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+# DICOM De-Identification
+
+DICOM anonymization (a type of de-identification) is the commonest workflow in
+_ChRIS_. This page describes the DICOM anonymization pipeline.
+
+## Background
+
+The [DICOM file format](https://nipy.org/nibabel/dicom/dicom_intro.html) is
+the international standard for how medical images are produced, transmitted,
+and stored. Hence, de-identification of DICOM data is the first step of any
+imaging research analysis.
+
+Protected health information (PHI) can be found in DICOM files in three forms:
+
+- **Metadata** as DICOM tags, e.g. PatientName, PatientSex, PatientBirthDate, etc.
+- **Burned text**, where PHI appears as text in the image (the PixelData).
+- **Identifiable anatomy** e.g. the face which is present on head MRI scans.
+
+Currently, metadata anonymization and burned text removal are available as _ChRIS_ pipelines.
+
+### DICOM Tag Anonymization
+
+PHI found in DICOM tags can be anonymized by deleting those tags. This can be done:
+
+- Manually
+- Automatically, using an "allow-list" (i.e. remove all tags except for a few)
+- Automatically, using a "deny-list" (i.e. keep all tags except for a few)
+
+:::tip
+
+Generally, "allow-list" approaches are considered safer. The risk of accidental
+PHI leakage is lower using an "allow-list" policy because it is stricter.
+
+:::
+
+## DICOM De-Identification in _ChRIS_
+
+The following _ChRIS_ plugins are available:
+
+| Plugin Name | Description |
+|-------------|-------------|
+| [pl-dicomize](https://github.com/FNNDSC/pl-dicomize) | Among its multiple purposes, does DICOM anonymization by removing _all_ DICOM tags except for the image data. |
+| [pl-dicom_headerEdit](https://github.com/FNNDSC/pl-dicom_headerEdit) | Edits specified DICOM tags. This is a "deny-list" approach. |
+| [pl-dcm_textlocr](https://github.com/FNNDSC/pl-dcm_txtlocr) | Detects burned text PHI using optical-character recognition (OCR). |
+| [pl-image_textRemove](https://github.com/FNNDSC/pl-image_textRemove) | Detects and removes burned-in text from images using OCR. |
+
+These plugins are the building blocks of our DICOM de-identification pipelines.
+We have many pipelines tailored for specific workflows (e.g. ultrasound
+anonymization, anon → analyze → upload, ...),
+please <a href="mailto:fnndsc_atlas-dl@childrens.harvard.edu">contact us</a>
+for more information.
+