Clinical multicenter imaging studies are frequent and rely on a wide range of existing tools for sharing data and processing pipelines. This is not the case for preclinical (small animal) studies. Animal population imaging is still in infancy, especially because a complete standardization and control of initial conditions in animal models across labs is still difficult and few studies aim at standardization of acquisition and post-processing techniques. Clearly, there is a need of appropriate tools for the management and sharing of data, post-processing and analysis methods dedicated to small animal imaging. Solutions developed for Human imaging studies cannot be directly applied to this specific domain. In this paper, we present the Small Animal Shanoir (SAS) solution for supporting animal population imaging using tools compatible with open data. The integration of automated workflow tools ensures accessibility and reproducibility of research outputs. By sharing data and imaging processing tools, hosted by SAS, we promote data preparation and tools for reproducibility and reuse, and participation in multicenter or replication “open science” studies contributing to the improvement of quality science in preclinical domain. SAS is a first step for promoting open science for small animal imaging and a contribution to the valorization of data and pipelines of reference.

中文翻译：

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小动物 Shanoir (SAS) 用于管理临床前 MR 脑成像研究的基于云的解决方案


临床多中心成像研究很频繁，并且依赖于各种现有工具来共享数据和处理流程。临床前（小动物）研究并非如此。动物群体成像仍处于起步阶段，特别是因为跨实验室动物模型初始条件的完全标准化和控制仍然很困难，而且很少有研究旨在标准化采集和后处理技术。显然，需要适当的工具来管理和共享专用于小动物成像的数据、后处理和分析方法。为人类成像研究开发的解决方案不能直接应用于该特定领域。在本文中，我们提出了小动物 Shanoir (SAS) 解决方案，用于使用与开放数据兼容的工具支持动物种群成像。自动化工作流程工具的集成确保了研究成果的可访问性和可重复性。通过共享由 SAS 主办的数据和图像处理工具，我们促进数据准备和可重复性和重用工具，以及参与多中心或重复“开放科学”研​​究，从而有助于提高临床前领域的质量科学。 SAS 是促进小动物成像开放科学的第一步，也是对数据和参考管道增值的贡献。