k-dense-ai/scientific-agent-skillsMIT13 files

Pydicom

Use pydicom to read, inspect, write, transform, and safely preflight local DICOM datasets and pixel data. Applies to DICOM metadata, transfer syntaxes, compression plugins, frames, private elements, JSON, and bounded de-identification review.

Specification
Skill ID
k-dense-ai/scientific-agent-skills/pydicom
Publisher
k-dense-ai
Repository
scientific-agent-skills
Installs
0
Files
13
License
MIT
Requires
Python 3.10+ with pydicom 3.0.2; optional pinned NumPy, Pillow, and pixel plugins. Helper CLIs are local-only and require authorized data.
Synced
Sep 23, 2026
How to use it

Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.

k-dense-ai/scientific-agent-skills/pydicomInstalls these files
  • SKILL.md
  • references/common_tags.md
  • references/transfer_syntaxes.md
  • scripts/__init__.py
  • scripts/_common.py
  • scripts/anonymize_dicom.py
  • scripts/deidentification_audit.py
  • scripts/dicom_inventory.py
  • scripts/dicom_to_image.py
  • scripts/extract_metadata.py
  • scripts/pixel_frame_planner.py
  • scripts/transfer_syntax_inspector.py
  • scripts/uid_mapping_validator.py

What this skill tells the agent

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, while the live DICOM Standard may be newer.

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 exact reviewed release:

uv pip install "pydicom==3.0.2"

Uncompressed pixel arrays and image rendering:

uv pip install "pydicom==3.0.2" "numpy==2.5.1" "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==2.5.1" "pylibjpeg==2.1.0" \
  "pylibjpeg-libjpeg==2.4.0" "pylibjpeg-openjpeg==2.5.0" \
  "pylibjpeg-rle==2.2.0"

# JPEG-LS encoder/decoder
uv pip install "numpy==2.5.1" "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 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 YCbCr pixel data to RGB when possible; 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. MONOCHROME1 may require presentation inversion. Palette Color requires

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