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https://github.com/aces/DICAT

Tool to anonymize DICOM data headers.
https://github.com/aces/DICAT

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Tool to anonymize DICOM data headers.

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README 

        
# DICAT (DICOM Anonymization Tool)



DICAT is a simple graphical tool that facilitates DICOM (Digital Imaging and 

Communications in Medicine) de-identification directly on a local workstation. 

It was designed to work on all major operating systems (Windows, Linux and OSX) 

and is very light in terms of dependencies (Python). Binaries (with no 

dependencies) have been compiled for all operating systems and made available 

with each [release of DICAT](https://github.com/aces/DICAT/releases).



With the increasing use of web-based database systems, such as 

[LORIS](http://www.loris.ca) 

([Das *et al.*, 2011](http://journal.frontiersin.org/article/10.3389/fninf.2011.00037/full), 

[Das *et al.*, 2016](http://www.sciencedirect.com/science/article/pii/S1053811915008009)), 

for large scale imaging studies, de-identification of DICOM datasets becomes a 

requirement before they can be uploaded in such databases.



![Flow Chart](docs/images/TypicFlowChartOfDICOMdeidentification.png)



***Typical Flow Chart of DICOM de-identification.***



Before DICOM datasets can be uploaded into a web-based database, identifying 

information stored in the DICOM header (such as patient name, date of birth) 

should be removed.



DICAT produces two archival outputs: a back-up of the original DICOM files, and 

a de-identified DICOM dataset that can then be uploaded or transferred to other 

systems.



DICAT also features an ID key log that can be used to keep a record of the 

original candidate name (participant/patient) linked to their anonymized study 

identifier, for reference by study coordinators. 



DICAT was first developed during the 

[2014 and 2015 brainhacks](http://brainhack.org) held at the 

[Organization of Human Brain Mapping (OHBM)](http://www.humanbrainmapping.org/i4a/pages/index.cfm?pageid=1) 

conferences.



## How to install and run DICAT



Installation instructions vary depending on the operating system used. 

See below for detailed information.



Running DICAT will open a window with three different tabs:



* A simple **"Welcome to DICAT"** tab giving a short description of the tool

* A **"DICOM de-identifier"** tab, in which DICOM de-identification will take place 

* An **"ID Key"** tab, containing the key between candidates' name and their IDs



![Welcome page](docs/images/Welcome_DicAT.png)



***Welcome page of DICAT.***



### Using DICAT executables/applications



Executables of DICAT have been created for most systems and can be found with 

each [release](https://github.com/aces/DICAT/releases) of DICAT in the Github 

repository. Download the executable relevant to your system and move it to any 

folder of your choice.



To open DICAT, simply double click on the executable.



Please note that for some obscure reason, DICAT is extremely slow to run on 

Windows OS.



### From the source code



###### Requirements 



Before running DICAT, make sure your system contains a 

[Python 3](https://www.python.org) compiler with the 

[TkInter](https://wiki.python.org/moin/TkInter) library (usually, TkInter comes 

by default with most Python installations).



For Ubuntu distributions, TkInter can be installed via apt-get:

```sudo apt-get install python-tk```



The [PyDICOM](https://pydicom.github.io/) 

package is also required by DICAT. 



For most platform, PyDICOM can be installed via `pip`: 

```pip install pydicom``` 



###### DICAT installation



To install DICAT source code on a computer, download and save the content of 

the current Github repository into a workstation.



DICAT can be started by executing `DICAT.py` script with a Python compiler. 

On UNIX computers (Linux and Mac OS X), open a terminal, go to the main 

directory of DICAT source code (`dicat` directory) and run the following:

```python DICAT.py```



## How to use the DICOM de-identifier of DICAT?



![DICOM deidentifier 1](docs/images/DICOM_deidentification.png)



***DICOM de-identification with the DICAT GUI*** 



In the *"DICOM de-identifier"* tab (1), use the select button (2) to choose a 

directory containing DICOM files to de-identify.



Once a directory containing DICOM files have been selected (as described in the 

above section), the DICOM fields can be viewed when clicking on the 

*“View DICOM fields”* button (3).



The DICOM identifiable fields will be displayed in a table with editable fields (4). 



Users can choose to delete all identifiable fields using the *“Clear All Fields”* 

button (5). 



Users can also directly edit the fields (6) in the table and all values present in 

the table will be inserted into the corresponding DICOM fields in the imaging files.

> Note that the PatientName field is required (7) and will need to be filled 

with new IDs in order to label the scan for that session.



Finally, once the user has finalized the edits, clicking on the *“De-identify”* 

button (8) will run the de-identification tool on the DICOM dataset. 



***Mass DICOM de-identification using `mass_deidentify.py`***



The script `mass_deidentify.py` in the `dicat` directory allows a user to 

mass de-identify a series of DICOM studies. Running `mass_deidentify.py -h` 

will display the following information on how to run the script:



![Mass_deidentify_help](docs/images/mass_deidentify_help.png)



## How to use the ID Key of DICAT



The ID Key feature of DICAT allows storage of the key between identifiable 

candidates's information (*Real Name* and *Date of Birth*) and its study’s 

identifier. This information will be stored locally on the workstation within 

an XML file (candidate.xml) in DICAT's directory. See the following figure for 

detailed information on how to use this feature.



![ID Key](docs/images/ID_Mapper.png)



***ID key feature of DICAT.*** 



This feature (1) allows storage of the mapping information between candidates’s 

information and study IDs. This information will be stored in an XML file that 

can be either created (2) or opened (3). Changes will be automatically saved. 



A candidate (participant/patient) can be looked up using the 

*“Search candidate”* button (5) after having entered either the *“Identifier”* 

or the *“Real Name”* text fields available in (4). 



The *“Clear fields”* button (6) allows clearing the text in those text fields. 



A new candidate can be registered using the *“Add candidate”* button (7) after 

having entered the *“Identifier”*, *“Real Name”* and *“Date of birth”* 

information in the text fields of (4). 



Clicking on a subject row (8) of the table displayed at the bottom of the 

application will automatically populate the text fields (4) with the 

information of the candidate. 



The *“Real Name”* or *“Date of birth”* of that candidate can be edited if 

needed by altering the field and clicking on the *“Edit candidate”* button (9). 



Finally, the data table of candidate is sortable by clicking on any of the 

column headers (10).



## Authors



Ayan Sengupta               - Concept, Pydicom implementation   



Cecile Madjar        - GUI implementation, PyDICOM implementation, python integration of DICOM-toolkit, ID key



Dave MacFarlane  - ID Key



Samir Das                - Concept and guidance



Daniel Krötz          - Documentation, testing on Windows



Christine Rogers  - Documentation



Leigh Evans           - Video tutorial



Derek Lo                  - Logo design