The human brain is understood to contain multiple, nested levels of hierarchical complexity. This complexity is represented in the multiple disciplines within modern neuroscience, from molecular neuroscience to systems neuroscience and beyond to social neuroscience. Due to the intrinsic complexity of brain, multimodal mulitparametric neuroimaging is increasingly demanded in clinical neurology, psychiatry, and basic neuroscience research. Positron emission tomography (PET) with computed tomography (CT) (PET-CT) and magnetic resonance imaging (MRI) are considered as a main multimodal neuroimaging technology to study brain structure and function in normal and abnormal conditions, and different levels of hierarchical structure and function. While PET imaging is better suited than MRI to address questions of molecular neuroscience, functional MRI may be better suited than PET to address questions of posed by systems neuroscience. Thus, multiparametric images generated from PET (or PET-CT) and MRI are complementary with the derived information which cannot be reliably obtained from either modality separately. Tremendous efforts have been made in the last decade in the integration of PET and MRI. The remarkable development of hybrid PET-MRI (simultaneous PET-MRI imaging) scanners provides a state of the art imaging tool in clinical and preclinical research. However, there are no existing universal statistical and mathematical models that are ideally suited to integrate signals (either sequential or simultaneous) detected from PET and MRI. The full potential of multimodal neuroimaging technology has yet to be explored. This Research Topic aims to collect the most recent progress in integrative multimodal mulitparametric neuroimaging, including theory development, algorithms, and software in image processing and parameter estimation, and its applications. We welcome articles with focus on the following topics:
1) Optimization in multiparametric PET-MRI data acquisition, where the PET and MRI images can be acquired sequentially from PET and MRI, or simultaneously from hybrid PET-MRI scanner. Multiparametric PET can be multi-tracer or single tracer dynamic or static PET, and multiparametric MRI can be multi-sequence structural or functional MRI.
2) MRI- and CT guided or constraint PET image reconstruction, image process including but not limited to partial volume correction, volume of interest definition and segmentation, brain parcellation, image derived input function for kinetic modeling, and kinetic modeling and parametric imaging, where MRI and CT can be structural or functional images.
3) Statistical and mathematical modeling of the spatial-temporal signals measured from PET, and MRI at functional and molecular levels.
4) Multimodal mulitparametric imaging rest state brain network, brain with psychopharmacological stimulation, interventional neuroradiology, and neurological disorders.
5) Applying artificial intelligence technology in integrative multimodal quantitative image reconstruction, image process, quantification, and clinical application.
All types of articles are accepted to this Research Topic: Original Research Articles, Hypotheses & Theories, Review Articles, Mini Review Articles, Perspectives, Method Articles, Clinical Trials, Case Reports, Brief Research Report, Technology Report, Book Reviews, Opinion, and General Commentaries.
The human brain is understood to contain multiple, nested levels of hierarchical complexity. This complexity is represented in the multiple disciplines within modern neuroscience, from molecular neuroscience to systems neuroscience and beyond to social neuroscience. Due to the intrinsic complexity of brain, multimodal mulitparametric neuroimaging is increasingly demanded in clinical neurology, psychiatry, and basic neuroscience research. Positron emission tomography (PET) with computed tomography (CT) (PET-CT) and magnetic resonance imaging (MRI) are considered as a main multimodal neuroimaging technology to study brain structure and function in normal and abnormal conditions, and different levels of hierarchical structure and function. While PET imaging is better suited than MRI to address questions of molecular neuroscience, functional MRI may be better suited than PET to address questions of posed by systems neuroscience. Thus, multiparametric images generated from PET (or PET-CT) and MRI are complementary with the derived information which cannot be reliably obtained from either modality separately. Tremendous efforts have been made in the last decade in the integration of PET and MRI. The remarkable development of hybrid PET-MRI (simultaneous PET-MRI imaging) scanners provides a state of the art imaging tool in clinical and preclinical research. However, there are no existing universal statistical and mathematical models that are ideally suited to integrate signals (either sequential or simultaneous) detected from PET and MRI. The full potential of multimodal neuroimaging technology has yet to be explored. This Research Topic aims to collect the most recent progress in integrative multimodal mulitparametric neuroimaging, including theory development, algorithms, and software in image processing and parameter estimation, and its applications. We welcome articles with focus on the following topics:
1) Optimization in multiparametric PET-MRI data acquisition, where the PET and MRI images can be acquired sequentially from PET and MRI, or simultaneously from hybrid PET-MRI scanner. Multiparametric PET can be multi-tracer or single tracer dynamic or static PET, and multiparametric MRI can be multi-sequence structural or functional MRI.
2) MRI- and CT guided or constraint PET image reconstruction, image process including but not limited to partial volume correction, volume of interest definition and segmentation, brain parcellation, image derived input function for kinetic modeling, and kinetic modeling and parametric imaging, where MRI and CT can be structural or functional images.
3) Statistical and mathematical modeling of the spatial-temporal signals measured from PET, and MRI at functional and molecular levels.
4) Multimodal mulitparametric imaging rest state brain network, brain with psychopharmacological stimulation, interventional neuroradiology, and neurological disorders.
5) Applying artificial intelligence technology in integrative multimodal quantitative image reconstruction, image process, quantification, and clinical application.
All types of articles are accepted to this Research Topic: Original Research Articles, Hypotheses & Theories, Review Articles, Mini Review Articles, Perspectives, Method Articles, Clinical Trials, Case Reports, Brief Research Report, Technology Report, Book Reviews, Opinion, and General Commentaries.
