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High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

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Dynamic X-ray speckle-tracking imaging with high-accuracy phase retrieval based on deep learning.

Fucheng Yu¹, Kang Du², Xiaolu Ju²

¹Shanghai Synchrotron Radiation Facility/Zhang Jiang Lab, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201800, People's Republic of China.

|December 14, 2023

Summary

A new deep-learning method enhances X-ray speckle-tracking imaging for accurate dynamic phase retrieval. This advancement overcomes limitations in traditional methods, enabling precise visualization of microstructures in materials science and biomedicine.

Keywords:

X-ray microscopy computed tomography deep learning dynamic X-ray imaging phase contrast X-ray imaging phase retrieval speckle tracking

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Area of Science:

Advanced imaging techniques
Materials science
Biomedical imaging

Background:

Speckle-tracking X-ray imaging offers flexible dynamic imaging with phase, transmission, and scattering data.
Traditional methods exhibit phase distortion with abrupt density changes, limiting real-world sample analysis.
Accurate phase retrieval is crucial for quantitative dynamic X-ray imaging.

Purpose of the Study:

To develop a deep-learning based method for high-accuracy phase retrieval in dynamic X-ray speckle-tracking imaging.
To overcome the phase distortion limitations of conventional speckle-tracking techniques.
To enable precise quantitative analysis of dynamic processes using X-ray imaging.

Main Methods:

Implementation of a deep-learning algorithm for X-ray speckle-tracking imaging.
Utilizing simultaneous phase, transmission, and scattering image acquisition.
Validation using phantom calibration and polyurethane foaming experiments.

Main Results:

The deep-learning method achieved high-accuracy phase retrieval, consistent with theoretical profiles in phantom tests.
Accurate visualization of complex bubble microstructure evolution during polyurethane foaming was demonstrated.
The proposed technique effectively mitigates phase distortion issues inherent in traditional methods.

Conclusions:

The developed deep-learning approach provides a promising solution for dynamic X-ray imaging with accurate phase retrieval.
This method significantly enhances the application scope of speckle-tracking X-ray imaging.
Potential for extensive applications in metrology and quantitative dynamic analysis across material science, physics, chemistry, and biomedicine.