Small Animal Shanoir (SAS) A Cloud-Based Solution for Managing Preclinical MR Brain Imaging Studies

Area of Science:

• Preclinical imaging research
• Open science initiatives
• Data management in biomedical research

Background:

• Clinical imaging studies benefit from established data sharing and processing tools, unlike preclinical small animal imaging.
• Standardization and control of initial conditions in animal models across laboratories remain challenging.
• Existing solutions for human imaging are not directly applicable to small animal imaging studies.

Purpose of the Study:

• To introduce the Small Animal Shanoir (SAS) solution for supporting small animal population imaging.
• To promote the use of open data compatible tools in preclinical research.
• To enhance accessibility, reproducibility, and reuse of research outputs in small animal imaging.

Main Methods:

• Development and implementation of the Small Animal Shanoir (SAS) platform.
• Integration of automated workflow tools for data processing and analysis.
• Provision of a centralized system for sharing imaging data and processing pipelines.

Main Results:

• SAS provides tools compatible with open data principles for small animal imaging.
• Automated workflows integrated within SAS ensure research accessibility and reproducibility.
• The platform facilitates data preparation, tool sharing, and participation in multicenter open science studies.

Conclusions:

• SAS represents a significant step towards promoting open science in small animal imaging.
• The solution contributes to the valorization of data and reference pipelines in preclinical research.
• SAS aims to improve the quality and impact of scientific research in the small animal imaging domain.