  In this work, we present X-Diffusion, a cross-sectional diffusion model
tailored for Magnetic Resonance Imaging (MRI) data. X-Diffusion is capable of
generating the entire MRI volume from just a single MRI slice or optionally
from few multiple slices, setting new benchmarks in the precision of
synthesized MRIs from extremely sparse observations. The uniqueness lies in the
novel view-conditional training and inference of X-Diffusion on MRI volumes,
allowing for generalized MRI learning. Our evaluations span both brain tumour
MRIs from the BRATS dataset and full-body MRIs from the UK Biobank dataset.
Utilizing the paired pre-registered Dual-energy X-ray Absorptiometry (DXA) and
MRI modalities in the UK Biobank dataset, X-Diffusion is able to generate
detailed 3D MRI volume from a single full-body DXA. Remarkably, the resultant
MRIs not only stand out in precision on unseen examples (surpassing
state-of-the-art results by large margins) but also flawlessly retain essential
features of the original MRI, including tumour profiles, spine curvature, brain
volume, and beyond. Furthermore, the trained X-Diffusion model on the MRI
datasets attains a generalization capacity out-of-domain (e.g. generating knee
MRIs even though it is trained on brains). The code is available on the project
website https://emmanuelleb985.github.io/XDiffusion/ .
