About this Research Topic
Computational neuroimaging is a rapidly evolving field that plays a crucial role in the diagnosis and management of neurological diseases. Medical imaging provides a non-invasive means to visualize the internal structures of the human body, offering critical insights into the brain and spinal cord's structural and pathological changes. The integration of image processing methods enhances the efficiency, accuracy, and reliability of clinical diagnoses by enabling the detection, 3D segmentation, and reconstruction of human organs and tissues. Recent advancements in artificial intelligence, particularly deep learning, have significantly impacted neuroimaging, allowing for more sophisticated analysis and interpretation of imaging data. Despite these advancements, challenges remain in fully harnessing these technologies to improve patient outcomes. There is a need for further research to address existing gaps, such as the development of more precise algorithms and the translation of computational techniques into clinical practice.
This research topic aims to explore the latest advancements in computational neuroimaging and their applications in diagnosing and treating neurological diseases. The primary objectives include investigating novel computational techniques, enhancing image processing methods, and integrating artificial intelligence into neuroimaging. Key questions to be addressed include how these technologies can be optimized for clinical use and what new insights they can provide into the pathophysiology of neurological disorders. The research will also test hypotheses related to the efficacy of these methods in improving diagnostic accuracy and patient outcomes.
To gather further insights in the realm of computational neuroimaging for neurological diseases, we welcome articles addressing, but not limited to, the following themes:
- Neuroimaging and image data analysis, including segmentation, classification, and detection, from traditional to deep learning methods.
- Innovations in neurological image processing for brain and spinal cord diseases.
- The application of artificial intelligence and computer-aided detection/diagnosis in neurological diseases.
- Clinical translation of neuroimaging techniques onto biomedical devices.
- Integration of medical image processing with genetic, signaling, or molecular studies to identify novel targets in neurological diseases.
This research topic aims to explore the latest advancements in computational neuroimaging and their applications in diagnosing and treating neurological diseases. The primary objectives include investigating novel computational techniques, enhancing image processing methods, and integrating artificial intelligence into neuroimaging. Key questions to be addressed include how these technologies can be optimized for clinical use and what new insights they can provide into the pathophysiology of neurological disorders. The research will also test hypotheses related to the efficacy of these methods in improving diagnostic accuracy and patient outcomes.
To gather further insights in the realm of computational neuroimaging for neurological diseases, we welcome articles addressing, but not limited to, the following themes:
- Neuroimaging and image data analysis, including segmentation, classification, and detection, from traditional to deep learning methods.
- Innovations in neurological image processing for brain and spinal cord diseases.
- The application of artificial intelligence and computer-aided detection/diagnosis in neurological diseases.
- Clinical translation of neuroimaging techniques onto biomedical devices.
- Integration of medical image processing with genetic, signaling, or molecular studies to identify novel targets in neurological diseases.
Keywords: neuroimage processing, data analysis, neurological diseases, deep learning, spinal cord, biomedical devices
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