Stroke is the fourth leading cause of death and the leading cause of serious
long-term disability in the United States,[1] with approximately 795,000 cases of new or recurrent stroke each year.[1] Computerized tomography examination remains the standard imaging modality for
triaging acute ischemic infarcts at many institutions.[2] However, magnetic resonance imaging (MRI) is playing an increasing role in
determining if such patients might benefit from endovascular interventions such as
mechanical thrombectomy, particularly beyond 6 hours. Recent studies (EXTEND-IA,[3] MR CLEAN,[4] ESCAPE,[5] and SWIFT PRIME[6]) have demonstrated that mechanical thrombectomy can be highly efficacious
compared with intravenous tissue plasminogen activator therapy when patients are
screened using imaging biomarkers able to identify at-risk tissue and its viability,
such as large-vessel occlusion,[3-5] infarct core volume,[3],[5] and the extent of collateral circulation.[5] However, it is currently not possible to definitively predict a positive
response to recanalization among patients with emergent large vessel occlusion
(ELVO) or even small-core ELVO.Susceptibility-weighted imaging (SWI) is a relatively recent MRI sequence with unique
characteristics for evaluating cerebral tissue metabolism in patients with acute
ischemic stroke. SWI is highly sensitive to paramagnetic substances such as
deoxyhemoglobin, which is paramagnetic in contrast to diamagnetic oxyhemoglobin by
virtue of its four unpaired electrons.[7],[8] SWI uses phase difference to enhance the depiction of magnetic susceptibility
difference between regions containing paramagnetic substances, such as iron in
deoxygenated blood, and the surrounding tissues.[7-9] This phase difference is
manifested in patients with acute ischemic stroke by increased prominence of the
cerebral veins, given that underperfused tissues respond by increasing the percent
oxygen extraction from the blood (misery perfusion).[10-12] SWI can therefore show the
presence or absence of the last physiologic response (increased oxygen extraction
fraction) of ischemic tissue before cellular metabolism can no longer be
maintained.[13-15] Furthermore,
increased prominence of the cerebral veins on SWI has been associated with the
presence of vigorous collateral flow.[15] These characteristics of SWI suggest that the presence or absence of
prominent veins may serve as a valuable biomarker for tissue viability, and may thus
indicate the potential benefit to the patient of endovascular revascularization.
However, SWI is a qualitative technique and does not provide a quantitative
measurement of venous oxygen saturation or deoxyhemoglobin.Quantitative susceptibility mapping (QSM)[16] was recently developed to quantify the level of deoxyhemoglobin in cerebral
veins using various techniques,[17],[18] notably by using a SWI mapping (SWIM) program.[9],[19-22] Although subjective SWI
findings have been previously associated with National Institutes of Health stroke
scale (NIHSS) scores,[15],[23] QSM offers an objective method for estimating the venous deoxyhemoglobin
burden, which is not as subject to interobserver variability. QSM asymmetry in the
cortical veins has recently been described in patients with ischemic stroke;[19] however quantitative susceptibility has not yet been characterized in the
deep cerebral (thalamostriate veins) veins, which drain the basal ganglia, and has
not been correlated with stroke severity.In this study, we investigated the relationship between quantitative susceptibility
in the superficial versus deep cerebral veins and baseline NIHSS scores in patients
with hyperacute stroke who were selected for endovascular intervention by advanced
imaging.
Materials and methods
Study design
We conducted a retrospective observational cohort study in patients who presented
to our institution with hyperacute anterior circulation stroke from 2012 to
2014. Information on patient demographics, side of stroke, initial NIHSS score,
clinical covariates, laboratory data, and medications were collected from
electronic medical records. QSM was then utilized to assess the relationships
between superficial and deep cerebral venous susceptibilities and the patients’
presenting NIHSS scores. This study was approved by the independent
institutional review board at Loma Linda University, who waived the need for
patient consent.
Patients
Patients with hyperacute stroke who are being considered for mechanical
thrombectomy at our institution undergo computed tomography angiography (or
magnetic resonance angiography) plus MRI, unless contraindicated (e.g., cardiac
pacemaker), under our hyperacute stroke protocol. Among 52 patients with
hyperacute stroke who were selected for mechanical thrombectomy from 2010 to
2012, 15 with posterior circulation stroke, four with intracranial hemorrhage,
and eight patients with spoiled SWI sequences were excluded. Twenty-five
patients with confirmed large-vessel occlusion, small presenting infarct core
volume (<70 cc), and NIHSS score ≥8 were included in our cohort.
Magnetic resonance imaging
MRI was performed using a 3T scanner in the axial plane (Siemens Healthcare,
Erlangen, Germany). The following sequences were obtained: axial
fluid-attenuated inversion recovery, SWI, and diffusion-weighted imaging with
apparent diffusion coefficient maps, with or without perfusion. The SWI
parameters were as follows: echo time, 20 ms; repetition time, 30 ms; flip
angle, 15°; slice thickness, 2 mm; matrix size 256 × 256 mm; and in-plane
resolution, 0.5 × 0.5 mm.SWI maps were created from the magnitude and phase images of the SWI sequence
using post-processing software (SPIN, MR Institute of Detroit, MI, USA). Brain
extraction was performed using the brain extraction tool with a high-pass filter
of 32 × 32. Iterative SWIM was then applied with a k-space threshold of one with
three iterations. QSM measurements were obtained from the most prominent vein in
each of the four standard regions of interest: the cortical and thalamostriate
veins ipsilateral and contralateral to the stroke, respectively. For each
measured vein, a linear region of interest was placed along the long axis and
the mean susceptibility and standard deviation was calculated from three repeat
measurements and recorded in parts per billion (ppb) (Figure 1). All measurements were
performed by a medical student research assistant and verified by a fellow
within the diagnostic neuroradiology program.
Figure 1.
Representative susceptibility-weighted imaging map showing the region of
interest (red line) place within the most prominent thalamostriate vein.
Inset: magnified region of interest.
Representative susceptibility-weighted imaging map showing the region of
interest (red line) place within the most prominent thalamostriate vein.
Inset: magnified region of interest.
Statistical analysis
Pairwise comparisons of mean quantitative susceptibility measurements were made
between each venous group using Wilcoxon’s signed rank test. Correlations
between susceptibility and presenting NIHSS score were analyzed for each group
using Spearman’s rho. Statistical significance was defined as
P < 0.05.
Results
Our cohort of 25 patients included 10 men and 15 women, with a median age of 66 years
(Table 1).
Twenty-one patients had occlusion of the middle cerebral artery and four of the
internal carotid artery. The presenting NIHSS scores ranged from 1 to 25. The median
stroke-onset to imaging time was 5.5 hours (Table 1).
Table 1.
Characteristics of patients with anterior emergent large vessel occlusion
selected for thrombectomy.
Number
25
Mean age (years)
66.4 SD ± 14.9
Male sex n (%)
10 (40)
Past medical history n (%)
Atrial fibrillation
10 (40)
Cancer
1 (4)
Congestive heart failure
4 (16)
Diabetes
14 (56)
Hypercholesterolemia
13 (52)
Hypertension
22 (88)
Recreational drug use
2 (8)
Smoking
19 (76)
Transient ischemic attack
3 (3)
Median NIHSS score
14 IQR (12.5, 17)
Stroke onset to imaging time (hours) (median, IQR)
5.5 (4.9, 7.8)
Vital signs during MRI examination (mean ± SD)
Systolic blood pressure (mmHg)
139 ± 21.7
Diastolic blood pressure (mmHg)
78 ± 18.6
Heart rate
80 ± 14.8
Median presenting core size (cm3)
21.6 IQR (17.7, 48.0)
Laboratory values
Bicarbonate (mMol/L) (mean ± SD)
23 ± 2.6
Glucose (mg/dL) (median, IQR)
122 (104, 139)
Hemoglobin (g/dL) (mean ± SD)
13 ± 1.3
Tissue plasminogen activator n (%)
6
SD: standard deviation, IQR: interquartile range, NIHSS: National
Institutes of Health stroke scale, MRI: magnetic resonance imaging.
Characteristics of patients with anterior emergent large vessel occlusion
selected for thrombectomy.SD: standard deviation, IQR: interquartile range, NIHSS: National
Institutes of Health stroke scale, MRI: magnetic resonance imaging.An obvious initial finding was qualitative asymmetric prominence of the superficial
and deep cerebral veins in the cerebral hemisphere ipsilateral to the stroke (Figure 2). Quantitative
analysis showed the highest susceptibility in the cortical veins ipsilateral to the
stroke compared with the other studied vein groups. The mean susceptibility (95%
confidence interval) within the ipsilateral cortical veins was 174 ppb (139–209)
compared with 107 ppb (92–120) for the contralateral cortical veins
(P = 0.002), 129 ppb (104–155) for the ipsilateral
thalamostriate veins (P = 0.05), and 107 ppb (92–123) for the
contralateral thalamostriate veins (P = 0.001) (Figure 3). Comparisons between
other groups did not reach adjusted statistical significance.
Figure 2.
Diffusion-weighted imaging (DWI), mean transit time (MTT),
susceptibility-weighted imaging (SWI), and quantitative susceptibility
mapping (QSM) in a 61-year-old woman with acute left-sided weakness, NIHSS
score of 13, and right M1 occlusion. (a) Axial DWI demonstrates core infarct
in the right insula. (b) Axial MTT demonstrates a large mismatched ischemic
penumbra in the right middle cerebral artery (MCA) territory. (c) Axial SWI
demonstrates increased prominence of the cortical and deep cerebral veins in
the right MCA territory. (d) QSM map demonstrates quantifiable
susceptibility changes in the cerebral veins in parts per billion.
Figure 3.
Susceptibility of cerebral veins in the regions ipsilateral and contralateral
to the acute infarct. The cortical veins ipsilateral to the stroke showed
the greatest susceptibility followed by the ipsilateral thalamostriate
veins.
Diffusion-weighted imaging (DWI), mean transit time (MTT),
susceptibility-weighted imaging (SWI), and quantitative susceptibility
mapping (QSM) in a 61-year-old woman with acute left-sided weakness, NIHSS
score of 13, and right M1 occlusion. (a) Axial DWI demonstrates core infarct
in the right insula. (b) Axial MTT demonstrates a large mismatched ischemic
penumbra in the right middle cerebral artery (MCA) territory. (c) Axial SWI
demonstrates increased prominence of the cortical and deep cerebral veins in
the right MCA territory. (d) QSM map demonstrates quantifiable
susceptibility changes in the cerebral veins in parts per billion.Susceptibility of cerebral veins in the regions ipsilateral and contralateral
to the acute infarct. The cortical veins ipsilateral to the stroke showed
the greatest susceptibility followed by the ipsilateral thalamostriate
veins.The susceptibilities of both the ipsilateral and contralateral thalamostriate veins
showed strong inverse correlations with the presenting NIHSS score (rho = −0.626,
P = 0.001, and rho = −0.498, P = 0.013,
respectively) (Figure 4).
The correlation between ipsilateral thalamostriate vein susceptibility and
presenting NIHSS score was not significantly different after adjusting for the one
outlier within the cohort who presented with near complete occlusion of the left
internal carotid artery with inadequate collateral support yet extremely high
ipsilateral thalamostriate vein susceptibility (rho = −0.597,
P = 0.003) (Figure
5).
Figure 4.
Correlation between ipsilateral and contralateral thalamostriate vein
susceptibility and presenting NIHSS score. Both the (a) ipsilateral and (b)
contralateral thalamostriate veins showed a strong inverse correlation with
the presenting NIHSS score.
Figure 5.
Correlation between ipsilateral thalamostriate vein susceptibility and
presenting NIHSS score adjusting for outliers. Excluding a single patient
who presented with complete left internal carotid artery occlusion, poor
collateral supply, and an extremely high ipsilateral thalamostriate vein
susceptibility (360 ppb) did not significantly affect the correlation.
Correlation between ipsilateral and contralateral thalamostriate vein
susceptibility and presenting NIHSS score. Both the (a) ipsilateral and (b)
contralateral thalamostriate veins showed a strong inverse correlation with
the presenting NIHSS score.Correlation between ipsilateral thalamostriate vein susceptibility and
presenting NIHSS score adjusting for outliers. Excluding a single patient
who presented with complete left internal carotid artery occlusion, poor
collateral supply, and an extremely high ipsilateral thalamostriate vein
susceptibility (360 ppb) did not significantly affect the correlation.These results demonstrate a strong inverse correlation between ipsilateral deep
venous quantitative susceptibility and presenting stroke severity, and increased
cerebral venous susceptibility (cortical > thalamostriate) ipsilateral to the
occlusion in patients with ELVO stroke.
Discussion
Previous reports correlating cerebral venous susceptibility with neurologic outcome
have produced varied results. Park et al.[15] showed that multiple hypointense vessels ipsilateral to the infarct on SWI
were associated with lower initial NIHSS score, which was corroborated by the
current findings. In contrast, Mucke et al.[23] found that asymmetry of the deep medullary veins on SWI was associated with
increased initial stroke severity, and Li et al.[21] reported that decreasing oxygenation of the ipsilateral cerebral veins was
associated with poorer outcomes. Zhang et al.[24] recently showed that ipsilateral thalamostriate vein prominence on SWI was
associated with reduced reperfusion and a poor outcome following thrombolysis using
intravenous thrombolytics. However, to the best of our knowledge, the current study
may be the first to compare QSM data and stroke severity using both the cortical and
deep venous systems. However, the equivocal nature of the above results indicates
the need for further investigations.The apparently conflicting results also indicate differing conceptual understandings
of misery perfusion. Increased cerebral venous susceptibility can indicate the area
of viable tissue at risk of infarction[13],[14],[25] (i.e., the ischemic penumbra), but also reveals that the tissue is responding
appropriately by increasing oxygen extraction, since dark signal and QSM ppb
increase with elevated deoxyhemoglobin concentration. In contrast, lack of
susceptibility within a tissue suggests that the tissue is metabolically inactive
and, in the setting of stroke, most likely unsalvageable. This phenomenon can be
observed in the infarct core, where the oxygen extraction fraction has been shown to
be markedly decreased.[26] In other words, increased oxygen extraction indicated by increased venous
susceptibility indicates an active compensating, homeostatic process, and may
therefore correlate with decreased clinical stroke severity, as suggested by the
current results.A continued supply of oxygen within the ischemic penumbra must be maintained by
sufficient collateral flow or tissue infarctin will occur. Park et al.[15] showed that increased venous susceptibility was associated with favorable
collateralization within the tissue, as well as a greater diffusion-perfusion
mismatch volume and decreased diffusion lesion volume. In contrast, Verma et al.[27] found that increased susceptibility was associated with decreased collateral
flow, and suggested that the degree of collateral flow determined the level of
deoxyhemoglobin in the cerebral veins. Park et al.,[15] however, stated that the converse was true: that the degree of collateral
flow was determined by the oxygen demand, represented by venous deoxyhemoglobin
levels. The above finding of decreased deoxyhemoglobin levels in the ischemic core
appears to substantiate the latter claim.[26] If collateral flow is best characterized as a response to increased oxygen
demand by the affected tissues, the ability of SWI to estimate deoxyhemoglobin
levels would allow it to characterize collateral flow, as another critical factor in
determining stroke severity.[28],[29]The current results also show quantitative susceptibility asymmetry of the cortical
veins in acute ischemic stroke with increased venous susceptiblity on the side of
the occluded artery, as recently demonstrated by Xia et al.[19] Our study corroborated these findings and expanded on them to include
quantitative susceptibility data for the deep cerebral veins. In contrast to the
cortical veins, there was no significant asymmetry in QSM between the deep cerebral
veins in patients with acute ischemic stroke the median 20% increase of
susceptibility in ipsilateral versus contralateral thalamostriate veins did not rise
to the level of statistical significance. This may be attributed to the smaller
tissue volume served by the deep venous system, decreased basal ganglia collateral
supply, or the small sample size of the study. It is also possible that the lack of
significance was the result of an increased compensatory response (interhemispheric
diaschisis) by the unaffected side during a time of regional ischemic insult.The current study demonstrated the above findings specifically in patients who
underwent endovascular interventions. Some suggest that all patients with ELVO
should proceed immediately to mechanical thrombectomy rather than undergoing
selection by MRI,[30] while others believe that this strategy fails by exposing the approximately
50% of patients with ELVO with large core infarcts to invasive, futile, and possibly
harmful treatment.[31] In particular, MRI plays an important role in the assessment of patients with
hyperacute stroke with ELVO who do not present early or do not respond to tissue
plasminogen activator.[32]Diffusion-weighted imaging combined with perfusion-weighted imaging is an accepted
method for characterizing the ischemic penumbra, and can help to determine if a
patient should receive endovascular therapy.[3,33,34] The ability of SWI to
characterize the ischemic penumbra, reflect adaptive changes in the tissue, and
suggest the presence of nontrivial collateral flow suggests that it also may be a
valuable tool for aiding patient selection. The current results indicate that QSM
values derived from SWI are associated with decreased initial NIHSS score. Given
that initial NIHSS score is viewed as the most important predictor of long-term
outcomes,[35-37] it is
plausible that quantitative susceptibility may also be transitively correlated with
long-term outcomes in this population.Our study was limited by its small sample size. Potential cofounders included age,
stroke severity, hyperglycemia, time from onset to imaging, and variability of
collateral blood supply. However, we believe, that the demonstrated relationship
between QSM values and stroke severity provides the basis for further larger
studies.In conclusion, ipsilateral thalamostriate vein susceptibility, derived from QSM data,
shows a strong inverse correlation with presenting NIHSS in adult patients with
hyperacute stroke who were selected for endovascular intervention by advanced
imaging. Quantitative susceptibility values are also asymmetrically elevated in the
cortical veins on the ischemic side. Although these results remain to be validated
in larger studies, the results suggest that SWI may be an important biomarker for
guiding decision making and predicting long-term neurologic outcomes in patients who
present with ELVO.
Authors: Eric E Smith; Nandavar Shobha; David Dai; Daiwai M Olson; Mathew J Reeves; Jeffrey L Saver; Adrian F Hernandez; Eric D Peterson; Gregg C Fonarow; Lee H Schwamm Journal: Circulation Date: 2010-09-27 Impact factor: 29.690
Authors: Alan S Go; Dariush Mozaffarian; Véronique L Roger; Emelia J Benjamin; Jarett D Berry; Michael J Blaha; Shifan Dai; Earl S Ford; Caroline S Fox; Sheila Franco; Heather J Fullerton; Cathleen Gillespie; Susan M Hailpern; John A Heit; Virginia J Howard; Mark D Huffman; Suzanne E Judd; Brett M Kissela; Steven J Kittner; Daniel T Lackland; Judith H Lichtman; Lynda D Lisabeth; Rachel H Mackey; David J Magid; Gregory M Marcus; Ariane Marelli; David B Matchar; Darren K McGuire; Emile R Mohler; Claudia S Moy; Michael E Mussolino; Robert W Neumar; Graham Nichol; Dilip K Pandey; Nina P Paynter; Matthew J Reeves; Paul D Sorlie; Joel Stein; Amytis Towfighi; Tanya N Turan; Salim S Virani; Nathan D Wong; Daniel Woo; Melanie B Turner Journal: Circulation Date: 2014-01-21 Impact factor: 29.690
Authors: P A Barber; D G Darby; P M Desmond; Q Yang; R P Gerraty; D Jolley; G A Donnan; B M Tress; S M Davis Journal: Neurology Date: 1998-08 Impact factor: 9.910
Authors: Rajeev K Verma; Kety Hsieh; Pascal P Gratz; Adrian C Schankath; Pasquale Mordasini; Christoph Zubler; Frauke Kellner-Weldon; Simon Jung; Gerhard Schroth; Jan Gralla; Marwan El-Koussy Journal: Eur J Radiol Date: 2014-05-17 Impact factor: 3.528
Authors: Jeffrey L Saver; Mayank Goyal; Alain Bonafe; Hans-Christoph Diener; Elad I Levy; Vitor M Pereira; Gregory W Albers; Christophe Cognard; David J Cohen; Werner Hacke; Olav Jansen; Tudor G Jovin; Heinrich P Mattle; Raul G Nogueira; Adnan H Siddiqui; Dileep R Yavagal; Thomas G Devlin; Demetrius K Lopes; Vivek Reddy; Richard du Mesnil de Rochemont; Reza Jahan Journal: Int J Stroke Date: 2015-04 Impact factor: 5.266
Authors: Ramon Gilberto González; William A Copen; Pamela W Schaefer; Michael H Lev; Stuart R Pomerantz; Otto Rapalino; John W Chen; George J Hunter; Javier M Romero; Bradley R Buchbinder; Mykol Larvie; Joshua Adam Hirsch; Rajiv Gupta Journal: J Neurointerv Surg Date: 2013-03-14 Impact factor: 5.836
Authors: Brenda L Bartnik-Olson; Arlin B Blood; Michael H Terry; Shawn Fl Hanson; Christopher Day; Daniel Kido; Paggie Kim Journal: J Cereb Blood Flow Metab Date: 2021-12-08 Impact factor: 6.960