  Field-of-view (FOV) tissue truncation beyond the lungs is common in routine
lung screening computed tomography (CT). This poses limitations for
opportunistic CT- based body composition (BC) assessment as key anatomical
structures are missing. Traditionally, extending the FOV of CT is considered as
a CT reconstruction problem using limited data. However, this approach relies
on the projection domain data which might not be available in application. In
this work, we formulate the problem from the semantic image extension
perspective which only requires image data as inputs. The proposed two-stage
method identifies a new FOV border based on the estimated extent of the
complete body and imputes missing tissues in the truncated region. The training
samples are simulated using CT slices with complete body in FOV, making the
model development self-supervised. We evaluate the validity of the proposed
method in automatic BC assessment using lung screening CT with limited FOV. The
proposed method effectively restores the missing tissues and reduces BC
assessment error introduced by FOV tissue truncation. In the BC assessment for
a large-scale lung screening CT dataset, this correction improves both the
intra-subject consistency and the correlation with anthropometric
approximations. The developed method is available at
https://github.com/MASILab/S-EFOV.
