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

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 colle...

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Autores principales: Qingsong Gong, Botao Yu, Mengjie Wang, Min Chen, Haowen Xu, Jianbo Gao
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Publicado: Hindawi Limited 2021
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spelling oai:doaj.org-article:b55eaf8dc8d9436c839e8a4ad13ceb292021-11-22T01:10:25ZPredictive 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 Stroke2040-230910.1155/2021/4463975https://doaj.org/article/b55eaf8dc8d9436c839e8a4ad13ceb292021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/4463975https://doaj.org/toc/2040-2309Our 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.Qingsong GongBotao YuMengjie WangMin ChenHaowen XuJianbo GaoHindawi LimitedarticleMedicine (General)R5-920Medical technologyR855-855.5ENJournal of Healthcare Engineering, Vol 2021 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine (General)
R5-920
Medical technology
R855-855.5
spellingShingle Medicine (General)
R5-920
Medical technology
R855-855.5
Qingsong Gong
Botao Yu
Mengjie Wang
Min Chen
Haowen Xu
Jianbo Gao
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
description 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.
format article
author Qingsong Gong
Botao Yu
Mengjie Wang
Min Chen
Haowen Xu
Jianbo Gao
author_facet Qingsong Gong
Botao Yu
Mengjie Wang
Min Chen
Haowen Xu
Jianbo Gao
author_sort Qingsong Gong
title 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
title_short 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_sort 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
publisher Hindawi Limited
publishDate 2021
url https://doaj.org/article/b55eaf8dc8d9436c839e8a4ad13ceb29
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