/extracting-dicom-metadata

name: extracting-dicom-metadata
description: "Reads DICOM file headers and DICOM-SR (Structured Report) content to pull study/series metadata and embedded report text, and flags PHI carried in header tags. Use before OpenMed processing when ingesting imaging data (CT/MR/CR/US, radiology SR) and you need the report narrative de-identified and analyzed, plus a list of header tags that must be scrubbed. Hand SR/report text to openmed.deidentify and openmed.analyze_text; use pydicom to read tags. Trigger keywords: DICOM, pydicom, DICOM-SR, structured report, PatientName, study metadata, PACS, radiology report, PS3."
license: Apache-2.0
metadata:
  project: OpenMed
  category: data-ingestion
  pairs: before
  version: "1.0"

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:

| Tag | Name | VR | Notes | | --- | --- | --- | --- | | (0010,0010) | PatientName | PN | direct identifier | | (0010,0020) | PatientID | LO | MRN | | (0010,0030) | PatientBirthDate | DA | DOB | | (0010,1040) | PatientAddress | LO | address | | (0008,0090) | ReferringPhysicianName | PN | provider | | (0008,0020/0030) | StudyDate / StudyTime | DA/TM | dates | | (0008,0050) | AccessionNumber | SH | order id | | (0008,103E) | SeriesDescription | LO | free text — may leak PHI | | (0020,4000) | ImageComments | LT | free text — may leak PHI | | (0040,A730) | ContentSequence | SQ | DICOM-SR report tree |

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). 2. **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. 3. **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. 4. **De-identify → analyze** the report narrative with OpenMed. 5. **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 t