Deep Scatter Estimation (DSE): Accurate Real-Time Scatter Estimation for X-Ray CT Using a Deep Convolutional Neural Network

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 metrolo...

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Bibliographic Details
Published inJournal of nondestructive evaluation Vol. 37; no. 3; pp. 1 - 9
Main Authors Maier, Joscha, Sawall, Stefan, Knaup, Michael, Kachelrieß, Marc
Format Journal Article
LanguageEnglish
Published New York Springer US 01.09.2018
Springer Nature B.V
Subjects
Artificial neural networks
Characterization and Evaluation of Materials
Classical Mechanics
Computed tomography
Computer simulation
Control
Data acquisition
Dynamical Systems
Engineering
Forecasting
Image quality
Neural networks
Real time
Scattering
Solid Mechanics
Vibration
X-ray scattering
CT
Deep neural network
X-ray scatter correction
Cone-beam CT (CBCT)
Convolutional neural network
Artifact reduction
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Summary: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.
ISSN:0195-9298
1573-4862
DOI:10.1007/s10921-018-0507-z