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Anatomically consistent field-of-view (FOV) completion to recover truncatedbody sections has important applications in quantitative analyses of computedtomography (CT) with limited FOV. Existing solution based on conditionalgenerative models relies on the fidelity of synthetic truncation patterns attraining phase, which poses limitations for the generalizability of the methodto potential unknown types of truncation. In this study, we evaluate azero-shot method based on a pretrained unconditional generative diffusionprior, where truncation pattern with arbitrary forms can be specified atinference phase. In evaluation on simulated chest CT slices with synthetic FOVtruncation, the method is capable of recovering anatomically consistent bodysections and subcutaneous adipose tissue measurement error caused by FOVtruncation. However, the correction accuracy is inferior to the conditionallytrained counterpart.

Zero-shot CT Field-of-view Completion with Unconditional Generative Diffusion Prior