Date of Award
Master of Science
Yi Hu, Chu Zhao
convolution, deep learning, frequency, neural networks, Scatter radiation
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
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.
Jerez Gonzalez, Carlos Ivan, "Scatter Reduction By Exploiting Behaviour of Convolutional Neural Networks in Frequency Domain" (2019). Theses and Dissertations. 2312.
Available for download on Thursday, January 07, 2021