  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.
