Download Effect of field inhomogeneity due to head motion on BOLD fMRI signal

Effect of field inhomogeneity due to head motion on BOLD fMRI signal
Anahita Talebi Amiri1,2, F. Işik Karahanoğlu3, Paul Wighton2, Dara Manoach4, Dimitri Van De Ville1
André van der Kouwe2
1Ecole
Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, 2Massachusetts General Hospital, A.A. Martinos
Center for Biomedical Imaging, Charlestown, MA, USA, 3Massachusetts General Hospital, Harvard Medical
School, Boston, MA, USA, 4Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
Introduction
Recent studies have focused on the effect of motion on the BOLD
signal in terms of geometry, neglecting how BOLD is influenced by
field inhomogeneity (FI) [1-2]. Even after real-time motion tracking or
image registration, changes in field due to motion cause the field to
change abruptly. We hypothesize a relationship between the amount
of change in the field, reflected in the phase map, and the
proportion of variation in the BOLD signal, through the influence
∗
of FI on 𝑻𝟐 .
Results
Effect of field inhomogeneity (FI) in the MR images
Mean signal intensity
• Problem 1: Geometric distortion
• Problem 2: Decrements in the baseline of signal
intensity .
8
• Problem 3:
BOLD fMRI signal
Variation in
Changes in
6
BOLD fMRI
amplitude
4
signal before
and after
2
movement are
0
not coherent.
There is not any
-2
solution for this
Stimulus
issue, yet.
-4
0
10
20
30
Volume
40
50
60
Data acquisition and methods
Acquiring phase and magnitude images:
• under water-excitation condition to suppress shifted fat
Brain
activation
during
visual
stimulation based on estimated 𝑻𝟐∗ .
Accurately modeling the relationship
between field changes and changes in
T2* may provide a method to correct
for the secondary effect of motion on
the BOLD signal in brain activation
studies.
Histogram of estimated FI using Eq. 2 and 3
Distribution of FI using Eq. 2 and 3
(water phantom), in a well-shimmed
state. Little variation of FI is expected.
𝑭𝑰𝑻𝟐∗ shows far less variation than
𝑭𝑰𝒑𝒉𝒂𝒔𝒆−𝒎𝒂𝒑 . This suggests that these
methods are not equivalent.
Average signal intensity inside VOI
A11 =
-241.8
[uT/m]
942
938
934
A11 =
-191.8
[uT/m]
A11 =
-216.8
[uT/m]
930
100
90
80
70
60
50
40
30
20
10
0
-80
(1)
𝑰 = 𝑰𝟎
• a 3D EPI pulse seq. to eliminate the spin-history effect
d)
c)
Whole brain is excited multiple times and after each
excitation, a slice of k-space is acquired. Volumes depict kspace.
To prove that FI decreases signal intensity, the data sets
are acquired:
• from a water phantom with 4 different 𝑇𝐸s (FLASH
pulse sequence)
• for each measurement, FI was manipulated by
applying a field gradient along the X-axis
To test whether equations 2 and 3 are equivalent [1-3]:
• 12 data sets with 𝑇𝐸s between 2 and 24 ms (step size
2 ms) were collected on the water phantom.
• For each measurement, we varied the shim
parameter, A11, between 10 and 80 µT/m (step size
10 µT/m).
𝛾
∗
′
𝑅2 = 𝑅2 + 𝑅2 = 𝑅2 +
∆𝛽 [𝐻𝑧] (2)
2𝜋
𝑭𝑰𝑻𝟐∗ = 𝑹𝟐∗ − 𝑹𝟐 [𝑯𝒛]
𝑭𝑰𝒑𝒉𝒂𝒔𝒆−𝒎𝒂𝒑 = 𝜸∆𝜷 =
[𝑯𝒛], 𝜸 =
𝜸
𝟐𝝅
-40
-10
-20
-30
0
10
• The motion-induced signal drop cannot be fully eliminated by
regressing out the motion parameters [4-6] , i.e., the residual drop can
be explained by changes in FI. This variation may be used to compensate
for the residual signal difference.
• Accurately predicting the R2' contribution to the BOLD signal requires
more careful modeling of the underlying field variation and cannot be
deduced simply from Eq. 3.
• It is shown that 𝐹𝐼𝑇2∗ and 𝐹𝐼𝑝ℎ𝑎𝑠𝑒−𝑚𝑎𝑝 are not equivalent. Increasing FI, by
manipulating the shim, leads to a shift and broadening of the T2*
distribution and more variation of 𝑭𝑰𝒑𝒉𝒂𝒔𝒆−𝒎𝒂𝒑 .
𝑭𝑰𝑻𝟐∗ , 𝐀𝟏𝟏 = −𝟏𝟗𝟏. 𝟖𝟒
μ𝑻
[ ]
𝒎
𝐀𝟏𝟏 = −𝟐𝟎𝟏. 𝟖𝟒
80
80
60
60
40
40
20
20
0
0
3
3.5
𝑯𝒛
𝑭𝑰𝒑𝒉𝒂𝒔𝒆−𝒎𝒂𝒑 , 𝐀𝟏𝟏 = −𝟏𝟗𝟏. 𝟖𝟒
μ𝑻
[ ]
𝒎
(3)
100
80
20
0
3.5
𝑯𝒛
𝐀𝟏𝟏 = −𝟐𝟎𝟏. 𝟖𝟒
3.5
𝑯𝒛
3
4
μ𝑻
[ ]
𝒎
4
𝐀𝟏𝟏 = −𝟐𝟏𝟏. 𝟖𝟒
μ𝑻
[ ]
𝒎
50
60
60
40
40
30
20
20
20
0
-100
𝐀𝟏𝟏 = −𝟐𝟏𝟏. 𝟖𝟒
μ𝑻
[ ]
𝒎
60
40
3
4
μ𝑻
[ ]
𝒎
40
∆𝝋
𝟐𝝅∆𝑻𝑬
-50
Conclusion
−𝑻𝑬
∗
𝑻
𝒆 𝟐
b)
-60
Average signal intensity decreases
as the shim parameter increases in
the negative direction along the Xaxis. This suggests that in brain
studies, underlying field changes
due to changes in position cause
variation of the BOLD signal. We
observed concomitant variation in
the estimated T2*.
A11 =
-291.8
[uT/m]
Suppress shifted fat
To generate the brain activation map, 𝑇2∗ is estimated
(equation 1) using
• three data sets with interleaved 𝑇𝐸s
a)
-70
FI [Hz]
0 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6
presence of shifted fat
𝑭𝑰𝑻𝟐∗
𝑭𝑰𝒑𝒉𝒂𝒔𝒆−𝒎𝒂𝒑
10
-50
0
𝑯𝒛
50
100
0
-100
-50
0
50
𝑯𝒛
100
0
-100
-50
0
50
100
𝑯𝒛
Distribution of the estimated field inhomogeneity (Eq. 2 and 3) for three different
data sets. Shim values were manipulated manually.
References
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