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Proceedings Paper

Deep scatter estimation (DSE): feasibility of using a deep convolutional neural network for real-time x-ray scatter prediction in cone-beam CT
Author(s): Joscha Maier; Yannick Berker; Stefan Sawall; Marc Kachelrieß
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Paper Abstract

The contribution of scattered x-rays to the acquired projection data is a severe issue in cone-beam CT (CBCT). Due to the large cone angle, scatter-to-primary ratios may easily be in the order of 1. The corresponding artifacts which appear as cupping or dark streaks in the CT reconstruction may impair the diagnostic value of the CT examination. Therefore, appropriate scatter correction is essential. The gold standard is to use a Monte Carlo photon transport code to predict the distribution of scattered x-rays which can be subtracted from the measurement subsequently. However, long processing times of Monte Carlo simulations prohibit them to be used routinely. To enable fast and accurate scatter estimation we propose the deep scatter estimation (DSE). It uses a deep convolutional neural network which is trained to reproduce the output of Monte Carlo simulations using only the acquired projection data as input. Once the network is trained, DSE performs in real-time. In the present study we demonstrate the feasibility of DSE using simulations of CBCT head scans at different tube voltages. The performance is tested on data sets that significantly differ from the training data. Thereby, the scatter estimates deviate less than 2% from the Monte Carlo ground truth. A comparison to kernel-based scatter estimation techniques, as they are used today, clearly shows superior performance of DSE while being similar in terms of processing time.

Paper Details

Date Published: 9 March 2018
PDF: 6 pages
Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 105731L (9 March 2018); doi: 10.1117/12.2292919
Show Author Affiliations
Joscha Maier, German Cancer Research Ctr. (DKFZ) (Germany)
Ruprecht-Karls-Univ. (Germany)
Yannick Berker, German Cancer Research Ctr. (DKFZ) (Germany)
Stefan Sawall, German Cancer Research Ctr. (DKFZ) (Germany)
Ruprecht-Karls-Univ. (Germany)
Marc Kachelrieß, German Cancer Research Ctr. (DKFZ) (Germany)
Ruprecht-Karls-Univ. (Germany)

Published in SPIE Proceedings Vol. 10573:
Medical Imaging 2018: Physics of Medical Imaging
Joseph Y. Lo; Taly Gilat Schmidt; Guang-Hong Chen, Editor(s)

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