The Value of Susceptibility-Weighted Imaging (SWI) in Evaluating the Ischemic Penumbra of Patients with Acute Cerebral Ischemic Stroke

Wang, Yongren; Li, Z.; Huang, Wei; H, Yang; Gao, Bo; Chen, Yuting

doi:10.2147/ndt.s301870

Cited by 8 publications

(4 citation statements)

References 24 publications

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“…The above comparison results suggested that the denoised DWI image had a more prominent effect on the display of the IP phenomenon, which was worthy of clinical promotion. The study suggested that conventional DWI detection alone could not effectively diagnose IP in patients with early cerebral infarction [ 27 – 29 ], which was consistent with the above comparison results. In the study, ADC, DWI, and PET images were compared.…”

Section: Discussionsupporting

confidence: 78%

Deep Learning‐Based Diffusion‐Weighted Magnetic Resonance Imaging in the Diagnosis of Ischemic Penumbra in Early Cerebral Infarction

Sheng

Xue-ling

Jiang

et al. 2022

Contrast Media & Molecular Imaging

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The prefiltered image was imported into the local higher-order singular value decomposition (HOSVD) denoising algorithm (GL-HOSVD)-optimized diffusion-weighted imaging (DWI) image, which was compared with the deviation correction nonlocal mean (NL mean) and low-level edge algorithm (LR + edge). Regarding the peak signal-to-noise ratio (PSNR), root mean square error (RMSE), sensitivity, specificity, accuracy, and consistency, the application effect of the GL-HOSVD algorithm in DWI was investigated, and its adoption effect in the examination of ischemic penumbra (IP) of early acute cerebral infarction (ACI) patients was evaluated. A total of 210 patients with ACI were selected as the research subjects, who were randomly rolled into two groups. Those who were checked by conventional DWI were set as the control group, and those who used DWI based on the GL-HOSVD denoising algorithm were set as the observation group, with 105 people in each. Positron emission tomography (PET) test results were set as the gold standard to evaluate the application value of the two examination methods. It was found that under different noise levels, the peak signal-to-noise ratio (PSNR) of the GL-HOSVD algorithm and the root mean square error (RMSE) of the FA parameter were better than those of the nonlocal means (NL-means) of deviation correction and low-rank edge algorithm (LR + edge). The sensitivity, specificity, accuracy, and consistency (8.76%, 81.25%, 87.62%, and 0.52) of the observation group were higher than those of the control group (57.78%, 53.33%, 57.14%, and 0.35) ( P < 0.05 ). Moreover, the apparent diffusion coefficient (ADC) of the DWI images of the observation group was basically consistent with that of the PET images, while the control group had a poor display effect and low definition. In summary, under different noise levels, the GL-HOSVD algorithm had a good denoising effect and greatly reduced fringe artifacts. DWI after denoising had high sensitivity, specificity, accuracy, and consistency in the detection of IP, which was worthy of clinical application and promotion.

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Section: Discussionsupporting

confidence: 78%

Deep Learning‐Based Diffusion‐Weighted Magnetic Resonance Imaging in the Diagnosis of Ischemic Penumbra in Early Cerebral Infarction

Sheng

Xue-ling

Jiang

et al. 2022

Contrast Media & Molecular Imaging

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“…Thus, the analysis of perfusion and SvO 2 parameters in patients provides more insights into cerebral perfusion and oxygenation status, which enables physicians to assess the severity of the stroke and guide treatment selection and adjustment. In the past, many studies have demonstrated the value of perfusion and SvO 2 parameters in stroke patients’ status assessment and prognosis prediction ( 13 , 35 , 36 ). However, these studies only focused on the connection between the parameters and the patients but ignored the deeper information within the parameters.…”

Section: Discussionmentioning

confidence: 99%

Ischemic perfusion radiomics: assessing neurological impairment in acute ischemic stroke

Lu,

Yassin,

Guo

et al. 2024

Front. Neurol.

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IntroductionAccurate neurological impairment assessment is crucial for the clinical treatment and prognosis of patients with acute ischemic stroke (AIS). However, the original perfusion parameters lack the deep information for characterizing neurological impairment, leading to difficulty in accurate assessment. Given the advantages of radiomics technology in feature representation, this technology should provide more information for characterizing neurological impairment. Therefore, with its rigorous methodology, this study offers practical implications for clinical diagnosis by exploring the role of ischemic perfusion radiomics features in assessing the degree of neurological impairment.MethodsThis study employs a meticulous methodology, starting with generating perfusion parameter maps through Dynamic Susceptibility Contrast-Perfusion Weighted Imaging (DSC-PWI) and determining ischemic regions based on these maps and a set threshold. Radiomics features are then extracted from the ischemic regions, and the t-test and least absolute shrinkage and selection operator (Lasso) algorithms are used to select the relevant features. Finally, the selected radiomics features and machine learning techniques are used to assess the degree of neurological impairment in AIS patients.ResultsThe results show that the proposed method outperforms the original perfusion parameters, radiomics features of the infarct and hypoxic regions, and their combinations, achieving an accuracy of 0.926, sensitivity of 0.923, specificity of 0.929, PPV of 0.923, NPV of 0.929, and AUC of 0.923, respectively.ConclusionThe proposed method effectively assesses the degree of neurological impairment in AIS patients, providing an objective auxiliary assessment tool for clinical diagnosis.

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“…The neurological deficit was assessed by the Chinese Stroke patients' Neurological Deficit Rating Scale (CSS) [ 12 ]. It included myodynamia, speech disorder, mental disorder, walking ability, hemiplegia, and eyeball disorder.…”

Section: Methodsmentioning

confidence: 99%

Integrated Medical Care and the Continuous 4C Nursing Model to Improve Nursing Quality and Clinical Treatment of Patients with Acute Stroke: Based on a Retrospective Case‐Control Study

Zhang

et al. 2022

Contrast Media & Molecular Imaging

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Objective. This research paper is based on a retrospective case-control study for exploring the effects of medical nursing integration and the continuous 4C nursing model to improve the clinical treatment and nursing quality of patients with acute stroke. Method. For this purpose, a total of 313 patients with acute stroke, treated in our hospital from February 2020 to April 2021, were enrolled. They were divided into control and study groups with an even number of patients. The control group received integrated medical care number (N = 156), while the study group received integrated medical care and a continuous 4C nursing model (N = 157). In integrated medical care, the general data, self-nursing ability, degree of neurological impairment, Fugl–Meyer Assessment (FMA) score, Barthel index score, and quality of life score were compared between the two groups. Result. The self-nursing concept, self-nursing responsibility, self-nursing skills, health knowledge, and total score of the patients in the study group were higher than those in the control group ( P < 0.05). The neurological function scores of the study group were lower than those of the control group at 1, 3, and 6 months after discharge ( P < 0.05). The scores of the study group were higher than those of the control group at 1, 3, and 6 months after discharge ( P < 0.05). The Barthel index score of the study group was higher than that of the control group at 1, 3, and 6 months after discharge. The scores of physical function, psychological function, social function, and health self-cognition in the study group were lower than those in the control group ( P < 0.05). Conclusion. The application of integrated medical care and the continuous 4C nursing model for patients with acute stroke is beneficial to enhance the degree of neurological impairment of stroke patients, improve activities of daily life and motor function, and facilitate patients’ quality of life. It is helpful to strengthen the attitude and feeling of cooperation between doctors and nurses, promote cooperation between doctors and nurses, reduce the defects of nursing work, heighten the quality of nursing, and achieve the requirement and goal of effectively promoting high-quality nursing.

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The Value of Susceptibility-Weighted Imaging (SWI) in Evaluating the Ischemic Penumbra of Patients with Acute Cerebral Ischemic Stroke

Cited by 8 publications

References 24 publications

Deep Learning‐Based Diffusion‐Weighted Magnetic Resonance Imaging in the Diagnosis of Ischemic Penumbra in Early Cerebral Infarction

Deep Learning‐Based Diffusion‐Weighted Magnetic Resonance Imaging in the Diagnosis of Ischemic Penumbra in Early Cerebral Infarction

Ischemic perfusion radiomics: assessing neurological impairment in acute ischemic stroke

Integrated Medical Care and the Continuous 4C Nursing Model to Improve Nursing Quality and Clinical Treatment of Patients with Acute Stroke: Based on a Retrospective Case‐Control Study

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