Data Management in Small Animal Imaging: Conceptual and Technical Considerations

Abstract

Small animal imaging in general and multimodal tomographic imaging in particular generate a substantial amount of heterogeneous data that can be challenging to handle. Besides computed tomographic images, there are also the primarily acquired raw data such as listmode data in positron emission tomography (PET), projection data in X-ray computed tomography (CT), or even k-space data in magnetic resonance imaging (MRI). Additionally, further image data might be created by postprocessing (e.g., filtering) or by using alternative image reconstruction methods. All these data have to be stored; thus, the required disk space can easily exceed several terabyte (TB) over time. Therefore, good data storage planning and management strategies are required. In this context, data management obviously does not just mean storing the data. Rather, the data have to be easily accessible for all involved researchers, they also have to remain accessible years after the measurement, and the data have to be backed up in a save and secured place.