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Originally Published 26 January 2016
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Cortical Venous Filling on Dynamic Computed Tomographic Angiography: A Novel Predictor of Clinical Outcome in Patients With Acute Middle Cerebral Artery Stroke

Ido R. van den Wijngaard, MD, Marieke J.H. Wermer, MD, PhD, Jelis Boiten, MD, PhD, Ale Algra, MD, PhD, Ghislaine Holswilder, MSc, Frederick J.A. Meijer, MD, PhD, Diederik W.J. Dippel, MD, PhD, Birgitta K. Velthuis, MD, PhD, Charles B.L.M. Majoie, MD, PhD, and Marianne A.A. van Walderveen, MD, PhDAuthor Info & Affiliations
Stroke
Volume 47, Number 3
https://doi.org/10.1161/STROKEAHA.115.012279
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Abstract

Background and Purpose—

Venous flow in the downstream territory of an occluded artery may influence patient prognosis after ischemic stroke. Our aim was to study cortical venous filling (CVF) in a time-resolved manner with dynamic computed tomographic angiography and to assess the relationship with clinical outcome.

Methods—

Patients with a proximal middle cerebral artery occlusion underwent noncontrast CT and whole-brain CT perfusion/dynamic CT angiography within 9 hours after stroke-onset. We defined poor outcome as a modified Rankin Scale score of ≥3. Association between the extent and velocity of CVF and poor outcome at 3 months was analyzed with Poisson-regression. Prognostic value of optimal CVF (maximum opacification of cortical veins) in addition to age, stroke severity, treatment, Alberta Stroke Program Early CT score, cerebral blood flow, and collateral status was assessed with logistic regression and summarized with the area under the curve.

Results—

Eighty-eight patients were included, with a mean age of 67 years. By combining the extent and velocity of optimal CVF, we observed a decreased risk of poor outcome in patients with good and fast optimal CVF, risk ratio of 0.5 (95% confidence interval, 0.3–0.7). Extent and velocity of optimal CVF had additional prognostic value (area under the curve, 0.88; 95% confidence interval, 0.77–0.98; P<0.02) compared with a model without CVF information.

Conclusions—

The combination of extent and velocity of optimal CVF, as assessed with dynamic CT angiography, is useful to identify patients with acute middle cerebral artery stroke at higher risk of poor clinical outcome at 3-month follow-up.

Clinical Trial Registration—

URL: http://www.trialregister.nl/trialreg and http://www.clinicaltrials.gov. Unique identifier: NTR1804 and NCT00880113, respectively.

Introduction

Patients with acute ischemic stroke as a result of a proximal anterior circulation occlusion still have a substantial risk of poor clinical outcome, despite recent advances in acute stroke treatment.1–4 Imaging markers, such as small infarct core and good collateral status, may identify patients most likely to benefit from reperfusion therapies, and they have been used to extend time windows for treatment.2,5 Multimodel computed tomographic (CT) imaging, including noncontrast CT, CT angiography (CTA), and CT perfusion (CTP) is most commonly used in clinical practice to evaluate these imaging markers in patients with acute ischemic stroke because this modality is fast, non-invasive, inexpensive, and widely available.
Poor cortical venous filling (CVF) in the downstream territory of an occluded artery is a promising prognostic feature, which may be defined by the extent of collaterals and resultant perfusion.6,7 CVF can provide an indirect assessment of perfusion through the microcirculation.8,9 The Prognostic Evaluation on Cortical Vein Score Difference in Stroke (PRECISE) score, as assessed with single-phase CTA, predicted poor clinical outcome.7 From this observational study, the investigators concluded that the extent of CVF influenced prognosis in the setting of a proximal artery occlusion.7
Measuring delay in the opacification of cortical veins could have additional prognostic value in acute ischemic stroke, as has been suggested in a nonhuman primate model.6
Multiphase or dynamic CTA is superior to conventional single-phase CTA for assessment of vessel filling because both extent and velocity of vessel filling can be taken into account.10–13 Similarly, CVF may be better defined using dynamic CTA when compared with single-phase CTA. To our knowledge, CVF in acute ischemic stroke has not yet been studied in a time-resolved manner. The aim of our study was to investigate whether extent and velocity of CVF as assessed with dynamic CTA predicts clinical outcome at 3 months in patients with acute middle cerebral artery (MCA) stroke.

Methods

Study Design

Patients from 2 university medical centers (Leiden and Radboud University Medical Centers) were selected from the Dutch Acute Stroke (DUST) Study and the Multicenter Randomized Clinical trial of Endovascular Treatment for Acute ischemic stroke in the Netherlands (MR CLEAN). Protocol details of these clinical studies with inclusion and exclusion criteria have been published before.14,15 Patients with an acute ischemic stroke with a proximal MCA occlusion (M1 or M2 segments) were included, with or without occlusion of the internal carotid artery. Clinical data were retrieved from the study databases. The primary outcome measure was a poor clinical outcome defined as a modified Rankin Scale score of ≥3 at 90 days, indicating functional dependence or death.

CT Image Acquisition

For CT image acquisition, a 320-slice multidetector CT (Aquilion-One Toshiba Medical Systems, Tokyo, Japan) was used, resulting in whole-brain CTP coverage. All patients underwent a standard scanning protocol at presentation, including noncontrast CT, single-phase CTA from the aortic arch to the vertex, and whole-brain CTP/dynamic CTA.
Noncontrast CT was evaluated for early ischemic changes (Alberta Stroke Program Early CT score); CTP/CTA for extent of the arterial occlusion (clot burden score), collateral status, cerebral blood flow (CBF), cerebral blood volume, mean transit time, and time-to-peak (supplemental methods). For whole-brain CTP/dynamic CTA, a total of 19 volumes were obtained during a duration of 1 minute. Dynamic CTAs were derived from the 320-slice CTP by subtracting the first unenhanced volume of the CTP study from the subsequent contrast-enhanced volumes to ensure that only vessels remained visible. Maximum intensity projections of all 19 volumes were created and displayed in time herewith creating angiography-like movies of time-resolved maximum intensity projections. The total radiation dose amounted to 8.4 mSv with our current acquisition protocol. CVF was evaluated for the affected and nonaffected hemisphere on these time-resolved maximum intensity projections.

Cortical Venous Filling

All cortical veins that drain into the superior sagittal sinus and anastomotic veins were assessed in both hemispheres (Figures 1 and 2). By visual assessment, cortical venous contrast opacification and the number of cortical and anastomotic veins were evaluated in comparison with the contralateral hemisphere. We defined first CVF as the appearance of any cortical vein draining in the superior sagittal sinus. Optimal CVF was defined as maximum contrast opacification of all cortical veins. If discrepancies in opacification between different cortical veins were present, a choice was made for the moment when the majority of cortical veins showed highest contrast opacification. We defined the end of venous filling as the first moment that contrast medium in all the cortical veins had completely disappeared. Time points of different venous filling parameters were indicated according to timing acquisition of each of the 19 volumes in seconds. Moreover, venous parameters were related to first contrast opacification of the internal carotid artery to correct for differences in time delay of CTP/dynamic CTA acquisition after contrast bolus injection between patients.
Figure 1. Good versus poor extent of cortical venous filling. Dynamic computed tomographic (CT) angiography maximum intensity projections of a single volume in 2 patients. Both patients underwent multimodel CT imaging within 90 minutes after symptom-onset, which showed in both patients an occlusion of the M1 segment of the middle cerebral artery and a high Alberta Stroke Program Early CT score. A, Good extent of optimal cortical venous filling in the nonaffected (black arrows) as well as affected hemisphere (white arrows). Patient A was treated with intravenous thrombolysis and had a good clinical outcome. B, Asymmetry in cortical venous filling between both hemispheres with poor extent of cortical venous filling in the affected hemisphere (white arrows). Patient B was also treated with intravenous thrombolysis but had a poor clinical outcome at 3-month follow-up.
Figure 2. Time-resolved assessment of cortical venous filling. Dynamic computed tomographic angiography maximum intensity projections of 4 volumes displayed chronologically in the same patient with right middle cerebral artery stroke illustrating the importance of time-resolved assessment of cortical venous filling. A, Arterial-venous phase in which cortical veins are visible in the nonaffected hemisphere (black arrows), but not yet visible in the affected hemisphere (white arrow). B, Optimal cortical venous filling in the nonaffected hemisphere (black arrows), whereas optimal venous filling in the affected hemisphere is seen 5 s later as shown in C (white arrows). D, Volume in the late venous phase in which cortical venous filling is more prominent in the affected (white arrow) than nonaffected hemisphere (black arrow).
To assess the velocity of CVF, we calculated for all venous time points the median differences in seconds between venous filling of the ipsilateral (affected) versus the contralateral (nonaffected) hemisphere. Fast CVF was defined as time points smaller or equal to the median value and slow CVF as larger than the median value. Additionally for extent of CVF, we assessed at the time points of maximum venous opacification whether the number of cortical veins in the affected hemisphere was present in <50% (poor extent of CVF) or ≥50% (good extent of CVF) in comparison with the nonaffected hemisphere. Consensus reading for the extent of CVF was done in the cohort of the Leiden University Medical Center by a trained neurologist and neuroradiologist who were given information about the clinical symptoms only. All CVF times were independently assessed in the same cohort, and agreement between the 2 observers for velocity of CVF was assessed with multiple Bland–Altman plots. Reperfusion outcomes were independently assessed on digital subtraction angiography after endovascular treatment with the modified thrombolysis in cerebral ischemia score, and they were provided by the MR CLEAN investigators.

Statistical Analysis

We analyzed the association between clinical or radiological characteristics and poor clinical outcome (modified Rankin Scale score, ≥3) after 3 months with univariable and multivariable Poisson-regression analysis. The prognostic value of optimal CVF on clinical outcome in addition to characteristics including age, stroke severity, treatment, Alberta Stroke Program Early CT score, CBF, and collateral status was analyzed with logistic regression models. In a first basic model (model 1), the variables age, baseline National Institute of Health Stroke Scale (NIHSS) score, treatment, and Alberta Stroke Program Early CT score were used. In model 2, CBF was added to model 1. In model 3, the collateral status was added to model 2. For the final model 4, we added a combination of extent and velocity of optimal CVF to model 3. Subsequently, the area under the curve (AUC) of the receiver operating characteristic curves was calculated for all models and potential improvements between the final models and the initial models were determined. Comparison of AUC–receiver operating characteristics between hierarchical models was done with the likelihood ratio test.

Results

Patients

Between July 2010 and July 2014, a total of 96 patients were eligible for evaluation of CVF on dynamic CTA. Eight patients were excluded because of insufficient quality of the postprocessed images for assessment of venous filling. Forty-two patients were selected from the DUST and 46 patients from the MR CLEAN trial. Sixty-five patients were selected from the Leiden University Medical Center and 23 patients from the Radboud University Medical Center. The mean age of the study participants was 67 years; median NIHSS was 15, and 44 participants (50%) were women. Median time from symptom-onset to multimodel CT imaging was 77 minutes. Most patients were treated with intravenous thrombolysis (59%) alone, whereas IAT was performed in 22 patients (25%; Table I in the online-only Data Supplement). At 3 months of follow-up, 54 patients (61%) had a poor clinical outcome (modified Rankin Scale score, 3–6), and 22 of 88 patients (25%) were dead.
Bland–Altman analysis showed good agreement between the 2 observers for all venous time points (n=65, Figure I in the online-only Data Supplement). Therefore, average time points of the 2 observers were used for the final analyses. For the entire study population, the mean difference between the nonaffected and affected hemisphere was 2.8 s for first CVF, 3.7 s for optimal CVF, and 5.7 s for end of CVF (Table II in the online-only Data Supplement). The median difference of arterial contrast supply to the ipsilateral and contralateral internal carotid arteries was zero, whereas the median difference for optimal CVF between the nonaffected and affected hemisphere was 3 s (interquartile range, 1–5). Therefore, we defined slow filling to be present if the difference in optimal CVF in the affected hemisphere was >3 s in comparison with optimal CVF in the nonaffected hemisphere. Because of movement artifacts in few but relevant volumes, time points of optimal CVF could not be assessed with certainty in 2 patients and the end of CVF could not be assessed in 3 patients. In 37 of 86 patients (43%), the velocity of optimal CVF was slow, and in 49 of 86 patients (57%), it was fast. With regard to the extent of CVF, 61 patients (69%) had a good CVF (ie, ≥50% compared with the nonaffected hemisphere), whereas 27 patients (31%) had a poor CVF status.

Univariable Analyses

Venous predictors of poor clinical outcome at 3 months of follow-up were poor extent of optimal CVF (risk ratio, 1.8; 95% confidence interval [CI], 1.4–2.4) and slow velocity of optimal CVF (derived from the differences in optimal CVF between the affected and nonaffected hemisphere; risk ratios, 1.6; 95% CI, 1.1–2.2; Table, risk ratios of other clinical and radiological variables are presented in Table III in the online-only Data Supplement). Total duration of CVF in the affected hemisphere was not related to clinical outcome at follow-up.
Table. Poor Outcome at Follow-Up (mRS, 3–6) in Relation to CVF Characteristics Dichotomized at Median Values (N=88)
Dichotomized CVF CharacteristicsRisk of Poor Clinical Outcome at Follow-Up (n/N %)Risk Ratio (95% CI)P Value*
Poor Outcome (n)/Characteristic Present (N)Poor Outcome (n)/Characteristic Absent (N)
Extent CVF in affected hemisphere
 Poor extent of CVF†24/27 (89)30/61 (49)1.8 (1.4–2.4)<0.001
Velocities CVF in affected hemisphere
 Slow first CVF‡28/37 (76)26/51 (51)1.5 (1.1–2.1)0.02
 Slow optimal CVF‡26/36 (72)27/50 (54)1.4 (1.0–1.9)
 Slow end of CVF‡24/36 (67)29/49 (59)1.1 (0.8–1.6)
 Slow first to optimal CVF‡20/35 (57)33/51 (65)0.9 (0.6–1.3)
 Slow first to final CVF‡22/37 (59)31/48 (65)0.9 (0.7–1.3)
Interhemispheric time differences (Δ)
 Δ Internal carotid artery >median Δ16/26 (62)28/50 (56)1.1 (0.7–1.6) 
 Δ First venous filling >median Δ21/29 (72)33/59 (56)1.3 (0.9–1.8) 
 Δ Optimal venous filling >median Δ29/37 (78)24/49 (49)1.6 (1.1–2.2)0.006
 Δ End of venous filling >median Δ17/29 (59)36/56 (64)0.9 (0.6–1.3) 
Extent and delay of CVF
 <50% and slow13/15 (89)14/38 (37)§2.4 (1.5–3.7)<0.001
 <50% and fast10/11 (91)14/38 (37)§2.5 (1.6–3.9)<0.001
 >50% and slow16/22 (73)14/38 (37)§2.0 (1.2–3.2)0.006
 >50% and fast14/38 (37)39/48 (81)0.5 (0.3–0.7)<0.001
CI indicates confidence interval; CVF, cortical venous filling; and mRS, modified Rankin Scale.
*
P values are given in case <0.05.
†
Poor extent venous filling was defined as <50% of CVF in the affected hemisphere compared with the nonaffected hemisphere.
‡
Slow defined as longer than median value in seconds of specific venous time parameter.
§
>50% extent and fast CVF was taken as reference Numbers may not add up to a total population number because of missing values.
Combining the extent and velocity of CVF resulted in 4 subgroups (Table). Poor outcome was seen in 14 of 38 patients (37%) with good extent and fast CVF, as opposed to those with either poor extent, slow CVF, or both (39/48=81%; risk ratios, 0.5; 95% CI, 0.3–0.7).

Multivariable Analyses

Poisson Analysis

Adjustment for separate clinical and radiological variables did not alter the relationship much between extent plus velocity of optimal CVF and poor clinical outcome (Table IV in the online-only Data Supplement). Only when combining optimal CVF with the 4 most influential covariates (mean transit time, CBF, CBS, and collateral status), the relative risk for poor clinical outcome decreased but remained statistically significant (risk ratios, 1.7; 95% CI, 1.0–2.7; P<0.05).

Receiver Operating Characteristic Analysis

At receiver operating characteristic analysis (Figure II in the online-only Data Supplement), the AUC was 0.80 (95% CI, 0.68–0.92) for model 1, which included variables such as age, NIHSS, treatment, and Alberta Stroke Program Early CT score at admission to predict clinical outcome at 3 months of follow-up. Adding CBF to model 1 resulted in model 2 with an AUC of 0.80 (95% CI, 0.68–0.93). Adding collateral status to model 2 resulted in model 3 with an AUC of 0.84 (95% CI, 0.74–0.95). Adding the extent and velocity of CVF to model 3 resulted in model 4 with an AUC of 0.88 (95% CI, 0.77–0.98). The prediction of clinical outcome was better for model 4 when compared with model 3 (P<0.02).

CVF and Endovascular Treatment

Twenty-two patients (25%) of our cohort underwent mechanical thrombectomy (in 18 patients preceded by intravenous thrombolysis), of whom 8 had good clinical outcome at follow-up. One patient was excluded from this subgroup analysis because optimal CVF could not be determined with certainty because of a movement artifact. After mechanical thrombectomy with good reperfusion (modified thrombolysis in cerebral ischemia, 2b and 3), 4 of 6 patients with good extent and fast CVF had good clinical outcome, whereas 2 of 7 patients with good reperfusion had good clinical outcome in case of poor extent or slow CVF. However, in patients with poor reperfusion (modified thrombolysis in cerebral ischemia, 0–2a) CVF status did not much alter clinical outcome because none of the 4 patients with good CVF and only 1 of the 4 patients with poor CVF had good clinical outcome. The risk ratio for good clinical outcome in the group with good CVF and good reperfusion status was 3.3 (95% CI, 1.1–10.6; P<0.05) when compared with patient groups with poor CVF and poor reperfusion status combined (Table V in the online-only Data Supplement).

Discussion

Our study showed that the assessment of extent and velocity of optimal CVF is useful to identify patients with acute MCA stroke at higher risk of poor clinical outcome at 3-month follow-up. Previously, it was already demonstrated that extent of cortical venous drainage, as assessed with single-phase CTA, predicted clinical outcome.7 By adding information about the velocity of optimal CVF, we showed that good extent of optimal CVF is a necessary but insufficient condition for good clinical outcome. A combined assessment of extent and velocity of CVF had additional prognostic value over established predictors of outcome.
Reduced venous drainage in acute ischemic stroke could be explained by multiple factors.16 First of all, the blood exiting the brain via the venous system matches the amount of blood entering the brain from the arterial system.17 Interestingly, the combined assessment of extent and velocity of CVF had predictive value over the extent of arterial occlusion. Moreover, the differences in internal carotid artery filling times were small and had little influence on the interhemispheric difference in optimal CVF times. In experimental studies, additional mechanisms of narrowing of the venous lumen are active vein constriction,18 obstruction by leukocyte–platelet aggregates19,20 and compression of thin-walled venules by edema.16,17,21 Poor venous outflow from the affected hemisphere is associated with poor arterial collateral flow in animal studies.6 Our study confirms that venous assessment complements the information obtained from arterial collateral assessment and plays an important role in predicting stroke outcomes.9,22 These findings are in line with an earlier study from which it was hypothesized that venous filling represents a combined assessment of both the extent of collaterals and perfusion through the microcirculation.7 Discrepancies between venous filling and arterial collateral filling could be explained by additional factors, such as decreased perfusion through the microcirculation as a result of thrombus propagation and progressive microvascular obstruction.7,22
We found that among patients who underwent endovascular stroke treatment, good CVF status at presentation on dynamic CTA in combination with good reperfusion status on digital subtraction angiography after treatment, improved clinical outcome. However, the vast majority of patients with poor CVF status but good reperfusion, as well as patients with good CVF status but poor reperfusion, had poor clinical outcome at follow-up. A limitation in this analysis is the small number of patients who could be included in the subgroups. The role of venous outflow assessment for treatment decision making in acute ischemic stroke needs to be assessed in future prospective studies with larger sample sizes.
An important finding from our study is the role of velocity of optimal CVF in patients with acute MCA stroke. Assessment of the time lag between hemispheres in the opacification of cortical veins in acute ischemic stroke is an understudied phenomenon. However, analysis of the synchronicity of the venous phase is widely used in balloon test occlusions to predict tolerance to permanent carotid artery occlusion. A recent study showed that the validity of this cortical vein time lag in acute carotid occlusion is supported by evidence that the difference of cerebral circulation times reflect asymmetric CBF distribution.23
Limitations of our study mainly relate to the limited sample size. Moreover, we did not assess the variability in superficial venous drainage in our study population, which has been described in a small group of patients.7 Because we took all cortical veins (which drain to the superior sagittal sinus) into account, it is unlikely that physiologic variability in venous drainage influenced our results. We did not take the deep venous structures (ie, thalamostriate vein or internal cerebral veins) into account, since deep venous filling could not be properly evaluated with our postprocessed images. However, it has been shown that only cortical venous drainage, as opposed to deep venous drainage, predicts clinical outcome.7
A potential pitfall of the applied venous filling times in this study is that with our dynamic CTA protocol as used in current clinical practice, acquisition was done at intervals of 2 and 5 s to reduce radiation dose to patients. Therefore, caution is needed about the interpretation of absolute filling times. Application of shorter acquisition intervals to sample the venous phase in more detail is possible, but the trade-off is a higher radiation dose for patients. Reliable assessment of venous drainage patterns with our current acquisition protocol was possible despite these limitations as we took filling of the internal carotid artery into account and demonstrated good interobserver agreement for all different intra- and interhemispheric venous filling times.
In conclusion, combined assessment of extent and velocity of CVF, as evaluated with dynamic CTA, has additional prognostic value over established predictors of clinical outcome in patients with acute MCA stroke.

Supplemental Material

File (str_stroke-2015-012279d_supp1.pdf)

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The image is taken from an article in this issue, “Cortical Venous Filling on Dynamic Computed Tomographic Angiography: A Novel Predictor of Clinical Outcome in Patients With Acute Middle Cerebral Artery Stroke” by van den Wijngaard et al (Stroke. 2016;47:762–767).

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Stroke
Volume 47 • Number 3 • March 2016
Pages: 762 - 767
PubMed: 26814234

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© 2016 American Heart Association, Inc.

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History

Received: 30 November 2015
Revision received: 30 November 2015
Accepted: 18 December 2015
Published online: 26 January 2016
Published in print: March 2016

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Keywords

  1. angiography
  2. brain
  3. confidence intervals
  4. prognosis
  5. stroke

Subjects

  1. Cerebrovascular Disease/Stroke
  2. Computerized Tomography (CT)
  3. Imaging
  4. Ischemic Stroke
  5. Prognosis

Authors

Affiliations

Ido R. van den Wijngaard, MD
From the Departments of Radiology (I.R.v.d.W., G.H., M.A.A.v.W.), Neurology (M.J.H.W.), and Clinical Epidemiology (A.A.), Leiden University Medical Center, Leiden, The Netherlands; Department of Neurology, Medical Center Haaglanden, The Hague, The Netherlands (I.R.v.d.W., J.B.); Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus (A.A.) and Department of Radiology (B.K.V.), University Medical Center Utrecht, Utrecht, The Netherlands; Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands (F.J.A.M.); Department of Neurology, Erasmus University Medical Center, Rotterdam, The Netherlands (D.W.J.D.); and Department of Radiology, Academic Medical Center, Amsterdam, The Netherlands (C.B.L.M.M.).
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Marieke J.H. Wermer, MD, PhD
From the Departments of Radiology (I.R.v.d.W., G.H., M.A.A.v.W.), Neurology (M.J.H.W.), and Clinical Epidemiology (A.A.), Leiden University Medical Center, Leiden, The Netherlands; Department of Neurology, Medical Center Haaglanden, The Hague, The Netherlands (I.R.v.d.W., J.B.); Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus (A.A.) and Department of Radiology (B.K.V.), University Medical Center Utrecht, Utrecht, The Netherlands; Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands (F.J.A.M.); Department of Neurology, Erasmus University Medical Center, Rotterdam, The Netherlands (D.W.J.D.); and Department of Radiology, Academic Medical Center, Amsterdam, The Netherlands (C.B.L.M.M.).
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Jelis Boiten, MD, PhD
From the Departments of Radiology (I.R.v.d.W., G.H., M.A.A.v.W.), Neurology (M.J.H.W.), and Clinical Epidemiology (A.A.), Leiden University Medical Center, Leiden, The Netherlands; Department of Neurology, Medical Center Haaglanden, The Hague, The Netherlands (I.R.v.d.W., J.B.); Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus (A.A.) and Department of Radiology (B.K.V.), University Medical Center Utrecht, Utrecht, The Netherlands; Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands (F.J.A.M.); Department of Neurology, Erasmus University Medical Center, Rotterdam, The Netherlands (D.W.J.D.); and Department of Radiology, Academic Medical Center, Amsterdam, The Netherlands (C.B.L.M.M.).
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Ale Algra, MD, PhD
From the Departments of Radiology (I.R.v.d.W., G.H., M.A.A.v.W.), Neurology (M.J.H.W.), and Clinical Epidemiology (A.A.), Leiden University Medical Center, Leiden, The Netherlands; Department of Neurology, Medical Center Haaglanden, The Hague, The Netherlands (I.R.v.d.W., J.B.); Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus (A.A.) and Department of Radiology (B.K.V.), University Medical Center Utrecht, Utrecht, The Netherlands; Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands (F.J.A.M.); Department of Neurology, Erasmus University Medical Center, Rotterdam, The Netherlands (D.W.J.D.); and Department of Radiology, Academic Medical Center, Amsterdam, The Netherlands (C.B.L.M.M.).
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Ghislaine Holswilder, MSc
From the Departments of Radiology (I.R.v.d.W., G.H., M.A.A.v.W.), Neurology (M.J.H.W.), and Clinical Epidemiology (A.A.), Leiden University Medical Center, Leiden, The Netherlands; Department of Neurology, Medical Center Haaglanden, The Hague, The Netherlands (I.R.v.d.W., J.B.); Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus (A.A.) and Department of Radiology (B.K.V.), University Medical Center Utrecht, Utrecht, The Netherlands; Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands (F.J.A.M.); Department of Neurology, Erasmus University Medical Center, Rotterdam, The Netherlands (D.W.J.D.); and Department of Radiology, Academic Medical Center, Amsterdam, The Netherlands (C.B.L.M.M.).
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Frederick J.A. Meijer, MD, PhD
From the Departments of Radiology (I.R.v.d.W., G.H., M.A.A.v.W.), Neurology (M.J.H.W.), and Clinical Epidemiology (A.A.), Leiden University Medical Center, Leiden, The Netherlands; Department of Neurology, Medical Center Haaglanden, The Hague, The Netherlands (I.R.v.d.W., J.B.); Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus (A.A.) and Department of Radiology (B.K.V.), University Medical Center Utrecht, Utrecht, The Netherlands; Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands (F.J.A.M.); Department of Neurology, Erasmus University Medical Center, Rotterdam, The Netherlands (D.W.J.D.); and Department of Radiology, Academic Medical Center, Amsterdam, The Netherlands (C.B.L.M.M.).
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Diederik W.J. Dippel, MD, PhD
From the Departments of Radiology (I.R.v.d.W., G.H., M.A.A.v.W.), Neurology (M.J.H.W.), and Clinical Epidemiology (A.A.), Leiden University Medical Center, Leiden, The Netherlands; Department of Neurology, Medical Center Haaglanden, The Hague, The Netherlands (I.R.v.d.W., J.B.); Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus (A.A.) and Department of Radiology (B.K.V.), University Medical Center Utrecht, Utrecht, The Netherlands; Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands (F.J.A.M.); Department of Neurology, Erasmus University Medical Center, Rotterdam, The Netherlands (D.W.J.D.); and Department of Radiology, Academic Medical Center, Amsterdam, The Netherlands (C.B.L.M.M.).
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Birgitta K. Velthuis, MD, PhD
From the Departments of Radiology (I.R.v.d.W., G.H., M.A.A.v.W.), Neurology (M.J.H.W.), and Clinical Epidemiology (A.A.), Leiden University Medical Center, Leiden, The Netherlands; Department of Neurology, Medical Center Haaglanden, The Hague, The Netherlands (I.R.v.d.W., J.B.); Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus (A.A.) and Department of Radiology (B.K.V.), University Medical Center Utrecht, Utrecht, The Netherlands; Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands (F.J.A.M.); Department of Neurology, Erasmus University Medical Center, Rotterdam, The Netherlands (D.W.J.D.); and Department of Radiology, Academic Medical Center, Amsterdam, The Netherlands (C.B.L.M.M.).
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Charles B.L.M. Majoie, MD, PhD
From the Departments of Radiology (I.R.v.d.W., G.H., M.A.A.v.W.), Neurology (M.J.H.W.), and Clinical Epidemiology (A.A.), Leiden University Medical Center, Leiden, The Netherlands; Department of Neurology, Medical Center Haaglanden, The Hague, The Netherlands (I.R.v.d.W., J.B.); Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus (A.A.) and Department of Radiology (B.K.V.), University Medical Center Utrecht, Utrecht, The Netherlands; Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands (F.J.A.M.); Department of Neurology, Erasmus University Medical Center, Rotterdam, The Netherlands (D.W.J.D.); and Department of Radiology, Academic Medical Center, Amsterdam, The Netherlands (C.B.L.M.M.).
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Marianne A.A. van Walderveen, MD, PhD
From the Departments of Radiology (I.R.v.d.W., G.H., M.A.A.v.W.), Neurology (M.J.H.W.), and Clinical Epidemiology (A.A.), Leiden University Medical Center, Leiden, The Netherlands; Department of Neurology, Medical Center Haaglanden, The Hague, The Netherlands (I.R.v.d.W., J.B.); Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus (A.A.) and Department of Radiology (B.K.V.), University Medical Center Utrecht, Utrecht, The Netherlands; Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands (F.J.A.M.); Department of Neurology, Erasmus University Medical Center, Rotterdam, The Netherlands (D.W.J.D.); and Department of Radiology, Academic Medical Center, Amsterdam, The Netherlands (C.B.L.M.M.).
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Notes

The online-only Data Supplement is available with this article at http://stroke.ahajournals.org/lookup/suppl/doi:10.1161/STROKEAHA.115.012279/-/DC1.
Correspondence to Ido R. van den Wijngaard, MD, Department of Neurology, Medical Center Haaglanden, PO Box 432, The Hague, The Netherlands. E-mail [email protected]

Disclosures

None.

Sources of Funding

Dr Wermer is supported by a personal grant from the Netherlands Organisation for Scientific Research (NWO/ZonMw-VENI grant) and the Dutch Heart Foundation (2011T055). The Dutch Acute Stroke Trial study was supported by the Dutch Heart Foundation (2008T034) and the NutsOhra Foundation (0903-012). Dr Velthuis is a regular presenter for Philips Healthcare. The Multicenter Randomized Clinical trial of Endovascular treatment for Acute ischemic stroke in the Netherlands trial was supported by the Dutch Heart Foundation and by unrestricted grants from AngioCare BV, Medtronic/Covidien/EV3, Medac/Lamepro, Penumbra, Stryker, and Top Medical/Concentric. Erasmus MC received funds from Stryker for consultations by Dr Dippel. Academic Medical Center (Amsterdam) received funds from Stryker for lectures by Dr Majoie.

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Cortical Venous Filling on Dynamic Computed Tomographic Angiography
Stroke
  • Vol. 47
  • No. 3

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