  We propose X2CT-FLOW for the reconstruction of volumetric chest computed
tomography (CT) images from uni- or biplanar digitally reconstructed
radiographs (DRRs) or chest X-ray (CXR) images on the basis of a flow-based
deep generative (FDG) model. With the adoption of X2CT-FLOW, all the
reconstructed volumetric chest CT images satisfy the condition that each of
those projected onto each plane coincides with each input DRR or CXR image.
Moreover, X2CT-FLOW can reconstruct multiple volumetric chest CT images with
different likelihoods. The volumetric chest CT images reconstructed from
biplanar DRRs showed good agreement with ground truth images in terms of the
structural similarity index (0.931 on average). Moreover, we show that
X2CT-FLOW can actually reconstruct such multiple volumetric chest CT images
from DRRs. Finally, we demonstrate that X2CT-FLOW can reconstruct multiple
volumetric chest CT images from a real uniplanar CXR image.
