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INRR3CT: CT Reconstruction from Few Planar X-Rays with Application Towards Low-Resource Radiotherapy
This is the official pytorch implementation of the deep leraning model INRR3CT for 3D CT reconstruction from few planar X-rays. The paper is originally published on MICCAI 2023 Deep Generative Models Workshop (DGM4MICCAI 2023). The arxiv version of the paper is available here.
Note: The previous code repository of INRR3CT is here: https://github.com/wanderinrain/Xray2CT.
Code release
The code will be ready before Sep. 30, 2024.
Abstract
CT scans are the standard-of-care for many clinical ailments, and are needed for treatments like external beam radiotherapy. Unfortunately, CT scanners are rare in low and mid-resource settings due to their costs. Planar X-ray radiography units, in comparison, are far more prevalent, but can only provide limited 2D observations of the 3D anatomy. In this work, we propose a method to generate CT volumes from few (<5) planar X-ray observations using a prior data distribution, and perform the first evaluation of such a reconstruction algorithm for a clinical application: radiotherapy planning. We propose a deep generative model, building on advances in neural implicit representations to synthesize volumetric CT scans from few input planar X-ray images at different angles. To focus the generation task on clinically-relevant features, our model can also leverage anatomical guidance during training (via segmentation masks). We generated 2-field opposed, palliative radiotherapy plans on thoracic CTs reconstructed by our method, and found that isocenter radiation dose on reconstructed scans have <1% error with respect to the dose calculated on clinically acquired CTs using <4 X-ray views. In addition, our method is better than recent sparse CT reconstruction baselines in terms of standard pixel and structure-level metrics (PSNR, SSIM, Dice score) on the public LIDC lung CT dataset.
Citing our work
If you find the paper useful in your research, please cite the paper:
@inproceedings{sun2023ct,
title={CT Reconstruction from Few Planar X-Rays with Application Towards Low-Resource Radiotherapy},
author={Sun, Yiran and Netherton, Tucker and Court, Laurence and Veeraraghavan, Ashok and Balakrishnan, Guha},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={225--234},
year={2023},
organization={Springer}
}
Contents
Requirements
- pytorch 1.9.0 version had been tested
- python 3.8 was tested
- python dependencies, please see the requirements.txt file
- CUDA11.8 had been tested
Installation
- Install Python 3.8.0
- pip install -r requirements.txt
- Install pytorch 1.9.0 or above
- Make sure CUDA and cudnn are installed
- Download the source code and put the data file to the right location according to the code structure below
Acknowledgement
This work was supported by NSF CAREER: IIS-1652633.
The public datasets were used in this paper LIDC-IDRI and LUNA 16 are under Creative Commons Attribution 3.0 Unported License and Creative Commons Attribution 4.0 International License.
MONAI and clinical level pre-trained nn-UNet from MD Anderson are used during evaluation stage.