About this Research Topic
Small-animal imaging is a dynamic research field with tremendous progress regarding capabilities concerning high spatial resolution, high temporal resolution, and detection sensitivity. The developments and improvements of preclinical imaging methods have substantially strengthened the options of in vivo visualization of disease processes over time. Through the available technologies and specifically multi-modal imaging approaches, a better understanding of disease processes can be obtained. In addition, the effects of (new) therapies on disease mitigation or eradication can be assessed at the anatomical, physiological, and molecular level, providing insight into the factors that determine successful treatment effects. These insights can then be used in designing new and/or improved diagnostic and therapeutic strategies in patients.
This Research Topic aims at generating a widespread understanding of the importance of robust and standardized methods in small animal imaging within the field of biomedical research. While major steps have already been taken concerning these aspects, there is still a need for further technological improvements and standardized methods to further strengthen the translational power of such research. Various aspects can majorly influence study outcomes including choices made regarding the imaging modality, the imaging protocol, the animal model, animal handling protocols, image analysis method, and data processing tools. In this topic, we will address these issues and define the most urgent needs for in the field to achieve robust data and high translational power.
This Research Topic welcomes original research and reviews on technological developments and standardization of methodology for preclinical imaging in biomedical research. These include description and evaluation of new technology, study designs, data processing, and analysis tools, and new applications of existing technology. Articles should address how the described technology/methodology will contribute to the goal of acquiring more robust data and a better understanding of disease processes and/or disease response to therapy. Studies comparing disease models and interspecies variations in study results also fall within the scope of the Research Topic. Authors are encouraged to address clinical translatability or clinical impact of the methodology or the study results.
This Research Topic aims at generating a widespread understanding of the importance of robust and standardized methods in small animal imaging within the field of biomedical research. While major steps have already been taken concerning these aspects, there is still a need for further technological improvements and standardized methods to further strengthen the translational power of such research. Various aspects can majorly influence study outcomes including choices made regarding the imaging modality, the imaging protocol, the animal model, animal handling protocols, image analysis method, and data processing tools. In this topic, we will address these issues and define the most urgent needs for in the field to achieve robust data and high translational power.
This Research Topic welcomes original research and reviews on technological developments and standardization of methodology for preclinical imaging in biomedical research. These include description and evaluation of new technology, study designs, data processing, and analysis tools, and new applications of existing technology. Articles should address how the described technology/methodology will contribute to the goal of acquiring more robust data and a better understanding of disease processes and/or disease response to therapy. Studies comparing disease models and interspecies variations in study results also fall within the scope of the Research Topic. Authors are encouraged to address clinical translatability or clinical impact of the methodology or the study results.
Keywords: preclinical imaging, translational research, molecular imaging, technological advancements, standardized methodology
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