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XNAT Anonymization 101

This course will teach you the importance of, and techniques for, image data anonymization in XNAT

Enrollment is Closed
  • Course Number

  • Self-Paced

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About This Course

Welcome to the XNAT Anonymization course on the XNAT Academy. In this course, we will introduce the key concepts and techniques associated with data anonymization and deidentification and how those are applied to your XNAT data. This course is geared toward users who have a basic understanding of XNAT and how it handles subject and image session data. 

This course contains a set of lectures, quizzes, and practical exercises. The practical exercises are optional, but highly recommended. 

Course Goals

By the end of this course, you will have a clear understanding of the importance of data anonymization and deidentification to your XNAT instance. You will become aware of the many places that PHI and potentially identifying information can be stored, and you will be armed with a series of techniques for masking or removing this information.


We assume that you are already familiar with the imaging research environment and workflow, so we don't stop to explain terms such as "PACS" or "PET Session" or "Freesurfer".

For practical exercises, we recommend that you have access to a computer with at least 4GB of RAM (8GB recommended), and the ability to install software as an admin user. You should be familiar with using a command-line interface (i.e. Terminal on Mac OS, Command prompt or Git Bash on Windows) to access your system as well.

Recommended Prerequisites

  • XNAT Admin 101 - Explains XNAT data management and the image session uploading/archiving process in depth
  • XNAT Local Installation - Required for the practical exercises in this course
  • Docker Desktop installed - Required for the demo PACS machine used in the practical exercise  

Course Staff

Course Staff Image #1

Will Horton

Will Horton is the lead UI/UX/Instructional Designer on the XNAT team, and the head of XNAT Academy. Will has worked on the XNAT team since 2010.

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