$^{90}\mathrm{Y}$ SPECT Scatter Estimation and Voxel Dosimetry in Radioembolization using a Unified Deep Learning Framework

This repository contains the official implementation of Y90 SPECT Scatter Estimation and Voxel Dosimetry in Radioembolization using a Unified Deep Learning Framework.

Pipeline Overview

This study explores a deep-learning-based absorbed dose-rate estimation method for 90Y that mitigates the impact of poor SPECT image quality on dosimetry and the accuracy–efficiency trade-off of Monte Carlo (MC)-based scatter estimation and voxel dosimetry methods.

Code and Datasets

The majority of the code is written in Python. Neural networks are built and trained using the PyTorch automatic differentiation framework. The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Stage I: CNN for SPECT Scatter Estimation

Paper: http://doi.org/10.1007/s00259-020-04840-9

Code template: https://github.com/haoweix/spect-scatter-deep-learning

Stage II: OS-EM for SPECT Reconstruction

Toolbox: https://web.eecs.umich.edu/~fessler/code/

Stage III: DblurDoseNet for Dose-rate Map Generation

Paper: https://pubmed.ncbi.nlm.nih.gov/34882821/

Code template: https://github.com/ZongyuLi-umich/DblurDoseNet

Note: use the script run_train.sh and run_test.sh in the folder stageIII for training and testing.

Citation

If you find this work useful, please cite it as follows:

@article{jia202390y,
  title={90Y SPECT scatter estimation and voxel dosimetry in radioembolization using a unified deep learning framework},
  author={Jia, Yixuan and Li, Zongyu and Akhavanallaf, Azadeh and Fessler, Jeffrey A and Dewaraja, Yuni K},
  journal={EJNMMI physics},
  volume={10},
  number={1},
  pages={82},
  year={2023},
  url={https://doi.org/10.1186/s40658-023-00598-9},
  publisher={Springer}
}