  Field-of-view (FOV) recovery of truncated chest CT scans is crucial for
accurate body composition analysis, which involves quantifying skeletal muscle
and subcutaneous adipose tissue (SAT) on CT slices. This, in turn, enables
disease prognostication. Here, we present a method for recovering truncated CT
slices using generative image outpainting. We train a diffusion model and apply
it to truncated CT slices generated by simulating a small FOV. Our model
reliably recovers the truncated anatomy and outperforms the previous
state-of-the-art despite being trained on 87% less data.
