Abstract
X-ray scatter is a major cause of image quality degradation in dimensional CT. Especially, in case of highly attenuating components scatter-to-primary ratios may easily be higher than 1. The corresponding artifacts which appear as cupping or dark streaks in the CT reconstruction may impair a metrological assessment. Therefore, an appropriate scatter correction is crucial. Thereby, the gold standard is to predict the scatter distribution using a Monte Carlo (MC) code and subtract the corresponding scatter estimate from the measured raw data. MC, however, is too slow to be used routinely. To correct for scatter in real-time, we developed the deep scatter estimation (DSE). It uses a deep convolutional neural network which is trained to reproduce the output of MC simulations using only the acquired projection data as input. Once trained, DSE can be applied in real-time. The present study demonstrates the potential of the proposed approach using simulations and measurements. In both cases the DSE yields highly accurate scatter estimates that differ by< 3% from our MC scatter predictions. Further, DSE clearly outperforms kernel-based scatter estimation techniques and hybrid approaches, as they are in use today.
Highlights
CT image reconstruction algorithms rely on the assumption that the acquired projection data correspond to the line integral over the spatial distribution of the attenuation coefficient
The network internally performs similar operations as kernelbased methods. In contrast to these methods, the deep scatter estimation (DSE) network is much more flexible since it is able to use non-linear mappings and varying scatter kernels depending on local features of the input image
This manuscript describes the application of a deep convolutional neural network to estimate X-ray scatter in real-time
Summary
CT image reconstruction algorithms rely on the assumption that the acquired projection data correspond to the line integral over the spatial distribution of the attenuation coefficient. Thereby, the scatter estimate can either be derived using dedicated hardware such as beam blockers or primary modulation grids [3,7,8,20,24,28,38,39] or using software-based approaches that rely on physical or empirical models to predict X-ray scattering [1,2,11,17,18, 21,22,30,32,33,34,36,37] Among these methods the gold standard is to use a Monte Carlo (MC) photon transport code [27]. Even highly optimized code does not perform in real-time on conventional hardware
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