  Transforming two-dimensional (2D) images into three-dimensional (3D) volumes
is a well-known yet challenging problem for the computer vision community. In
the medical domain, a few previous studies attempted to convert two or more
input radiographs into computed tomography (CT) volumes. Following their
effort, we introduce a diffusion model-based technology that can rotate the
anatomical content of any input radiograph in 3D space, potentially enabling
the visualization of the entire anatomical content of the radiograph from any
viewpoint in 3D. Similar to previous studies, we used CT volumes to create
Digitally Reconstructed Radiographs (DRRs) as the training data for our model.
However, we addressed two significant limitations encountered in previous
studies: 1. We utilized conditional diffusion models with classifier-free
guidance instead of Generative Adversarial Networks (GANs) to achieve higher
mode coverage and improved output image quality, with the only trade-off being
slower inference time, which is often less critical in medical applications;
and 2. We demonstrated that the unreliable output of style transfer deep
learning (DL) models, such as Cycle-GAN, to transfer the style of actual
radiographs to DRRs could be replaced with a simple yet effective training
transformation that randomly changes the pixel intensity histograms of the
input and ground-truth imaging data during training. This transformation makes
the diffusion model agnostic to any distribution variations of the input data
pixel intensity, enabling the reliable training of a DL model on input DRRs and
applying the exact same model to conventional radiographs (or DRRs) during
inference.
