Functional and molecular imaging are fundamental tools to study biological pathways in oncology, neurology, cardiology, and infectious disease. The technological development of in-vivo biomedical imaging has made hybrid multimodal scanners (PET/CT, PET/MRI, and SPECT/CT) available to investigate these pathways non-invasively. Furthermore, digital PET scanners are becoming widely available and have a profound clinical impact. Finally, the development of new approaches to bioimaging, such as innovative acquisition protocols, novel radiotracers, refined image reconstruction algorithms, as well as standardization, quantification, and post-processing methods increase the accuracy of an in-depth exploration of different pathophysiological aspects of biological processes and diseases.
The implementation, dissemination and standardization of methods both in pre-clinical and clinical molecular imaging are of paramount importance in the scientific community. The goal of this research topic is to spread and to share technological advances in nuclear medicine through the collection of propositions, preliminary studies, or evidence evaluating the potential clinical impact of innovative approaches to images, including PET/CT, PET/MRI, and SPECT in oncological and non-oncological fields, as well as novel radiotracers.
Manuscripts covering innovative approaches to nuclear medicine techniques, including pre-clinical and clinical PET/CT, PET/MRI, and SPECT will be welcome. As innovative approaches, radiotracers, acquisition protocols, image reconstruction algorithms, standardization, quantification, and post-processing methods are considered. Original research, technological notes, brief reports, or reviews will be accepted both in oncological and in non-oncological diseases, including neurology, cardiology, and others.
M.H. is a recipient of grants from GE Healthcare, grants for translational and clinical cardiac and oncological research from the Alfred and Annemarie von Sick Grant legacy, and grants from the Artificial Intelligence in oncological Imaging Network by the University of Zurich. The other Topic Editors declare no conflicts of interest
Functional and molecular imaging are fundamental tools to study biological pathways in oncology, neurology, cardiology, and infectious disease. The technological development of in-vivo biomedical imaging has made hybrid multimodal scanners (PET/CT, PET/MRI, and SPECT/CT) available to investigate these pathways non-invasively. Furthermore, digital PET scanners are becoming widely available and have a profound clinical impact. Finally, the development of new approaches to bioimaging, such as innovative acquisition protocols, novel radiotracers, refined image reconstruction algorithms, as well as standardization, quantification, and post-processing methods increase the accuracy of an in-depth exploration of different pathophysiological aspects of biological processes and diseases.
The implementation, dissemination and standardization of methods both in pre-clinical and clinical molecular imaging are of paramount importance in the scientific community. The goal of this research topic is to spread and to share technological advances in nuclear medicine through the collection of propositions, preliminary studies, or evidence evaluating the potential clinical impact of innovative approaches to images, including PET/CT, PET/MRI, and SPECT in oncological and non-oncological fields, as well as novel radiotracers.
Manuscripts covering innovative approaches to nuclear medicine techniques, including pre-clinical and clinical PET/CT, PET/MRI, and SPECT will be welcome. As innovative approaches, radiotracers, acquisition protocols, image reconstruction algorithms, standardization, quantification, and post-processing methods are considered. Original research, technological notes, brief reports, or reviews will be accepted both in oncological and in non-oncological diseases, including neurology, cardiology, and others.
M.H. is a recipient of grants from GE Healthcare, grants for translational and clinical cardiac and oncological research from the Alfred and Annemarie von Sick Grant legacy, and grants from the Artificial Intelligence in oncological Imaging Network by the University of Zurich. The other Topic Editors declare no conflicts of interest