Magnetic resonance imaging of human cerebral infarction: Enhancement with Gd-DTPA

1987 ◽  
Vol 29 (5) ◽  
pp. 422-429 ◽  
Author(s):  
S. Imakita ◽  
T. Nishimura ◽  
H. Naito ◽  
N. Yamada ◽  
K. Yamamoto ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Cerebral Infarction ◽  
Resonance Imaging

scholarly journals Differential immunological profiles herald magnetic resonance imaging-defined perioperative cerebral infarction

2018 ◽  
Vol 11 ◽  
pp. 175628641875949 ◽  
Author(s):  
Jonathon P. Fanning ◽  
Louise E. See Hoe ◽  
Margaret R. Passmore ◽  
Adrian G. Barnett ◽  
Barbara E. Rolfe ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Cerebral Infarction ◽  
Resonance Imaging

scholarly journals Noninvasive assessment of ischemic penumbra by using MR-SWI during the acute phase of cerebral infarction: a comparison to PWI

2015 ◽  
Vol 42 (S1) ◽  
pp. S36-S36
Author(s):  
S Luo ◽  
X Wu ◽  
F Deng ◽  
Y Zhang ◽  
J Miao ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Cerebral Infarction ◽  
Cerebral Blood Volume ◽  
Unmet Need ◽  
Acute Stage ◽  
Ischemic Penumbra ◽  
Resonance Imaging ◽  
Stroke Evolution ◽  
Work Up

Background: Assessment of ischemic penumbra during the acute stage of cerebral infarction is crucial for a decision to initiate thrombolytic therapy and for predicting stroke evolution. Although controversial as a perfect equivalence to penumbra, perfusion weighted imaging (PWI)-diffusion weighted imaging (DWI) mismatch may predict the response to thrombolysis. Due to the reliance on contrast agents in PWI, noninvasive alternatives remain an unmet need. Methods: We herein investigate the potentials of SWI as an alternative to PWI in defining ischemic penumbra and in predicting stroke outcome. A multimodal magnetic resonance imaging work-up which includes conventional magnetic resonance imaging sequences (T1WI, T2WI and FLAIR), DWI, PWI and SWI was performed. The Alberta Stroke Programme Early CT Score (ASPECTS) was used to evaluate the changes in DWI, SWI and PWI. Results: The mismatch of SWI-DWI was comparable with that of PWI-DWI (p>0.05). Furthermore, the grade of prominent vein and the cerebral blood volume in the ipsilateral brain tissue were positively correlated. Conclusions: SWI can be used as a noninvasive alternative to identify occlusive arteries and to evaluate the ischemic penumbra. The susceptibility vein sign may represent thrombosis in arteries whereby being helpful to identify responsible blood vessels in ischemic stroke.

Central nervous system inflammatory response after cerebral infarction as detected by magnetic resonance imaging

2004 ◽  
Vol 17 (4) ◽  
pp. 163-169 ◽  
Author(s):  
Andreas Saleh ◽  
Dirk Wiedermann ◽  
Michael Schroeter ◽  
Cornelia Jonkmanns ◽  
Sebastian Jander ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Central Nervous System ◽  
Nervous System ◽  
Magnetic Resonance ◽  
Inflammatory Response ◽  
Cerebral Infarction ◽  
Resonance Imaging

scholarly journals Novel gradient echo sequence-based amide proton transfer magnetic resonance imaging in hyperacute cerebral infarction

2015 ◽  
Vol 11 (5) ◽  
pp. 3279-3284 ◽  
Author(s):  
DEXIAO HUANG ◽  
SHENKAI LI ◽  
ZHUOZHI DAI ◽  
ZHIWEI SHEN ◽  
GEN YAN ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Cerebral Infarction ◽  
Proton Transfer ◽  
Amide Proton ◽  
Resonance Imaging ◽  
Gradient Echo ◽  
Amide Proton Transfer

scholarly journals Magnetic Resonance Imaging Features of Cerebral Infarction in Critical Patients Based on Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yi Bo ◽  
Junli Xie ◽  
Jianguo Zhou ◽  
Shikun Li ◽  
Yuezhan Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Artificial Intelligence ◽  
Magnetic Resonance ◽  
Cerebral Infarction ◽  
Convolutional Neural Network ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Gray Level ◽  
Resonance Imaging

The clinical application of the artificial intelligence-assisted system in imaging was investigated by analyzing the magnetic resonance imaging (MRI) influence characteristics of cerebral infarction in critically ill patients based on the convolutional neural network (CNN). Fifty patients with cerebral infarction were enrolled and examined by MRI. Besides, a CNN artificial intelligence system was established for learning and training. The features were extracted from the MRI image results of the patients, and then, the data were calculated by computer technology. The gray-level cooccurrence matrix (GLCM) of T1-weighted images was 0.872 ± 0.069; the reasonable prediction (ALL) result was 0.766 ± 0.112; the gray-level run-length matrix (GLRLM) was 0.812 ± 0.101; the multigray-level area size matrix (MGLSZM) result was 0.713 ± 0.104; and the result of gray-scale area size matrix (GLSZM) was 0.598 ± 0.099. The GLCM, ALL, GLRLM, MGLSZM, and GLSZM of enhanced T1-weighted images were 0.710 ± 0.169, 0.742 ± 0.099, 0.778 ± 0.096, 0.801 ± 0.104, and 0.598 ± 0.099, respectively. The GLCM, ALL, GLRLM, MGLSZM, and GLSZM of T2-weighted images were 0.780 ± 0.096, 0.798 ± 0.087, 0.888 ± 0.086, 0.768 ± 0.112, and 0.767 ± 0.100, respectively. In short, the image diagnosis method that could reduce the subjective visual judgment error to a certain extent was found by analyzing the characteristics of MRI images of critically ill patients with cerebral infarction based on CNN.

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