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The NOVA project - maximizing beam time efficiency through synergistic analyses of SRμCT data

机译:Nova项目 - 通过SRμCT数据的协同分析来最大化光束时间效率

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Beamtime and resulting SRμCT data are a valuable resource for researchers of a broad scientific community in life sciences. Most research groups, however, are only interested in a specific organ and use only a fraction of their data. The rest of the data usually remains untapped. By using a new collaborative approach, the NOVA project (Network for Online Visualization and synergistic Analysis of tomographic data) aims to demonstrate, that more efficient use of the valuable beam time is possible by coordinated research on different organ systems. The biological partners in the project cover different scientific aspects and thus serve as model community for the collaborative approach. As proof of principle, different aspects of insect head morphology will be investigated (e.g., biomechanics of the mouthparts, and neurobiology with the topology of sensory areas). This effort is accomplished by development of advanced analysis tools for the ever-increasing quantity of tomographic datasets. In the preceding project ASTOR, we already successfully demonstrated considerable progress in semi-automatic segmentation and classification of internal structures. Further improvement of these methods is essential for an efficient use of beam time and will be refined in the current NOVA-project. Significant enhancements are also planned at PETRA Ⅲ beamline p05 to provide all possible contrast modalities in x-ray imaging optimized to biological samples, on the reconstruction algorithms, and the tools for subsequent analyses and management of the data. All improvements made on key technologies within this project will in the long-term be equally beneficial for all users of tomography instrumentations.
机译:BeamTime并产生SRμCT数据是生命科学社区广泛科学界的研究人员的宝贵资源。然而,大多数研究组只对特定器官感兴趣,只使用其数据的一小部分。其余数据通常仍未开发。通过使用新的协作方法,Nova项目(网络可视化网络和断层数据的协同分析)旨在证明,通过对不同器官系统的协调研究,可以更有效地使用有价值的光束时间。该项目的生物合作伙伴涵盖了不同的科学方面,并因此成为合作方法的模型界。作为原理的证据,将研究昆虫头形态的不同方面(例如,口感的生物力学,以及感觉区域拓扑的神经生物学)。这项努力是通过开发用于不断增加的断层性数据集的高级分析工具来实现的。在前面的项目Astor中,我们已经成功地表明了半自动细分和内部结构分类的相当大的进展。这些方法的进一步改进对于有效使用光束时间并且将在当前的Nova-Project中改进。在PetraⅢBeamline P05中还规划了显着的增强,以在重建算法上提供对生物样本的X射线成像中的所有可能的对比模式,以及用于随后分析和管理数据的工具。在该项目中的关键技术所作的所有改进都将长期同样有利于所有断层扫描仪器的用户。

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