Comparison of Robarts’s 3T and 7T MRI Machines …...MRI machines. The Robarts research facility at the University of Western Ontario has, in addition to its more common 3T Siemens

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


• TR=2.47, multi-echo (5)

• 193 volumes (no dummies)

• First 5 volumes discarded in

analysis

• first 12.5s of movie missing


• 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


7T resting and 7T movie

7T resting 7T movie

Mean 1.962435 1.979769

Variance 0.000187 7.67E-05





3T resting and 7T

resting

3T resting 7T resting

Mean 1.686316 1.955343

Variance 1.98E-05 7.96E-05




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.


resting and 7T movie SNR values. The result of


the 7T resting and 7T movie SNR values.


resting and 7T resting SNR values. The result of


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



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.


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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• Total components: 17

• Non-noise components: 4

• Ratio: 0.24

• Non-noise ID: 11, 12, 13, 15



• Ratio: 0.38

• Non-noise ID: 7, 10, 15, 19, 21, 22,

23, 25, 26, 27, 29, 32



• 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.




• Ratio: 0.14

• Non-noise ID: 37, 40, 47, 48,

51, 52, 53



• Ratio: 0.29

• Non-noise ID: 7, 14, 16, 19, 22, 23,

24, 26, 30



• 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


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



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

Documents

Comparison of Robarts’s 3T and 7T MRI Machines …...MRI machines. The Robarts research facility at the University of Western Ontario has, in addition to its more common 3T Siemens