  A fundamental problem in X-ray Computed Tomography (CT) is the scatter due to
interaction of photons with the imaged object. Unless corrected, scatter
manifests itself as degradations in the reconstructions in the form of various
artifacts. Scatter correction is therefore critical for reconstruction quality.
Scatter correction methods can be divided into two categories: hardware-based;
and software-based. Despite success in specific settings, hardware-based
methods require modification in the hardware, or increase in the scan time or
dose. This makes software-based methods attractive. In this context,
Monte-Carlo based scatter estimation, analytical-numerical, and kernel-based
methods were developed. Furthermore, data-driven approaches to tackle this
problem were recently demonstrated. In this work, two novel physics-inspired
deep-learning-based methods, PhILSCAT and OV-PhILSCAT, are proposed. The
methods estimate and correct for the scatter in the acquired projection
measurements. They incorporate both an initial reconstruction of the object of
interest and the scatter-corrupted measurements related to it. They use a
common deep neural network architecture and cost function, both tailored to the
problem. Numerical experiments with data obtained by Monte-Carlo simulations of
the imaging of phantoms reveal significant improvement over a recent purely
projection-domain deep neural network scatter correction method.
