  Molecular imaging generates large volumes of heterogeneous biomedical imagery
with an impelling need of guidelines for handling image data. Although several
successful solutions have been implemented for human epidemiologic studies, few
and limited approaches have been proposed for animal population studies.
Preclinical imaging research deals with a variety of machinery yielding tons of
raw data but the current practices to store and distribute image data are
inadequate. Therefore, standard tools for the analysis of large image datasets
need to be established. In this paper, we present an extension of XNAT for
Preclinical Imaging Centers (XNAT-PIC). XNAT is a worldwide used, open-source
platform for securely hosting, sharing, and processing of clinical imaging
studies. Despite its success, neither tools for importing large, multimodal
preclinical image datasets nor pipelines for processing whole imaging studies
are yet available in XNAT. In order to overcome these limitations, we have
developed several tools to expand the XNAT core functionalities for supporting
preclinical imaging facilities. Our aim is to streamline the management and
exchange of image data within the preclinical imaging community, thereby
enhancing the reproducibility of the results of image processing and promoting
open science practices.
