
The angular momentum of its precession can be determined with the following formula:
ω0 = γ B0 = f 2 π
Using this formula the angular frequency ω0 (a measure of the rate of rotation, measured in
radians per second) can be found from the gyromagnetic magnetic ratio γ (an inherent measure of the
strength of the magnetic moment, unique for each atom, measured in MHz per Tesla) and the primary
magnetic field strength B0 (measured in Tesla). The frequency of precession (another measure of the rate
of precession, measured in Hz) can also be found, as it is equivalent to the angular frequency divided by
2 π. As the gyromagnetic ratio for each atom is unique, so too is the angular frequency of precession. It
is in this way that a particular type of atom can be selected for measurement in an MRI image.
The frequency is the rate of precession about the B0 axis for any given atom. If we create an RF
pulse at the same frequency, we can cause all atoms of that type in a sample to tilt away from the B0 axis
and begin precessing. This phenomenon is known as resonance. This is what the second magnetic field,
the B1 field, is used for. An RF pulse is created by an RF coil rotating in the plane transverse to the B0
field at the precession frequency. While it is much weaker than the B0 field, it does rise proportionally to
the B0 field, giving rise to some inhomogeneity which will be discussed later.
While an atom is precessing it is in a state of imbalance. After the RF pulse has started the
precession, the magnetization vector of the dipole will return to its equilibrium state. During the return
Figure 2: Showing the spin axis of a
hydrogen atom precessing about the
primary axis of rotation, that of the B0
field.
Equation 1: for determining precession frequency
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to equilibrium, the atom emits its own RF pulse (an emission of the energy it required to put it into a
precessing state). This energy is detected by the RF coil, and recorded as image data. Each tissue type
emits a unique pulse and so can be differentiated from the surrounding tissue. For example, white and
grey matter in the brain both contain ample water, and therefore hydrogen, but the energy emitted from
the hydrogen atoms in each will be different.
This information can tell us about the amount of an atom, and the tissue type it resides in, but
does not include information on its location. This is where the third magnetic field used in MRI comes
in; the Gradient fields. The gradient fields consist of three graded magnetic fields parallel to each axis,
stronger at one end of the axis then the other. All three field gradients have the same properties, but are
applied at distinct moments in different directions to spatially encode the entire image. The first field
applied is along the z-axis. This field is the slice selection gradient (GSS) and alters the strength of the
B0 field along the z-axis just enough so that only a particular plane (perpendicular to the z-axis) is
subjected to the exact precession frequency. The second field applied acts along the y-axis. This field is
the phase encoding gradient (GPE) and acts for a short time to alter the phase of each row of atoms
without affecting the frequency. In this way all of the atoms in the plane are still precessing, but each
row is slightly phase shifted, which leads to the image signal being slightly out of phase, and encoding
for position along the y-axis. The third field applied is along the x-axis. This field is the frequency
encoding gradient (GFE) and acts to alter the receiving frequencies along the x-axis. In this way each
column of atoms has shifted frequencies which encode their position along the x-axis. Every atoms
position in space can be determined from the use of these three gradient fields. Figure 3 shows an image
of the gradient coils and their respective field axes.
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With those basic principles of MRI determined, one can go about creating an MRI sequence,
which includes the previous discussed parameters as well as a number of parameters I will not go into
detail on as they are beyond the scope of this project including: Echo types and Contrast type
(determined by varying T1 and T2 times), reconstruction methods, sequence acceleration, and artifact
reduction. These variables determine a sequence within you can further alter another set of parameters.
These include the TR (the time between two RF pulses), the TE (the time between the RF pulse and the
signal data being collected), and the field of view (the sections of the entire MRI field for which signal
will be recorded).
The MRI sequence used in this experiment was a Magnetization Prepared Rapid Acquisition
Gradient Echo (MPRAGE). This sequence is used to obtain a high contrast, high spatial resolution 3D
structural. This can be used as a high quality reference image for the fMRI data we obtain. This
sequence is T1 weighted, giving us a stereotypical MRI image in which fats appear brighter than water.
Figure 3: Showing the three gradient
coils and their respective axes, used to
spatially encode the MRI signal data.
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The Mechanics of fMRI
Functional magnetic resonance imaging (fMRI) is a form of MRI adapted to measure brain
activity with high temporal resolution. It relies on fast repetitive imaging sequences which collect entire
brain volumes on a second by second basis. fMRI uses BOLD contrast (blood oxygen level dependent)
to detect active brain regions. BOLD Contrast relies on a sequence of physiological events following
brain activation. First the brain is activated with a task (motor control, such as thumb movement is
popular. A movie with auditory and visual stimulation was used in our experiment). O2 consumption to
the activated regions of the brain is increased, and local blood flow increases within that region. The
ratio of oxyhemoglobin to deoxyhemoglobin increases in the region due to the increased influx of
oxygenated blood. This ratio increase is detected as a weak transient rise in a T2 weighted signal. Thus
areas of the brain being activated at any particular point in time will show as having a higher signal
(brighter on our image).
The fMRI sequence used in this experiment was an Echo Planar Imaging (EPI) sequence. This is
a fast repetitive imaging sequence which provides us with an entire brain volume in a short period of
time (TR = 2 seconds for this experiment). The trade-off for this rapid image acquisition is the decreased
spatial frequency. This sequence is T2 weighted, giving a less traditional image in which water appears
brighter than fats.
High Powered MRI
Increased B0 field strength in MRI has a number of theoretical benefits and consequences, a few
of which will be discussed in this paper. One of the primary benefits of high powered MRI is the
increased signal to noise ratio (SNR). From Boltzmann Distribution, an increase in field strength should
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accentuate the difference in parallel and anti-parallel spins, increasing the signal to noise ratio. The
potential signal will vary with the square of the B0 field, while the noise will progress linearly. Thus the
SNR should increase linearly with field strength.
SNR is a measurement which can be very difficult to measure, especially in MRI images. SNR at
its core it determined from the following relationship:
Signal-to-Noise-Ratio = Mean Signal in a region / Mean Noise in a Region
However determining true signal and noise measurements is nearly impossible. Signal
measurements are always affected by noise, and vary across the image. Noise in the image comes from
many sources. The noise we want to isolate is that due to the scanner collecting the data, the noise which
is not representative of any biological structure or process. However, there are many sources of noise
intermingled from physiological processes, natural noise in the brain activation, and noise from slight
movements of the subject which we might later correct for. Most MRI SNR measurements simply use
the average signal in a given region of interest (ROI) as the signal measurement, ignoring the
proportionally tiny contribution of noise. Noise measurements are collected in various ways. A method
used in a prior study of high field strength MRI used the mean of the artifact free image background
(outside the skull at the edge of the image) as a way of determining the noise which could not be due to
physiological processes. This paper uses a slightly more common method in imaging, which us to take
the standard deviation of signal across the signal as the noise for the entire image. A similar alternative
is to use the standard deviation from only the ROI the mean signal was found in.
Relaxation times are also directly proportional to the B0 field strength. Therefore TR times and
overall scan times are to be expected to increase at high field strength. Also the specific absorption rate
(SAR), which is a measure of the energy deposited into the body being scanned, increases with the
square of the B0 field. This means that certain sequences cannot be as long, or cannot be done back to
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back under health regulations. Another downfall is that the auditory levels increase in the higher
powered scanner, giving it some potential to interfere with the auditory portion of our fMRI stimuli.
It was mentioned early that the increased B1 field or RF pulse is subject to some inhomogeneity
at higher field strength. This is due to a standing wave pattern. As B0 increases, the precession frequency
increases proportionally. Precession frequency is related to wavelength by the following formula:
λ = v / f
Using this formula the wavelength λ (the distance between peaks of the electromagnetic wave, in
meters) is equal to the speed v (of the electromagnetic wave front, measured in meters per second, in this
case v is the speed of light 3x108 m/s) divided by the precession frequency f (a measure of the rate of
oscillation of the wave, measured in Hz). Thus, as precession frequency increases due to increased B0
field strength, the wavelength of the RF pulse decreases. In low powered MRI machines the 3T
machine, the wavelength is long enough that the portion of the wave inside the scanner bore is
essentially linear. However, in high powered machines like the 7T machine the wavelength is short
enough that the portion of the wave in the scanner bore has significant amplitude differences throughout.
This is magnified by the use of modern RF coils, which have multiple coils and produce nodes of low
signal throughout the image.
Another source of inhomogeneity is due to the size of the head bore in the 7T machine. There is very
little space inside the head bore, and the subjects head is very close to the RF coils. The nature of the RF
coils means they create a less homogenous field near the edges of the head bore, giving the images a
squared off look to the top and back of head, along with distortion in the face.
Equation 3: relating precession frequency to wavelength
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Methods
Data Acquisition and Image Analysis
After the initial decision was made to compare the two machines at Robarts, appointments were
made for both machines in back to back time slots, to help avoid variation in our subject or testing
patterns. Two sets of images were obtained from each machine. From both machines an MPRAGE
structural was obtained. This gives us a single high quality brain volume, which is an excellent reference
image, and is good for qualitatively comparing the machines.
Next two EPI sequences were obtained from each machine. The first EPI was a resting state
image set, in which the subject was to lie still, with his eyes open or closed, but not asleep. This serves
as a base line reference to compare the brain activation in the next EPI against. The first six volumes
collected were dummy volumes, and are not used in later analysis. This negates any noise from the
machine beginning its sequence and the subject possibly reacting to it. The second EPI was collected
while the subject watched a movie. This movie was projected (front projection for both machines) for
the subject to see, and audio was fed to the subject through headphones. The constant audio visual
stimulation provides a large amount of activation to be studied.
A few other scans, including an MP2RAGE and field maps, were obtained as well, but were not
used in this experiment.
Image data was delivered to the Cusack Lab imaging server in dicom format. The data from the
3T scans was delivered separated by sequence, with sequence parameters stored in the dicom header.
The data from the 7T scans was delivered as one large block of dicom image files in the order the
sequences were conducted. This is one particularly annoying disadvantage of the 7T scanner and
software.
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Analysis of the images was done using Matlab Software Modules SPM (Statistical Parametric
Mapping), a well developed, well documented, free Matlab module evolved from early MRI software
used in the 90s. The second piece of software used was AA (Automatic Analysis), a piece of software
developed in Cambridge by a small team including my supervisor Dr. Rhodri Cusack. AA is used first
and automatically performs a number of tasks. The first and most relevant step is the conversion of
dicom image sets into more easily analyzed NIfTI files. AA then goes on to run a number of post-
processing corrections on the image sets, such as image realignment and image smoothing, although for
this experiment we worked primarily with the raw NIfTI files. With the files in NIfTI format they are
easily manipulated with SPM which can read the images into Matlab as 3D or 4D (in the case of our
time course EPIs) matrices.
Spatial SNR Calculations
The first section of the experiment was to determine the spatial SNR of the four sets of EPIs as a
comparison of the two machines. As discussed in the theory section, SNR was calculated from the mean
signal in an ROI and the standard deviation of the entire image. First an ROI was selected. We chose the
auditory cortex due its previously established activity levels with our movie stimuli. Using SPM, a
binary image of the auditory cortex was resliced to align it with our EPI sets (separately for the 3T and
7T image sets). It was then applied to each volume of the EPI sequence to isolate voxels in the ROI
region. The signal mean of these voxels was calculated and divided by the standard deviation of all the
voxels in the volume. This was done with a Matlab script I wrote from scratch just for the image set. The
script created a matrix of mean signals, standard deviations, and SNR values for every volume fed into
the script. The SNR values were exported to Excel for statistical analysis.
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Excel was used to compare the exported SNRs. Four sets of data were exported; 171 SNR values
for both the 3R resting and 3T movie EPI sets, as well as 240 SNR values for both the 7T resting and 7T
movie EPI sets. One-tailed paired t-tests were used to analyze the data.
Individual Component Analysis
The second part of the experiment was to run Individual Component Analysis (ICA) on the
acquired data. ICA is a relatively new and incredibly value type of fMRI analysis. Without any pre-
definition of important spatial regions or temporal profiles, ICA can take an entire fMRI time course and
determine brain activity patterns in either the spatial or temporal domain. For example, one of the
reported components of ICA for our fMRI set was for the auditory cortex. The ICA analysis recognizes
that the signal values in that region are changing in sync with each other across the time course, and
reports it as a component, although it does not automatically identify the brain region it belongs to. ICA
returns a number of noise related components, due to the consistent nature of many sources of noise (an
oscillating inhomogeneity, or consistent movement of the subject’s head.
The software we used for ICA analysis was FSL. Three sets of data were provided to FSL; the
3T EPI sequences (both resting and movie), the 7T EPI sequences (both resting and movie), and a group
study done previously in Cambridge with similar parameters in a 3T MRI (Using the same movie
stimuli). The Group Study, consisting of 20 subjects, serves as a type of average data set for comparing
the component analysis of our data sets with. A summary of the data sets is shown in table 1.
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3T EPI 7T EPI 3T Group Study
• TR=2, iPAT=3, 2x2x2 voxel size
• 240 volumes (includes 6
dummies)
•
• Last ≈12s of movie missing
• 38 slices, descending sequential
• Screen crash around volume ≈185
• TR=2
• 240 volumes (includes 6
dummies)
•
• Last ≈12s of movie missing
• 48 slices, descending sequential
• TR=2.47, multi-echo (5)
• 193 volumes (no dummies)
• First 5 volumes discarded in
analysis
• first 12.5s of movie missing
• 32 slices, descending sequential
• 20 subjects
Table 1: Summarizing the parameters of the three data sets submitted for ICA analysis
ICA was run twice with two slightly different sets of images. During acquisition of the 3T EPI
movie sequence the projection screen fell onto the subject, compromising the last 60 volumes of the
data. ICA was run once on the entire data set, which produces a number of excess noise components in
the 3T data, and a second time omitting the last 60 volumes from all data sets (to keep the time course
matched between sets). These sets were named Dummycut and Moviecut, respectively. A Summary of
the parameters of each analysis is shown in table 2.
ICA - Moviecut ICA - Dummycut
• Uses raw data, pre-processed with FSL
• Excludes last 60 volumes (to avoid screen crash)
• Excludes 6 dummy scans
• 173 volumes in total
• For comparison to group ICA results, the first 12s of the
movie need to be cut because group ICA discards first 5
volumes
• Need to adjust movie soundtrack to correlate with
auditory component timecourse
• Uses raw data, pre-processed with FSL
•
• Excludes 6 dummy scans
• 236 volumes in total
• For comparison to group ICA results, the first 12s of the
movie need to be cut because group ICA discards first 5
volumes
• Need to adjust movie soundtrack to correlate with auditory
component timecourse
Table 2: Summarizing the distinctions between the two ICA analyses used
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FSL processes the data and returns a list of all suspected components, with images of the
component brain regions highlighted in a number of slices through a brain volume. All components were
correlated with the known and identified components from the group study. This allowed us to identify
the component representing the auditory cortex. The components representing the auditory cortex in all
three sets of data were then correlated against each other, and against the sound envelope from the
stimulus movie. The sound envelope is a simple time course of the intensity of the audio track in the
movie, the theory being that peaks in audio intensity should correspond to peaks in activation of the
auditory cortex.
Results
Sample Images
For reference and qualitative comparison four sample images have been provided below. The
two MPRAGE structurals and a sample volume of the EPI sequences are shown below.
Figure 3: 3T MPRAGE Structural Image
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Figure 4: 7T MPRAGE Structural
Figure 5: 3T EPI fMRI
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Figure 6: 7T EPI fMRI
A quick comparison of the 3T and 7T images clearly shows the improved SNR, with much of the
grainy background noise not existent in the 7T images. Also apparent is the previously discussed field
inhomogeneity, expressed in the squared off top and back of the head visible in figure 4. You can also
see some darker patches in figure 4 towards the top and each side of the brain, caused by the standing
field patterns.
Spatial SNR Calculations
Spatial SNR for each volume was found using the custom Matlab script. The mean SNR found
for the four EPI image sets are summarized table 3.
3T EPI -
Resting
3T EPI -
Movie
7T EPI -
Resting
7T EPI -
Movie
SNR considering mean signal in auditory cortex ROI
and using standard deviation of image as noise (average
of 5 volumes)
1.6863 1.6891 1.9624 1.9798
Table 3: Showing average SNR for each of the four EPI image sets
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Excel was used to conduct One-Tailed Paired Two Sample t-Tests between the two 3T scans, the
two 7T scans, the two resting state scans, and the two movie scans. Tables 4 – 7 show the results of the
t-tests.
t-Test: Paired Two Sample for Means
3T resting and 3T movie
3T resting 3T movie
Mean 1.686316 1.689094
Variance 1.98E-05 3.14E-05
Observations (n) 171 171
P(T<=t) one-tail 9.26E-08
t Critical one-tail 1.653866
t-Test: Paired Two Sample for Means
7T resting and 7T movie
7T resting 7T movie
Mean 1.962435 1.979769
Variance 0.000187 7.67E-05
Observations (n) 240 240
P(T<=t) one-tail 1.4E-35
t Critical one-tail 1.651254
t-Test: Paired Two Sample for Means
3T resting and 7T
resting
3T resting 7T resting
Mean 1.686316 1.955343
Variance 1.98E-05 7.96E-05
Observations (n) 171 171
P(T<=t) one-tail 8.6E-240
t Critical one-tail 1.653866
Table 4: The results of the t-test comparing the 3T
resting and 3T movie SNR values. The result of
the test indicates a significant difference between
the 3T resting and 3T movie SNR values.
Table 5: The results of the t-test comparing the 7T
resting and 7T movie SNR values. The result of
the test indicates a significant difference between
the 7T resting and 7T movie SNR values.
Table 6: The results of the t-test comparing the 3T
resting and 7T resting SNR values. The result of
the test indicates a significant difference between
the 3T resting and 3T resting SNR values.
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t-Test: Paired Two Sample for
Means
3T movie and 7T movie
3T movie 7T movie
Mean 1.689094 1.98256
Variance (n) 3.14E-05 6.89E-05
Observations 171 171
P(T<=t) one-tail 3.6E-245
t Critical one-tail 1.653866
The results of all four t-tests indicated a significant difference between the relevant sets of spatial
SNRs. These results validate two key points. First, the tests between the two 3T scans and the two 7T
scans demonstrate the signal increase due to brain activation when the subject experiences the movie
stimuli. As discussed in the theory section this increase is very small, an increase of 0.16% in the 3T
data and 0.88% in the 7T data. The second, and more relevant, point the test demonstrate is the increased
SNR in the 7T scanner compared to that of the 3T scanner. A 16.0% increase is seen in SNR values of
the resting states between the two machines, while a 17.4% increase is seen in SNR values of the
stimulated states between the two machines.
ICA Results
The first set of results returned from FSL includes a summary of all found components and their
corresponding images. The non-noise components were manually identified. This was done for both the
Moviecut and Dummycut analyses. The summary of Components for the Moviecut and Dummycut
analyses, respectively, are shown below in tables 8 and 9.
Table 7: The results of the t-test comparing the 3T
movie and 7T movie SNR values. The result of the
test indicates a significant difference between the
3T movie and 7T movie SNR values.
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3T EPI 7T EPI 3T Group Study
• Total components: 17
• Non-noise components: 4
• Ratio: 0.24
• Non-noise ID: 11, 12, 13, 15
• Total components: 32
• Non-noise components: 12
• Ratio: 0.38
• Non-noise ID: 7, 10, 15, 19, 21, 22,
23, 25, 26, 27, 29, 32
• Total components: 57
• Non-noise components: 18
• Ratio: 0.32
• Non-noise ID: 1-11, 13, 17, 20, 23,
30, 31, 54
Table 8: Summarizing the component findings of the Moviecut ICA analysis.
3T EPI 7T EPI 3T Group Study
• Total components: 56
• Non-noise components: 8
• Ratio: 0.14
• Non-noise ID: 37, 40, 47, 48,
51, 52, 53
• Total components: 31
• Non-noise components: 9
• Ratio: 0.29
• Non-noise ID: 7, 14, 16, 19, 22, 23,
24, 26, 30
• Total components: 57
• Non-noise components: 18
• Ratio: 0.32
• Non-noise ID: 1-11, 13, 17, 20, 23,
30, 31, 54
Table 9: Summarizing the component findings of the Dummycut ICA analysis.
After the non-noise components were identified, correlation was run comparing the components
from both our 3T and 7T data with that of the group study. Previous work with the group study data has
identified non-noise components related to specific brain regions. By running this type of correlation we
can easily determine which of the components in our 3T and 7T data correspond to particular brain
regions. In our case, we were trying to identify the component representing the auditory cortex.
Correlation was run with a significance p<0.05. The results of the correlation are summarized below in
tables 10 and 11.
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3T EPI – 3T Group Study 7T EPI – 3T Group Study Group 3T Study
• total: 5
• pairs: 1-9, 11-2, 13-3, 15-23,
17-9
• Auditory: 11
• total: 7
• pairs: 1-16, 6-17, 7-2, 10-9,
17-16, 22-4, 32-11
• Auditory: 7
• Known non-noise components (18):
• 1 = Visual
• 2 = Auditory
• 3 = Frontal-Parietal
• 4 = Frontal
• 5-11, 13, 17, 20, 23, 30, 31, 54 = other
Table 10: Summarizing the correlation of the components of our 3T and 7T data with that of the
known 3T group study data for the Moviecut ICA analysis.
3T EPI – 3T Group Study 7T EPI – 3T Group Study Group 3T Study
• total: 25
• pairs: 1-11, 2-16, 7-5, 9-6, 13-6,
14-8, 16-1, 17-3, 20-31, 21-9,
25-8, 27-9, 30-16, 32-13, 36-20,
37-2
• Auditory: 37
• total: 15
• pairs: 2-17, 6-13, 7-2, 9-17,
11-1, 12-8, 15-6, 20-9, 21-16,
22-4, 23-5, 24-23, 26-6, 29-
13, 30-8
• Auditory: 7
• Known non-noise components (18):
• 1 = Visual
• 2 = Auditory
• 3 = Frontal-Parietal
• 4 = Frontal
• 5-11, 13, 17, 20, 23, 30, 31, 54 = other
Table 11: Summarizing the correlation of the components of our 3T and 7T data with that of the
known 3T group study data for the Dummycut ICA analysis.
The above correlation data shows that components 11, 7, 37, and 7 for their respective data sets
represent the auditory cortex, as determined by their correlation with the know auditory component of
the 3T group study analysis. Shown below in figure 7 is the image provided for component 2 of the 3T
EPI in the Moviecut analysis. The corresponding image for the Dummycut analysis looks similar.
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Figure 7: The highlighted brain regions for component 2 of the 3T EPI Moviecut analysis. The
highlighted brain regions correspond with what is classically thought of as the auditory cortex,
confirming the correlations results.
With the auditory cortex components isolated, correlation can now be done between our 3T and
7T auditory components and that of the 3T group study, as well as correlation between our 3T and 7T
auditory components and the sound envelope produced from the audio track of the stimulus movie.
Shown below in figure 8 are the normalized time courses for the Sound Envelope, 3T auditory
component signal, and 7T auditory component signal used in the Moviecut analysis. Corresponding time
courses for the Dummycut analysis look similar.
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Figure 8: Showing the normalized time courses for the Sound Envelope, 3T auditory component
signal, and 7T auditory component signal used in the Moviecut analysis.
Statistical correlations were run between the above time courses. Correlations were calculated
between both our 3T and 7T data with the 3T group study data, as well as between both our 3T and 7T
data with the sound envelope. Correlations were run for both the Moviecut and the Dummycut analyses.
The results are summarized in tables 12 and 13 below.
ICA Moviecut Correlations for auditory component
3T – 7T r = .73, p<.0001
3T – Group r = 0.82, p<.0001
7T – Group r = 0.87, p<.0001
3T – Sound Envelope r = .33, p<.0001
7T – Sound Envelope r = .39, p<.0001
Table 12: Summarizing the correlations between the data sets of the Moviecut analysis.
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ICA Dummycut Correlations for auditory component
3T – 7T r = .71, p<.0001
3T – Group r = 0.82, p<.0001
7T – Group r = 0.90, p<.0001
3T – Sound Envelope r = .23, p<.0005
7T – Sound Envelope r = .30, p<.0001
Table 13: Summarizing the correlations between the data sets of the Dummycut analysis. Note
that unlike all other results shown, correlation of our 3T data with the sound envelope is shown at a
significance level of 0.0005 as opposed to 0.0001.
The above data shows strong correlations amongst the time courses. The correlations are stronger
in the Moviecut data, due to the omission of the noise creating portion of the 3T EPI data set. Especially
relevant to this experiment, note that in all four sets of correlations the 7T timecourse correlated more
strongly with the “true” data sets (the sound envelope representing the system input, and the group study
representing a corrected average).
Discussion
Previous papers (1) comparing low and high field strength MRIs have shown consistently
increased SNR for the high field strength machines, in various regions of the brain. Our goal was to
show that a similar relationship existed between the two MRI scanners at Robarts. Our results showed
the results we expected. SNR was increased significantly for the 7T scanner. Interestingly the increase in
SNR that was found for the Robarts machines was less than in paper (1). There are a number of possible
reasons for this. The other paper calculated SNR values for five different regions of the brain, and used a
different method of calculation, considering the mean of a background area their measure of noise. This
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in itself could easily explain the differences, although does not show that either method is better than the
other, simply that they likely cannot be compared directly with each other, since the noise measurement
is quite arbitrary. It is also possible that the auditory cortex ROI was in a region of nodal inhomogeneity,
with a slightly lower average signal than other brain regions. Further tests using additional ROIs, and
more exploration into the degree and possible correction of the field inhomogeneities would answer
these questions.
An interesting thing I came across when determining the SNRs for our data was related to the
above noted choice of noise measurement. I also wrote a script to determine SNR using the mean of a
background region as the noise measurement, as in paper (1). My results appeared to lack a pattern.
Although I only ran it on a few images, the 3T images had higher SNR values, and there was the resting
and movie states didn’t seem to have any consistent effect. It is noted in paper (1) that the background
region was “artifact free” and some post processing was mentioned. So it is possible that working with
the raw image data, as we did, prevented us from accurately using this measurement.
The ICA results were very interesting, and perhaps more relevant to future studies. ICA being a
technique that is becoming more common for data analysis in research experiments, it bodes well for the
7T machine to see increased correlations in all comparisons with the 3T data. Correlation of additional
ICA components would help to solidify the degree of improvement the 7T machine shows over the 3T.
Although our numerical results show improved performance across the board for the 7T scanner,
it was not without its drawbacks. Qualitative comparison of the resulting images shows that the field
inhomogeneities are a large factor in the 7T scanner. This renders the 7T scanner almost useless for
research of certain brain regions. Further study into correcting for this in either post-processing or by
tweaking the sequence parameters, could potentially make the 7T scanner a universally better choice for
fMRI experiments.
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Conclusions
The purpose of this research was to obtain equivalent image sets from both machines
given the constraints of the hardware involved, and to compare, both qualitatively and quantitatively, the
image sets from each machine.
We found that the images we obtained from the 7T machine appeared to have noticeably higher
SNRs than those of the 3T machine. However, field inhomogeneities were evident as both squared edges
of the head in the image, and dark spots in certain areas of the brain. These inhomogeneities were not
present in the 3T data.
We found in our research that the 7T MRI provided a statistically significant increase in SNR
over the 3T MRI, both with the subject in a resting state, and when presented stimulus; increases of
16.0% and 17.4% were observed respectively. We also validated an increased signal when a subject was
presented stimulus in comparison with a resting state for both our 3T and 7T image sets; increases of
0.16% and 0.88% were observed respectively.
ICA analysis showed good correlation of the auditory components of both our 3T and 7T data
with previously analysed 3T group study data, and with the sound envelope of our auditory stimulus. In
all comparable correlations the 7T data showed a higher coefficient of correlation than the 3T data.
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References
1. Vaughan J, Garwood M, Collins G, Liu W, DelaBarre L, Ardriany G, Andersen P, Merkle
H, Goebel R, Smith M, Ugurbil K. 7T vs. 4T: RF Power, Homogeneity, and Signal-to-Noise
Comparison in Head Images. Magnetic Resonance in Medicine 46: 24-30, 2001
2. Smith S, Fox P, Miller K, Glahn D, Fox P, Mackay C, Filippini N, Watkins K, Toro R,
Laird A, Beckmann C, Raichle M. Correspondence of the Brain's Functional Architecture
during Activation and Rest. Proceedings of the National Academy of Sciences of the United
States of America 106: 13040-13045, 2009
3. Gelman N. Medical Biophysics 3505F Mathematical Transform Applications in Medical
Biophysics. Lecture Slides, 2011
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