  Purpose: In some proton therapy facilities, patient alignment relies on two
2D orthogonal kV images, taken at fixed, oblique angles, as no 3D on-the-bed
imaging is available. The visibility of the tumor in kV images is limited since
the patient's 3D anatomy is projected onto a 2D plane, especially when the
tumor is behind high-density structures such as bones. This can lead to large
patient setup errors. A solution is to reconstruct the 3D CT image from the kV
images obtained at the treatment isocenter in the treatment position.
  Methods: An asymmetric autoencoder-like network built with vision-transformer
blocks was developed. The data was collected from 1 head and neck patient: 2
orthogonal kV images (1024x1024 voxels), 1 3D CT with padding (512x512x512)
acquired from the in-room CT-on-rails before kVs were taken and 2
digitally-reconstructed-radiograph (DRR) images (512x512) based on the CT. We
resampled kV images every 8 voxels and DRR and CT every 4 voxels, thus formed a
dataset consisting of 262,144 samples, in which the images have a dimension of
128 for each direction. In training, both kV and DRR images were utilized, and
the encoder was encouraged to learn the jointed feature map from both kV and
DRR images. In testing, only independent kV images were used. The full-size
synthetic CT (sCT) was achieved by concatenating the sCTs generated by the
model according to their spatial information. The image quality of the
synthetic CT (sCT) was evaluated using mean absolute error (MAE) and
per-voxel-absolute-CT-number-difference volume histogram (CDVH).
  Results: The model achieved a speed of 2.1s and a MAE of <40HU. The CDVH
showed that <5% of the voxels had a per-voxel-absolute-CT-number-difference
larger than 185 HU.
  Conclusion: A patient-specific vision-transformer-based network was developed
and shown to be accurate and efficient to reconstruct 3D CT images from kV
images.
