This repository accompanies our award-winning paper:

"ASTRA: Atomic Surface Transformations for Radiotherapy Quality Assurance"
Presented at the 45th IEEE Engineering in Medicine and Biology Conference (EMBC), 2023.

Authors: Amith Kamath, Robert Poel, Jonas Willmann, Ekin Ermis, Nicolaus Andratschke, Mauricio Reyes

See a short video description of this work here:

🔗 Project Website

ASTRA introduces a deep learning-based framework to assess the sensitivity of radiotherapy dose predictions to local variations in organ-at-risk (OAR) segmentations. By simulating atomic surface transformations, ASTRA provides clinicians with dose-aware sensitivity maps, highlighting regions where segmentation inaccuracies could significantly impact dose distributions.

• Dose-Aware Sensitivity Mapping: ASTRA predicts the potential impact of local segmentation variations on radiotherapy dose distributions, aiding in quality assurance.
• Simulation of Segmentation Variability: Introduces a method to simulate realistic local perturbations in OAR contours, reflecting inter-observer variability.
• Clinical Applicability: Demonstrated the utility of ASTRA in identifying critical regions in OARs susceptible to dose changes, facilitating informed decision-making in treatment planning.

• Data: Utilized a dataset of 100 glioblastoma patients, including CT scans, OAR segmentations, and corresponding dose distributions.
• Perturbation Techniques: Applied atomic surface transformations to OAR contours to simulate local segmentation variability.
• Model Architecture: Employed convolutional neural networks to predict dose sensitivity maps based on perturbed segmentations.
• Evaluation Metrics: Assessed the model's performance using metrics such as Mean Absolute Error (MAE) and Dose-Volume Histogram (DVH) differences.

• Python 3.8+
• PyTorch
• MONAI
• NumPy
• SciPy
• Matplotlib

git clone https://github.com/amithjkamath/astra.git
cd astra
pip install -r requirements.txt

If this is useful in your research, please consider citing:

@inproceedings{kamath2023astra,
title={ASTRA: Atomic Surface Transformations for Radiotherapy quality Assurance},
author={Kamath, Amith and Poel, Robert and Willmann, Jonas and Ermis, Ekin and Andratschke, Nicolaus and Reyes, Mauricio},
booktitle={45th IEEE Engineering in Medicine and Biology Conference (EMBC)},
year={2023}
}

Major props to the code and organization in https://github.com/LSL000UD/RTDosePrediction, which is what this model is based on (looks like this repo is not maintained/available anymore!)