LORIS: DICOM anonymizer

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Das, Samir;Madjar, Cécile;Sengupta, Ayan;Mohades, Zia

Description

The purpose of this Brainhack project was to create a simple application, with the least dependencies, for anonymization of DICOM files directly on a workstation.
Anonymization of DICOM datasets is a requirement before an imaging study can be uploaded in a web-based database system, such as LORIS. Currently, a simple and efficient interface for the anonymization of such imaging datasets, which works on all operating systems and is very light in terms of dependencies, is not available.
Here, we created a DICOM anonymizer that is a simple graphical tool that uses PyDICOM package to anonymize DICOM datasets easily on any operating system, with no dependencies except for the default Python and NumPy packages. DICOM anonymizer is available for all UNIX systems (including Mac OS) and can be easily installed on Windows computers as well (see PyDICOM installation). The GUI (using tkinter) and the processing pipeline were designed in Python. Executing the anonymizer_gui.py script with a python compiler will start the program. Figure 1 illustrates how to use the program to anonymize a DICOM study.
The DICOM anonymizer is a simple standalone graphical tool that facilitates anonymization of DICOM datasets on any operating system. These anonymized studies can be uploaded to a web-based database system, such as LORIS, without compromising the patient or participants identity.

Citations (0)

Mentions (0)

Metrics

Dataset Index

0.5

FAIR Score

31%

Citations

1

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

GigaScience Database

License

Creative Commons Zero v1.0 Universal

Assigned Domain

Subfield

Materials Chemistry

Field

Materials Science

Domain

Physical Sciences

Confidence Score

37%

Source

Scholar Data Model

Keywords

ImagingNeuroscience

Normalization Factors

FT

50.00

CTw

1.00

MTw

1.00