Date of Award

December 2019

Degree Type

Thesis

Degree Name

Master of Science

Department

Engineering

First Advisor

Jun Zhang

Committee Members

Yi Hu, Chu Zhao

Keywords

convolution, deep learning, frequency, neural networks, Scatter radiation

Abstract

In X-ray imaging, scattered radiation can produce a number of artifacts that greatly

undermine the image quality. There are hardware solutions, such as anti-scatter grids.

However, they are costly. A software-based solution is a better option because it is

cheaper and can achieve a higher scatter reduction. Most of the current software-based

approaches are model-based. The main issues with them are the lack of flexibility, expressivity, and the requirement of a model. In consideration of this, we decided to apply

Convolutional Neural Networks (CNNs), since they do not have any of the previously

mentioned issues.

In our approach we split the image into three frequency bands: low, high low and high

high and process each of them separately with a CNN. Then, we downsample the low

frequency band and upsample the high frequency band, so that the frequency is increased

and decreased respectively. Finally, we train three CNNs with each of the components

and put them back together to have the reconstruction of the image. We demonstrate

theoretically that doing this leads to better results, and provide comprehensive empirical

evidence of the capability of our algorithm for doing scatter correction.

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