Yiran Sun, Tucker Netherton, Laurence E. Court, Ashok Veeraraghavan, Guha Balakrishnan.
Rice University, MD Anderson Cancer Center
This is the official code release of the 2023 paper "CT reconstruction from few planar X-rays with application towards low-resource radiotherapy".
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.
- 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
- 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
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.
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