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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Time-frequency domain prior constrained deep unfolding network for low-dose CT reconstruction.

Xiong Zhang1, Xinbo Zhang1, Xinzhong Li2

  • 1Taiyuan University of Science and Technology, Tai yuan, China.

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|September 24, 2025
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Summary

This study introduces an interpretable deep unfolding network for low-dose computed tomography (LDCT) reconstruction. The novel network effectively removes noise and artifacts, significantly improving image quality in LDCT scans.

Keywords:
deep unfolding networkiterative reconstructionlow-dose computed tomography (LDCT)time-frequency regularization term

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Signal Processing

Background:

  • Low-dose computed tomography (LDCT) reduces radiation exposure but introduces image noise and artifacts.
  • Existing Convolutional Neural Networks (CNNs) show promise but have limitations in adaptability for CT reconstruction.
  • Further improvements in LDCT reconstruction are needed to balance dose reduction with image quality.

Purpose of the Study:

  • To develop an interpretable deep unfolding network for low-dose CT reconstruction.
  • To enhance reconstruction performance by integrating iterative algorithm principles with deep learning.
  • To achieve superior image quality in LDCT by effectively mitigating noise and artifacts.

Main Methods:

  • Proposed an interpretable deep unfolding network leveraging time-frequency and image domain priors.
  • Mapped the iterative optimization process into a deep unfolding network architecture.
  • Introduced a Stage Information Memory Network (SIMN) to preserve information across network stages.

Main Results:

  • The proposed model demonstrated superior performance on Mayo and Piglet datasets.
  • Achieved state-of-the-art results in both quantitative metrics and visual quality.
  • Validated the effectiveness of the interpretable network in complex CT reconstruction tasks.

Conclusions:

  • The developed network effectively removes artifacts and noise from low-dose CT images.
  • Achieved excellent reconstruction performance, enhancing diagnostic capabilities of LDCT.
  • The interpretable deep unfolding approach offers a promising direction for future CT reconstruction research.