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Open AccessArticle

A Deep Learning-Based Scatter Correction of Simulated X-ray Images

Artificial Intelligence Lab., Division of Computer Science and Engineering, Chonbuk National University, Jeonju-si 54896, Korea
Author to whom correspondence should be addressed.
Electronics 2019, 8(9), 944;
Received: 10 July 2019 / Revised: 19 August 2019 / Accepted: 24 August 2019 / Published: 27 August 2019
(This article belongs to the Special Issue Deep Neural Networks and Their Applications)
X-ray scattering significantly limits image quality. Conventional strategies for scatter reduction based on physical equipment or measurements inevitably increase the dose to improve the image quality. In addition, scatter reduction based on a computational algorithm could take a large amount of time. We propose a deep learning-based scatter correction method, which adopts a convolutional neural network (CNN) for restoration of degraded images. Because it is hard to obtain real data from an X-ray imaging system for training the network, Monte Carlo (MC) simulation was performed to generate the training data. For simulating X-ray images of a human chest, a cone beam CT (CBCT) was designed and modeled as an example. Then, pairs of simulated images, which correspond to scattered and scatter-free images, respectively, were obtained from the model with different doses. The scatter components, calculated by taking the differences of the pairs, were used as targets to train the weight parameters of the CNN. Compared with the MC-based iterative method, the proposed one shows better results in projected images, with as much as 58.5% reduction in root-mean-square error (RMSE), and 18.1% and 3.4% increases in peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), on average, respectively. View Full-Text
Keywords: X-ray image; scatter correction; deep learning; CNN; monte carlo simulation X-ray image; scatter correction; deep learning; CNN; monte carlo simulation
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Lee, H.; Lee, J. A Deep Learning-Based Scatter Correction of Simulated X-ray Images. Electronics 2019, 8, 944.

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