Neuroimaging scan is the use of various techniques to either directly or indirectly image the structure, function, or pharmacology of the nervous system. Those scans are being used more and more to help detect and diagnose many medical disorders and illnesses. Currently, brain scans for mental disorders are in research studies to learn more about the disorders. Many neuroimaging methods are used in hospitals and research institutes, such as computed tomography, event-related optical imaging, magnetic resonance imaging (MRI), functional MRI, positron emission tomography, single-photon emission computed tomography, magnetoencephalography, etc. Brain scans alone are commonly used to diagnose neurological and psychiatric diseases, such as Meningioma, glioma, Herpes encephalitis, Huntington’s disease, Pick’s disease, Alzheimer’s disease, Multiple sclerosis, and cerebral palsy toxoplasmosis, Sarcoma, Subdural hematoma, etc.
This topic aims to report the recent intelligent recognition and detection methods/applications in the field of neuroimaging. With the help of intelligent recognition and detection methods, we can realize analysis, enhancement, reconstruction, segmentation, and classification of various neuroimaging scans. We expect the Research Topic will provide a comprehensive picture of recent intelligent recognition and detection methods using artificial intelligence and deep learning. This Research Topic will serve as a starting point for the design of more complex intelligent recognition and detection methods, which may be applied in neuroimaging and related fields.
Intelligent Recognition and Detection in Neuroimaging. Special attention should be devoted to the clinical results to make your intelligent method trustable and verifiable.
Neuroimaging methods include but are not limited to
• Computed tomography
• Event-related optical imaging
• Magnetic resonance imaging (MRI)
• Functional MRI
• Positron emission tomography
• Single-photon emission computed tomography
• Magnetoencephalography
Intelligent methods include but are not limited to:
• Artificial intelligence (AI)
• Deep learning
• Transfer learning
• Convolutional neural network
• Graph neural network
• Attention neural network
• Explainable AI
• Trustworthy AI
Neuroimaging scan is the use of various techniques to either directly or indirectly image the structure, function, or pharmacology of the nervous system. Those scans are being used more and more to help detect and diagnose many medical disorders and illnesses. Currently, brain scans for mental disorders are in research studies to learn more about the disorders. Many neuroimaging methods are used in hospitals and research institutes, such as computed tomography, event-related optical imaging, magnetic resonance imaging (MRI), functional MRI, positron emission tomography, single-photon emission computed tomography, magnetoencephalography, etc. Brain scans alone are commonly used to diagnose neurological and psychiatric diseases, such as Meningioma, glioma, Herpes encephalitis, Huntington’s disease, Pick’s disease, Alzheimer’s disease, Multiple sclerosis, and cerebral palsy toxoplasmosis, Sarcoma, Subdural hematoma, etc.
This topic aims to report the recent intelligent recognition and detection methods/applications in the field of neuroimaging. With the help of intelligent recognition and detection methods, we can realize analysis, enhancement, reconstruction, segmentation, and classification of various neuroimaging scans. We expect the Research Topic will provide a comprehensive picture of recent intelligent recognition and detection methods using artificial intelligence and deep learning. This Research Topic will serve as a starting point for the design of more complex intelligent recognition and detection methods, which may be applied in neuroimaging and related fields.
Intelligent Recognition and Detection in Neuroimaging. Special attention should be devoted to the clinical results to make your intelligent method trustable and verifiable.
Neuroimaging methods include but are not limited to
• Computed tomography
• Event-related optical imaging
• Magnetic resonance imaging (MRI)
• Functional MRI
• Positron emission tomography
• Single-photon emission computed tomography
• Magnetoencephalography
Intelligent methods include but are not limited to:
• Artificial intelligence (AI)
• Deep learning
• Transfer learning
• Convolutional neural network
• Graph neural network
• Attention neural network
• Explainable AI
• Trustworthy AI
