Limited Angle Tomography for Transmission X-Ray Microscopy Using Deep Learning

Huang Y, Wang S, Guan Y, Maier A (2020)

Publication Language: English

Publication Type: Journal article, Original article

Publication year: 2020

Journal

Journal of Synchrotron Radiation International Union of Crystallography

URI: https://onlinelibrary.wiley.com/iucr/doi/10.1107/S160057752000017X

DOI: 10.1107/S160057752000017X

Abstract

In transmission X-ray microscopy (TXM) systems, the rotation of a scanned sample might be restricted to a limited angular range to avoid collision to other system parts or high attenuation at certain tilting angles. Image reconstruction from such limited angle data suffers from artifacts due to missing data. In this work, deep learning is applied to limited angle reconstruction in TXMs for the first time. With the challenge to obtain sufficient real data for training, training a deep neural network from synthetic data is investigated. Particularly, the U-Net, the state-of-the-art neural network in biomedical imaging, is trained from synthetic ellipsoid data and multi-category data to reduce artifacts in filtered back-projection (FBP) reconstruction images. 

The proposed method is evaluated on synthetic data and real scanned chlorella data in 100-degree limited angle tomography. For synthetic test data, the U-Net significantly reduces root-mean-square error (RMSE) from 2.55 X 10^{-3}/μm in the FBP reconstruction to 1.21 X 10^{-3}/μm in the U-Net reconstruction, and also improves structural similarity (SSIM) index from 0.625 to 0.920. With penalized weighted least square denoising of measured projections, the RMSE and SSIM are further improved to 1.16 X 10^{-3}/μm and 0.932, respectively.

For real test data, the proposed method remarkably improves the 3-D visualization of the subcellular structures in the chlorella cell, which indicates its important value for nano-scale imaging in biology, nanoscience and materials science.

Authors with CRIS profile

Yixing Huang Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung) (LME) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Institute of High Energy Physics (IHEP) / 中国科学院高能物理研究所 CN China (CN) University of Science and Technology of China (USTC) CN China (CN)

How to cite

APA:

Huang, Y., Wang, S., Guan, Y., & Maier, A. (2020). Limited Angle Tomography for Transmission X-Ray Microscopy Using Deep Learning. Journal of Synchrotron Radiation. https://doi.org/10.1107/S160057752000017X

MLA:

Huang, Yixing, et al. "Limited Angle Tomography for Transmission X-Ray Microscopy Using Deep Learning." Journal of Synchrotron Radiation (2020).

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