CIM-XNAT

Welcome to the CIM-XNAT information website!

A XNAT service for archiving and processing preclinical medical images is available at http://cim-xnat.unito.it.
Small animal imaging facilities are highly specialized centers that provide the research community access to cutting-edge imaging technologies. These centers have therefore to deal with the complexity and the variety of preclinical image datasets in terms of archiving and retrieving image datasets as well as for the management and processing. To date, no custom or standard solutions are available to imaging centers to fullfill these tasks.
XNAT natively supports multiple imaging modalities, such as MR, PET, CT, and US. We are extending XNAT datatypes to other preclinical imaging modalities, such as Optoacoustic (OA) and Optical Imaging (OI).

We have overcome these limitations through the integration of an open-source archiving platform commonly exploited at clinical level based on XNAT with customizable tools for automated image processing. The developed platform can provide the following workflow:
  • importing multiple imaging datasets acquired through several instrumentations and modalities either in DICOM (1) or proprietary formats via DICOM converter (2);
  • managing different experimental protocols importing user-defined variables (treated/untreated groups, different timepoints, doses, ...);
  • image processing tool accepting raw data (3) or an XNAT image processing pipeline accepting as input DICOM files (4) to produce parametric images by calling user-proprietary image-analysis script.


In the near future, we offer the following services:
  • Each user can, after registration, create his/her own projects and upload DICOM data.
  • Users can run external applications and shell scripts (pipeline) passing the required parameters to the application to download the data, process it and import the processed data back to XNAT. To date Diffusion Weigthed Imaging (DWI) processing pipelines that cun run at project/subject level are available on our XNAT instance.
  • Users can either use previously imported mask on XNAT or create mask directly on XNAT (beta) to obtain parametric images.
Contact information

XNAT administrators: Sara Zullino, Alessandro Paglialonga

Acknowledgements

Links/References

XNAT home page: http://www.xnat.org

User documentation: https://wiki.xnat.org/documentation


Group Leaders:

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