  Cone-beam computed tomography (CBCT) has become a vital imaging technique in
various medical fields but scatter artifacts are a major limitation in CBCT
scanning. This challenge is exacerbated by the use of large flat panel 2D
detectors. The scatter-to-primary ratio increases significantly with the
increase in the size of FOV being scanned. Several deep learning methods,
particularly U-Net architectures, have shown promising capabilities in
estimating the scatter directly from the CBCT projections. However, the
influence of varying FOV sizes on these deep learning models remains
unexplored. Having a single neural network for the scatter estimation of
varying FOV projections can be of significant importance towards real clinical
applications. This study aims to train and evaluate the performance of a U-Net
network on a simulated dataset with varying FOV sizes. We further propose a new
method (Aux-Net) by providing auxiliary information, such as FOV size, to the
U-Net encoder. We validate our method on 30 different FOV sizes and compare it
with the U-Net. Our study demonstrates that providing auxiliary information to
the network enhances the generalization capability of the U-Net. Our findings
suggest that this novel approach outperforms the baseline U-Net, offering a
significant step towards practical application in real clinical settings where
CBCT systems are employed to scan a wide range of FOVs.
