Kain Michael, Bodin Marjolaine, Loury Simon, Chi Yao, Louis Julien, Simon Mathieu, Lamy Julien, Barillot Christian, Dojat Michel
INRIA U1228, INSERM, Université de Rennes, Rennes, France.
INSERM U1216, Grenoble Institut des Neurosciences, Université Grenoble Alpes, CHU Grenoble Alpes, Grenoble, France.
Front Neuroinform. 2020 May 19;14:20. doi: 10.3389/fninf.2020.00020. eCollection 2020.
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 "" 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.
临床多中心成像研究很常见，且依赖于大量现有的数据共享和处理流程工具。而临床前（小动物）研究并非如此。动物群体成像仍处于起步阶段，尤其是因为跨实验室对动物模型初始条件进行完全标准化和控制仍然困难，而且很少有研究旨在实现采集和后处理技术的标准化。显然，需要有专门用于管理和共享数据、后处理及分析方法的合适工具，以用于小动物成像。为人类成像研究开发的解决方案不能直接应用于这个特定领域。在本文中，我们介绍了小动物Shanoir（SAS）解决方案，该方案使用与开放数据兼容的工具来支持动物群体成像。自动化工作流程工具的集成确保了研究成果的可获取性和可重复性。通过共享由SAS托管的数据和成像处理工具，我们推动了数据准备以及用于可重复性和再利用的工具，并促进参与多中心或重复性研究，从而有助于提高临床前领域的科学质量。SAS是推动小动物成像开放科学的第一步，也是对数据和参考流程增值的贡献。
