Incorporating deep learning into X-ray CT imaging

In recent years, deep learning gained a lot of attention and made impressive achievements in various applications. Incorporating deep learning in X-ray CT has become a non-reversible trend.

In this webinar, we’ll give a brief overview of deep learning technology. On this basis, focusing on the key issues in CT imaging, including denoising, artefact suppression, image reconstruction, we will discuss the methodology of incorporating deep learning into different data-processing missions by addressing the deep-learning framework, neural network design, loss functions, multiple domain learning, as well as some of our preliminary research results. Some of the key issues in the current field and technological-development challenges will also be discussed.

Yuxiang Xing received her PhD from the State University of New York at Stony Brook in 2003 and then joined Tsinghua University as a faculty member. She is currently a professor of the department of engineering physics at Tsinghua University, China. Since 2003, she has been devoted to research on the theories and technologies for the development and application of X-ray imaging systems. She has authored or co-authored more than 150 research publications and more than 50 patents. Her current interests include X-ray imaging physics, reconstruction methods for CT, radiation image processing and performance evaluation, especially cutting-edge deep-learning methods for CT reconstruction and artefacts reduction.

Speaker relationship with IOP Publishing

Editorial board member for Physics in Medicine & Biology.

The post Incorporating deep learning into X-ray CT imaging appeared first on Physics World.

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