Extracting DICOM Metadata & Report Text for OpenMed

DICOM (Digital Imaging and Communications in Medicine) files carry far more than
pixels: a header of tagged attributes (patient, study, series, equipment)
and, for DICOM-SR (Structured Reports), a content tree holding the actual
radiology/cardiology report text. Two jobs sit here: pull the report narrative
for NLP, and flag the PHI in the header so it gets scrubbed. This skill does
both, then hands narrative to OpenMed. Header tags are read with pydicom
(external, MIT-licensed); de-identification of the extracted text is OpenMed's.

When to use

• You ingest DICOM from PACS/VNA or a research archive and want the SR report

text mined with clinical NLP.

• You must enumerate PHI-bearing header tags before sharing/exporting images.
• You have DICOM-SR objects (e.g. radiology measurements + impression) whose

content tree contains the dictated report.

DICOM headers in one minute

Every attribute has a tag (gggg,eeee) (group, element), a VR (value
representation, e.g. PN person name, DA date, UI UID), and a value. PHI
clusters in well-known tags:

Quick start

Read the header, pull SR report text, flag PHI tags, hand off to OpenMed:

import pydicom
import openmed

ds = pydicom.dcmread("study.dcm")

# 1) Enumerate PHI-bearing header tags (report, do not log values).
PHI_TAGS = [
    (0x0010, 0x0010), (0x0010, 0x0020), (0x0010, 0x0030), (0x0010, 0x1040),
    (0x0008, 0x0090), (0x0008, 0x0050), (0x0008, 0x0020), (0x0008, 0x0030),
]
present_phi = [hex_pair for hex_pair in PHI_TAGS if hex_pair in ds]

# 2) Extract report text from a DICOM-SR content tree (recursively).
def sr_text(dataset):
    chunks = []
    for item in dataset.get("ContentSequence", []):
        vt = item.get("ValueType")
        if vt == "TEXT" and "TextValue" in item:
            chunks.append(item.TextValue)
        if "ContentSequence" in item:          # nested CONTAINER
            chunks.append(sr_text(item))
    return "\n".join(c for c in chunks if c)

report = sr_text(ds)
# Some modalities stash narrative in free-text header tags too:
for tag in ("ImageComments", "SeriesDescription", "StudyDescription"):
    if tag in ds and isinstance(ds.get(tag), str):
        report += "\n" + ds.get(tag)

# 3) De-identify the narrative, then run NER.
if report.strip():
    deid = openmed.deidentify(report, method="replace", policy="hipaa_safe_harbor")
    result = openmed.analyze_text(deid.text, output_format="dict")

pydicom reads tags by keyword (ds.PatientName) or by (group, element).
DICOM-SR text lives in the recursive ContentSequence content tree.

Workflow

1. Read the dataset with pydicom.dcmread (use stop_before_pixels=True

for header-only/metadata work — faster, avoids loading pixels).

1. Walk the SR content tree. ContentSequence nests CONTAINER, TEXT,

CODE, NUM, PNAME nodes; concatenate TEXT.TextValue (and relevant
CODE/NUM measurements) in document order to reconstruct the report.

1. Inventory PHI tags. Flag the standard identifier tags and free-text

tags (ImageComments, *Description) that frequently leak PHI. Report tag
presence — never echo the values into logs.

1. De-identify → analyze the report narrative with OpenMed.
2. Scrub the header before any image export using a DICOM de-identification

profile (PS3.15 Annex E / Basic Application Level Confidentiality). OpenMed
de-identifies the narrative; header scrubbing is a separate DICOM step.

Hand-off to / from OpenMed

• To OpenMed: SR report text (and free-text header tags) →

openmed.deidentify → openmed.analyze_text.

• Header de-id is out of scope for OpenMed — OpenMed handles the text

narrative; use a DICOM-native de-identifier (pydicom + PS3.15 profile, or a
PACS de-id node) to scrub (0010,xxxx) and burned-in-pixel PHI. This skill's
job is to flag those tags so they aren't missed.

• Re-link by UID, not PHI. Carry StudyInstanceUID/SeriesInstanceUID as

rejoin keys; these are not identifiers but should be re-mapped consistently if
the profile requires UID remapping.

Edge cases & gotchas

• Pixel-burned PHI. Ultrasound and secondary-capture images often burn name/

MRN/date into the pixels — header scrubbing alone is insufficient; flag
modalities (US, SC, XC) for pixel review/OCR. OpenMed's multimodal/OCR intake
can read burned-in text for redaction screening.

• Private tags. Vendor (gggg,eeee) odd-group private tags can hide PHI;

PS3.15 requires removing or whitelisting them — don't trust unknown tags.

• Date shifting must be consistent. If you date-shift StudyDate, shift all

related dates by the same offset to preserve temporal relationships.

• SR value types. Not all SR content is narrative — NUM (measurements),

CODE (coded findings), PNAME (person names, PHI!) need different handling;
don't dump PNAME into NLP text.

• Character sets. Honor SpecificCharacterSet (0008,0005); non-Latin

patient names need correct decoding before de-id.

• Read-only intake. Treat source DICOM as immutable; write de-identified

copies, never overwrite originals.

Standards & references

• DICOM standard (PS3.x), Part 6 Data Dictionary (tags):

https://www.dicomstandard.org/current

• PS3.15 Annex E — Attribute Confidentiality Profiles (de-identification):

https://dicom.nema.org/medical/dicom/current/output/html/part15.html#chapter_E

• DICOM-SR (PS3.3 Structured Reporting; PS3.16 templates):

https://dicom.nema.org/medical/dicom/current/output/html/part03.html

• pydicom documentation: https://pydicom.github.io/
• DICOM PS3.16 TID 2000 (Basic Diagnostic Imaging Report):

https://dicom.nema.org/medical/dicom/current/output/html/part16.html