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Diagnosis of Early Neurological Deterioration after Intravenous Thrombolysis for Patients with Cerebral Ischemic Stroke Using Compressed Sensing-Magnetic Resonance Imaging Algorithm
This study was to explore the risk factors and prognosis of early neurological deterioration (END) after intravenous thrombolysis in patients with cerebral ischemic stroke (CIS) with the guidance of magnetic resonance imaging (MRI) under the compressed sensing-MRI (CSMRI) algorithm. 187 patients wit...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8318764/ https://www.ncbi.nlm.nih.gov/pubmed/34377103 http://dx.doi.org/10.1155/2021/2529936 |
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author | Wang, Junjun Wang, Benxiao Tang, Yongliang Yan, Hui |
author_facet | Wang, Junjun Wang, Benxiao Tang, Yongliang Yan, Hui |
author_sort | Wang, Junjun |
collection | PubMed |
description | This study was to explore the risk factors and prognosis of early neurological deterioration (END) after intravenous thrombolysis in patients with cerebral ischemic stroke (CIS) with the guidance of magnetic resonance imaging (MRI) under the compressed sensing-MRI (CSMRI) algorithm. 187 patients with CIS in the hospital were selected and grouped into a deterioration group and a control group according to whether they had END. The CSMRI algorithm was constructed and compared with digital television (DTV) algorithm and Bayesian compressed sensing (BCS) algorithm. It was found that the reconstruction time of CSMRI algorithm in platform I (1134.9 s) and platform II (2615.8 s) was visibly lower than that of DTV algorithm (2634.6 s, 3963.4 s) and BCS algorithm (5631.5 s, 7412.3 s), showing statistically obvious differences (P < 0.05). In addition, the reconstruction efficiency of the CSMRI algorithm was the best. After 4 hours of intravenous thrombolysis, the stroke scale score (12.3 scores) of the deterioration group was much higher than that of the control group (8.4 scores) (P < 0.05). The occlusion of responsible great vessel in the deterioration group (30 cases, 83.33%) was obviously higher in contrast to that in the control group (74 cases, 49%) (P < 0.05). Stroke scale score and occlusion of responsible great vessel were risk factors for EBD after intravenous thrombolysis. |
format | Online Article Text |
id | pubmed-8318764 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-83187642021-08-09 Diagnosis of Early Neurological Deterioration after Intravenous Thrombolysis for Patients with Cerebral Ischemic Stroke Using Compressed Sensing-Magnetic Resonance Imaging Algorithm Wang, Junjun Wang, Benxiao Tang, Yongliang Yan, Hui Contrast Media Mol Imaging Research Article This study was to explore the risk factors and prognosis of early neurological deterioration (END) after intravenous thrombolysis in patients with cerebral ischemic stroke (CIS) with the guidance of magnetic resonance imaging (MRI) under the compressed sensing-MRI (CSMRI) algorithm. 187 patients with CIS in the hospital were selected and grouped into a deterioration group and a control group according to whether they had END. The CSMRI algorithm was constructed and compared with digital television (DTV) algorithm and Bayesian compressed sensing (BCS) algorithm. It was found that the reconstruction time of CSMRI algorithm in platform I (1134.9 s) and platform II (2615.8 s) was visibly lower than that of DTV algorithm (2634.6 s, 3963.4 s) and BCS algorithm (5631.5 s, 7412.3 s), showing statistically obvious differences (P < 0.05). In addition, the reconstruction efficiency of the CSMRI algorithm was the best. After 4 hours of intravenous thrombolysis, the stroke scale score (12.3 scores) of the deterioration group was much higher than that of the control group (8.4 scores) (P < 0.05). The occlusion of responsible great vessel in the deterioration group (30 cases, 83.33%) was obviously higher in contrast to that in the control group (74 cases, 49%) (P < 0.05). Stroke scale score and occlusion of responsible great vessel were risk factors for EBD after intravenous thrombolysis. Hindawi 2021-07-20 /pmc/articles/PMC8318764/ /pubmed/34377103 http://dx.doi.org/10.1155/2021/2529936 Text en Copyright © 2021 Junjun Wang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Wang, Junjun Wang, Benxiao Tang, Yongliang Yan, Hui Diagnosis of Early Neurological Deterioration after Intravenous Thrombolysis for Patients with Cerebral Ischemic Stroke Using Compressed Sensing-Magnetic Resonance Imaging Algorithm |
title | Diagnosis of Early Neurological Deterioration after Intravenous Thrombolysis for Patients with Cerebral Ischemic Stroke Using Compressed Sensing-Magnetic Resonance Imaging Algorithm |
title_full | Diagnosis of Early Neurological Deterioration after Intravenous Thrombolysis for Patients with Cerebral Ischemic Stroke Using Compressed Sensing-Magnetic Resonance Imaging Algorithm |
title_fullStr | Diagnosis of Early Neurological Deterioration after Intravenous Thrombolysis for Patients with Cerebral Ischemic Stroke Using Compressed Sensing-Magnetic Resonance Imaging Algorithm |
title_full_unstemmed | Diagnosis of Early Neurological Deterioration after Intravenous Thrombolysis for Patients with Cerebral Ischemic Stroke Using Compressed Sensing-Magnetic Resonance Imaging Algorithm |
title_short | Diagnosis of Early Neurological Deterioration after Intravenous Thrombolysis for Patients with Cerebral Ischemic Stroke Using Compressed Sensing-Magnetic Resonance Imaging Algorithm |
title_sort | diagnosis of early neurological deterioration after intravenous thrombolysis for patients with cerebral ischemic stroke using compressed sensing-magnetic resonance imaging algorithm |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8318764/ https://www.ncbi.nlm.nih.gov/pubmed/34377103 http://dx.doi.org/10.1155/2021/2529936 |
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