MaskIt  - A DICOM Anonymizer for Clinical Researchers

Introduction

Researchers in medicine often need to anonymize DICOM datasets before use them in their studies due to HIPAA regulations. MaskIt is a free lightweight DICOM anonymizer for all DICOM modalities. It runs on Windows platforms. The current version is 3.1 released on July 15, 2023.  

To help you check the integrity of a dataset, DICOM objects will be sorted and presented in a hierarchical structure in which they were actually internally articulated. DICOM objects of commonly used modalities are indicated in distinctive icons for you to get a good picture of the dataset. 

Installation

User's Manual

    1. Three ways to specify DICOM datasets to be anonymized

    A.  Click [Load a Folder] button to locate the DICOM source folder.

    B.  Type in the path in the text input slot and press <Enter> key.

    C.  Click [Load Files] button to select multiple DICOM files.

      2. Anonymize the whole dataset of the selected patient

    A.  Select any item of a patient.

    B.  Click [Anonymize] button to anonymize the dataset in given name and ID. A user input window will pop up to take new name and ID.

    C.  The original DICOM files would be overridden if they writable. Otherwise the user would be asked to pick a new folder with write permission.

      3. Sort and copy

    There might be multiple patients in the source folder. MiaskIt helps you sort the datasets and copy out DICOM objects of the selected patient.

      4. Use the dataset tree

    A.  The dataset tree presents patient's dataset in a hierarhical structure.

    B.  It helps the user sort DICOM objects, check and confirm the integrity of the dataset.

    C.  It also lets the user select a patient when multiple patients are listed.


Contact Technical Support

  Yulong Yan Ph.D.

  Department of Radiation Oncology

  University of Texas Southwestern Medical Center

  2280 Inwood Road,  Dallas, TX 75390

  Tel: (214) 645 7636    Fax: (214) 645 2885

  Email: Yulong.Yan@utsouthwestern.edu     or   YanYulong@gmail.com