Ahmed Khalil, Ann-Christin Ostwaldt, Till Nierhaus ... · Ahmed Khalil, Ann-Christin Ostwaldt, Till...
Transcript of Ahmed Khalil, Ann-Christin Ostwaldt, Till Nierhaus ... · Ahmed Khalil, Ann-Christin Ostwaldt, Till...
Introduction
Assessing perfusion in acute stroke is relevant to
identify patients more likely to benefit from
treatment with thrombolysis (1). Currently used
techniques either require intravenous contrast,
are unreliable, or are expensive and thus not
widely used in routine clinical practice (2).
Aim: To investigate the performance of a non-invasive method (3), which measures the relative delay in local blood oxygen level dependent (BOLD) signal using resting-state functional MRI (rsfMRI) data, for the diagnosis and
follow-up of perfusion deficits in patients with acute stroke in a clinical setting by directly comparing it to an established method for assessing brain perfusion (dynamic susceptibility contrast MRI, DSC-MRI).
Methods • 76 patients • <24 hours of ischaemic stroke onset • Perfusion (DSC-MRI) and BOLD delay (rsfMRI) maps
Results Image quality
Diagnostic agreement
Volumetric agreement
Spatial agreement
Discussion BOLD delay maps are sensitive to perfusion
deficits in acute stroke but have a high rate of
false-positives, possibly due to CSF artefacts.
BOLD delay maps agree, in terms of location, with
perfusion maps very early (<4.5h) following
stroke onset.
BOLD delay lesions underestimate perfusion
lesion volumes, resulting in low sensitivity for
perfusion-diffusion mismatch.
Head motion adversely affects the spatial
overlap between BOLD delay and perfusion
lesions, resulting in false negative BOLD delay
maps.
BOLD delay in stroke circumvents the infarct core
and specifically affects the surrounding tissue.
Contrary to previous studies (4), this suggests
that BOLD delay has neuronal rather than
vascular origins.
BOLD delay maps are reliable for following up
stroke patients, as changes in lesion volumes
between the first and second days following
stroke onset were similar between BOLD delay
and perfusion maps.
Ahmed Khalil, Ann-Christin Ostwaldt, Till Nierhaus, Kersten Villringer, and Jochen Fiebach
International Graduate Program Medical Neurosciences, Charité Universitätsmedizin Berlin
Further information [email protected]
1. BOLD signal across time in one voxel (local) and whole brain (global) are extracted.
2. Signal corresponding to one repetition time (TR) at the start and end of the voxel
time series is removed.
3. Local time series is passed through the global time series in a series of sequential
steps (step=1TR).
4. For each step, the correlation between the local and global time series is calculated.
5. 1-4 repeated for each voxel, resulting in a TSA map showing the step yielding the
maximum correlation between local and global time series.
Perfusion deficits Perfusion-diffusion
mismatch
Sensitivity 82% (66 to 92%) 54% (34 to 72%)
Specificity 41% (24 to 61%) 90% (76 to 97%)
Positive predictive value 65% (50 to 78%) 79% (54 to 94%)
Negative predictive value 63% (38 to 84%) 73% (58 to 85%)
Yield 89%
Dic
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t0 .0
0 .2
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Dic
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< 4 .5 h rs 4 .5 -9 h rs > 9 h rs
0 .0
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Longitudinal reliability
Effect of head motion
D a y 2 p e r fu s io n le s io n v o lu m e (m l)
Da
y 2
BO
LD
de
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sio
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l)
0 2 0 4 0 6 0 8 0
0
5 0
1 0 0
1 5 0
2 0 0
Ch
an
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vo
lum
e (m
l)
Perfusion BOLD delay-50
0
50
100
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Independent
variable
B P-value
Maximum motion -0.023 (-0.067 to -0.005) 0.023
Mean motion -0.157 (-0.308 to -0.006) 0.042
Number of
movements >0.5mm
-0.002 (-0.004 to -0.001) 0.006
Time shift analysis (TSA):
Pe
rc
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t o
f m
ap
s (
%)
P e r fu s io n B O L D d e la y
0
2 0
4 0
6 0
8 0
1 0 0
C e rta in
U n c e rta in
U n in te rp re ta b le
Pe
rc
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t o
f m
ap
s (
%)
P e r fu s io n B O L D d e la y
0
2 0
4 0
6 0
8 0
1 0 0
H ig h
M o d e ra te
L o w
BOLD delay Perfusion
DWI BOLD delay Perfusion
Example of a false-negative BOLD delay map associated with severe head motion. The perfusion map
shows a large lesion corresponding to the infarct on the right side.
Results of multiple logistic regression analysis: head motion is associated with decreased spatial overlap between perfusion and BOLD delay lesions. Regression coefficients (B) represent the effect sizes on the Dice similarity coefficient.
Positive correlation between BOLD delay and perfusion lesions on the second day of stroke onset (rho=0.78, p<0.0001).
Tukey boxplot showing lesion volume changes
between days 1 and 2 of stroke onset using the two
techniques (p=0.11)
Examples of high spatial overlap between BOLD delay and perfusion lesions.
Distribution of the degree of spatial overlap between BOLD delay and perfusion lesions in the study sample. Middle bar
is median, whiskers are interquartile range.
Bland-Altman plot showing the bias (20 ml, red line) and 95% limits of agreement (-117 to 157 ml , black lines) between perfusion and BOLD delay lesion volumes.
Image quality (diagnostic confidence, left; noisiness, right) of the BOLD delay and perfusion maps as assessed by the raters. The associations between map type and image quality were statistically significant (confidence, p<0.0001 ; noisiness, p<0.0001)
Coverage of the infarct core
Co
ve
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P e r fu s io n B O L D d e la y
0 .0
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1 .0
DWI BOLD delay Perfusion
Example of low
DWI lesion coverage by a
BOLD delay lesion and high coverage
by a perfusion lesion in the same
patient.
Tukey boxplots showing the degree of spatial overlap between BOLD delay and
perfusion lesions in patients presenting in different time
periods following stroke onset (p=0.26).
Values in brackets are 95% confidence intervals
Values in brackets are 95% confidence intervals
Acknowledgments Thanks to Peter Brunecker for the technical advice and
support, and Alexander Nave and Peter Koch for helping
with the assessment of the maps.
Proportion of voxels in the DWI lesion covered by perfusion and BOLD delay lesions (p=0.005).
NO DELAY
DELAY
Literature cited (1) Thomalla et al Stroke 2006
(2) Goyal et al Radiology 2013
(3) Lv et al Annals of Neurology 2013
(4) Amemiya et al Radiology 2013