Tomographic reconstruction with a generative adversarial network.

Journal of Synchrotron Radiation
Xiaogang YangChristian G Schroer

Abstract

This paper presents a deep learning algorithm for tomographic reconstruction (GANrec). The algorithm uses a generative adversarial network (GAN) to solve the inverse of the Radon transform directly. It works for independent sinograms without additional training steps. The GAN has been developed to fit the input sinogram with the model sinogram generated from the predicted reconstruction. Good quality reconstructions can be obtained during the minimization of the fitting errors. The reconstruction is a self-training procedure based on the physics model, instead of on training data. The algorithm showed significant improvements in the reconstruction accuracy, especially for missing-wedge tomography acquired at less than 180° rotational range. It was also validated by reconstructing a missing-wedge X-ray ptychographic tomography (PXCT) data set of a macroporous zeolite particle, for which only 51 projections over 70° could be collected. The GANrec recovered the 3D pore structure with reasonable quality for further analysis. This reconstruction concept can work universally for most of the ill-posed inverse problems if the forward model is well defined, such as phase retrieval of in-line phase-contrast imaging.

References

Sep 25, 2010·Nature·Martin DierolfFranz Pfeiffer
Oct 28, 2010·Chemical Society Reviews·Jan-Dierk Grunwaldt, Christian G Schroer
Oct 11, 2013·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Daniël Maria Pelt, Kees Joost Batenburg
Sep 2, 2014·Journal of Synchrotron Radiation·Dogˇa GürsoyChris Jacobsen
Dec 24, 2014·Advanced Materials·Albert G MachokeWilhelm Schwieger
Sep 15, 2015·Journal of Microscopy·Andreas KupschIngo Manke
May 4, 2016·Journal of Synchrotron Radiation·Daniël M PeltKees Joost Batenburg
Mar 1, 2017·Journal of Synchrotron Radiation·Xiaogang YangDogˇa Gürsoy
Jun 24, 2017·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society· Kyong Hwan JinMichael Unser
Feb 9, 2018·Scientific Reports·Xiaogang YangDoğa Gürsoy
Mar 23, 2018·Nature·Bo ZhuMatthew S Rosen

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Citations

Aug 14, 2020·Journal of Applied Crystallography·Andreas SchroppChristian G Schroer
Mar 28, 2021·Optics Express·Björn De SamberJan Sijbers
Sep 8, 2021·Scientific Reports·Semih BarutcuDoğa Gürsoy
Sep 17, 2021·Chemical Reviews·Teo LombardoAlejandro A Franco

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Methods Mentioned

BETA
X-ray
chip
electron microscopy

Software Mentioned

Gridrec
Net
Tomopy
MLEM
GANrec

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