A comparative analysis of MRI and CT brain images for stroke diagnosis

Jeena, R. S.; Kumar, Sukesh

doi:10.1109/aicera-icmicr.2013.6575935

Cited by 19 publications

(6 citation statements)

References 6 publications

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“…There is now a wealth of neuroimaging techniques available to study the brain; from electroencephalogram (EEG) which offers superb temporal resolution (Gui et al, 2010), to Computed Tomography (CT) scans that offer a more structural view of the brain (Jeena & Kumar, 2013). One of the most widely used neuroimaging technique is Magnetic Resonance Imaging (MRI) and functional MRI (fMRI; Glover, 2011).…”

Section: Neuroimaging Techniquesmentioning

confidence: 99%

The neural correlates of reinforcement sensitivity theory: A systematic review of the (f)MRI literature.

Standen¹,

Firth²,

Sumich³

et al. 2022

Psychology & Neuroscience

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Objective: Reinforcement Sensitivity Theory (RST) is a theory of motivation, emotion, and learning, that has been translated into an account of personality. RST proposes neural structures that form the basis of systems responsible for reward (behavioral approach system; BAS), punishment (flight–fight–freeze system; FFFS) and conflict processing (behavioral inhibition system; BIS). This systematic review collated studies examining psychometric measures of RST alongside structural and function Magnetic Resonance Imaging (MRI) data to (a) examine how psychometric RST is associated with the proposed neural topologies of RST, (b) identify any common associations between psychometric RST and other brain regions, and (c) provide recommendations for advancing the current literature base. Method: Initial search terms identified 10,952 papers. After processing, 39 papers that investigated the association between RST scales and neural functioning in healthy adult samples were included in this review. Results: There was general support for associations between the BAS and the structure/activity of the prefrontal cortex and ventral striatum with some additional findings for the ventral pallidum and ventral tegmental area. There was also some support for associations between BIS/FFFS and structure/activity of frontal regions, cingulate cortices and the amygdala. Conclusions: Overall, psychometric correlates of RST were associated with activity in proposed neural circuitry, with the most consistent support being found for the BAS; however, psychometric and experimental limitations still hamper the differentiation of the BIS and FFFS systems in their activation of deeper brain networks. Future studies need to include revised RST scales that separate the BIS and FFFS and implement more rigorous tasks that allow for the examination of each system both independently and codependently.

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Section: Neuroimaging Techniquesmentioning

confidence: 99%

The neural correlates of reinforcement sensitivity theory: A systematic review of the (f)MRI literature.

Standen¹,

Firth²,

Sumich³

et al. 2022

Psychology & Neuroscience

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“…However, it is quite difficult to detect the hypointense lesion, a primary indication of ischaemic stroke, with CT images in the first few hours of a stroke. By comparison, MRI generates high-resolution images that outline the presence, size and location of a hyperacute cerebral ischaemic stroke ( Heit et al, 2017 ; Kidwell et al, 2004 ; Jeena and Kumar, 2013 ).…”

Section: The Imaging Of Strokementioning

confidence: 99%

Current approaches and advances in the imaging of stroke

Kakkar

Patankar

et al. 2021

Disease Models &Amp; Mechanisms

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A stroke occurs when the blood flow to the brain is suddenly interrupted, depriving brain cells of oxygen and glucose and leading to further cell death. Neuroimaging techniques, such as computed tomography and magnetic resonance imaging, have greatly improved our ability to visualise brain structures and are routinely used to diagnose the affected vascular region of a stroke patient's brain and to inform decisions about clinical care. Currently, these multimodal imaging techniques are the backbone of the clinical management of stroke patients and have immensely improved our ability to visualise brain structures. Here, we review recent developments in the field of neuroimaging and discuss how different imaging techniques are used in the diagnosis, prognosis and treatment of stroke.

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“…Indeed, Shuai et al, achieved a study that was based on COVID-19 changes radiologically from CT scans. They developed a deep learning method that can extract the graphical features of the lungs of the infected patients [13].…”

Section: Introductionmentioning

confidence: 99%

Computer Aided Deep Learning Based Assessment of Stroke From Brain Radiological CT Images

Ural

2022

European Journal of Science and Technology

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The aim of the study is to detect the abnormal area(s) from brain CTs of stroke patients using Image Processing and to accurately evaluate the stroke changes in brain tissues among patients with Deep Learning models in MATLAB 2019b interface. 1000 patients (500 stroke suspected, 500 healthy participants) were chosen between 25 and 75 age ranges from TOBB ETU and Yıldırım Beyazıt University Hospitals according to the ethics committee certificate. For this study, for increasing the accuracy and eliminating the redundancy, from the image data of the patients, only lateral and 4th ventricle CT images were used. Firstly, these images were processed via Image Processing methods (Image Acquisition, Preprocessing, Thresholding, Segmentation, Morphological Operations etc.). After these methods, the resulted lateral ventricle image was split into 6 specific areas and 4th ventricle image was split into 14 specific areas like automated computerized Alberta Stroke Scoring, respectively. For 1000 images, totally 20x1000=20000 pieces of CT subimages were obtained with the specific class names (as healthy and stroke) and were used as the input of Artificial Intelligence (AI) and Deep Learning (DL) models (optimized ANN with Levenberg-Marquardt method and CNN). This approach can give an important chance to the doctors for supporting their results with a decision support system, speeding up the diagnosis time and also decreasing the possible rate of misdiagnosis.

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A comparative analysis of MRI and CT brain images for stroke diagnosis

Cited by 19 publications

References 6 publications

The neural correlates of reinforcement sensitivity theory: A systematic review of the (f)MRI literature.

The neural correlates of reinforcement sensitivity theory: A systematic review of the (f)MRI literature.

Current approaches and advances in the imaging of stroke

Computer Aided Deep Learning Based Assessment of Stroke From Brain Radiological CT Images

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