CT Reconstruction from Few Planar X-rays with Application towards Low-resource Radiotherapy

• URL: http://arxiv.org/abs/2308.02100v1
• Date: Fri, 4 Aug 2023 01:17:57 GMT
• Title: CT Reconstruction from Few Planar X-rays with Application towards Low-resource Radiotherapy
• Authors: Yiran Sun, Tucker Netherton, Laurence Court, Ashok Veeraraghavan, Guha Balakrishnan
• Abstract summary: We propose a method to generate CT volumes from few (5) planar X-ray observations using a prior data distribution. To focus the generation task on clinically-relevant features, our model can also leverage anatomical guidance during training. Our method is better than recent sparse CT reconstruction baselines in terms of standard pixel and structure-level metrics.
• Score: 20.353246282326943
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. Code is available at: https://github.com/wanderinrain/Xray2CT.

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