Zero-shot CT Field-of-view Completion with Unconditional Generative Diffusion Prior
This work addresses a domain-specific problem in medical imaging for CT analysis with limited field-of-view, but it is incremental as it shows reduced accuracy compared to existing approaches.
The authors tackled the problem of anatomically consistent field-of-view completion in CT scans to recover truncated body sections, using a zero-shot method based on a pretrained unconditional generative diffusion prior that allows arbitrary truncation patterns at inference, but found it inferior in correction accuracy to conditionally trained methods.
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.