  Computed tomography (CT) is widely utilized in clinical settings because it
delivers detailed 3D images of the human body. However, performing CT scans is
not always feasible due to radiation exposure and limitations in certain
surgical environments. As an alternative, reconstructing CT images from
ultra-sparse X-rays offers a valuable solution and has gained significant
interest in scientific research and medical applications. However, it presents
great challenges as it is inherently an ill-posed problem, often compromised by
artifacts resulting from overlapping structures in X-ray images. In this paper,
we propose DiffuX2CT, which models CT reconstruction from orthogonal biplanar
X-rays as a conditional diffusion process. DiffuX2CT is established with a 3D
global coherence denoising model with a new, implicit conditioning mechanism.
We realize the conditioning mechanism by a newly designed tri-plane decoupling
generator and an implicit neural decoder. By doing so, DiffuX2CT achieves
structure-controllable reconstruction, which enables 3D structural information
to be recovered from 2D X-rays, therefore producing faithful textures in CT
images. As an extra contribution, we collect a real-world lumbar CT dataset,
called LumbarV, as a new benchmark to verify the clinical significance and
performance of CT reconstruction from X-rays. Extensive experiments on this
dataset and three more publicly available datasets demonstrate the
effectiveness of our proposal.
