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.

翻译：解剖学一致的视野（FOV）补全以恢复截断的身体区域，在有限视野的计算机断层扫描（CT）定量分析中具有重要应用。现有基于条件生成模型的解决方案依赖于训练阶段合成截断模式保真度，这限制了该方法对潜在未知截断类型的泛化能力。本研究评估了一种基于预训练无条件生成扩散先验的零样本方法，其中可在推理阶段指定任意形式的截断模式。在模拟合成FOV截断的胸部CT切片评估中，该方法能够恢复解剖学一致的身体区域，并纠正因FOV截断导致的下皮下脂肪组织测量误差。然而，其校正精度低于条件训练方法。