Skip to main content

Extracts AIRC data from DICOM files and uploads to SQLite Database

Project description

AI-Rad Companion Chest CT Extractor

This is a python library and command line tool for extracting the results of Siemens' AI-Rad companion Chest CT software into a relational database.
NOTICE: Command line tool only for use with an appropriate DicomConquest PACS

To install, simply use

pip install airc_extract

This will install the package and the two necessary command line programs to your python environment.

Usage

Python API

If you simply want to use the extraction method stored in the AIRCReport class, you can import and use it in any python script with

from airc_extract.airc_report import AIRCReport
# This is the list of structured report (SR) dicoms that make up the AIRC Chest CT output
report_dicoms = ['dcm1.dcm', 'dcm2.dcm', ...]
report = AIRCReport(report_dicoms)
report.extract_report()  # This is the method to pull the data
print(report.report_data)  # A dictionary containing the results

Command Line

If you are using a DicomConquest server, you can use the two command line tools provided in this package to automatically create and update a SQLite database, storing the AIRC results in 6 tables. More information on these tables can be found here.

To start, you will need to run airc-create-config.

airc-create-config \
--dicom-db /path/to/conquest.db3 \ # Required (usually dicomserver/data/dbase/conquest.db3)
--dicom-data-dir /path/to/conquest/data \ # Required (usually dicomserver/data)
--data-db /path/to/output/database.db3 \ # Required. Can NOT be on a network share.
--log-level-term INFO # Terminal logging level \
--log-level-file DEBUG # Log file logging level \
--log-dir . # Directory for log files to be stored

This will setup a package config.ini file that will be used for extraction, as well as test the connection to the DicomConquest database and the output database. It will create a new output database with all necessary tables at the specified path if it does not exist.

After this, just run

airc-extract

and all the series not found in the output database will be extracted and inserted into the output database.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

airc_extract-0.2.3.tar.gz (11.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

airc_extract-0.2.3-py3-none-any.whl (12.4 kB view details)

Uploaded Python 3

File details

Details for the file airc_extract-0.2.3.tar.gz.

File metadata

  • Download URL: airc_extract-0.2.3.tar.gz
  • Upload date:
  • Size: 11.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.7.2

File hashes

Hashes for airc_extract-0.2.3.tar.gz
Algorithm Hash digest
SHA256 b0720e44bc025703742938b0339a98da31334e6821db55850fc02ab4f135aa2c
MD5 b6bc79281c1d70546d250218c707c033
BLAKE2b-256 44e81b6ea091550750c4a433dd916047c1298f86f5462d957b21292e8ed0af52

See more details on using hashes here.

File details

Details for the file airc_extract-0.2.3-py3-none-any.whl.

File metadata

File hashes

Hashes for airc_extract-0.2.3-py3-none-any.whl
Algorithm Hash digest
SHA256 4322f8c0189f9995a5aac29237298c5630a1a352a53bf843a64e3d99d5a6e93d
MD5 f3b642e0e9ccf2961c9a239a6296c771
BLAKE2b-256 23515f5152ee42cea6873c6cd4f772ee9733eb65252fa9135d6e9dbfee6b6f29

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page