S.-C. Cheng

Blood-brain barrier permeability assessed by perfusion ct predicts symptomatic hemorrhagic transformation and malignant edema in acute ischemic stroke

Symptomatic hemorrhagic transformation and malignant edema are the most feared complications of cerebral infarction, particularly after systemic thrombolysis; why are patients prone to develop them? These investigators retrospectively analyzed data obtained from 32 patients and gave special attention to the permeability of the blood-brain barrier (as measured by perfusion CT). Six patients developed SHT and/or ME and most had received either intravenous or intra-arterial tPA plus mechanical clot retrieval. Abnormal admission BBB permeability measurements were 100% sensitive and 79% specific in identifying patients who developed these dreaded complications. Also, all of these patients were older than 65 years of age. BACKGROUND AND PURPOSE: SHT and ME are feared complications in patients with acute ischemic stroke. They occur >10 times more frequently in tPA-treated versus placebo-treated patients. Our goal was to evaluate the sensitivity and specificity of admission BBBP measurements derived from PCT in predicting the development of SHT and ME in patients with acute ischemic stroke. MATERIALS AND METHODS: We retrospectively analyzed a dataset consisting of 32 consecutive patients with acute ischemic stroke with appropriate admission and follow-up imaging. We calculated admission BBBP by using delayed-acquisition PCT data and the Patlak model. Collateral flow was assessed on the admission CTA, while recanalization and reperfusion were assessed on the follow-up CTA and PCT, respectively. SHT and ME were defined according to ECASS III criteria. Clinical data were obtained from chart review. In our univariate and forward selection−based multivariate analysis for predictors of SHT and ME, we incorporated both clinical and imaging variables, including age, admission NIHSS score, admission blood glucose level, admission blood pressure, time from symptom onset to scanning, treatment type, admission PCT–defined infarct volume, admission BBBP, collateral flow, recanalization, and reperfusion. Optimal sensitivity and specificity for SHT and ME prediction were calculated by using ROC analysis. RESULTS: In our sample of 32 patients, 3 developed SHT and 3 developed ME. Of the 3 patients with SHT, 2 received IV tPA, while 1 received IA tPA and treatment with the Merci device; of the 3 patients with ME, 2 received IV tPA, while 1 received IA tPA and treatment with the Merci device. Admission BBBP measurements above the threshold were 100% sensitive and 79% specific in predicting SHT and ME. Furthermore, all patients with SHT and ME—and only those with SHT and ME—had admission BBBP measurements above the threshold, were older than 65 years of age, and received tPA. Admission BBBP, age, and tPA were the independent predictors of SHT and ME in our forward selection−based multivariate analysis. Of these 3 variables, only BBBP measurements and age were known before making the decision of administering tPA and thus are clinically meaningful. CONCLUSIONS: Admission BBBP, a pretreatment measurement, was 100% sensitive and 79% specific in predicting SHT and ME.

Dynamic Perfusion CT Assessment of the Blood-Brain Barrier Permeability: First Pass versus Delayed Acquisition

BACKGROUND AND PURPOSE: The Patlak model has been applied to first-pass perfusion CT (PCT) data to extract information on blood-brain barrier permeability (BBBP) to predict hemorrhagic transformation in patients with acute stroke. However, the Patlak model was originally described for the delayed steady-state phase of contrast circulation. The goal of this study was to assess whether the first pass or the delayed phase of a contrast bolus injection better respects the assumptions of the Patlak model for the assessment of BBBP in patients with acute stroke by using PCT. MATERIALS AND METHODS: We retrospectively identified 125 consecutive patients (29 with acute hemispheric stroke and 96 without) who underwent a PCT study by using a prolonged acquisition time up to 3 minutes. The Patlak model was applied to calculate BBBP in ischemic and nonischemic brain tissue. Linear regression of the Patlak plot was performed separately for the first pass and for the delayed phase of the contrast bolus injection. Patlak linear regression models for the first pass and the delayed phase were compared in terms of their respective square root mean squared errors (√MSE) and correlation coefficients (R) by using generalized estimating equations with robust variance estimation. RESULTS: BBBP values calculated from the first pass were significantly higher than those from the delayed phase, both in nonischemic brain tissue (2.81 mL × 100 g−1 × min−1 for the first pass versus 1.05 mL × 100 g−1 × min−1 for the delayed phase, P < .001) and in ischemic tissue (7.63 mL × 100 g−1 × min−1 for the first pass versus 1.31 mL × 100 g−1 × min−1 for the delayed phase, P < .001). Compared with regression models from the first pass, Patlak regression models obtained from the delayed data were of better quality, showing significantly lower √MSE and higher R. CONCLUSION: Only the delayed phase of PCT acquisition respects the assumptions of linearity of the Patlak model in patients with and without stroke.

Automated versus manual post-processing of perfusion-CT data in patients with acute cerebral ischemia: influence on interobserver variability

IntroductionThe purpose of this study is to compare the variability of PCT results obtained by automatic selection of the arterial input function (AIF), venous output function (VOF) and symmetry axis versus manual selection.MethodsImaging data from 30 PCT studies obtained as part of standard clinical stroke care at our institution in patients with suspected acute hemispheric ischemic stroke were retrospectively reviewed. Two observers performed the post-processing of 30 CTP datasets. Each observer processed the data twice, the first time employing manual selection of AIF, VOF and symmetry axis, and a second time using automated selection of these same parameters, with the user being allowed to adjust them whenever deemed appropriate. The volumes of infarct core and of total perfusion defect were recorded. The cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT) and blood–brain barrier permeability (BBBP) values in standardized regions of interest were recorded. Interobserver variability was quantified using the Bland and Altmans approach.ResultsAutomated post-processing yielded lower coefficients of variation for the volume of the infarct core and the volume of the total perfusion defect (15.7% and 5.8%, respectively) compared to manual post-processing (31.0% and 12.2%, respectively). Automated post-processing yielded lower coefficients of variation for PCT values (11.3% for CBV, 9.7% for CBF, and 9.5% for MTT) compared to manual post-processing (23.7% for CBV, 32.8% for CBF, and 16.7% for MTT).ConclusionAutomated post-processing of PCT data improves interobserver agreement in measurements of CBV, CBF and MTT, as well as volume of infarct core and penumbra.

Optimal Duration of Acquisition for Dynamic Perfusion CT Assessment of Blood-Brain Barrier Permeability Using the Patlak Model

BACKGROUND AND PURPOSE: A previous study demonstrated the need to use delayed acquisition rather than first-pass data for accurate blood-brain barrier permeability surface product (BBBP) calculation from perfusion CT (PCT) according to the Patlak model, but the optimal duration of the delayed acquisition has not been established. Our goal was to determine the optimal duration of the delayed PCT acquisition to obtain accurate BBBP measurements while minimizing potential motion artifacts and radiation dose. MATERIALS AND METHODS: We retrospectively identified 23 consecutive patients with acute ischemic anterior circulation stroke who underwent a PCT study with delayed acquisition. The Patlak model was applied for the full delayed acquisition (90–240 seconds) and also for truncated analysis windows (90–210, 90–180, 90–150, 90–120 seconds). Linear regression of Patlak plots was performed separately for the full and truncated analysis windows, and the slope of these regression lines was used to indicate BBBP. The full and truncated analysis windows were compared in terms of the resulting BBBP values and the quality of the Patlak fitting. RESULTS: BBBP values in the infarct and penumbra were similar for the full 90- to 240-second acquisition (95% confidence intervals for the infarct and penumbra: 1.62–2.47 and 1.75–2.41 mL ×100 g−1 × min−1, respectively) and the 90- to 210-second analysis window (1.82–2.76 and 2.01–2.74 mL × 100 g−1 × min−1, respectively). BBBP values increased significantly with shorter acquisitions. The quality of the Patlak fit was excellent for the full 90- to 240-second and 90- to 210-second acquisitions, but it degraded with shorter acquisitions. CONCLUSIONS: The duration for the delayed PCT acquisition should be at least 210 seconds, because acquisitions shorter than 210 seconds lead to significantly overestimated BBBP values.

The Triple Rule-Out for Acute Ischemic Stroke: Imaging the Brain, Carotid Arteries, Aorta, and Heart

BACKGROUND AND PURPOSE: Ischemic stroke is commonly embolic, either from carotid atherosclerosis or from cardiac origin. These potential sources of emboli need to be investigated to accurately prescribe secondary stroke prevention. Moreover, the mortality in ischemic stroke patients due to ischemic heart disease is greater than that of age-matched controls, thus making evaluation for coronary artery disease important in this patient population. The purpose of this study was to evaluate the image quality of a comprehensive CTA protocol in patients with acute stroke that expands the standard CTA coverage to include all 4 chambers of the heart and the coronary arteries. MATERIALS AND METHODS: One hundred twenty patients consecutively admitted to the emergency department with suspected cerebrovascular ischemia undergoing standard-of-care CTA were prospectively enrolled in our study. We used an original tailored acquisition protocol using a 64-section CT scanner, consisting of a dual-phase intravenous injection of iodinated contrast and saline flush, in conjunction with a dual-phase CT acquisition, ascending from the top of the aortic arch to the vertex of the head, then descending from the top of the aortic arch to the diaphragm. No beta blockers were administered. The image quality, attenuation, and CNRs of the carotid, aortic, vertebral, and coronary arteries were assessed. RESULTS: Carotid, aorta, and vertebral artery image quality was 100% diagnostic (rated good or excellent) in all patients. Coronary artery image quality was diagnostic in 58% of RCA segments, 73% of LAD segments, and 63% of LCX segments. When we considered proximal segments only, the diagnostic quality rose to 71% in the RCA, 83% in the LAD, and 74% in the LCX. CONCLUSIONS: Our stroke protocol achieved excellent opacification of the left heart chambers, the cervical arteries, and each coronary artery, in addition to adequate carotid and coronary artery image quality.

Carotid Atherosclerosis Does Not Predict Coronary, Vertebral, or Aortic Atherosclerosis in Patients With Acute Stroke Symptoms

Background and Purpose— The purpose of this study was to determine whether significant atherosclerotic disease in the carotid arteries predicts significant atherosclerotic disease in the coronary arteries, vertebral arteries, or aorta in patients with symptoms of acute ischemic stroke. Methods— Atherosclerotic disease was imaged using CT angiography in a prospective study of 120 consecutive patients undergoing emergent CT evaluation for symptoms of stroke. Using a comprehensive CT angiography protocol that captured the carotid arteries, coronary arteries, vertebral arteries, and aorta, we evaluated these arteries for the presence and severity of atherosclerotic disease. Significant atherosclerotic disease was defined as >50% stenosis in the carotid, coronary, and vertebral arteries, or ≥4 mm thickness and encroaching in the aorta. Presence of any and significant atherosclerotic disease was compared in the different types of arteries assessed. Results— Of these 120 patients, 79 had CT angiography examinations of adequate image quality and were evaluated in this study. Of these 79 patients, 33 had significant atherosclerotic disease. In 26 of these 33 patients (79%), significant disease was isolated to 1 type of artery, most often to the coronary arteries (N=14; 54%). Nonsignificant atherosclerotic disease was more systemic and involved multiple arteries. Conclusions— Significant atherosclerotic disease in the carotid arteries does not predict significant atherosclerotic disease in the coronary arteries, vertebral arteries, or aorta in patients with symptoms of acute ischemic stroke. Significant atherosclerotic disease is most often isolated to 1 type of artery in these patients, whereas nonsignificant atherosclerotic disease tends to be more systemic.

Clinical risk factors and CT imaging features of carotid atherosclerotic plaques as predictors of new incident carotid ischemic stroke: A retrospective cohort study

BACKGROUND AND PURPOSE: Parameters other than luminal narrowing are needed to predict the risk of stroke more reliably, particularly in patients with <70% stenosis. The goal of our study was to identify clinical risk factors and CT features of carotid atherosclerotic plaques, in a retrospective cohort of patients free of stroke at baseline, that are independent predictors of incident stroke on follow-up. MATERIALS AND METHODS: We identified a retrospective cohort of patients admitted to our emergency department with suspected stroke between 2001–2007 who underwent a stroke work-up including a CTA of the carotid arteries that was subsequently negative for acute stroke. All patients also had to receive a follow-up brain study at least 2 weeks later. From a random sample, we reviewed charts and imaging studies of patients with subsequent new stroke on follow-up as well as those who remained stroke-free. All patients were classified either as “new carotid infarct patients” or “no-new carotid infarct patients” based on the Causative Classification for Stroke. Independently, the baseline CTA studies were processed using a custom, CT-based automated computer classifier algorithm that quantitatively assesses a set of carotid CT features (wall thickness, plaque ulcerations, fibrous cap thickness, lipid-rich necrotic core, and calcifications). Univariate and multivariate statistical analyses were used to identify any significant differences in CT features between the patient groups in the sample. Subsequent ROC analysis allowed comparison to the classic NASCET stenosis rule in identifying patients with incident stroke on follow-up. RESULTS: We identified a total of 315 patients without a new carotid stroke between baseline and follow-up, and 14 with a new carotid stroke between baseline and follow-up, creating the main comparison groups for the study. Statistical analysis showed age and use of antihypertensive drugs to be the most significant clinical variables, and maximal carotid wall thickness was the most relevant imaging variable. The use of age ≥75 years, antihypertensive medication use, and a maximal carotid wall thickness of at least 4 mm was able to successfully identify 10 of the 14 patients who developed a new incident infarct on follow-up. ROC analysis showed an area under the ROC curve of 0.706 for prediction of new stroke with this new model. CONCLUSIONS: Our new paradigm of using age ≥75 years, history of hypertension, and carotid maximal wall thickness of >4 mm identified most of the patients with subsequent new carotid stroke in our study. It is simple and may help clinicians choose the patients at greatest risk of developing a carotid infarct, warranting validation with a prospective observational study.

Accuracy and anatomical coverage of perfusion CT assessment of the blood-brain barrier permeability: one bolus versus two boluses.

Purpose: To assess whether blood-brain barrier permeability (BBBP) values, extracted with the Patlak model from the second perfusion CT (PCT) contrast bolus, are significantly lower than the values extracted from the first bolus in the same patient. Materials and Methods: 125 consecutive patients (29 with acute hemispheric stroke and 96 without stroke) who underwent a PCT study using a prolonged acquisition time up to 3 min were retrospectively identified. The Patlak model was applied to calculate the rate of contrast leakage out of the vascular compartment. Patlak plots were created from the arterial and parenchymal time enhancement curves obtained in multiple regions of interest drawn in ischemic brain tissue and in nonischemic brain tissue. The slope of a regression line fit to the Patlak plot was used as an indicator of BBBP. Square roots of the mean squared errors and correlation coefficients were used to describe the quality of the linear regression model. This was performed separately for the first and the second PCT bolus. Results from the first and the second bolus were compared in terms of BBBP values and the quality of the linear model fitted to the Patlak plot, using generalized estimating equations with robust variance estimation. Results: BBBP values from the second bolus were not lower than BBBP values from the first bolus in either nonischemic brain tissue [estimated mean with 95% confidence interval: 1.42 (1.10–1.82) ml·100 g–1·min–1 for the first bolus versus 1.64 (1.31–2.05) ml·100 g–1·min–1 for the second bolus, p = 1.00] or in ischemic tissue [1.04 (0.97–1.12) ml·100 g–1·min–1 for the first bolus versus 1.19 (1.11–1.28) ml·100 g–1·min–1 for the second bolus, p = 0.79]. Compared to regression models from the first bolus, the Patlak regression models obtained from the second bolus were of similar or slightly better quality. This was true both in nonischemic and ischemic brain tissue. Conclusion: The contrast material from the first bolus of contrast for PCT does not negatively influence measurements of BBBP values from the second bolus. The second bolus can thus be used to increase anatomical coverage of BBBP assessment using PCT.

Delay Correction for the Assessment of Blood-Brain Barrier Permeability Using First-Pass Dynamic Perfusion CT

SUMMARY: Hemorrhagic transformation is a serious potential complication of ischemic stroke with damage to the BBB as one of the contributing mechanisms. BBB permeability measurements extracted from PCT by using the Patlak model can provide a valuable assessment of the extent of BBB damage. Unfortunately, Patlak assumptions require extended PCT acquisition, increasing the risk of motion artifacts. A necessary correction is presented for obtaining accurate BBB permeability measurements from first-pass PCT.

Contrast Delay on Perfusion CT as a Predictor of New, Incident Infarct A Retrospective Cohort Study

Background and Purpose— The purpose of this study was to determine if the assessment of intracranial collateral circulation by CT angiography and/or perfusion CT (PCT) can predict the risk of future ischemic stroke in a large, retrospective cohort study. Methods— We identified 135 consecutive patients who underwent CT angiography of the head and neck and PCT of the brain at baseline and with subsequent follow-up brain imaging. Clinical and demographic information and carotid wall features were collected. Collateral circulation was assessed anatomically at CT angiography and functionally by measuring the mean transit time delay at PCT. The clinical, carotid, CT angiography, and PCT variables were compared between those with and without new incident infarct at follow-up imaging using mixed effect logistic statistical models. Results— During the follow-up period, 15 patients developed a new infarct and 120 patients did not. Clinical features associated with the stroke risk were age, hypertension, hyperlipidemia, and atrial fibrillation. The carotid features associated with stroke risk were wall thickness. Anatomic assessment of collaterals on CT angiography was not associated with stroke risk, whereas the functional assessment of collaterals (mean transit time delay on PCT) was associated with stroke risk. In a multivariate model, age, atrial fibrillation, and mean transit time delay (OR, 22.8; P<0.001) were the only covariates that were independent predictors of future ischemic stroke. Conclusions— The mean transit time delay on PCT contains important physiological information and should not be discarded. Along with age and atrial fibrillation, this functional assessment of intracranial collateral circulation predicts the risk of future hemispheric infarct.