Shallow U-Net deep learning approach for phase retrieval in propagation-based phase-contrast Imaging

Samuel Z. Li; Matthew G. French; Konstantin M. Pavlov; Heyang Thomas Li

doi:10.1117/12.2644579

14 October 2022 Shallow U-Net deep learning approach for phase retrieval in propagation-based phase-contrast Imaging

Samuel Z. Li, Matthew G. French, Konstantin M. Pavlov, Heyang Thomas Li

Proceedings Volume 12242, Developments in X-Ray Tomography XIV; 122421Q (2022) https://doi.org/10.1117/12.2644579
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Conference Poster

Abstract

X-Ray Computed Tomography (CT) has revolutionised modern medical imaging. However, X-Ray CT imaging requires patients to be exposed to radiation, which can increase the risk of cancer. Therefore there exists an aim to reduce radiation doses for CT imaging without sacrificing image accuracy. This research combines phase retrieval with the ShallowU-Net CNN method to achieve the aim. This paper shows that a significant change in existing machine learning neural network algorithms could improve the X-ray phase retrieval in propagationbased phase-contrast imaging. This paper applies deep learning methods, through a variant of the existing U-Net architecture, named ShallowU-Net, to show that it is possible to perform two distance X-ray phase retrieval on composite materials by predicting a portion of the required data. ShallowU-Net is faster in training and in deployment. This method also performs data stretching and pre-processing, to reduce the numerical instability of the U-Net algorithm thereby improving the phase retrieval images.

Citation Download Citation

Samuel Z. Li, Matthew G. French, Konstantin M. Pavlov, and Heyang Thomas Li "Shallow U-Net deep learning approach for phase retrieval in propagation-based phase-contrast Imaging", Proc. SPIE 12242, Developments in X-Ray Tomography XIV, 122421Q (14 October 2022); https://doi.org/10.1117/12.2644579

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