Brain imaging has seen considerable advances over the recent years. Both developments and validation of advanced image acquisition techniques as well as post-processing and analyses pipelines contribute to contemporary imaging, including parallel imaging, (semi-)automated segmentation, generation of synthetic images, and application of machine learning and radiomics. Multi-modal approaches using structural, metabolic, and functional imaging are emerging to build a framework for a better understanding of anatomical features and physiological processes of the brain.
This Research Topic intends to cover a broad theme, welcoming contributions spanning across the fields of (neuro)radiology, medical image analysis including machine learning, neurosurgery, and neurology, to reflect latest advances in the field of brain imaging. Original research, systematic / narrative / mini review, methods, perspective, clinical trial, case report, brief research report, general commentary, opinion, and technology and code manuscripts are welcome.
A special focus is set on the following topics:
- Multi-modal imaging (e.g., combination of structural, metabolic, molecular and functional imaging)
- Utility of neuronavigation / virtual reality based on imaging data
- Application of machine learning, artificial intelligence, and radiomics
- Development of (semi-)automated image processing pipelines (e.g., segmentation algorithms)
- Image acquisition acceleration / parallel imaging
- Translational efforts that describe the transfer of advanced imaging approaches to the clinical setup and/or to a broader application in research
- Emerging technology (e.g., mobile bedside MRI, functional ultrasound, magnetic particle imaging)
Brain imaging has seen considerable advances over the recent years. Both developments and validation of advanced image acquisition techniques as well as post-processing and analyses pipelines contribute to contemporary imaging, including parallel imaging, (semi-)automated segmentation, generation of synthetic images, and application of machine learning and radiomics. Multi-modal approaches using structural, metabolic, and functional imaging are emerging to build a framework for a better understanding of anatomical features and physiological processes of the brain.
This Research Topic intends to cover a broad theme, welcoming contributions spanning across the fields of (neuro)radiology, medical image analysis including machine learning, neurosurgery, and neurology, to reflect latest advances in the field of brain imaging. Original research, systematic / narrative / mini review, methods, perspective, clinical trial, case report, brief research report, general commentary, opinion, and technology and code manuscripts are welcome.
A special focus is set on the following topics:
- Multi-modal imaging (e.g., combination of structural, metabolic, molecular and functional imaging)
- Utility of neuronavigation / virtual reality based on imaging data
- Application of machine learning, artificial intelligence, and radiomics
- Development of (semi-)automated image processing pipelines (e.g., segmentation algorithms)
- Image acquisition acceleration / parallel imaging
- Translational efforts that describe the transfer of advanced imaging approaches to the clinical setup and/or to a broader application in research
- Emerging technology (e.g., mobile bedside MRI, functional ultrasound, magnetic particle imaging)
