portpy 1.0.4.6

First open-source radiation treatment planning system in Python

What is PortPy?

PortPy, short for Planning and Optimization for Radiation Therapy, is an initiative aimed at creating an open-source Python library for cancer radiotherapy treatment planning optimization. This project encompasses planning methodologies for Intensity Modulated Radiation Therapy (IMRT), Volumetric Modulated Arc Therapy (VMAT), and other emerging modalities. PortPy offers clinical-grade benchmark datasets and coding resources to promote benchmarking, reproducibility, and community development.

Note: If you are new to the field, we suggest reviewing relevant literature review papers (Zarepisheh et al. 2021, Breedveld et al. 2019, Ehrgott et al. 2010) and watching YouTube videos (Edelman competition, Varian IMRT, Elekta VMAT). In the near future, we plan to launch an educational YouTube channel to assist researchers new to this field.

Contents

• Quick start and examples
• Benchmark data, benchmark algorithms, and PortPy toolkit
• What can you do with PortPy?
• How to contribute?
• Data
• Installation
• Team

Quick start and examples

The easiest way to start is through the PorPy following examples.

Example File	Description
1_basic_tutorial.ipynb	Demonstrates the main functionalities of PortPy (e.g., Access data, create an IMRT plan, visualize)
vmat_scp_tutorial.ipynb	Creates a VMAT plan using sequential convex programming
vmat_scp_dose_prediction.ipynb	Predicts 3D dose distribution using deep learning and converts it into a deliverable VMAT plan
3d_slicer_integration.ipynb	Creates an IMRT plan and visualizes it in 3D-Slicer
imrt_tps_import.ipynb	1. Outputs IMRT plan in DICOM RT format and imports it into TPS. 2. Outputs IMRT plan optimal fluence in an Eclipse-compatable format and imports it into Eclipse
vmat_tps_import.ipynb	Outputs VMAT plan in DICOM RT format and imports it into TPS
imrt_dose_prediction.ipynb	Predicts 3D dose distribution using deep learning and converts it into a deliverable IMRT plan
vmat_global_optimal.ipynb	Finds a globally optimal VMAT plan
beam_orientation_global_optimal.ipynb	Finds globally optimal beam angles for IMRT
dvh_constraint_global_optimal.ipynb	Finds a globally optimal plan meeting Dose Volume Histogram (DVH) constraints
structure_operations.ipynb	Creates new structures by expanding/shrinking the existing ones or using boolean operations
inf_matrix_down_sampling.pynb	Down-samples beamlets and/or voxels for computational efficiency
inf_matrix_sparsification.ipynb	Sparsifies (i.e., truncates) the influence matrix for computational efficiency

Benchmark data, benchmark algorithms, and PortPy toolkit

The figure above illustrates the inspiration behind developing PortPy, drawing from successful open-source practices in the AI and computer science communities. Tools like PyTorch and TensorFlow, along with benchmark datasets such as ImageNet and algorithms like AlexNet, have revolutionized AI and data science. Our goal is to replicate this success by providing researchers with a benchmark dataset (currently featuring 50 curated lung patients), benchmark algorithms for finding globally optimal plans using Mixed Integer Programming, and the PortPy toolkit for creating, evaluating, and visualizing treatment plans.

What can you do with PortPy?

PortPy facilitates the design, testing, and clinical validation of new treatment planning algorithms. This includes both cutting-edge AI-based models and traditional optimization techniques. The above figure illustrates the PortPy design and its three main modules: “Data Management”, “Plan Generation”, and “Plan Evaluation”, which are discussed below. We recommend reviewing our Jupyter Notebooks examples for a more comprehensive understanding of these modules.

1. Data Management

   • This module provides access to the curated benchmark PortPy dataset, allowing researchers to test their algorithms on a standardized dataset (see basic_tutorial.ipynb notebook)
   • The available data includes:
     1. CT images and contours
     2. all necessary data for optimization extracted from Eclipse using its API (version 16.1)
     3. expert-selected beams for each patient
     4. an IMRT plan for each patient, generated using our in-house automated planning system, ECHO (YouTube Video, Paper). More information about data can be found in Data section.
   • In the current version, you can only work with the benchmark dataset provided in this PortPy repo and cannot use your own dataset for now. We will address this problem in the near future

2. Plan Generation

   • This module facilitates the generation of treatment plans using either classical optimization methods or emerging AI-based techniques
   • For optimization tasks, PortPy has been integrated with CVXPy, a widely-used open-source package. CVXPy enables the high-level formulation of optimization problems and offers out-of-the-box access to a range of free (e.g., SCIP, SCIPY) and commercial (e.g., MOSEK, CPLEX, GUROBI) optimization engines (available for free for research purposes) (see basic_tutorial.ipynb notebook)
   • PortPy.AI module is equipped with essential functionalities for AI-based planning. These include data access, data pre-processing, model training and testing, and patient-specific 3D dose prediction (see imrt_dose_prediction.ipynb notebook)

3. Plan Visualization and Evaluation

   • Basic built-in visualization tools (e.g., DVH, dose distribution) are integrated into PortPy
   • Enhanced visualizations are available through the integration with the popular open-source 3DSlicer package (see 3d_slicer_integration.ipynb notebook)
   • Plans can be quantitatively evaluated using well-established clinical protocols (e.g., Lung 2Gyx30, see basic_tutorial.ipynb)
   • Plans can be imported into any TPS for final clinical evaluations (see imrt_tps_import.ipynb)

How to contribute?

As illustrated in the above figure, PortPy organization includes "PortPy", which is the current repository, and PortPy extensions, which are the repositories developed using the PortPy as a platform. To maintain the lightweight nature and user-friendliness of PortPy modules, our aim is to include only fundamental functionalities, along with benchmark data and algorithms in the PortPy repo, and establish separate repositories for other projects, similar to what we've done for projects like LowDimRT and ECHO VMAT.

If you're interested in contributing to existing PortPy modules or wish to create a new module, we encourage you to contact us first. This will help ensure that our objectives and priorities are aligned. If you use PortPy to build your own package, you're welcome to host your package within the PortPy-Project orgainization. Alternatively, you can host your package on your own GitHub page. In this case, please inform us so that we can fork it and feature it under the PortPy-Project organization. For those keen on creating a logo for their repository, we offer the option to customize our pre-designed logo.

Data

PortPy equips researchers with a robust benchmark patient dataset, sourced from the FDA-approved Eclipse commercial treatment planning system through its API. This dataset embodies all necessary elements for optimizing various machine configurations such as beam angles, aperture shapes, and leaf movements. It includes

1. Dose Influence Matrix (AKA dose deposition matrix, dij matrix): The dose contribution of each beamlet to each voxel,
2. Beamlets/Voxels Details: Detailed information about the position and size of beamlets/voxels,
3. Expert-Selected Benchmark Beams: An expert clinical physicist has carefully selected benchmark beams, providing reference beams for comparison and benchmarking,
4. Benchmark IMRT Plan: A benchmark IMRT plan generated using our in-house automated treatment planning system called ECHO (YouTube Video, Paper). This plan serves as a benchmark for evaluating new treatment planning algorithms.
5. Benchmark Clinical Criteria: A set of clinically relevant mean/max/DVH criteria for plan evaluation. Currently, this set encompasses only the Lung 2Gy×30 protocol but will be expanded in the future to more protocols as well as TCP/NTCP evaluation functions.

To access these resources, users are advised to download the latest version of the dataset, which can be found here. Subsequently, create a directory titled './data' in the current project directory and transfer the downloaded file into it. For example, ./data/Lung_Phantom_Patient_1. We have adopted the widely-used JSON and HDF5 formats for data storage. HDFViwer can be utilized to view the contents of the HDF5 files.

Note: Initially, we will utilize a lung dataset from TCIA. The original DICOM CT images and structure sets are not included in the PortPy dataset and need to be directly downloaded from the TCIA. Users can fetch the TCIA collection ID and the TCIA subject ID for each PortPy patient using the get_tcia_metadata() method in PortPy and subsequently download the data from TCIA (see imrt_tps_import)

Installation

1. Install using pip:

   • Run the command 'pip install portpy'

2. Install using conda:

   • Run the command 'conda install -c conda-forge portpy'

3. Install from source:

   • Clone this repository using 'git clone https://github.com/PortPy-Project/PortPy.git'

   • Navigate to the repository with 'cd portpy'

   • Install the dependencies within a Python virtual environment or Anaconda environment. To set up in a Python virtual environment, install all the dependencies specified in requirements.txt as follows:

     • Create the virtual environment with 'python3 -m venv venv'
     • Activate the environment with 'source venv/bin/activate'
     • Install the requirements using '(venv) pip install -r requirements.txt'

Team

PortPy is a community project initiated at Memorial Sloan Kettering Cancer Center. It is currently developed and maintained by:

Name	Expertise	Institution
Masoud Zarepisheh	Treatment Planning and Optimization	Memorial Sloan Kettering Cancer Center
Saad Nadeem	Computer Vision and AI in Medical Imaging	Memorial Sloan Kettering Cancer Center
Gourav Jhanwar	Algorithm Design and Development	Memorial Sloan Kettering Cancer Center
Mojtaba Tefagh	Mathematical Modeling and Reinforcement Learning	University of Edinburgh, Scotland
Vicki Taasti	Physics and Planning of Proton Therapy	Aarhus University, Denmak
Seppo Tuomaala	Eclispe API Scripting	Varian Medical Systems

License

PortPy code is distributed under Apache 2.0 with Commons Clause license, and is available for non-commercial academic purposes.

Reference

If you find our work useful in your research or if you use parts of this code please cite our AAPM'23 abstract :

@article{jhanwar2023portpy,
  title={Portpy: An Open-Source Python Package for Planning and Optimization in Radiation Therapy Including Benchmark Data and Algorithms},
  author={Jhanwar, Gourav and Tefagh, Mojtaba and Taasti, Vicki T and Alam, Sadegh R and Tuomaala, Seppo and Nadeem, Saad and Zarepisheh, Masoud},
  journal={AAPM 65th Annual Meeting & Exhibition},
  year={2023}
}

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