scholarly journals Signal of Carotid Intraplaque Hemorrhage on MR T1-Weighted Imaging: Association with Acute Cerebral Infarct

2020 ◽  
Vol 41 (5) ◽  
pp. 836-843 ◽  
Author(s):  
D. Yang ◽  
Y. Liu ◽  
Y. Han ◽  
D. Li ◽  
W. Wang ◽  
...  
Keyword(s):  
Cerebral Infarct ◽  
Intraplaque Hemorrhage ◽  
Acute Cerebral Infarct

Cisplatin induced acute cerebral infarct

2008 ◽  
Vol 29 (4) ◽  
pp. 45
Author(s):  
K Sambasivaiah ◽  
Srikanth Reddy ◽  
BS Praveen Kumar ◽  
P Suneetha
Keyword(s):  
Cerebral Infarct ◽  
Acute Cerebral Infarct

scholarly journals Comparison between xenogeneic and allogeneic adipose mesenchymal stem cells in the treatment of acute cerebral infarct: proof of concept in rats

2015 ◽  
Vol 13 (1) ◽  
pp. 46 ◽  
Author(s):  
María Gutiérrez-Fernández ◽  
Berta Rodríguez-Frutos ◽  
Jaime Ramos-Cejudo ◽  
Laura Otero-Ortega ◽  
Blanca Fuentes ◽  
...  
Keyword(s):  
Stem Cells ◽  
Mesenchymal Stem Cells ◽  
Cerebral Infarct ◽  
Proof Of Concept ◽  
Adipose Mesenchymal Stem Cells ◽  
Acute Cerebral Infarct

scholarly journals Acute cerebral infarct in patients with severe white matter lesions: an analysis using diffusion-weighted MRI

Nosotchu ◽  
2007 ◽  
Vol 29 (4) ◽  
pp. 514-519
Author(s):  
Eiichi Araki ◽  
Toshihiro Hokonohara ◽  
Yuji Kanamori ◽  
Yoshihide Taniwaki ◽  
Takeshi Yamada
Keyword(s):  
White Matter ◽  
White Matter Lesions ◽  
Cerebral Infarct ◽  
Diffusion Weighted Mri ◽  
Diffusion Weighted ◽  
Acute Cerebral Infarct

scholarly journals Adoption of computerized tomography perfusion imaging in the diagnosis of acute cerebral infarct under optimized deconvolution algorithm

2021 ◽  
Vol 37 (6-WIT) ◽  
Author(s):  
Bo Fang ◽  
Hongjiang Zhai
Keyword(s):  
Total Variation ◽  
Computerized Tomography ◽  
Perfusion Imaging ◽  
Cerebral Blood Volume ◽  
Brain Perfusion ◽  
Absolute Error ◽  
Cerebral Infarct ◽  
Deconvolution Algorithm ◽  
Before And After ◽  
Acute Cerebral Infarct

Objectives: To explore the significance of the hemodynamic parameters of Computerized Tomography Perfusion Imaging (CTPI) under the deconvolution optimization algorithm for the diagnosis and treatment of patients with acute cerebral infarct (ACI). Methods: A hundred and ten patients with ACI from December 2018 to September 2019 were selected for research, and CTPI was performed before and after Edaravone injection treatment. Then, the CTPI deconvolution algorithm based on the weighted adaptive (WA) total variation (TV) (WA-TV) optimization was constructed, which was compared with tensor total variation (TTV) and Motion-adaptive sparse parity (MASP). Brain Perfusion 4.0 was applied to obtain the relative time to peak (rTTP), the relative transit time of mean (rMTT), relative cerebral blood volume (rCBV), and relative cerebral blood flow (rCBF) of the core infarction area (CIA) and penumbra ischemic (PI). Results: In four parameters of rTTP, rMTT, rCBV, and CBF, the peak signal to noise ratio (PSNR) of the WA-TV algorithm was higher than the MSAP and TTV algorithms, while the Mean Square Error (MSE) and Mean Absolute Error (MAE) were lower than MSAP and TTV algorithms (P<0.05); the parameters of rCBV (71.56±9.87), rCBF (43.17±7.06) of the CIA before treatment were higher than PI (23.66±7.22; 18.37±3.99), rMTT (124.83±9.73) and rTTP (122.57±7.41) were lower than the PI (183.17±10.16); 150.74±9.74) (P<0.05). After treatment, the rCBV and rCBF of PI were higher than before treatment, and rMTT and rTTP were lower than before treatment (P<0.05), and there was no obvious difference in rCBV, rCBF, rMTT, and rTTP before and after treatment in the CIA (P>0.05). Conclusion: Compared with TTV and MASP, the WA-TV algorithm performs better in noise reduction and artifact reduction. The CTPI parameters of rCBV, rCBF, rMTT, and rTTP are all important indications for the diagnosis of PI and ACI. doi: https://doi.org/10.12669/pjms.37.6-WIT.4884 How to cite this:Fang B, Zhai H. Adoption of computerized tomography perfusion imaging in the diagnosis of acute cerebral infarct under optimized deconvolution algorithm. Pak J Med Sci. 2021;37(6):1687-1692. doi: https://doi.org/10.12669/pjms.37.6-WIT.4884 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

scholarly journals Characterization of clot composition in acute cerebral infarct using machine learning techniques

2019 ◽  
Vol 6 (4) ◽  
pp. 739-747 ◽  
Author(s):  
Jong‐Won Chung ◽  
Yoon‐Chul Kim ◽  
Jihoon Cha ◽  
Eun‐Hyeok Choi ◽  
Byung Moon Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cerebral Infarct ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Acute Cerebral Infarct

Relationship Between Chinese Medicine Pattern Types, Clinical Severity, and Prognosis in Patients With Acute Cerebral Infarct

EXPLORE ◽  
2013 ◽  
Vol 9 (4) ◽  
pp. 226-231 ◽  
Author(s):  
Mao-chi Jhong ◽  
Nou-Ying Tang ◽  
Chung-Hsiang Liu ◽  
Wei-Hsih Huang ◽  
Yi-Ting Hsu ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Cerebral Infarct ◽  
Clinical Severity ◽  
Acute Cerebral Infarct
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