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Limited-angle tomography reconstruction via deep end-to-end learning on synthetic data

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author/s: Thomas Germer, Stefan Harmeling, Sebastian Konietzny, Jan Robine, Tobias Uelwer
journal:Applied Mathematics for Modern Challenges
copyright:American Institute of Mathematical Sciences
keywords:Helsinki tomography challenge 2022, limited angle tomography, computed tomography, deep learning, artificial neural networks, inverse problems

Computed tomography (CT) has become an essential part of modern science and medicine. A CT scanner consists of an X-ray source that is spun around an object of interest. On the opposite end of the X-ray source, a detector captures X-rays that are not absorbed by the object. The reconstruction of an image is a linear inverse problem, which is usually solved by filtered back projection. However, when the number of measurements is small, the reconstruction problem is ill-posed. This is for example the case when the X-ray source is not spun completely around the object, but rather irradiates the object only from a limited angle. To tackle this problem, we present a deep neural network that is trained on a large amount of carefully-crafted synthetic data and can perform limited-angle tomography reconstruction even for only 30° or 40° sinograms. With our approach we won the first place in the Helsinki Tomography Challenge 2022.

Heinrich Heine Universität

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