DICOM Anonymization
DICOM anonymization software for research and sharing
De-identify DICOM studies with reusable profiles — strip or replace patient identifiers, keep the attributes research needs, and apply it automatically by routing rule or on demand.
The basics
What is DICOM anonymization?
DICOM anonymization (de-identification) removes or replaces the attributes in a study that identify a patient — name, medical record number, birth date, accession, institution, and the dates and identifiers scattered through the header — so the images can be used for research, teaching, algorithm development, or vendor evaluation without carrying PHI.
Doing it well is more than blanking a few tags. Identifiers appear in places people forget: referring physician, station and operator names, study and series descriptions, private vendor tags, and the UIDs themselves. A usable tool applies a consistent, repeatable profile rather than relying on someone to remember every field each time.
Why RadFuzion
Anonymization that fits the workflow
De-identification usually fails not because the tool cannot strip tags, but because it sits outside the path studies actually travel. RadFuzion is already in that path.
Reusable profiles
Define what gets removed, replaced, or kept once, then apply that profile every time — so two exports for the same study are de-identified the same way.
Automatic by rule
Attach a profile to a routing rule so studies bound for a research destination or an outside vendor are de-identified on the way out, without anyone remembering to do it.
The original stays intact
Anonymization applies to the copy being routed or exported. The clinical study in your archive is untouched, so care is never working from a stripped record.
Under the hood
Anonymization capabilities
- check_circleReusable anonymization profiles
- check_circleRemove or replace patient identifiers
- check_circleHandle dates and date shifting
- check_circleStrip institution and operator details
- check_circleApply automatically by routing rule
- check_circleApply on demand to a selected study
- check_circleKeep attributes research needs
- check_circleConsistent, repeatable output
- check_circleWorks with cloud and on-prem destinations
- check_circleAudit trail of exports
- check_circleCombine with tag editing
- check_circleBulk apply across a study set
Research, AI, and vendor evaluations
The usual triggers are an algorithm vendor asking for a sample set, a research collaboration needing a cohort, or a teaching file. All three share a pattern: studies must leave the clinical environment, they must not carry PHI, and they still need enough metadata — modality, body part, acquisition parameters, sometimes age — to be scientifically useful. A profile draws that line explicitly and applies it identically every time.
Because RadFuzion already routes the studies, the de-identified copy can be sent straight to wherever it needs to go — a cloud bucket, a DICOMweb endpoint, a partner node — rather than passing through an export folder and a manual upload. Sharing between RadFuzion sites is covered on the RadShare page.
FAQ
DICOM anonymization questions
What does DICOM anonymization remove?add
Patient identifiers such as name, ID, and birth date, plus the identifying values spread through the header — accession, referring physician, institution, station and operator, and descriptions that can carry names. Profiles decide what is removed, what is replaced with a placeholder, and what is deliberately kept.
Can studies be de-identified automatically?add
Yes. An anonymization profile can be attached to a routing rule, so studies going to a research destination or an outside vendor are de-identified as they are sent, without depending on anyone to remember the step.
Does anonymizing change the original study?add
No. The profile applies to the copy being routed or exported; the clinical study in your archive is unchanged, so patient care is never working from a de-identified record.
Can we keep some attributes for research?add
Yes. Profiles are explicit about what is kept, so scientifically necessary attributes — modality, body part, acquisition parameters, and similar — survive while identifiers do not.
Can it anonymize a whole set of studies at once?add
Yes. A profile can be applied across a set rather than study by study, which is the normal case for building a research cohort or a vendor sample.
Is anonymization the same as HIPAA Safe Harbor de-identification?add
They are related but not identical. Profiles let you implement the attribute handling a given standard or agreement requires, but whether a particular data set meets a specific regulatory definition is a determination your compliance team makes — the tool enforces the policy you define, consistently.
See anonymization profiles
Open the live preview to see profiles, rule-based de-identification, and routing on sample studies.