dicompare

dicompare is a DICOM validation tool designed to ensure compliance with study-specific imaging protocols and domain-specific guidelines while preserving data privacy. It provides multiple interfaces, including support for validation directly in the browser, leveraging WebAssembly (WASM), Pyodide, and the underlying pip package dicompare. dicompare is suitable for multi-site studies and clinical environments without requiring software installation or external data uploads.

dicompare supports DICOM session validation against templates based on:

• Reference sessions: JSON schema files can be generated based on a reference MRI scanning session;
• [TESTING] domain guidelines: Flexible guidelines for specific domains (currently QSM);
• [FUTURE] landmark studies: Schema files based on landmark studies such as the HCP, ABCD, and UK BioBank projects.

Command-line interface (CLI) and application programming interface (API)

While you can run dicompare in your browser now without any installation, you may also use the underlying dicompare pip package if you wish to use the command-line interface (CLI) or application programming interface (API).

pip install dicompare

Command-line interface (CLI)

The package provides the following CLI entry points:

• dcm-gen-session: Generate JSON schemas for DICOM validation.
• dcm-check-session: Validate DICOM sessions against predefined schemas.

1. Generate a session template

dcm-gen-session \
    --in_session_dir /path/to/dicom/session \
    --out_json_ref schema.json \
    --acquisition_fields ProtocolName SeriesDescription \
    --reference_fields EchoTime RepetitionTime

This will create a JSON schema describing the session based on the specified fields.

2. Validate a DICOM Session

dicompare-session \
    --in_session /path/to/dicom/session \
    --json_ref schema.json \
    --out_json compliance_report.json

The tool will output a compliance summary, indicating deviations from the session template.

Python API

The dicompare package provides a Python API for programmatic schema generation and validation.

Generate a schema:

from dicompare.io import read_dicom_session

reference_fields = ["EchoTime", "RepetitionTime"]
acquisition_fields = ["ProtocolName", "SeriesDescription"]

session_data = read_dicom_session(
    session_dir="/path/to/dicom/session",
    acquisition_fields=acquisition_fields,
    reference_fields=reference_fields
)

# Save the schema as JSON
import json
with open("schema.json", "w") as f:
    json.dump(session_data, f, indent=4)

Validate a session:

from dicompare.io import read_json_session, read_dicom_session
from dicompare.compliance import check_session_compliance

# Load the schema
acquisition_fields, reference_fields, ref_session = read_json_session(json_ref="schema.json")

# Read the input session
in_session = read_dicom_session(
    session_dir="/path/to/dicom/session",
    acquisition_fields=acquisition_fields,
    reference_fields=reference_fields
)

# Perform compliance check
compliance_summary = check_session_compliance(
    in_session=in_session,
    ref_session=ref_session,
    series_map=None  # Optional: map series if needed
)

# Print compliance summary
for entry in compliance_summary:
    print(entry)