CT Perfusion Imaging Using Bayesian-Based Deconvolution Method: Determination of the Optimal Threshold to Identify Core Infarct and Penumbra in Acute Ischemic Stroke

Introduction/purpose: A significant complication in the intervention of acute ischemic stroke is hemorrhagic transformation (HT). It has been postulated that perfusion permeability imaging showing increased blood brain barrier permeability can be used to predict hemorrhagic transformation and possibly alter therapies. Materials and Methods: We retrospectively reviewed 1040 sequential CT perfusion scans with permeability surface area product maps calculated using the Patlak model for all patients that exhibited stroke like symptoms between October 2011 and November 2012. The size of the permeability surface product was ranked on a qualitative three-part scale of small, moderate and large permeability changes. A change smaller than 25% of the image was considered a small result. A moderate result is a permeability change that is approximately 25% of the image. A large permeability change exceeds 25% of the image. Follow up non-contrast CT images (>24 hours but <15 days after initial perfusion imaging) were used to determine if HT had occurred in the cases where an increase in permeability surface product was observed. Results: There was a positive increase in permeability maps in 142 of the 1040 cases. The size of the permeability change was moderate to large in 101 of the positive cases (71%). Hemorrhagic transformation was observed in 12 patients that showed an increase in permeability surface product (8.4%). Of the cases that resulted in HT, nine (75%) resulted in an HI1 and HI2 subtypes. There were three (25%) of the more severe parenchymal hemorrhages (PH1, PH2) observed. Out of the 12 positive hemorrhagic transformations four (33%) were treated with iv-TPA and two (17%) received endovascular thrombectomies, while six (50%) did not receive TPA or endovascular intervention. Of the major parenchymal hemorrhages (PH1/2) two occurred after iv-TPA treatment of the stroke, with the other arising after endovascular thrombectomy. No difference was found in the size or degree of the permeability changes and the incidence of HT. Conclusions: Elevated permeability on CT perfusion imaging had no relevant predictive value for hemorrhagic transformation in acute ischemic stroke at our institution.

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Abstract WMP15: A Simple Tool to Predict Target Mismatch in Ischemic Stroke Patients with Anterior Circulation Occlusion

Stroke ◽

10.1161/str.48.suppl_1.wmp15 ◽

2017 ◽

Vol 48 (suppl_1) ◽

Author(s):

Adam de Havenon ◽

Steve O’Donnell ◽

Alex Linn ◽

Scott McNally ◽

Bailey Dunleavy ◽

...

Keyword(s):

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Perfusion Imaging ◽

Time Window ◽

Ct Perfusion ◽

Volumetric Analysis ◽

Stroke Patients ◽

Anterior Circulation ◽

Ct Angiogram

Introduction: The efficacy of endovascular thrombectomy in an extended time window for acute ischemic stroke patients with Target Mismatch (TM) on perfusion imaging was shown in a recent study and the ongoing DEFUSE-3 trial is studying thrombectomy in a 6-16 hour window for TM patients. A limitation of TM is that perfusion imaging is not widely available. We sought to identify a tool to predict TM based on clinical factors and CT angiogram (CTA) imaging, which is available at most hospitals. Methods: We reviewed acute ischemic stroke patients from 2010-2014 with proximal middle cerebral artery occlusion, CTA and CT perfusion (CTP) at hospital admission. TM was identified on CTP using the Olea Sphere volumetric analysis software with Bayesian deconvolution. TM was defined by the DEFUSE-3 criteria. ASPECTS was derived from the non-contrast CT head and the CTA source images (CTA-ASPECTS). Two collateral scores were derived from CTA source images. Results: 61 patients met inclusion criteria. The mean±SD age was 61±18 years and 61% were male. Mean NIH Stroke Scale (NIHSS) was 14.1±8.0 and median (IQR) follow-up modified Rankin Scale was 3 (1,6). TM was present in 35/61 (57%), who had lower mRS at follow-up (z=3.5, p<0.001). The predictor variables are shown in Table 1. The best combination of predictors was CTA-ASPECTS >4 and NIHSS <16, which had a sensitivity of 80% and specificity of 85% for TM (Figure 1). Discussion: We report a reliable, accessible, and clinically useful tool for predicting TM. This score warrants further study as a tool to guide transfer decisions from primary or secondary stroke centers to tertiary centers where endovascular intervention would be possible for selected patients.

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Predictive Value of CT Perfusion Imaging on the Basis of Automatic Segmentation Algorithm to Evaluate the Collateral Blood Flow Status on the Outcome of Reperfusion Therapy for Ischemic Stroke

Journal of Healthcare Engineering ◽

10.1155/2021/4463975 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Qingsong Gong ◽

Botao Yu ◽

Mengjie Wang ◽

Min Chen ◽

Haowen Xu ◽

...

Keyword(s):

Blood Flow ◽

Ischemic Stroke ◽

Perfusion Imaging ◽

Ct Perfusion ◽

Automatic Segmentation ◽

Reperfusion Therapy ◽

Segmentation Algorithm ◽

Collateral Flow ◽

Collateral Blood Flow ◽

Ct Perfusion Imaging

Our objective was to study the predictive value of CT perfusion imaging based on automatic segmentation algorithm for evaluating collateral blood flow status in the outcome of reperfusion therapy for ischemic stroke. All data of 30 patients with ischemic stroke reperfusion in our hospital were collected and examined by CT perfusion imaging. Convolutional neural network (CNN) algorithm was used to segment perfusion imaging map and evaluate the results. The patients were grouped by regional leptomeningeal collateral score (rLMCs). Binary logistic regression was used to analyze the independent influencing factors of collateral blood flow on brain CT perfusion. The modified Scandinavian Stroke Scale was used to evaluate the prognosis of patients, and the effects of different collateral flow conditions on prognosis were obtained. The accuracy of CNN segmentation image is 62.61%, the sensitivity is 87.42%, the similarity coefficient is 93.76%, and the segmentation result quality is higher. Blood glucose (95% CI = 0.943, P = 0.028 ) and ischemic stroke history (95% CI = 0.855, P = 0.003 ) were independent factors affecting the collateral blood flow status of stroke patients. CBF (95% CI = 0.818, P = 0.008 ) and CBV (95% CI = 0.796, P = 0.016 ) were independent influencing factors of CT perfusion parameters. After 3 weeks of onset, the prognostic function defect score of the good collateral flow group (11.11%) was lower than that of the poor group (41.67%) ( P < 0.05 ). The automatic segmentation algorithm has more accurate segmentation ability for stroke CT perfusion imaging and plays a good auxiliary role in the diagnosis of clinical stroke reperfusion therapy. The collateral blood flow state based on CT perfusion imaging is helpful to predict the treatment outcome of patients with ischemic stroke and further predict the prognosis of patients.

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