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The deep radon prior-based stationary CT image reconstruction algorithm for two phase flow inspection.

Jiahao Chang1,2, Shuo Xu3, Zirou Jiang1,2

  • 1Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China.

Journal of X-Ray Science and Technology
|July 2, 2025
PubMed
Summary

A new deep learning algorithm, Deep Radon Prior (DRP), accurately measures high-velocity two-phase flow in pipes. This method enhances visualization of small bubbles, improving reactor safety and efficiency.

Keywords:
CT image reconstructiondeep learningstationary CTtwo phase flow

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Area of Science:

  • Nuclear Engineering
  • Fluid Dynamics
  • Medical Imaging

Background:

  • Accurate measurement of two-phase flow is vital for reactor safety and efficiency.
  • Existing methods struggle with high-velocity flow in small pipes, limiting void fraction and flow pattern identification.
  • Stationary computed tomography (CT) offers a potential solution but faces challenges with sparse data acquisition.

Purpose of the Study:

  • To develop and validate a novel method for measuring high-velocity two-phase flow in small-diameter pipes.
  • To address the limitations of sparse data in CT-based flow measurements.
  • To improve the visualization and analysis of two-phase flow patterns and void fractions.

Main Methods:

  • Proposed an unsupervised deep learning algorithm, Deep Radon Prior (DRP), for image reconstruction from sparse projection data.
  • DRP optimizes errors in the radon domain, integrating neural network learning with iterative algorithms.
  • Compared DRP's performance against traditional Filtered Back Projection (FBP) and ADMM-TV algorithms.

Main Results:

  • DRP significantly suppressed image artifacts and noise, outperforming FBP and ADMM-TV.
  • Achieved superior image reconstruction quality, enabling visualization of 0.3 mm bubbles.
  • Demonstrated the algorithm's effectiveness with sparse projection data inherent in the CT system's physical constraints.

Conclusions:

  • The DRP algorithm provides a robust and effective solution for high-velocity two-phase flow measurement in small pipes.
  • DRP enhances visualization capabilities for small flow structures, crucial for safety and efficiency.
  • The algorithm shows broad applicability for various fluid flow patterns and bubble flow measurements.