About this Research Topic
Data and processing tools sharing are now well recognized as key factors for improving quality and reproducibility of scientific findings and overcoming current methodological limitations. Some large data repositories (e.g. ADNI, ConnectomeDB) and specific architectures (e.g. COINS, LONI, FLI-IAM) are now available mainly for human population imaging. The animal imaging community has also growing requirements for multicenter studies, but only few tools are available today and few studies aim at standardization of acquisition and post-processing techniques.
This Research Topic addresses the ongoing efforts towards Animal Population Imaging, a domain still in infancy, notably because a complete standardization and control of initial conditions in animal models across labs is difficult. However, sharing requirements will grow in the next future, as it has grown for human research, i.e. optimize costs and subject participation, improve science quality (use of sufficiently large animal cohorts for ensuring statistical result validity cf. drug development process) and enhancement of research discovery. The APPNING Research Topic is focused on conceptual and methodological aspects and solutions to support the sharing of animal imaging data and processing tools: data structures, application ontologies, new paradigms for handling data, atlas construction, interoperability of repositories, semantic queries, image processing composition, local or grid-access execution, software and hardware architectures, and pros and cons of existing working solutions. This collection of articles will help to promote the federation of multiple sources of information, processing tools and diffusion of knowledge distributed in various preclinical imaging centers.
Submitted papers should be related to methodological issues for Animal Population Imaging including data management, data processing pipelines, multicenter studies, and processing of large imaging databases.
Topics of interest include, but are not limited to, the following:
• Infrastructure for facilitating data and software sharing and reuse; grid access facilitation.
• Conceptual and technical methods for solving specific difficult points (domain ontology development, interoperability of repositories, image quality control, atlas construction, image processing pipeline development, ...);
• Case studies using specific platforms (pros and cons...), multicenter preclinical evaluations, needs and requirements for specific federated animal studies.
• Industrial requirements for animal imaging studies
Keywords: Data sharing, Image processing, Multicenter studies, Preclinical, Animal model