  Scattered radiation is a major concern impacting X-ray image-guided
procedures in two ways. First, back-scatter significantly contributes to
patient (skin) dose during complicated interventions. Second, forward-scattered
radiation reduces contrast in projection images and introduces artifacts in 3-D
reconstructions. While conventionally employed anti-scatter grids improve image
quality by blocking X-rays, the additional attenuation due to the anti-scatter
grid at the detector needs to be compensated for by a higher patient entrance
dose. This also increases the room dose affecting the staff caring for the
patient. For skin dose quantification, back-scatter is usually accounted for by
applying pre-determined scalar back-scatter factors or linear point spread
functions to a primary kerma forward projection onto a patient surface point.
However, as patients come in different shapes, the generalization of
conventional methods is limited. Here, we propose a novel approach combining
conventional techniques with learning-based methods to simultaneously estimate
the forward-scatter reaching the detector as well as the back-scatter affecting
the patient skin dose. Knowing the forward-scatter, we can correct X-ray
projections, while a good estimate of the back-scatter component facilitates an
improved skin dose assessment. To simultaneously estimate forward-scatter as
well as back-scatter, we propose a multi-task approach for joint back- and
forward-scatter estimation by combining X-ray physics with neural networks. We
show that, in theory, highly accurate scatter estimation in both cases is
possible. In addition, we identify research directions for our multi-task
framework and learning-based scatter estimation in general.
