  Anatomically consistent field-of-view (FOV) completion to recover truncated
body sections has important applications in quantitative analyses of computed
tomography (CT) with limited FOV. Existing solution based on conditional
generative models relies on the fidelity of synthetic truncation patterns at
training phase, which poses limitations for the generalizability of the method
to potential unknown types of truncation. In this study, we evaluate a
zero-shot method based on a pretrained unconditional generative diffusion
prior, where truncation pattern with arbitrary forms can be specified at
inference phase. In evaluation on simulated chest CT slices with synthetic FOV
truncation, the method is capable of recovering anatomically consistent body
sections and subcutaneous adipose tissue measurement error caused by FOV
truncation. However, the correction accuracy is inferior to the conditionally
trained counterpart.
