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CT scans are the standard-of-care for many clinical ailments, and are neededfor treatments like external beam radiotherapy. Unfortunately, CT scanners arerare in low and mid-resource settings due to their costs. Planar X-rayradiography units, in comparison, are far more prevalent, but can only providelimited 2D observations of the 3D anatomy. In this work, we propose a method togenerate CT volumes from few (<5) planar X-ray observations using a prior datadistribution, and perform the first evaluation of such a reconstructionalgorithm for a clinical application: radiotherapy planning. We propose a deepgenerative model, building on advances in neural implicit representations tosynthesize volumetric CT scans from few input planar X-ray images at differentangles. To focus the generation task on clinically-relevant features, our modelcan also leverage anatomical guidance during training (via segmentation masks).We generated 2-field opposed, palliative radiotherapy plans on thoracic CTsreconstructed by our method, and found that isocenter radiation dose onreconstructed scans have <1% error with respect to the dose calculated onclinically acquired CTs using <=4 X-ray views. In addition, our method isbetter than recent sparse CT reconstruction baselines in terms of standardpixel and structure-level metrics (PSNR, SSIM, Dice score) on the public LIDClung CT dataset. Code is available at: https://github.com/wanderinrain/Xray2CT.

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