Magnetic Resonance imaging (MRI) based neuroimaging methods have shown great potential to reflect brain structure and functional changes of age related development or diseases. Multiple advanced imaging methods have been frequently applied in the field of neuroscience to provide multi-dimensional information, e.g., 1) high resolution anatomy imaging giving information about brain structure such as cortical thickness and brain region volume; 2) advanced diffusion models reflecting cellular-level microstructure; 3) perfusion imaging quantifying cerebral blood flow with or without contrast agents; and 4) susceptibility imaging quantifying iron deposition in deep brain nuclei. In addition to imaging methods, the post-processing and analysis tools are also essential to fully gather multi-dimensional information in the brain. Recently, brain subregion segmentation technique, combined with structure and functional imaging, has helped identify region-specific quantitative biomarkers. Moreover, artificial intelligence (AI) based methods, such as radiomics or deep learning, were increasingly acknowledged for accurate diagnosis and prognosis prediction of brain diseases.
In this Research Topic, we would like to facilitate the development of novel MRI neuroimaging methods as well as their validation. This is a dynamic field as new MRI based neuroimaging methods are emerging every year. Their application and validation thus need to be widely explored and carefully compared to conventional methods. Advanced post-processing methods for neuroimaging data are highly anticipated, as such approaches can bridge the gap between neuroimaging methods and clinical applications. Robust and automatic evaluation methods are the focus of interest here, and well-designed methods that could extract additional information from the original neuroimaging data are also welcome.
Example topics include but are not limited to:
- Development of novel MRI based neuroimaging methods to provide neural functional and microstructure information, especially on hybrid methods acquiring multiple quantitative maps within single scan, and micro-scale methods reflecting cellular level information or tissue metabolism.
- Development of novel MRI post-processing methods for robust, specific or automatic data quantification, with a potential application for brain development and disease diagnosis.
- Applications of MRI neuroimaging and post-processing methods in evaluating microstructure changes of brain development, diagnosis and prognosis of brain diseases.
- Jointly usage of multi-parameter MRI for brain disease.
- Targeting on clinical practice, comprehensive comparison of the clinical significance, robustness, speed and efficiency of new neuroimaging acquisition strategy and/or post-processing pipeline with conventional methods.
Xu Yan Ph.D. is a MR research scientist in Siemens Healthineers, a commercial producer of diagnosis imaging devices. This should not pose any conflict for this project, as he will maintain objectivity.
Magnetic Resonance imaging (MRI) based neuroimaging methods have shown great potential to reflect brain structure and functional changes of age related development or diseases. Multiple advanced imaging methods have been frequently applied in the field of neuroscience to provide multi-dimensional information, e.g., 1) high resolution anatomy imaging giving information about brain structure such as cortical thickness and brain region volume; 2) advanced diffusion models reflecting cellular-level microstructure; 3) perfusion imaging quantifying cerebral blood flow with or without contrast agents; and 4) susceptibility imaging quantifying iron deposition in deep brain nuclei. In addition to imaging methods, the post-processing and analysis tools are also essential to fully gather multi-dimensional information in the brain. Recently, brain subregion segmentation technique, combined with structure and functional imaging, has helped identify region-specific quantitative biomarkers. Moreover, artificial intelligence (AI) based methods, such as radiomics or deep learning, were increasingly acknowledged for accurate diagnosis and prognosis prediction of brain diseases.
In this Research Topic, we would like to facilitate the development of novel MRI neuroimaging methods as well as their validation. This is a dynamic field as new MRI based neuroimaging methods are emerging every year. Their application and validation thus need to be widely explored and carefully compared to conventional methods. Advanced post-processing methods for neuroimaging data are highly anticipated, as such approaches can bridge the gap between neuroimaging methods and clinical applications. Robust and automatic evaluation methods are the focus of interest here, and well-designed methods that could extract additional information from the original neuroimaging data are also welcome.
Example topics include but are not limited to:
- Development of novel MRI based neuroimaging methods to provide neural functional and microstructure information, especially on hybrid methods acquiring multiple quantitative maps within single scan, and micro-scale methods reflecting cellular level information or tissue metabolism.
- Development of novel MRI post-processing methods for robust, specific or automatic data quantification, with a potential application for brain development and disease diagnosis.
- Applications of MRI neuroimaging and post-processing methods in evaluating microstructure changes of brain development, diagnosis and prognosis of brain diseases.
- Jointly usage of multi-parameter MRI for brain disease.
- Targeting on clinical practice, comprehensive comparison of the clinical significance, robustness, speed and efficiency of new neuroimaging acquisition strategy and/or post-processing pipeline with conventional methods.
Xu Yan Ph.D. is a MR research scientist in Siemens Healthineers, a commercial producer of diagnosis imaging devices. This should not pose any conflict for this project, as he will maintain objectivity.
