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Quantifying reperfusion of the ischemic region on whole-brain computed tomography perfusion.

ABSTRACT: To derive the reperfusion index best predicting clinical outcome of ischemic stroke patients, we retrospectively analysed the acute and 24-h computed tomography perfusion data of 116 patients, collected from two centres equipped with whole-brain computed tomography perfusion. Reperfusion index was defined by the percentage of the ischemic region reperfused from acute to 24-h computed tomography perfusion. Recanalization was graded by arterial occlusive lesion system. Receiver operator characteristic analysis was performed to assess the prognostic value of reperfusion and recanalization in predicting good clinical outcome, defined as modified Rankin Score of 0-2 at 90 days. Among previous reported reperfusion measurements, reperfusion of the Tmax>6 s region resulted in higher prognostic value than recanalization at predicting good clinical outcome (area under the curve = 0.88 and 0.74, respectively, p = 0.002). Successful reperfusion of the Tmax>6 s region (≥60%) had 89% sensitivity and 78% specificity in predicting good clinical outcome. A reperfusion index defined by Tmax>2 s or by mean transit time>145% had much lower area under the curve in comparison to Tmax>6 s measurement (p < 0.001 and p = 0.003, respectively), and had no significant difference to recanalization at predicting clinical outcome (p = 0.58 and 0.63, respectively). In conclusion, reperfusion index calculated by Tmax>6 s is a stronger predictor of clinical outcome than recanalization or other reperfusion measures.

SUBMITTER: Lin L

PROVIDER: S-EPMC5464706 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Quantifying reperfusion of the ischemic region on whole-brain computed tomography perfusion.

Lin Longting L Cheng Xin X Bivard Andrew A Levi Christopher R CR Dong Qiang Q Parsons Mark W MW

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism 20160101 6

To derive the reperfusion index best predicting clinical outcome of ischemic stroke patients, we retrospectively analysed the acute and 24-h computed tomography perfusion data of 116 patients, collected from two centres equipped with whole-brain computed tomography perfusion. Reperfusion index was defined by the percentage of the ischemic region reperfused from acute to 24-h computed tomography perfusion. Recanalization was graded by arterial occlusive lesion system. Receiver operator characteri ...[more]

PMID: 27461903

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Project description:PurposeComputed tomography (CT)-derived ventilation methods compute respiratory induced volume changes as a surrogate for pulmonary ventilation. Currently, there are no known methods to derive perfusion information from noncontrast CT. We introduce a novel CT-Perfusion (CT-P) method for computing the magnitude mass changes apparent on dynamic noncontrast CT as a surrogate for pulmonary perfusion.MethodsCT-Perfusion is based on a mass conservation model which describes the unknown mass change as a linear combination of spatially corresponding inhale and exhale HU estimated voxel densities. CT-P requires a deformable image registration (DIR) between the inhale/exhale lung CT pair, a preprocessing lung volume segmentation, and an estimate for the Jacobian of the DIR transformation. Given this information, the CT-P image, which provides the magnitude mass change for each voxel within the lung volume, is formulated as the solution to a constrained linear least squares problem defined by a series of subregional mean magnitude mass change measurements. Similar to previous robust CT-ventilation methods, the amount of uncertainty in a subregional sample mean measurement is related to measurement resolution and can be characterized with respect to a tolerance parameter τ . Spatial Spearman correlation between single photon emission CT perfusion (SPECT-P) and the proposed CT-P method was assessed in two patient cohorts via a parameter sweep of τ . The first cohort was comprised of 15 patients diagnosed with pulmonary embolism (PE) who had SPECT-P and 4DCT imaging acquired within 24 h of PE diagnosis. The second cohort was comprised of 15 nonsmall cell lung cancer patients who had SPECT-P and 4DCT images acquired prior to radiotherapy. For each test case, CT-P images were computed for 30 different uncertainty parameter values, uniformly sampled from the range [0.01, 0.125], and the Spearman correlation between the SPECT-P and the resulting CT-P images were computed.ResultsThe median correlations between CT-P and SPECT-P taken over all 30 test cases ranged between 0.49 and 0.57 across the parameter sweep. For the optimal tolerance τ = 0.0385, the CT-P and SPECT-P correlations across all 30 test cases ranged between 0.02 and 0.82. A one-sample sign test was applied separately to the PE and lung cancer cohorts. A low Spearmen correlation of 15% was set as the null median value and two-sided alternative was tested. The PE patients showed a median correlation of 0.57 (IQR = 0.305). One-sample sign test was statistically significant with 96.5 % confidence interval: 0.20-0.63, P < 0.00001. Lung cancer patients had a median correlation of 0.57(IQR = 0.230). Again, a one-sample sign test for median was statistically significant with 96.5 percent confidence interval: 0.45-0.71, P < 0.00001.ConclusionCT-Perfusion is the first mechanistic model designed to quantify magnitude blood mass changes on noncontrast dynamic CT as a surrogate for pulmonary perfusion. While the reported correlations with SPECT-P are promising, further investigation is required to determine the optimal CT acquisition protocol and numerical method implementation for CT-P imaging.

Project description:OBJECTIVE:To compare assessment of collaterals by single-phase computed tomography (CT) angiography (CTA) and CT perfusion-derived 3-phase CTA, multiphase CTA and temporal maximum-intensity projection (tMIP) images to digital subtraction angiography (DSA), and relate collateral assessments to clinical outcome in patients with acute ischemic stroke. METHODS:Consecutive acute ischemic stroke patients who underwent CT perfusion, CTA, and DSA before thrombectomy with occlusion of the internal carotid artery, the M1 or the M2 segments were included. Two observers assessed all CT images and one separate observer assessed DSA (reference standard) with static and dynamic (modified American Society of Interventional and Therapeutic Neuroradiology) collateral grading methods. Interobserver agreement and concordance were quantified with Cohen-weighted ? and concordance correlation coefficient, respectively. Imaging assessments were related to clinical outcome (modified Rankin Scale, ? 2). RESULTS:Interobserver agreement (n = 101) was 0.46 (tMIP), 0.58 (3-phase CTA), 0.67 (multiphase CTA), and 0.69 (single-phase CTA) for static assessments and 0.52 (3-phase CTA) and 0.54 (multiphase CTA) for dynamic assessments. Concordance correlation coefficient (n = 80) was 0.08 (3-phase CTA), 0.09 (single-phase CTA), and 0.23 (multiphase CTA) for static assessments and 0.10 (3-phase CTA) and 0.27 (multiphase CTA) for dynamic assessments. Higher static collateral scores on multiphase CTA (odds ratio [OR], 1.7; 95% confidence interval [CI], 1.1-2.7) and tMIP images (OR, 2.0; 95% CI, 1.1-3.4) were associated with modified Rankin Scale of 2 or less as were higher modified American Society of Interventional and Therapeutic Neuroradiology scores on 3-phase CTA (OR, 1.5; 95% CI, 1.1-2.2) and multiphase CTA (OR, 1.7; 95% CI, 1.1-2.6). CONCLUSIONS:Concordance between assessments on CT and DSA was poor. Collateral status evaluated on 3-phase CTA and multiphase CTA, but not on DSA, was associated with clinical outcome.

Project description:Background and aimGood collateral circulation is recognized to maintain perfusion and contribute to favorable clinical outcomes in acute ischemic stroke. This study aimed to derive and validate an optimal collateral time measurement on perfusion computed tomography imaging for patients with acute ischemic stroke.MethodsThis study included 106 acute ischemic stroke patients with complete large vessel occlusions. In deriving cohort of 23 patients, the parasagittal region of the ischemic hemisphere was divided into six pial arterial zones according to pial branches of the middle cerebral artery. Within the 85 arterial zones with collateral vessels, the receiver operating characteristic analysis was performed to derive the optimal collateral time threshold for fast collateral flow on perfusion computed tomography. The reference for fast collateral flow was the peak contrast delay on the collateral vessels within each ischemic arterial zone compared to its contralateral normal arterial zone on dynamic computed tomography angiography. The optimal perfusion collateral time threshold was then tested in predicting poor clinical outcomes (modified Rankin score of 5-6) and final infarct volume in the validation cohort of 83 patients.ResultsFor the derivation cohort of 85 arterial zones, the optimal collateral time threshold for fast collateral flow on perfusion computed tomography was a delay time of 4.04 s [area under the curve = 0.78 (0.67, 0.89), sensitivity = 73%, and specificity = 77%]. Therefore, the delay time of 4 s was used to define the perfusion collateral time. In the validation cohort, the perfusion collateral time showed a slightly higher predicting power than dynamic computed tomography angiography collateral time in poor clinical outcomes (area under the curve = 0.72 vs. 0.67; P < 0.001). Compared to dynamic computed tomography angiography collateral time, the perfusion collateral time also had better performance in predicting final infarct volume (R-squared values = 0.55 vs. 0.23; P < 0.001).ConclusionOur results indicate that perfusion computed tomography can accurately quantify the collateral time after acute ischemic stroke.

Project description:ObjectiveIn this study, we investigate whether a Convolutional Neural Network (CNN) can generate informative parametric maps from the pre-processed CT perfusion data in patients with acute ischemic stroke in a clinical setting.MethodsThe CNN training was performed on a subset of 100 pre-processed perfusion CT dataset, while 15 samples were kept for testing. All the data used for the training/testing of the network and for generating ground truth (GT) maps, using a state-of-the-art deconvolution algorithm, were previously pre-processed using a pipeline for motion correction and filtering. Threefold cross validation had been used to estimate the performance of the model on unseen data, reporting Mean Squared Error (MSE). Maps accuracy had been checked through manual segmentation of infarct core and total hypo-perfused regions on both CNN-derived and GT maps. Concordance among segmented lesions was assessed using the Dice Similarity Coefficient (DSC). Correlation and agreement among different perfusion analysis methods were evaluated using mean absolute volume differences, Pearson correlation coefficients, Bland-Altman analysis, and coefficient of repeatability across lesion volumes.ResultsThe MSE was very low for two out of three maps, and low in the remaining map, showing good generalizability. Mean Dice scores from two different raters and the GT maps ranged from 0.80 to 0.87. Inter-rater concordance was high, and a strong correlation was found between lesion volumes of CNN maps and GT maps (0.99, 0.98, respectively).ConclusionThe agreement between our CNN-based perfusion maps and the state-of-the-art deconvolution-algorithm perfusion analysis maps, highlights the potential of machine learning methods applied to perfusion analysis. CNN approaches can reduce the volume of data required by deconvolution algorithms to estimate the ischemic core, and thus might allow the development of novel perfusion protocols with lower radiation dose deployed to the patient.

Project description:See Saver (doi:10.1093/awx020) for a scientific commentary on this article.Stroke shortens an individual's disability-free life. We aimed to assess the relative prognostic influence of pre- and post-treatment perfusion computed tomography imaging variables (e.g. ischaemic core and penumbral volumes) compared to standard clinical predictors (such as onset-to-treatment time) on long-term stroke disability in patients undergoing thrombolysis. We used data from a prospectively collected international, multicentre, observational registry of acute ischaemic stroke patients who had perfusion computed tomography and computed tomography angiography before treatment with intravenous alteplase. Baseline perfusion computed tomography and follow-up magnetic resonance imaging were analysed to derive the baseline penumbra volume, baseline ischaemic core volume, and penumbra salvaged from infarction. The primary outcome measure was the effect of imaging and clinical variables on Disability-Adjusted Life Year. Clinical variables were age, sex, National Institutes of Health Stroke Scale score, and onset-to-treatment time. Age, sex, country, and 3-month modified Rankin Scale were extracted from the registry to calculate disability-adjusted life-year due to stroke, such that 1 year of disability-adjusted life-year equates to 1 year of healthy life lost due to stroke. There were 772 patients receiving alteplase therapy. The number of disability-adjusted life-year days lost per 1 ml of baseline ischaemic core volume was 17.5 (95% confidence interval, 13.2-21.9 days, P < 0.001). For every millilitre of penumbra salvaged, 7.2 days of disability-adjusted life-year days were saved (β = -7.2, 95% confidence interval, -10.4 to -4.1 days, P < 0.001). Each minute of earlier onset-to-treatment time resulted in a saving of 4.4 disability-free days after stroke (1.3-7.5 days, P = 0.006). However, after adjustment for imaging variables, onset-to-treatment time was not significantly associated with savings in disability-adjusted life-year days. Pretreatment perfusion computed tomography can (independently of clinical variables) predict significant gains, or loss, of disability-free life in patients undergoing reperfusion therapy for stroke. The effect of earlier treatment on disability-free life appears explained by salvage of penumbra, particularly when the ischaemic core is not too large.

Dataset Information

Quantifying reperfusion of the ischemic region on whole-brain computed tomography perfusion.

Publications

Quantifying reperfusion of the ischemic region on whole-brain computed tomography perfusion.

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