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

Abstract

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.

Evaluation of collateral status and outcome in patients with middle cerebral artery stenosis in late time window by CT perfusion imaging

“…Among the cerebral perfusion parameters, cerebral blood volume (CBV) is observed to reflect the blood flow reserve capacity of ischemic brain tissue. Cerebral blood flow (CBF) measures blood flow velocity and is considered to be representative of the compensation degree of brain tissue blood flow ( 12 , 13 ). In this study, the ratios of the CTP-derived CBV and CBF values were calculated by dividing the values of the ischemic lesion by the corresponding values of the contralateral normal region (which we defined as the rCBV and rCBF).…”

Radiomics Analysis of Diffusion-Weighted Imaging and Long-Term Unfavorable Outcomes Risk for Acute Stroke

“…Many studies have attempted to combine these prognostic factors into standardized scores or use machine learning to predict clinical outcomes. [4][5][6] However, these studies were mainly based on computed tomography (CT) imaging in patients with AIS, and few have utilized magnetic resonance imaging (MRI). Nevertheless, diffusion-weighted imaging (DWI) has been shown to be more accurate than noncontrast CT or perfusion CT in estimating the ischemic core.…”

Retracted: 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

“…This article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”