Skip to main content

Film dosimetry and gamma analysis for dose distributions in radiotherapy

Project description

Dosepy-Logo

PyPI - Version PyPI - Python Version PyPI - Downloads GitHub commit activity

Welcome to Dosepy

Main documentation

Dosepy is an open-source Python library to perform radiochromic film dosimetry.

Dosepy is intended to be an accessible tool for medical physicists in radiation oncology to perform film dosimetry effortlessly.

Installation

Dosepy is distributed as a Python library under the Python Package Index (PyPI). Open a console (or 'terminal', or 'command prompt') and use the pip command:

pip install Dosepy

See the Python for Beginners getting started tutorial for an introduction to using your operating system’s console and interacting with Python.

Features

  • Automatic film detection.
  • Uncertainty analysis.
  • Quality control test for error detection.
  • Average of multiple scans for noise reduction.
  • Handle lateral scanner response artifact.

Gamma index

Dose distributions comparison can be performed using the 2-dimensional gamma index test according to Low's definition Daniel_Low_gamma_1998, as well as some AAPM TG-218 Miften_TG218_2018 recommendations:

  • The acceptance criteria for dose difference can be selected in absolute mode (in Gy) or relative mode (in %).
    • In relative mode, the percentage could be interpreted with respect to the maximum dose (global normalization), or with respect to the local dose (local normalization); according to user selection.
  • Dose threshold can be adjusted by the user.
  • The reference distribution can be selected by the user.
  • It is possible to define a search radius as an optimization process for calculation.
  • By default, percentile 99 from dose distribution is used as maximum dose. This is used to avoid the possible inclusion of artifacts or user markers.
  • Interpolation is not yet supported.

Used technologies

Warning!

To use a software as a medical device, it is required to demonstrate its safety and efficacy through a risk categorization structure, a quality management system and a clinical evaluation; as described in the International Forum of Medical Device Regulators working group guidelines (IMDRF).

Dosepy is currently under development to meet quality standards. To achieve this in Mexico the regulatory mechanism is through NOM-241-SSA1-2021, in addition to the IMDRF guidelines.

Contributing

Dosepy uses GitHub as a platform to store and develop the software.

  • To report software bugs, create an issue here
  • To commit changes, create an issue, fork the repository, make your changes, and make a new pull request.

Discussion

Have questions? Ask them on the Dosepy discussion forum.

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

dosepy-0.12.2.tar.gz (2.2 MB view details)

Uploaded Source

Built Distribution

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

dosepy-0.12.2-py3-none-any.whl (2.2 MB view details)

Uploaded Python 3

File details

Details for the file dosepy-0.12.2.tar.gz.

File metadata

  • Download URL: dosepy-0.12.2.tar.gz
  • Upload date:
  • Size: 2.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.11

File hashes

Hashes for dosepy-0.12.2.tar.gz
Algorithm Hash digest
SHA256 fe42a79dc1fc39e21afd9a20d68d0b461d67567d316cae48c7c4ae09d8b32335
MD5 512c3241a302e1a47e1c610ae1270e80
BLAKE2b-256 476b00150649ef74039aea0f0137868af4155bb8ae4dfcf349cf6b7ca9ae5b29

See more details on using hashes here.

File details

Details for the file dosepy-0.12.2-py3-none-any.whl.

File metadata

  • Download URL: dosepy-0.12.2-py3-none-any.whl
  • Upload date:
  • Size: 2.2 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.11

File hashes

Hashes for dosepy-0.12.2-py3-none-any.whl
Algorithm Hash digest
SHA256 92f78f76f8f3e6bf5fc0daea8f37b8a76dbec9ef61a273762a2cd2040363d422
MD5 8afd5f9463b35413d18dc53a9ff9a24b
BLAKE2b-256 8c2c489e15b513b4f77993726311392375587ee2ffcd0e66bcc2971ab5b3367c

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