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LONI De-identification Debablet

A simple to use Java application for removing patient-identifying information (e.g., patient name and id) from medical image files. Removal of this information is often necessary for enabling investigators to share image files in a HIPAA compliant manner.

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Features

  • Removes information that could potentially be used to identify a patient.
  • Substitutes a user-defined research ID for the original patient ID, allowing the investigator to maintain a linked list of patient and research identifiers while ensuring that the original patient identifier is not exposed.

Description

The LONI de-identification Debablet is an application for removing patient-identifying information from medical image files. Removing patient-identifying information is often necessary for enabling investigators to share image files in a HIPAA compliant manner. The name "Debablet" is derived from the underlying execution engine (the Debabeler), which is used to create and modify the de-identifications. De-identified files can be checked for patient-identifying information with the Inspector.

Usage

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System Requirements

  • OS: none
  • Processor: any
  • Memory: 128 Mb
  • Other: Java 1.4.2 or higher

Installation

On Windows systems, start the Debablet by double-clicking the loniDeidentifyDebablet.jar file.

On all systems, the Debablet can be started by typing the command line: java -jar loniDeidentifyDebablet.jar. Command line options for running the de-identifications without the GUI can be found by typing: java -jar loniDeidentifyDebablet.jar -help.

Purpose

Medical imaging data is often shared or pooled for large studies. Preserving patient confidentiality in this environment is crucial, and a requirement under federal regulations. To complicate matters, medical imaging data is produced in many different file formats. The de-identification debablet creates de-identified versions of MRI and PET image data in DICOM, ANALYZE, GE, MINC, ECAT, and HRRT Interfile file formats. These de-identified files are suitable for sharing with study collaborators.