Simultaneous Estimation of X-ray Back-Scatter and Forward-Scatter using Multi-Task Learning

• URL: http://arxiv.org/abs/2007.04018v1
• Date: Wed, 8 Jul 2020 10:47:37 GMT
• Title: Simultaneous Estimation of X-ray Back-Scatter and Forward-Scatter using Multi-Task Learning
• Authors: Philipp Roser, Xia Zhong, Annette Birkhold, Alexander Preuhs, Christopher Syben, Elisabeth Hoppe, Norbert Strobel, Markus Kowarschik, Rebecca Fahrig, Andreas Maier
• Abstract summary: Back-scatter significantly contributes to patient (skin) dose during complicated interventions. Forward-scattered radiation reduces contrast in projection images and introduces artifacts in 3-D reconstructions. We propose a novel approach combining conventional techniques with learning-based methods to simultaneously estimate the forward-scatter reaching the detector.
• Score: 59.17383024536595
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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