Development of Simulator Training to Reduce Head Motion Artifact in fMRI

60 Pages·2011·1.07 MB·English

Development of Simulator Training to Reduce Head
Motion Artifact in fMRI
Shawn Michael Ranieri
A thesis submitted in conformity with the requirements
for the degree of Master of Health Science in Clinical Biomedical Engineering
Institute of Biomaterials and Biomedical Engineering
University of Toronto

Supervised by
Dr. Simon J. Graham
Department of Medical Biophysics
University of Toronto
©Copyright by Shawn Ranieri 2011
Development of Simulator Training to Reduce Head Motion
Artifact in fMRI
Shawn Ranieri
Master of Health Science in Clinical Biomedical Engineering
Institute of Biomaterials and Biomedical Engineering
University of Toronto
2011
Abstract
Functional MRI (fMRI) is a primary tool in the study of brain function. The primary cause of
data corruption in fMRI is head motion while scanning. This problem is compounded by the fact
that subjects are asked to perform behavioural tasks, which can promote head motion. Random
and/or large head motions are often not handled well in post-processing correction algorithms.
This thesis investigates the use of an alternate method: an MRI simulator to help reduce head
motion in subjects through training. A simulator environment was developed where subjects
could be trained to reduce their head motion through closed loop visual feedback. The effect of
simulator training was investigated in young, old and stroke subjects. Performance of subjects
with respect to head motion was investigated prior, during and after feedback training, including
subsequent fMRI scans. This research helps improve fMRI image quality by reducing head
motion prior to scanning.
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Acknowledgments

I would like to thank Dr. Simon Graham for his indispensible guidance and knowledge in
supervising this work. To the thesis supervisory committee, Dr. Bradley MacIntosh and Dr. Tom
Schweizer, thank you for your advice and support throughout this project.
I would also like to thank all the members of the fMRI lab at the Rotman Research Institute, with
a special thanks to Annette Weekes-Holder and Tara Dawson for their invaluable help with the
project and for making the lab a welcome place for me. To Fred Tam, your resourcefulness
knows no limits, thank you. To Dr. Jon Ween, thank you for sharing your volunteer database. I
would also like to thank Dr. Shaun Boe for his contributions, including use of his task design and
pressure bulb hardware. Most importantly, I would like to show my gratitude to the volunteers,
all of whom were a pleasure to work with.
I would like to thank my friends and family for all their support. A special thanks to my mother
Jacqueline, who has supported me throughout my university career, and to my father Michael,
who is always with me.
Additional thanks are extended to the Institute of Biomaterials and Biomedical Engineering at
the University of Toronto. The Heart and Stroke Foundation of Ontario and the Natural Sciences
and Engineering Research Council of Canada are also thanked for providing funding support.

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Table of Contents
Acknowledgments .......................................................................................................................... iii
Table of Contents ........................................................................................................................... iv
List of Figures ................................................................................................................................ vi
List of Tables ............................................................................................................................... viii
List of Abbreviations ..................................................................................................................... ix
1 Introduction ................................................................................................................................ 1
1.1 Statement of Research Problem .......................................................................................... 1
1.2 Specific Aims ...................................................................................................................... 3
2 Background ................................................................................................................................ 5
2.1 Functional MRI and the BOLD Effect ................................................................................ 5
2.2 Motion Artifact in Functional MRI ..................................................................................... 6
2.2.1 Subject Motion ........................................................................................................ 8
2.3 fMRI Simulator ................................................................................................................. 10
2.3.1 Training ................................................................................................................. 11
2.4 Limitations of Current Methods ........................................................................................ 12
2.4.1 Physical Restraint .................................................................................................. 12
2.4.2 Retrospective Coregistration ................................................................................. 13
2.4.3 Navigator Echoes .................................................................................................. 14
2.4.4 PACE .................................................................................................................... 14
2.4.5 External Monitoring .............................................................................................. 14
3 Development of Simulator Training to Reduce Head Motion Artifact in fMRI ...................... 17
3.1 Introduction ....................................................................................................................... 17
3.2 Methods ............................................................................................................................. 18
3.2.1 Simulator Hardware .............................................................................................. 18
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3.2.2 Task Protocol ........................................................................................................ 20
3.2.3 Simulator Pilot Study ............................................................................................ 25
3.2.4 Cohort Study ......................................................................................................... 25
3.2.5 fMRI ...................................................................................................................... 26
3.2.6 Analysis ................................................................................................................. 26
3.3 Results ............................................................................................................................... 30
3.3.1 Pilot Study ............................................................................................................. 31
3.3.2 Cohort Study ......................................................................................................... 32
3.3.2.1 Simulator Data ........................................................................................ 32
3.3.2.2 fMRI Data ............................................................................................... 34
3.3.2.3 Activation Maps and Voxel Counts ....................................................... 37
3.4 Discussion ......................................................................................................................... 38
3.4.1 Pilot Study ............................................................................................................. 40
3.4.2 Cohort Study ......................................................................................................... 41
3.4.2.1 Motion in the Simulator .......................................................................... 41
3.4.2.2 Motion during fMRI ............................................................................... 42
3.4.2.3 Voxel Counts .......................................................................................... 44
4 Conclusions .............................................................................................................................. 45
4.1 Aim 1 ................................................................................................................................ 45
4.2 Aim 2 ................................................................................................................................ 45
4.3 Significance of Work ........................................................................................................ 46
4.4 Future Work ...................................................................................................................... 47
References ..................................................................................................................................... 49

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List of Figures
Figure 1: Representative percent signal change across a typical BOLD response. Image modified
from A Primer on MRI and Functional MRI28. ............................................................................... 6
Figure 2: Representative data during a pilot study with an fMRI simulator: (a) young, (b) elderly,
and (c) stroke subjects. Young subjects exhibit the least head motion. The stroke data exhibited
the largest motion amplitude and were significantly task correlated (10 tasks corresponding to 10
peaks) whereas the elderly data were intermediate in extent between the other two groups. The
majority of motion for both stroke and elderly groups lay in the inferior-superior direction. ....... 9
Figure 3: Illustration of experimental setup and visual-motor task. (a) Diagram of simulator
layout showing a representation of the visual field with real-time motion feedback. (b) Head of
simulator bed with miniBird apparatus and its respective coordinate axes. (c) Visual stimulus
(MRI only) for the gripping task with hand unit pressure bulb shown inset. ............................... 19
Figure 4: Diagram showing inclusion criteria for training eligibility applied to young and elderly
(initially) subjects. ......................................................................................................................... 23
Figure 5: Three event sample of square wave task timing (solid line) and test waveform
representing the task-related fMRI signal (dashed line). The test waveform is obtained by
mathematical convolution of the task waveform and the BOLD hemodynamic response function
(HRF). Timing is representative of a simulator run with 4 s events and 8 s rests. The HRF is
well modeled using a gamma distribution and adds a physiological BOLD response to the task,
such that the test waveform lags the task onset by 6-7 s. ............................................................. 28
Figure 6: Positional head motion data from pilot stroke subjects trained in a unilateral gripping
task with their affected hand. Data plotted in rows for: (a) Subject 1, (b) Subject 2 and (c)
Subject 3. The vertical scale between subjects is not equal. Feedback training substantially
reduced head motion during and after training. Note the major improvement in inferior-superior
motion, where the majority of displacement occurred prior to training. ...................................... 31
Figure 7: Plots for the three subject groups in the simulator are shown for (a) Absolute deviation
(AD) and (b) Cumulative deviation (CD). Error bars represent standard error of the mean. ...... 33
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Figure 8: Correlation values (CC) plotted for healthy young subjects in the (a) simulator and
during (b) fMRI. Corresponding behavioural data are given in (c) with respect to the task
performed during fMRI. All error bars represent the standard error of the mean. ...................... 35
Figure 9: Correlation values (CC) plotted for healthy elderly subjects in the (a) simulator and
during (b) fMRI. Corresponding behavioural data are given in (c) with respect to the task
performed during fMRI. All error bars represent the standard error of the mean. ...................... 35
Figure 10: Correlation values (CC) plotted for stroke subjects in the (a) simulator and during (b)
fMRI. Corresponding behavioural data are given in (c) with respect to the task performed during
fMRI. All error bars represent the standard error of the mean. ................................................... 36
Figure 11: Representative brain activity for: (a) young trained, (b) young untrained, (c) elderly
trained, (d) elderly untrained and (e) stroke individual subjects. Note the ipsilateral activity of
the stroke subject with right side paresis. Family-wise error rate was set at P = 0.001 with
nearest neighbour clustering at 20 voxels minimum volume. Colour scale is representative of t-
value. ............................................................................................................................................. 38

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List of Tables
Table 1: Summary of runs performed by each subject during time in the simulator and MRI
system. The longer fMRI runs were due to BOLD signal time constraints requiring longer rest
periods. The simulator runs were condensed to minimize time and avoid fatigue of the subjects.
……………………………………………………………………………………………………22
Table 2: Ratings from the subject groups on the difficulty of the two task conditions during
fMRI, where 1 is very easy and 8 is very difficult (mean +/- standard error). ………………….37

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List of Abbreviations
fMRI – Functional magnetic resonance imaging
BOLD – Blood oxygen level dependent
DOF – Degrees of freedom
TE – Excitation time
TR – Repetition time
HRF – Hemodynamic response function
EPI – Echo-planar imaging
EMG – Electromyography
CCD – Charge coupled device

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1
1 Introduction
1.1 Statement of Research Problem
Head motion has been widely regarded as a source of signal artifact in functional magnetic
resonance imaging (fMRI) that can be very difficult to distinguish from the brain activity signals
of interest1-6. Random head motion has been shown to decrease the number of activated voxels
detected in brain activation maps (false negative brain activity)6, whereas head motion correlated
with task-related behavior (particularly associated with motor performance)6 during fMRI has
led to false positive brain activity in both simulated and real data acquisition4,5. The threshold
for acceptable head motion has been shown to be approximately 1 mm, where motion exceeding
this threshold causes a significant increase in image artifact7-9.
Functional MRI is a widely used neuroimaging tool for the assessment of the natural processes
of aging, as well as neurological disorders such as stroke10,11. Participants in these groups have
demonstrated higher magnitudes of head motion during visually stimulated motor tasks6,12. For
example, it is believed that stroke patients have difficulty with head motion during motor tasks
due to the recruitment (co-contraction) of proximal muscles in their attempts to perform tasks
involving more distal muscles6. This is unfortunate, because fMRI has potential to provide new
and important information regarding how individuals recover from stroke, and to inform how
treatments can be developed to improve stroke recovery11.
It has also been shown6 that stroke participants exhibit significantly more task-correlated head
motion in the inferior-superior direction. Motion in this direction is considered “through-plane”
on a conventional axial (or oblique axial) fMRI scan and results in artifacts that are more
difficult to remove than those produced by motion in the orthogonal directions. Through-plane