pydicom – Writing & Content skill for AI agents

pydicom

Use pydicom for DICOM dataset I/O and pixel processing. Version 3.0.2 is the

current stable release reviewed here. It fixes CVE-2026-32711, a crafted

DICOMDIR path-traversal issue. pydicom 3.0.2 declares Python >=3.10; its

bundled DICOM dictionary is 2024c; the current DICOM Standard reviewed here is

2026d. No PACS, DIMSE, DICOMweb endpoint, or authenticated service is exercised

by this local-file skill.

Mandatory safety boundary

• Work only with local data that the user is authorized to access.
• DICOM metadata, file names, private elements, overlays, structured content,

and pixels may contain protected health information (PHI).

• Never print Dataset, export full metadata/JSON, or log element values by

default. Use a documented allowlist and aggregate output.

• pydicom is a general DICOM framework, not a diagnostic viewer. Pixel output,

validation, conversion, and plugin availability are not diagnostic claims.

• De-identification is profile-, purpose-, recipient-, jurisdiction-, and

threat-context-specific. It requires privacy/DICOM expert verification.

• Never claim that a tag-removal script is DICOM PS3.15, HIPAA, GDPR, or other

compliance. Preserve originals and audit derived outputs.

• Treat deterministic pseudonymization keys and UID maps as re-identification

secrets: use least privilege and encrypted/managed secret storage, never

commit, sync, log, or share them with derivatives, and define backup,

rotation, revocation, and destruction procedures. A leaked key invalidates

the intended separation; rotation also changes deterministic mappings.

• Set explicit input-file, file-count, frame-count, decoded-byte, and output

limits before parsing untrusted or unusually large datasets.

Installation

Create or activate an isolated environment, then install the reviewed release

(the pixel-stack pins below require Python 3.12+ because of NumPy):

uv pip install "pydicom==3.0.2"

Uncompressed pixel arrays and image rendering:

uv pip install "pydicom==3.0.2" "numpy==2.5.3" "Pillow==12.3.0"

Install only the transfer-syntax plugins required by the deployment:

# JPEG/JPEG-LS, JPEG 2000/HTJ2K, and faster RLE through pylibjpeg
uv pip install "numpy==2.5.3" "pylibjpeg==2.1.0" \
  "pylibjpeg-libjpeg==2.4.0" "pylibjpeg-openjpeg==2.6.0" \
  "pylibjpeg-rle==2.2.0"

# JPEG-LS encoder/decoder
uv pip install "numpy==2.5.3" "pyjpegls==1.5.1"

# Alternative decoder with platform-specific wheels
uv pip install "python-gdcm==3.2.6"

Plugin licenses and wheels differ by package/platform; review them before

deployment. Pillow has documented decoding limitations and pydicom cautions

that plugin output must be independently checked.

Native codec wheels widen the supply-chain and memory-safety boundary. For a

controlled deployment, resolve these exact pins on a trusted build host, lock

and verify wheel hashes/provenance, mirror approved artifacts internally, scan

them, and install with hash enforcement rather than resolving from the public

index at runtime.

Choose the workflow

1. Need an aggregate overview: run scripts/extract_metadata.py.

2. Need bounded technical checks: run scripts/dicom_inventory.py.

3. Need codec deployment preflight: run

scripts/transfer_syntax_inspector.py.

4. Need frame/memory planning: run scripts/pixel_frame_planner.py.

5. Need one non-diagnostic rendered frame: run

scripts/dicom_to_image.py.

6. Need a pseudonymized derivative: read the de-identification section, create

a site-reviewed action profile, then run scripts/anonymize_dicom.py and

scripts/deidentification_audit.py.

7. Need to check a sensitive UID map: run

scripts/uid_mapping_validator.py.

Read datasets safely

dcmread() returns a FileDataset, a Dataset subclass with File Format

state such as file_meta, preamble, and original encoding.

from pathlib import Path
import pydicom

path = Path("authorized/input.dcm")
ds = pydicom.dcmread(
    path,
    stop_before_pixels=True,
    specific_tags=[
        "SOPClassUID",
        "Modality",
        "Rows",
        "Columns",
        "NumberOfFrames",
    ],
)

technical = {
    "sop_class": ds.get("SOPClassUID"),
    "modality": ds.get("Modality"),
    "rows": ds.get("Rows"),
    "columns": ds.get("Columns"),
}

Use:

• stop_before_pixels=True for metadata-only work.
• specific_tags=[...] for a minimum allowlist.
• defer_size="1 MiB" when a later write must preserve large values.
• force=False (default). force=True only bypasses the File Format header

check; it does not prove the bytes are valid DICOM.

Do not call print(ds), repr(ds), or iterate values into logs on clinical

data.

Dataset, DataElement, and sequences

Access standard elements by keyword and check for absence:

modality = ds.get("Modality", "UNSPECIFIED")
if "ReferencedImageSequence" in ds:
    for item in ds.ReferencedImageSequence:
        referenced_class = item.get("ReferencedSOPClassUID")

Tag access, such as ds[0x0010, 0x0010], returns a DataElement; its .value

is separate. Sequence behaves like a list of nested Dataset items. Privacy

actions must recurse through every sequence item, not only the top level.

When creating a file, use FileMetaDataset for group 0002, keep dataset and

file-meta SOP UIDs consistent, set a Transfer Syntax UID, and write in enforced

File Format:

from pydicom import dcmwrite
from pydicom.dataset import FileDataset, FileMetaDataset
from pydicom.uid import CTImageStorage, ExplicitVRLittleEndian, generate_uid

meta = FileMetaDataset()
meta.MediaStorageSOPClassUID = CTImageStorage
meta.MediaStorageSOPInstanceUID = generate_uid()
meta.TransferSyntaxUID = ExplicitVRLittleEndian

ds = FileDataset(None, {}, file_meta=meta, preamble=b"\0" * 128)
ds.SOPClassUID = meta.MediaStorageSOPClassUID
ds.SOPInstanceUID = meta.MediaStorageSOPInstanceUID
# Add all attributes required by the selected IOD before writing.
dcmwrite("new.dcm", ds, enforce_file_format=True, overwrite=False)

write_like_original is deprecated in pydicom 3.0; use

enforce_file_format. A successful write is not full PS3.3 IOD conformance.

UIDs and transfer syntax

The File Meta Information Transfer Syntax UID controls dataset encoding and

pixel compression:

ts = ds.file_meta.TransferSyntaxUID
summary = {
    "uid": str(ts),
    "name": ts.name,
    "compressed": ts.is_compressed,
    "implicit_vr": ts.is_implicit_VR,
    "little_endian": ts.is_little_endian,
}

pydicom 3.0 chooses write encoding from the Transfer Syntax UID before legacy

dataset flags. Do not replace structural UIDs (Transfer Syntax, SOP Class, or

coding-scheme UIDs) during pseudonymization. Instance/reference UID replacement

must be one-to-one and consistent across the complete declared scope.

Read [references/transfer_syntaxes.md](references/transfer_syntaxes.md) before

compression, decompression, or encapsulation.

Pixel data and frames

The stable pydicom.pixels API supports path-based, frame-specific decoding:

from pydicom.pixels import pixel_array

# Reads only the selected frame where the source permits it.
frame = pixel_array("authorized/image.dcm", index=0, raw=False)

Shape semantics:

• grayscale single frame: (rows, columns)
• grayscale multi-frame: (frames, rows, columns)
• color single frame: (rows, columns, samples)
• color multi-frame: (frames, rows, columns, samples)

raw=False converts YBR_FULL/YBR_FULL_422 to RGB; codec-specific

JPEG 2000 processing may also produce RGB. It is not a universal YBR converter; raw=True retains

the decoded color space after mandatory minimal processing. Use

iter_pixels(path, indices=[...]) for bounded multi-frame iteration.

For grayscale display, apply transforms in this order:

from pydicom.pixels import apply_modality_lut, apply_voi_lut

modality_values = apply_modality_lut(frame, ds)
display_values = apply_voi_lut(modality_values, ds, index=0)

Modality LUT/rescale and VOI/windowing change display/value semantics.

Apply a declared Presentation LUT after VOI; avoid double inversion. Without

one, MONOCHROME1 requires reversed display polarity. Palette Color requires

apply_color_lut(). Presentation states and ICC behavior may require a

validated viewer. Never use per-frame min/max normalization for quantitative

analysis.

For enhanced multi-frame objects, inspect Shared/Per-Frame Functional Groups

for the selected frame before applying rescale or VOI transforms. The

[Pixel Value Transformation and Frame VOI macros](https://dicom.nema.org/medical/dicom/current/output/chtml/part03/sect_C.7.6.16.2.html)

may carry frame-specific parameters; top-level tags alone can be insufficient.

Confirm the output units and relevant real-world-value mapping before describing

decoded values as quantitative measurements. Do not assume a decoded array is

already in Hounsfield units or that every frame

… (full content is in the download package)