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Computed tomography and magnetic resonance perfusion imaging in ischemic stroke Definitions and thresholds.

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ORIGINAL ARTICLE
Computed Tomography and Magnetic
Resonance Perfusion Imaging in Ischemic
Stroke: Definitions and Thresholds
Krishna A. Dani, BSc (Hons), MBChB, MRCP,1,3 Ralph G.R. Thomas, MB ChB, BSc,
MRCP,2,3 Francesca M. Chappell, BSc, MSc, PhD,2,3 Kirsten Shuler, BSc,2,3
Mary J. MacLeod, MB ChB, FRCP, PhD,4 Keith W. Muir, MD, FRCP,1,3 and Joanna M.
Wardlaw, MB ChB, FRCR, FRCP, FMedSci2,3 on behalf of the Translational Medicine
Research Collaboration Multicentre Acute Stroke Imaging Study
Objective: Cerebral perfusion imaging with computed tomography (CT) or magnetic resonance (MR) is widely
available. The optimum perfusion values to identify tissue at risk of infarction in acute stroke are unclear. We
systematically reviewed CT and MR perfusion imaging in acute ischemic stroke.
Methods: We searched for papers on MR or CT perfusion performed <24 hours after stroke that assessed perfusion
thresholds, mean perfusion lesion values, or lesion volumes. We extracted definitions and perfusion values. We
compared definitions and evaluated perfusion thresholds for ‘‘nonviable’’/’’at risk’’ and ‘‘at risk’’/’’not at risk tissue’’
thresholds.
Results: Among 7,152 papers, 69 met inclusion criteria for analysis of definitions (49 MR and 20 CT), 21 MR (n ¼
551), and 10 CT (n ¼ 266) papers, median sample size 22, provided thresholds. We found multiple definitions for
tissue states, eg, tissue at risk, 18; nonviable tissue, 12; 16, no definition. Perfusion parameters varied widely; eg, 9
different MR, 6 different CT parameters for the ‘‘at risk’’/’’not at risk threshold.’’ Median threshold values varied up
to 4-fold, eg, for the ‘‘at risk’’/’’not at risk threshold,’’ median cerebral blood flow ranged from 18 to 37ml/100g/min;
mean transit time from 1.8 to 8.3 seconds relative to the contralateral side. The influence of reperfusion and
duration of ischemia could not be assessed.
Interpretation: CT and MR perfusion imaging viability thresholds in stroke are derived from small numbers of
patients, variable perfusion analysis methods and definitions of tissue states. Greater consistency of methods would
help determine reliable perfusion viability values for wider clinical use of perfusion imaging.
ANN NEUROL 2011;70:384–401
T
he target region of tissue for reperfusion therapy in
ischemic stroke is that which is hypoperfused and at
risk of infarction, but still viable—the ‘‘ischemic penumbra’’—as salvage of this tissue may improve functional
outcome. This tissue needs to be distinguished from definitely nonviable and from not at risk/normal tissue. In
experimental models, tissue viability was originally determined by reductions in cerebral blood flow (CBF) that
lead first to reversible functional neuronal electrical shutdown, then to failure of the cell membrane ion pumps
(‘‘at risk but still viable’’ or ‘‘penumbral’’ tissue), and
finally to cell death.1 An intermediate phase in which
CBF was reduced but not to the point of electrical shutdown was termed ‘‘benign oligemia.’’ Hence penumbral
tissue has been defined on quantitative perfusion thresholds,2 with similar viability threshold values found in
View this article online at wileyonlinelibrary.com. DOI: 10.1002/ana.22500
Received Mar 29, 2011, and in revised form May 6, 2011. Accepted for publication May 27, 2011.
Address correspondence to Prof Wardlaw, SINAPSE Collaboration, Brain Research Imaging Centre, Division of Clinical Neurosciences, Western General
Hospital, Crewe Rd, Edinburgh, Scotland EH4 2XU, UK. E-mail: joanna.wardlaw@ed.ac.uk
K.A.D. and R.G.R. contributed equally to this work.
1
From the Department of Neurology, Institute of Neurological Sciences, University of Glasgow Southern General Hospital, Glasgow, Scotland; 2Division of
Clinical Neurosciences, University of Edinburgh, Western General Hospital, Edinburgh, Scotland; 3Scottish Imaging Network, A Platform for Scientific
Collaboration (SINAPSE), Edinburgh, Scotland; 4Institute of Medical Sciences, University of Aberdeen, Aberdeen Royal Infirmary, Aberdeen.
Information on the Translational Medicine Research Collaboration Multicentre Acute Stroke Imaging Study is listed in the Appendix on page xx.
Additional Supporting Information can be found in the online version of this article.
C 2011 American Neurological Association
384 V
Dani et al: Perfusion Imaging in Stroke
patients using positron emission tomography (PET)
imaging.3,4
Magnetic resonance (MR) perfusion weighted imaging (PWI) and computed tomography perfusion imaging
(CTP), performed following a bolus intravenous injection of contrast agent, are now widely available. If perfusion imaging is to be used to guide treatment decisions
by identifying at risk but still viable tissue, then it should
differentiate benignly oligemic, at risk, and nonviable tissue. A previous systematic review,5 just of CBF in acute
stroke, found wide variation in tissue viability thresholds,
but included all perfusion imaging modalities and both
patients and experimental models, so some variation
could have resulted from the different perfusion imaging
techniques or species. Differing estimates of the extent of
tissue at risk result from use of different perfusion parameters,6 and it is unclear which is most reliable. Variation in case mix, acquisition parameters,7 use of acute
treatments, and in definitions of viable and nonviable tissue may further contribute to variability in PWI and
CTP thresholds but few if any of these factors have been
explored in previous studies or meta-analyses.
As several new, larger studies have been published
since 2005, the contribution of many factors to perfusion
value variability has not been assessed, and CT and MR
perfusion imaging are now widely available and used
increasingly in stroke clinical assessment, we systematically reviewed all published information on tissue viability thresholds measured with CT or MR contrast bolus
perfusion imaging, assessed study quality, definitions of
tissue viability, and hence the strength of the current
evidence.
Materials and Methods
We performed a systematic review of the literature according to
Cochrane Collaboration principles, taking into account specific
modifications for diagnostic tests8 and observational studies as
described in the Equator Network (www.equatornetwork.org).
Search Strategy
We searched MEDLINE and EMBASE (inception to the week
of August 4, 2009) using the following terms: stroke, brain ischemia, cerebral ischemia, magnetic resonance angiography, cerebral angiography, perfusion MRI, and perfusion CT (Supporting Appendix 1). We limited the electronic search to studies
published in English (due to resource limitations) and of adult
humans. We removed duplicate studies retrieved from both
MEDLINE and EMBASE and screened the remaining titles
and abstracts. We validated the electronic search by hand
searching review article references and the journals Stroke, American Journal of Neuroradiology (August 2003 to end July 2008),
and www.strokecenter.org.9 All potentially relevant studies were
evaluated by 2 reviewers independently, agreed a shortlist for
September 2011
inclusion by consensus, and a third reviewer (J.W.) arbitrated
over discrepancies.
Inclusion and Exclusion Criteria
We included studies of patients within 24 hours of symptom
onset, aged over 18 years, with perfusion imaging (MR or CT)
using bolus tracking of the first pass of an intravenous contrast
agent to measure thresholds between nonviable, at risk, and not
at risk/normal tissue, or mean values of perfusion parameters in
different tissue regions, or volumes of tissue regions for correlation with clinical or radiological parameters.
We excluded studies of: children (aged <18 years); animals and patients with hemorrhagic stroke, venous infarction,
chronic occlusive cerebrovascular disease; studies using steady
state techniques, arterial spin labeling, xenon-CT, single photon
emission computed tomography (SPECT); technical studies
describing development of new perfusion imaging techniques;
and papers that applied prespecified threshold values to delineate and measure a perfusion lesion volume. Studies using radioisotope scans (ie, positron emission tomography [PET] or
SPECT) were only included when these scans had been used to
validate CT or MR perfusion parameters. Where 2 papers contained the same study populations, both were included if they
reported different analyses but we only counted the patients
once.
Data Extraction
We assessed study quality using the QUADAS tool8 modified
for assessment of perfusion studies in stroke patients (Supporting Appendix 2). We extracted data on study design, patient
population and size, clinical and neurological assessments, definitions of thresholds and ischemic tissue, use of reperfusion
therapies, assessment of recanalization, clinical and radiological
outcomes and their timing, image acquisition, processing, and
validation, statistical methods, the perfusion parameters assessed
and threshold values, including whether these applied to gray
or white matter or mixed gray and white matter. Two reviewers
extracted the data, entered data into a spreadsheet and crosschecked a random sample; others cross-checked extracted data
or arbitrated over discrepancies.
We scrutinized the way that nonviable, at risk, and not at
risk tissues were identified. To standardize definitions, we condensed the individual studies’ definitions into three predefined
categories representing ‘‘nonviable,’’ ‘‘at risk,’’ and ‘‘not at risk’’
tissue according to how the tissue had been used in each study,
taking account of the time lapse between stroke and imaging
and whether the occluded artery had recanalized.
Three reviewers (R.G.R.T., K.A.D., and J.M.W.)
extracted the data from the papers. Data entry into a spreadsheet was divided between 2 reviewers (R.G.R.T. and K.A.D.),
who cross-checked a random sample of papers, and a third
reviewer (J.M.W.) arbitrated over discrepancies. Two other
reviewers (F.M.C. and K.S.) cross-checked data accuracy in the
spreadsheet. We were able to clarify some, but not all details of
overlapping study populations with original study authors.
385
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FIGURE 1: Flow chart depicting number of papers retrieved from electronic and manual search strategies. Note the papers
contributing to the analysis of definitions are the 69 in the ‘‘eligible papers for data extraction.’’ AJNR 5 American Journal of
Neuroradiology; ASL 5 arterial spin labeling; PET 5 positron emission tomography; SPECT 5 single photon emission computed tomography; XeCT 5 xenon computed tomography.
Data Analysis
We summarized study population demographics, design features
and QUADAS scores. We listed definitions of tissue states,
tabulated definitions for all tissue states and created diagrams
for the at risk tissue to display the range and complexity of the
different definitions. For the threshold analysis, we only
included studies which determined perfusion threshold values
for ‘‘nonviable’’ vs ‘‘at risk’’ and/or ‘‘at risk’’ vs ‘‘not at risk’’ tissue.
We identified the number of studies and patients contributing
data to each threshold per perfusion parameter, by reperfusion
386
status where available and compared the threshold values for
each tissue category by perfusion parameter against the experimentally-derived values. Prespecified sensitivity analyses
included the effect of time to imaging, recanalization status,
and thrombolysis/reperfusion therapy on threshold values.
Results
Search Results
The electronic search retrieved 7,152 papers (Fig 1).
Removal of duplicates and irrelevant papers left 273
Volume 70, No. 3
Dani et al: Perfusion Imaging in Stroke
abstracts. Further references were found through hand
searching bibliographies of review articles (n ¼ 16)5,10–24
and 1 further paper from manual searching of journals
and www.strokecenter.org,25 giving 289 papers to be
appraised. Of these, 220 papers were rejected based on
the full text, leaving 69 potentially relevant papers (49
MR and 20 CT). Data on definitions were obtained
from all 69 papers; 21 MR25–44 and 10 CT45–54 papers,
published by 19 different research groups worldwide,
provided threshold data.
Definitions of Tissue States
NONVIABLE TISSUE.. There were 8 different defini-
tions of nonviable tissue given in 43 of 49 MR and 4
different definitions in 10 of 20 CT studies (Table 1).
Most MR studies (28/49) used the acute diffusion
weighted image (DWI) lesion to indicate nonviable tissue, whereas the commonest CT study definition (6/10
that gave a definition) was the visible infarct on followup scanning performed at between 1 and 90 days. Six
MR and 10 CT studies gave no definition.
MR studies either used contemporaneous PET scans (n ¼ 5), or single or serial imaging at
various times after stroke. There were 10 different definitions in the MR studies and 8 different definitions in the
CT studies (see Table 1; Fig 2). Of 49 MR studies, 30
used only 1 definition, 14 used more than 1 definition,
and 5 studies did not define tissue at risk.34,42,55–57 Of
20 CT studies, 13 used 1 definition and 7 used more
than 1 definition.
Examination of Figure 2 indicates that these definitions result in many-fold differences in the extent of tissue defined as tissue at risk, both in MR and CT studies.
For example between studies that used DWI lesion
expansion (a in Fig 2A)33,37,58–62 and the DWI/PWI
mismatch tissue (see e in Fig 2A);36,58,63,64 or between
studies that used the entire region of hypoperfusion seen
on CTP (see a in Fig 2B)65–70 and the tissue present in
the final infarct (on DWI, T2, fluid attenuation inversion recovery [FLAIR], or CT; see b in Fig
2B),46,48,50,51,66–73
AT RISK TISSUE.
NOT AT RISK/NORMAL TISSUE. There were 6 defini-
tions for not at risk or normal tissue in 20 of 49 MR
studies, and 2 methods in 11 of 20 CT studies (see Table
1). Of those that provided a definition, most MR and
CT studies (14/20 and 10/11, respectively) used the perfusion value obtained from a mirror image region in the
contralateral hemisphere. Definitions were not specified
in 29 of 49 MR and 9 of 20 CT studies.
September 2011
The remainder of the results refer to the 21 MR
and 10 CT papers that provided threshold data.
Study Quality and Design
Scoring on modified QUADAS quality criteria was generally poor (Supporting Fig 1): just over one-half of studies described the patient selection criteria, assessment of
final outcome, or the image acquisition and analysis
adequately. Few studies mentioned blinding of perfusion
analysis to final infarct volume or of final infarct volume
to perfusion imaging and no studies provided a sample
size calculation or tested their data for normality prior to
analysis.
All studies were observational cohort studies, prospective in 12 MR25,26,28,30,32,33,35,37,40,42,43 and 6
CT,45,46,49,52–54 retrospective or not stated in the rest
(Supporting Table 1). No studies used a clinical functional outcome in the threshold analyses. Four
MR30,32,42,44 and 5 CT studies45–47,50,52 presented data
according to recanalization, but not for both recanalized
and nonrecanalized patients (details upon request). Perfusion thresholds were validated using perfusion values
obtained from PET imaging performed contemporaneously with MR imaging in 3 of 21 studies26–28 and with
an MR or CT imaging outcome, eg, infarct extent on
follow-up imaging, in 18 of 21 MR25,29–44,74 and all 10
CT studies, but only about one-half gave the timing of
follow-up imaging45–47,49,51,52 (Supporting Table 1),
which ranged from less than 7 days,29,34,36,39,74 to 90
days.35 Statistical analyses included voxel-based ROC
curve analysis in 11 studies (none corrected for multiple
intercorrelated voxels) and various correlation analyses
(univariate and multivariate) in the rest (4 did not provide details).
Study Populations
The median number of patients that provided thresholds
per study (‘‘final N,’’ Supporting Table 1) was 19 (range
5–97) for MR and 26 (range 13–101) for CT, overall
median 22. The total number of patients contributing
any threshold data was 551 for MR and 266 for CT, but
most of these did not contribute to each threshold: not
at risk/at risk threshold, n ¼ 399 (MR) and n ¼ 146
(CT); at risk/not viable threshold, n ¼ 55 (MR) and n
¼ 145 (CT). Three CT studies provided both thresholds.45–47 The same patients were definitely used in 3
pairs of studies from which we counted the patients only
once: Murphy and colleagues (2006)45 and Murphy and
colleagues (2008)52; Koenig and colleagues49 and Klotz
and Konig54; Sobesky and colleagues28 and Heiss and
colleagues.27 We were unable to establish the overlap in
patients, if any, that contributed to the analyses in 3
387
388
Nonviable
(range of days
to follow-up
imaging
where
applicable)
Tissue State
36, 101, 102
3
2
2
1
1
Tissue in both acute DWI
and final infarct (1–1,183
days)
Region of acute ADC
decline
Abnormal tissue in both
acute DWI and ADC map
ADC threshold: 80% of
that in normal hemisphere
CBV threshold: <2ml/
100g
6 including 5
PET studies
40, 63, 99, 100
4
Final infarct on follow up
T2, FLAIR, or CT (at 6–
79 days)
No definition
29, 42
2
Apparently ischemic tissue
on follow up DWI (at 4–7
days)
26–28, 105–107
38
74
37, 103
31, 32
6, 25, 30, 33, 34, 35
39, 41, 43, 44,
55–62, 64, 90–98
28
Apparently ischemic tissue
on acute DWI
References
n
Definition
MR
No definition
Tissue with ;CBF and
;CBV that is also within
final infarct (at 1–12 days)
CBF threshold: 12ml/
100g/min
Final infarct on follow up
(at 1–90 days)
Apparently ischemic tissue
on subacute/follow up
DWI (at 0.5–10 days)
Definition
10
1
1
6
2
n
CT
45, 48, 50, 51,
65–67, 69, 73, 108
47
104
46, 49, 52, 54, 68, 72
53, 71
References
TABLE 1: Definitions for Nonviable, at Risk, and Not at Risk Tissue Used in the 49 MR and 20 CT Studies Included in the Systematic Review of Perfusion
Imaging in Acute Ischemic Stroke
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Volume 70, No. 3
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At risk
Tissue State
References
33, 37, 58–62
(a) 6, 39, 41, 44
63, 90, 91, 92, 100
101, 103;
(b) 31, 32, 74, 103
29, 38, 40
93–96, 98, 107
58
36, 58, 63, 64
25, 32, 35, 37, 97
100, 102
7
(a) 11; (b) 4
9
1
4
7
The region of tissue present in follow up but not
acute DWI lesion (ie,
DWI lesion expansion).
The region of tissue present in the final infarct (on
T2, FLAIR, or CT), but
not present in either: (a)
the acute DWI lesion; or
(b) the acute ADC lesion
The entire final infarct,
measured on CT, T2, or
FLAIR
The entire region of tissue
present in the acute PWI
lesion
The region of tissue present in the acute PWI
lesion, but not in the acute
DWI lesion (DWI/PWI
mismatch)
Tissue in the acute DWI/
PWI mismatch region that
becomes part of the final
infarct (mismatch that
infarcts)
MR
n
Definition
TABLE 1 (Continued)
65
66
67
46, 48, 50, 51,
66–69, 71, 72
70
69
68
70
1
1
1
10
1
1
1
1
The entire region of hypoperfusion seen on CTP,
using MTT alone
The entire region of hypoperfusion seen on CTP,
using CBF alone
The entire region of hypoperfusion seen on CTP,
using MTT and CBV
The region of tissue present in the final infarct (on
DWI, T2, FLAIR, or CT)
The region of tissue present in the final infarct (on
DWI, T2, FLAIR, or CT)
expressed as a percentage
of the affected hemisphere
The entire region of hypoperfusion seen on CTP,
using by expressing CTP
lesion as a percentage of
the affected hemisphere
The entire region of hypoperfusion seen on CTP,
using TTP and CBV
The entire region of hypoperfusion seen on CTP,
using CBF, CBV, and TTP
References
n
Definition
CT
Dani et al: Perfusion Imaging in Stroke
389
Tissue State
390
References
25, 35, 90, 97, 101
102, 103
30, 32, 43, 61, 99
26–28, 105, 106
103
n
7
5
5
1
Tissue in the acute DWI/
PWI mismatch region that
does not become part of
the final infarct (mismatch
that survives)
Tissue in the acute PWI
lesion that does not
become part of the final
infarct
Tissue that satisfies criteria
for at risk tissue on PET
scan
Tissue that is present in
the acute DWI lesion, but
not in the final infarct
(DWI reversal)
MR
Definition
TABLE 1 (Continued)
73
49, 54, 73
52
45
53
47
47
1
3
1
1
1
1
1
The region of hypoperfusion on CTP minus the
final infarct in the gray
matter of patients who
recanalized
The region of hypoperfusion on CTP minus DWI
hyperintensity at 12 hours
Tissue in the final infarct
which had normal CBV
and reduced CBF on the
acute CTP scan (equivalent of DWI/PWI mismatch tissue that infarcts)
Normal tissue on follow
up which had normal
CBV but reduced CBF on
the acute CTP scan,
(equivalent of DWI/PWI
mismatch that survives)
The region of hypoperfusion on CTP minus the
final infarct in the white
matter of patients who
recanalized
The region of hypoperfusion on CTP minus the
final infarct in mixed gray/
white matter, irrespective
of recanalization status
The region of tissue present in the final infarct (on
DWI, T2, FLAIR, or CT)
expressed as a percentage
of the region of
hypoperfusion
References
n
Definition
CT
ANNALS
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Volume 70, No. 3
September 2011
43
35
38
1
1
1
1
29
Tissue out with brain
mask covering acute MTT
lesion and final infarct
Tissue other than ischemic
core, hypoperfused region
and final infarct
ROI in gray and white
matter cranial to lateral
ventricles
ROI outlining contralateral
MCA
No definition
6, 27, 28, 30, 34, 39
42, 44, 55–60, 63, 64
90, 91–96, 98, 100
103, 105–107
29
74, 101
2
Whole contralateral
hemisphere
25, 26, 31, 32, 33, 36
37, 40, 41, 61, 62
97, 99, 102
References
14
n
Mirrored ROI
Definition
MR
10
Mirrored ROI
No definition
9
1
73
1
Tissue that is hypoperfused
on acute CTP, but not on
follow up CTP at 24
hours, expressed as a percentage of the acute CTP
lesion (the reperfusion
area)
CBF threshold: >24ml/
100g/min
104
1
Tissue identified by predetermined CBF thresholds
(>12 but 24ml/100g/
min)
46, 51, 65–69, 73, 108
104
45, 47–50, 52–54, 71, 72
References
n
Definition
CT
Where possible, definitions for MR and CT that are broadly similar have been aligned in rows, but many definitions have no direct counterpart so appear in the same row as an apparently unrelated definition on the alternative modality.
ADC ¼ apparent diffusion coefficient; CBF ¼ cerebral blood flow; CBV ¼ cerebral blood flow; CT ¼ computed tomography; CTP ¼ computed tomography perfusion imaging;
DWI ¼ diffusion weighted imaging; FLAIR ¼ fluid attenuation inversion recovery; MCA ¼ middle cerebral artery; MR ¼ magnetic resonance; MTT ¼ mean transit time; PET ¼
positron emission tomography; PWI ¼ perfusion weighted imaging; ROI ¼ region of interest; TTP ¼ time to peak.
Not at risk/
normal tissue
Tissue State
TABLE 1 (Continued)
Dani et al: Perfusion Imaging in Stroke
391
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further groups of MR studies (first group,32,44,74 second
group,36,39,41 and third group38,40). Assuming that these
studies shared all their patients, then the maximum number of patients contributing threshold data from the 21
MR studies would be 397, not 551.
The mean ages ranged from 56 to 76 years (range
27–91) in MR and from 65 to 79 years (range 24–92)
in CT studies. Where mentioned, the mean or median
NIH Stroke Scale (NIHSS) scores on admission ranged
from 6 to 16 (16 MR studies26–30,32,33,35,37–39,41–44,74
and 4 CT studies45–47,52). Average time from stroke to
imaging was 90 to 978 minutes (mean/median; range
15–1,248 minutes). Some studies included patients
who received open label thrombolysis (9 MR
studies,27,29,30,32,38,42–44,74 n ¼ 162; 7/10 CT
studies,45–47,49,50,52,54 n ¼ 126), or patients randomized
to thrombolysis vs placebo (1 MR study37), or patients
treated with mechanical recanalization (2 CT studies,47,48
n ¼ 24).
~
Perfusion Thresholds
In general, there were more data for at risk/not at risk
than for nonviable/at risk thresholds. Few studies
reported data for both thresholds.45–47 Most MR and all
the CT studies reported perfusion values for combined
gray/white matter together rather than gray and white
matter separately. Additionally, each study tended to
report different perfusion parameters, further restricting
data comparisons. For example, 18 of 21 MR studies (n
¼ 399)25–41,44 and 7 of 10 CT studies (n ¼ 146)45–51
provided data on the not at risk/at risk threshold using 9
and 6 different perfusion parameters respectively; 3 of 21
MR (n ¼ 55)42,43,74 and 6 of 10 CT (n ¼ 145)45–47,52–
54
studies provided data on the nonviable/at risk threshold using 5 and 5 different perfusion parameters, respectively (Tables 2 and 3). The most commonly reported
perfusion values are summarized in Figure 3. There were
wide confidence intervals for each median threshold
value (where given) and wide variation between median
threshold values, resulting in substantial scatter in the
MR and CT perfusion threshold values compared with
392
FIGURE 2: Schematic diagram of definitions of tissue at risk
(highlighted in yellow) used in studies of perfusion imaging
in acute ischemic stroke. (A) MR studies: (a) DWI lesion
expansion. (b) Tissue in final infarct not part of acute DWI
(or ADC) lesion. (c) Entire final infarct tissue. (d) Entire acute
PWI lesion. (e) Acute DWI/PWI mismatch tissue. (f) Mismatch
that infarcts. (g) Mismatch that survives. (h) Tissue in acute
PWI lesion that survives. (i) Region of hypoperfusion defined
on a coregistered PET scan. (j) Tissue in acute DWI lesion
that survives (DWI reversal). (B) CT studies: (a) Entire CTP
lesion. (b) Entire final infarct tissue. (c) CTP lesion minus
final infarct tissue. (d) CTP lesion minus subacute DWI
hyperintensity. (e) Tissue in final infarct that had normal
CBV but reduced CBF acutely (mismatch that infarcts). (f)
Tissue that survives and had normal CBV, but reduced CBF
acutely (mismatch that survives). (g) Tissue identified by CBF
threshold >12ml/100g/min but <24ml/100g/min. (h) Hypoperfused tissue on acute CTP that is normal on follow up
CTP (reperfusion area). ADC 5 apparent diffusion coefficient; CBF 5 cerebral blood flow; CBV 5 cerebral blood volume; CT 5 computed tomography; CTP 5 computed
tomography perfusion imaging; DWI 5 diffusion weighted
imaging; MR 5 magnetic resonance; PET 5 positron emission tomography; PWI 5 perfusion weighted imaging.
Volume 70, No. 3
September 2011
6.8 (63.5, n
¼ 9)43
—
—
—
—
—
1.78 (72%, 75%, n ¼ 37)44,43; 4.0 (n ¼
12)29; 6.1 (68%, 91%, n ¼ 5)26a; 7.0
(57%, 93%, n ¼ 5)26b; 7.6 (44%, 83%, n
¼ 18)41; 8.1 (52%, 64%, n ¼ 48)39
0.58 (69%, 89%, n ¼ 18)41; 0.59 (91%,
73%, n ¼ 11)25; 0.61 (92%, 91%, n ¼
17)31
0.82 (56%, 95%, n ¼ 19)32; 0.85 (64%,
73%, n ¼ 11)25; 1.07 (88%, 82%, n ¼
17)31
1.63 (91%, 73%, n ¼ 11)25; 2.0 (n ¼
39)25; 2.11 (56%, 73%, n ¼ 17)31
1.45 (63%, 79%, n ¼ 37)44; 2.0 (n ¼
39)37; 4.0 (n ¼ 16)30; 5.40 (66%, 93%, n
¼ 5)26a; 5.46 (48%, 92%, n ¼ 5)26b
4.0 (n ¼ 39)37; 4.0 (82.7%, 76.8%, n ¼
11)27; 4.74 (41%, 94%, n ¼ 5)26b; 4.79
(66%, 97%, n ¼ 5)26a; 6.0 (76.5%, 87.5%,
n ¼ 11)27; 6.0 (77%, 86%, n ¼ 11)28; 6.0
(n ¼ 46)40; 7.0 (96.3%, 79.9%, n ¼ 11)34;
7.0 (50%, 80%, n ¼ 18)41
—
—
—
—
6 (71%, 63%,
n ¼ 14)40
—
MTT (seconds)
expressed as the
delay relative to
the opposite
hemisphere
rCBF
rCBV
rMTT
Tmax (seconds)
expressed as
delay relative to
the opposite
hemisphere
TTP (seconds)
—
—
—
0.87 (83%, 82%,
n ¼ 13)33
0.48 (88%, 66%,
n ¼ 13)33
4.94 (4.44–5.38,
61.8%, 62.3%, n
¼ 21)35
1.67 (1.39–2.17,
63.7%, 64.4%, n
¼ 21)35
34.6 (26.0–38.8,
64.6%, 65.4%, n
¼ 21)35
At Risk/Not at
Risk Threshold
—
—
—
—
—
—
—
—
—
—
5.15 (4.11–5.68,
64.1%, 65%, n
¼ 21)35
1.19 (0.94–1.53,
61.9%, 62.8%, n
¼ 21)35
1.7 (61.5, n
¼ 9)43
7.1 (63.4, n
¼ 9)43
20.8 (18.0–25.9,
65.9%, 67.2%, n
¼ 21)35
At Risk/Not at
Risk Threshold
12.3 (64.9, n
¼ 9)43
Nonviable/At
Risk
Threshold
White Matter
The standard deviation (6) or 95% confidence interval (X–Y), the sensitivity and specificity (where given) and number of patients are shown in parentheses.
a
Validated against PET CBF threshold <20ml/100g/min.
b
Validated 3 probabilistic PET criteria (CBF < 20ml/100g/min, OEF > 0.55 relative to controls, and CMRO2 > 63lmol/100g/min).CBF ¼ cerebral blood flow; CBV ¼ cerebral
blood volume; MTT ¼ mean transit time; OEF ¼ oxygen extraction fraction; PET ¼ positron emission tomography; rCBF ¼ regional cerebral blood flow; rCBV ¼ regional cerebral
blood volume; rMTT ¼ regional mean transit time; Tmax ¼ time to peak of the residue function; TTP ¼ time to peak.
2.4 (61.9, n
¼ 9)43
—
—
20 (610.2, n
¼ 9)43
18.0 (n ¼ 28)36; 25.0 (611, n ¼ 47)38;
35.0 (69%, 85%, n ¼ 18)41; 37.2 (80%,
37%, n ¼ 48)39
CBV (ml/100g)
Nonviable/At
Risk
Threshold
At Risk/Not at Risk Threshold
Gray Matter
12 (71%,
66%, n ¼
32)74
Nonviable/At
Risk
Threshold
Mixed Gray/White Matter
CBF (ml/100g/
min)
Parameter
TABLE 2: Threshold Values for MR Perfusion Imaging Parameters in Mixed Gray/White Matter, Gray Matter, and White Matter, Between Nonviable and
At Risk Tissue, and Between At Risk and Not at Risk Tissue
Dani et al: Perfusion Imaging in Stroke
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TABLE 3: Threshold Values for CT Perfusion Imaging Parameters in Mixed Gray/White Matter, Gray Matter,
and White Matter, Differentiating between Nonviable and At Risk Tissue, and between At Risk and Not at
Risk Tissue
Parameter
Mixed Gray/White Matter
Gray Matter
White Matter
Nonviable/At
Risk
Threshold
At Risk/Not
at Risk
Threshold
Nonviable/At
Risk
Threshold
At Risk/Not
at Risk
Threshold
Nonviable/At
Risk
Threshold
At Risk/Not
at Risk
Threshold
CBF (ml/
100g/min)
—
27.9 (91%,
75%,
n ¼ 13)50
—
25 (n ¼ 9)44
9a (81%,
91%,
n ¼ 16)51
—
CBV (ml/
100g)
2.0/2.3
(n ¼ 25/
49)45b
1.69 (84%,
59%,
n ¼ 13)50
—
—
0.82 (76%,
88%,
n ¼ 16)51
—
MTT
(seconds)
6.05
(84.6%,100%,
n ¼ 13)52
6.53 (84%,
86%, n ¼
13)50; 7.0
(n ¼ 46)45
—
—
—
—
rCBF
0.34
(n ¼ 14)46
0.50 (n ¼
14)46; 0.63
(91%, 71%,
n ¼ 13)50;
0.5
(n ¼ 26)47
0.20
(n ¼ 38)53
0.48 (76.1%,
73%,
n ¼ 34)48
—
—
rCBV
—
0.85 (69%,
71%, n ¼
13)50; 0.90
(n ¼ 26)47
—
0.60 (80.4%,
86.5%,
n ¼ 34)48
—
—
rMTT
—
1.45 (n ¼
46)45; 2.20
(88%, 89%,
n ¼ 13)50
—
1.60
(n ¼ 4)49
—
—
CBF*CBV
—
—
31.3 (97%,
97.2%,
n ¼ 16)44c
—
8.14 (97%,
72.2%,
n ¼ 16)51c
—
The sensitivity and specificity (where given) and number of patients contributing to the data are given in parentheses.
a
Specific values not reported by this study, but threshold reported in graphical form (9ml/100g/min).
b
Threshold was 2.0ml/100g based on DWI lesion, or on final infarct when this was small; 2.3ml/100g based on final infarct when
this was intermediate (50ml) or large (80ml). Ten patients overlapped between these 2 groups of patients.
c
The interaction term CBV*CBF was found to be superior to CBV or CBF alone in logistic regression analysis, suggesting the
CBV threshold for infarction may depend on the CBF.
CBF ¼ cerebral blood flow; CBV ¼ cerebral blood volume; MTT ¼ mean transit time; rCBF ¼ regional cerebral blood flow;
rCBV ¼ regional cerebral blood volume; rMTT ¼ regional mean transit time.
estimates obtain from experimental models (see Fig 3).
No studies presented the same perfusion parameter for
patients who did and did not recanalize, or by randomly
allocated thrombolytic treatment.
MIXED GRAY/WHITE MATTER THRESHOLDS. For
the nonviable/at risk tissue threshold we found 2 different MR perfusion parameters (CBF < 12ml/100g/min74
and Tmax 6 seconds42) based on 19 and 14 patients,
394
respectively, and 4 CT perfusion parameters (cerebral
blood volume [CBV] 2.0ml/100g for small or 2.3ml/
100g for medium to large lesions,46 mean transit time
[MTT] 6.05 seconds,53 and regional cerebral blood flow
[rCBF] 0.3447) based on 25 or 49, 13, and 14 patients,
respectively.
For the at risk/not at risk threshold, we found 9
MR perfusion parameters from between 1 and 7 studies
per parameter: quantitative CBF varied 2-fold (18–
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Dani et al: Perfusion Imaging in Stroke
37.2ml/100g/min, 4 studies, n ¼ 143); MTT varied 4fold (1.78–8.1 second delay relative to the contralateral
hemisphere, 5 studies, n ¼ 120); qualitative rCBF ranged
from 0.58 to 0.61 (3 studies, n ¼ 46), regional mean
transit time (rMTT) from 1.63 to 2.11 (3 studies, n ¼
67), time to peak (TTP) from 4 to 7 seconds (7 studies,
n ¼ 141), and time to peak of the residue function
(Tmax) varied 3-fold from 1.45 to 5.46 seconds (4 studies, n ¼ 97). For CT, there were 6 perfusion parameters
from between 1 and 3 studies per parameter, including
27.9ml/100g/min for CBF, 1.69ml/100g for CBV, and
6.53 to 7.0 seconds for MTT based on 13, 13, and 13
or 46 patients, respectively; and 0.50 to 0.63 for
rCBF,47,48,51 0.85 to 0.9 for regional cerebral blood volume (rCBV),48,51 and 1.45 to 2.2 for rMTT46,51 based
on 53, 39, and 59 patients, respectively.
GRAY AND WHITE MATTER SEPARATELY.. We
found CBF, CBV, and MTT values for white matter and
additionally rCBF and rCBV for gray matter in 3 MR
studies,33,35,43 but data came from a different study in
each case, of maximum sample size 21, precluding further analyses. In general, the white matter values were
lower than those for gray matter (see Fig 3) but with
wide confidence intervals around individual threshold
estimates.
Discussion
FIGURE 3: Perfusion thresholds for different tissue states.
(A) Cerebral blood flow values (ml/100g/min) and (B) mean
transit time values (s), on MR and CT perfusion imaging for
the nonviable/at risk and at risk/not at risk thresholds, in
mixed gray and white matter, gray matter only, and white
matter only. Study name, year of publication and sample
size are indicated in the left hand column, The point estimate of the threshold derived in each study is indicated by
the box (MR) or circle (CT), with the standard deviation or
95% confidence interval indicated by the horizontal lines (if
given). The literature values from experimental studies for
nonviable/at risk and at risk/not at risk thresholds are indicated by the gray shading. Note in B, all values are
expressed as delay in seconds with respect to the contralateral (normal) hemisphere. CT 5 computed tomography; MR
5 magnetic resonance.
September 2011
Cerebral perfusion levels have been central to the definition of the ischemic penumbra since the 1970s.1 Levels
suggested in experimental studies have broadly been confirmed in patient studies using PET imaging.2,3,75 Despite the promising number of studies on perfusion
imaging, and that rescuing the penumbra is a major
stroke treatment target, we found very limited amounts
of CT or MR data on perfusion thresholds for key tissue
states. The data were further fragmented by the small
study size, the variety of perfusion parameters reported
and the considerable heterogeneity of definitions of tissue
viability status and do not tell us if use of perfusion
imaging improves clinical outcomes because there were
no functional outcome data. Studies of advanced imaging
in acute stroke are difficult to do, which in part explains
the modest study sample sizes compared with, for example, studies of new drug treatments. Problems of study
size and variable methods are frequent and understandable when new diagnostic techniques first emerge, but
perfusion imaging has been available for almost 2 decades. Indeed, although 2 studies published in the last 5
years included over 50 patients (EPITHET and
DEFUSE) there was little indication otherwise that the
sample size was increasing over time, as might be
395
ANNALS
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expected if perfusion imaging were moving from the concept stage as ‘‘an exciting new technique’’ to more thorough testing to determine its clinical relevance (Supporting Table 1). Eighteen different definitions for tissue at
risk are unlikely to be helpful or necessary. Previous systematic5,76 and nonsystematic reviews of perfusion imaging,77 initiatives such as the Stroke Imaging Repository
(STIR, https://stir.ninds.nih.gov/html/projects.html) and
the Acute Stroke Imaging Roadmap78 recognize some,
but not all of the inconsistencies identified here, including the considerable variation in definition of tissue
states, missing details on the patient cohorts, variation in
which perfusion parameter was reported and by which
processing technique, etc. This limited evidence base on
thresholds is at odds with the increasing widespread use,
particularly of the more accessible CT perfusion imaging,
in clinical assessment of stroke. It also contrasts somewhat with the requirement for evidence from several
large clinical trials before new treatments are licensed or
recommended for clinical use.
Some quantitative thresholds for CT and MR perfusion were consistent with values reported in experimental and human PET imaging studies (see Tables 2 and 4,
Fig 3), but others varied as much as 4-fold between studies. Potential reasons include differences in tissue state
definitions, in accounting for recanalization or carotid
stenosis or the effects of ageing on CBF, the small sample
sizes, variation in the times to follow up imaging, and
not coregistering the PWI and DWI data so as to be
sure that flow values came from relevant tissue. In mixed
gray/white matter, the average perfusion values for CBF
for the at risk/not at risk threshold measured with MR
varied 2-fold (from 18 to 37.2ml/100mg/min, 4 studies)
while on CT the CBF value was 27.9ml/100mg/min (1
study) compared with the CBF value derived from experimental studies of 18 to 20ml/100mg/min.1 The corresponding MTT values measured with MR also varied 4fold, from 1.78 to 8.1 seconds delay relative to the opposite hemisphere (4 studies). Applying the uppermost of
these thresholds in stroke patients would class much
larger amounts of not at risk tissue as being at risk than
would use of the lowest threshold value—it is not clear
which is correct as we were unable to find any threshold
values which had been confirmed in subsequent studies.
The qualitative rCBF and rMTT threshold values (ratios)
were reported in more studies and were somewhat less
variable, but TTP and Tmax, both popular parameters
on MR (no data for CT) varied nearly 2-fold and 4-fold,
respectively. None of these studies also provided data on
the same perfusion parameters for the nonviable/at risk
threshold. Instead, 3 different studies reported on this
latter threshold, each using a different quantitative perfu396
sion parameter. We should be cautious in using the
threshold for tissue at risk derived from some studies and
the threshold for nonsalvageable or not at risk tissue
from other studies—the corollary would be to use data
on the benefit of a drug from 1 set of trials and on the
adverse effects from a different set of trials, performed in
different patients in different centers. Clinical treatment
decisions that involve diagnostic test results cannot be
based on such inconsistent values or on such variable definitions of tissue state any more than use of a therapy
should be based on inconsistent treatment trials.
The strengths of this review include the rigorous
application of the QUADAS criteria (Supporting Appendix 2), the independent study evaluation and cross-checking, and the assessment of tissue definitions and perfusion parameters. The limitations (beyond the limitations
of the primary studies themselves) are that we were only
able to include papers published in English and we may
have accidentally included some patients more than once.
The topic is highly likely to be subject to publication
bias although the data were not available to assess this
formally. However, there were few papers demonstrating
the absence of association between a particular perfusion
parameter, threshold value, other feature, and some outcome, suggesting that studies with neutral or negative
results may not be published. We may have overestimated the variation in definitions by focusing on specialist papers from expert groups that aimed to determine
critical tissue viability thresholds. Alternatively, one might
expect the greatest consistency to occur among the most
experienced and specialist research groups who presumably talk to each other regularly at conferences and read
each other’s work. Therefore, the variation in definitions
in the papers that did not meet the inclusion criteria
could be much worse. Further papers have been published since the end of the present search, but no agreement has been reached on definitions and no definite
threshold has emerged.
Can perfusion imaging ever define tissue viability
thresholds consistently? Some technological factors certainly contribute to the variation. In addition to the variability identified in the present work, even when applied
rigorously, different perfusion parameters produce different estimates of the extent of abnormal perfusion in the
same patient6,44 resulting in between none and 70% of
patients receiving a treatment if that treatment were only
to be administered to patients with a perfusion defect.
Even analyzing the same dataset with different manufacturers’ software that purports to do the same thing can
result in different perfusion deficits even for the same
perfusion parameter.79 Variation in image acquisition parameters probably also affects perfusion values. All this
Volume 70, No. 3
Dani et al: Perfusion Imaging in Stroke
variation could be minimized through standardization of
perfusion imaging. Coregistration of perfusion to diffusion or CT images would ensure that the perfusion values are from specific tissue regions80 and reporting ischemic lesion perfusion values relative to those in the
contralateral normal tissue81 would both improve consistency and avoid some problems; eg, those due to
aging. A working definition for nonviable tissue for use
in research studies could be the acute DWI-visible
lesion (accepting that some DWI abnormal tissue can
recover), for tissue at risk could be the difference
between the DWI and relative CBF lesion (accepting
that CBF is variable), and for not at risk tissue could
be anything outside the CBF lesion until such time as
a general consensus has been reached and accepting that
none of the definitions that are currently in use are
ideal and any definition in future should account for
reperfusion.
However, ultimately it is likely that major improvements in application of perfusion imaging will not be
through improved physics or poor engineering, or by
increasing the sophistication and power of scanners or
analysis software, because a large source of variation is
due to physiological factors,2 and (to paraphrase Logothetis82 discussing similar problems encountered in functional MR imaging [fMRI]) to the inappropriate experimental protocols that ignore these factors. Blood flow in
white matter is lower than in gray matter, yet only 3 of
21 MR and no CT studies provided threshold values for
white or gray matter separately. Most analysis software
assumes a fixed value for perfusion in white matter,
ignoring effects of ageing or leukoaraiosis.83 The major
arterial territories and their border zones vary between
and within subjects,84 increasing variation in perfusion
lesion extent.85 Cerebral perfusion declines with advancing age and with leukoaraiosis, but no thresholds
accounted for either chronological or biological age
(some studies did not even mention age). Thresholds for
tissue viability change with duration of ischemia2,3,86 but
the wide variation in perfusion parameters and lack of
detail on timing of scanning precluded any examination
of the effect of time. Experimental studies show wide
variation in functional flow thresholds for individual
neurons in different brain regions indicating differential
neuronal vulnerability.2 Reperfusion also influences tissue
viability but the available data did not allow us to
account for this. Ischemic stroke is a dynamic process.
Waves of spreading depolarization accompanied by transient vasospasm occur at regular intervals through the ischemic tissue causing transient reductions in perfusion
that do not return to prevasospasm levels, resulting in a
progressive stepwise decline in lesion perfusion the longer
September 2011
the duration of ischemia.87 Perfusion imaging performed
at the nadir of a spreading depression-associated vasospasm will indicate lower flow values than imaging performed even 10 or so minutes earlier or later.
This review suggests that if MR or CT perfusion
imaging is to be used to guide stroke treatment, then
clinical decisions should not be based on threshold values
at present.88 There are other ways in which perfusion
imaging can be used in stroke without determining
thresholds, for example to identify the extent of brain
with any degree of reduced blood flow as this may
increase confidence in the diagnosis of ischemic stroke
(eg, if no lesion is visible on plain CT). Some clinicians
may wish to be guided by threshold data from 1 study,
but the usual standard for implementation of a new treatment is at least to wait for a second confirmatory trial. As
there is no good reason to apply a lesser standard to diagnostic tests, it would be prudent for the results of 1 positive study to be confirmed by others before there is widespread implementation into routine practice. Perfusion
imaging has adverse effects including: adding extra time
to diagnosis thereby delaying time to treatment; contraindications in patients with renal impairment; and the substantial radiation dose from CT perfusion resulting in
recent legislation in the United States to limit its use.
The potential of the large amount of original perfusion imaging data already collected should be maximized
by combining it in a database for reanalysis using agreed
standards, as proposed by the Stroke Imaging Repository
(STIR) Consortium (https://stir.ninds.nih.gov/html/projects.html). The literature database of CT and MR perfusion imaging studies collected in this work is available at
the Brain Research Imaging Centre (http://www.bric.ed.
ac.uk/imageanalysis.html under ‘‘Information, Perfusion
imaging with CT and MR in acute ischemic stroke. A
systematic review’’) for use by anyone interested in
updating this work as new information becomes available. Stroke researchers should endeavor to follow minimum standards for perfusion acquisition,78,89 agree
standard definitions, analyses, and parameters for reporting, use key study quality criteria (Supporting Appendix
2), and appropriate statistics. Standardization of perfusion imaging is necessary before patient care can benefit
from the substantial technological advances that have
enabled the performance of perfusion imaging almost
routinely in stroke patients. However, even with such
standardization, perfusion imaging is still likely to remain
highly variable because the pathophysiology that it represents is dynamic and highly variable between individuals
for good biological reasons. The approach to measurement and analysis of cerebral perfusion in stroke should
acknowledge these factors if we are to progress.
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of Neurology
Acknowledgments
The work was supported by the Stroke Association
(TSA2006/11 to J.M.W.), the Translational Medicine
Research Collaboration (TMRC) (a consortium made up
of the Universities of Aberdeen, Dundee, Edinburgh, and
Glasgow, the 4 associated NHS Health Boards (Grampian,
Tayside, Lothian, and Greater Glasgow and Clyde), Scottish Enterprise and Pfizer (NS_EU_082 to J.M.W., K.M.,
and M.Mc.L.), the Patrick Berthoud Charitable Trust (to
K.A.D.), the Scottish Funding Council through the Scottish Imaging Network, A Platform for Scientific Excellence
(SINAPSE) Collaboration (www.sinapse.ac.uk, to J.M.W.).
This work was performed as part of the Multicentre
Acute Stroke Imaging Study and the Stroke Imaging Repository (STIR) Task Force 1 activities (https://stir.ninds.
nih.gov/html/projects.html). The database of extracted
data from the published studies and the data extraction
form are available at http://www.bric.ed.ac.uk/imageanalysis.
html; forms located under ‘‘Information’’ (‘‘Perfusion
imaging with CT and MR in acute ischaemic stroke. A
systematic review’’).
Potential conflict of interest
K.A.D. received grant(s) and support for travel to meetings
for the study or other purposes from Patrick Bethoud
Charitable Trust. M.J.M. received grant(s) and support for
travel to meetings for the study or other purposes from
TRMC Ltd. F.M.C. received grant(s) and support for
travel to meetings for the study or other purposes from
TMRC. K.W.M. received grant(s) from TMRC. K.S.
received grant(s) from TMRC. J.M.W. received grants
from TMRC and Scottish Funding Council; has grants/
grants pending from National Institutes of Health
Research, Wellcome Trust, and Scottish Funding Council;
and travel expenses for various invited talks at academic
meetings funded by the organization hosting the meeting,
not by pharma or other industry. R.G.R.T. received
grant(s) from the Translational Medicine Research Collaboration; has grant(s)/grant(s) pending from the Stroke
Association (Registered Charity SC037789); and travel/
accommodations/meeting expenses unrelated to activities
listed from Guarantors of Brain and Centre for Clinical
Brain Sciences, Edinburgh University.
APPENDIX
Aberdeen: Alison Murray, Olive Robb, Paul Acheampong
Edinburgh: Zoe Morris, Katherine Lymer, Trevor
Carpenter, Peter Keston, Chris Weir, Callum Grey, Will
Whiteley
398
Glasgow: Donald Hadley, Ferghal McVerry, Gordon
Lowe, Ann Rumley, Naveed Sattar, Paul Welch, Niall
MacDougall, Sally Baird
Dundee: Petra Rauchhaus, Daniel Crowther, Vackar
Afzal
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