scholarly journals Physiological motion of the carotid atherosclerotic plaque quantified using ultrasound B-Mode image analysis

2018 ◽  
Author(s):  
Baris Kanber ◽  
Timothy C. Hartshorne ◽  
James W. Garrard ◽  
A. Ross Naylor ◽  
Thompson G. Robinson ◽  
...  

AbstractBackgroundPhysical motion throughout the cardiac cycle may contribute to the rupture of the atherosclerotic carotid plaque, resulting in ischaemic stroke. The purpose of this study was to quantify the physiological motion of the atherosclerotic carotid plaque and to investigate any relationship between the quantified motion parameters and the degree of stenosis, greyscale plaque characteristics, and the presence of cerebrovascular symptoms.MethodsDisplacement, velocity and acceleration of 81 plaques (51% symptomatic, stenosis range 10%-95%) from 51 patients were measured using an automated system employing a block matching algorithm relative to the ultrasound probe and relative to the periadventitial tissues, over a mean duration of 5 cardiac cycles.ResultsAveraged across all plaques, the displacement amplitude was 1.2 mm relative to the probe, and 0.35 mm relative to the periadventitial tissues. Maximum and mean plaque velocities were 4.7 and 1.3 mm/s relative to the ultrasound probe, and 2.4 and 0.70 mm/s relative to the periadventitial tissues. The corresponding acceleration magnitudes were 69 and 22 mm/s2 relative to the probe, and 57 and 18 mm/s2 relative to the periadventitial tissues. There were no significant differences in any of the motion parameters, with respect to the presence of cerebrovascular symptoms, and none of the parameters showed a statistically significant relationship to the degree of stenosis, and the greyscale plaque characteristics (p≤0.05). The technique used was able to detect plaque motion amplitudes above 50μm.ConclusionsThis study provides quantitative data on the physiological motion of the atherosclerotic carotid plaque in-vivo. No significant relationship was found between the measured motion parameters and the presence of cerebrovascular symptoms, the degree of stenosis, and the greyscale plaque characteristics.

Author(s):  
V. A. Maksimenko ◽  
A. A. Harchenko ◽  
A. Lüttjohann

Introduction: Now the great interest in studying the brain activity based on detection of oscillatory patterns on the recorded data of electrical neuronal activity (electroencephalograms) is associated with the possibility of developing brain-computer interfaces. Braincomputer interfaces are based on the real-time detection of characteristic patterns on electroencephalograms and their transformation  into commands for controlling external devices. One of the important areas of the brain-computer interfaces application is the control of the pathological activity of the brain. This is in demand for epilepsy patients, who do not respond to drug treatment.Purpose: A technique for detecting the characteristic patterns of neural activity preceding the occurrence of epileptic seizures.Results:Using multi-channel electroencephalograms, we consider the dynamics of thalamo-cortical brain network, preceded the occurrence of an epileptic seizure. We have developed technique which allows to predict the occurrence of an epileptic seizure. The technique has been implemented in a brain-computer interface, which has been tested in-vivo on the animal model of absence epilepsy.Practical relevance:The results of our study demonstrate the possibility of epileptic seizures prediction based on multichannel electroencephalograms. The obtained results can be used in the development of neurointerfaces for the prediction and prevention of seizures of various types of epilepsy in humans. 


2009 ◽  
Vol 38 (2) ◽  
pp. 149-154 ◽  
Author(s):  
A.J. Patterson ◽  
J.M. U-King-Im ◽  
T.Y. Tang ◽  
D.J. Scoffings ◽  
S.P.S. Howarth ◽  
...  

Ultrasonics ◽  
2009 ◽  
Vol 49 (8) ◽  
pp. 779-785 ◽  
Author(s):  
Hairong Shi ◽  
Tomy Varghese ◽  
Carol C. Mitchell ◽  
Matthew McCormick ◽  
Robert J. Dempsey ◽  
...  

2015 ◽  
Vol 241 (1) ◽  
pp. e15
Author(s):  
J.T. Chai ◽  
L. Biasiolli ◽  
L. Li ◽  
A. Handa ◽  
J. Perkins ◽  
...  

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