A grid-line suppression technique based on deep convolutional neural networks

Kyongwoo Kim; Hyungkyu Kim; Heesin Lee; Joongeun Jung; Joshua J. Nam; Joonhyuk Park; Donghyun Kim; Hyewon Kim; Hojoon Kim

doi:10.1117/12.2549281

10 March 2020 A grid-line suppression technique based on deep convolutional neural networks

Kyongwoo Kim, Hyungkyu Kim, Heesin Lee, Joongeun Jung, Joshua J. Nam, Joonhyuk Park, Donghyun Kim, Hyewon Kim, Hojoon Kim

Author Affiliations +

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 1131327 (2020) https://doi.org/10.1117/12.2549281
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Anti-scatter grids are routinely used to prevent degradation of image quality caused by scattered X-ray beams. However, these grids might cause linear artifacts that represent the shadows of the radiopaque septa. In this paper, we propose a machine learning-based method for grid artifact suppression in radiography. There are two major difficulties in the application of a deep learning technique for the grid-line suppression problem. The first is quantitative shortage of learning data. It is difficult to acquire a sufficient amount of learning data from observed images in consideration of the various situations in which grid lines appear. The second is difficulty determining the target data. It is practically impossible to generate a clean target image for an arbitrary grid-line image. To overcome these problems, we propose a deep convolutional neural network architecture and a learning data construction method. A discrete cosine transform-based band-stop filtering technique and an image synthesizing algorithm were adopted for the learning data construction method. A patch sampling method was employed to overcome the shortage of the amount of learning data. The proposed method enables learning without clean target data and overcomes the weakness of conventional frequency analysis-based methods with regard to the grid-line suppression problem. This method makes it possible to expect complementary performance through the construction of a combined learning data set including observed images and artificially generated grid-line images.

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Kyongwoo Kim, Hyungkyu Kim, Heesin Lee, Joongeun Jung, Joshua J. Nam, Joonhyuk Park, Donghyun Kim, Hyewon Kim, and Hojoon Kim "A grid-line suppression technique based on deep convolutional neural networks", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 1131327 (10 March 2020); https://doi.org/10.1117/12.2549281

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KEYWORDS

Linear filtering

Convolutional neural networks

Data modeling

Image filtering

X-ray imaging

X-rays

Image restoration

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