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A springback TV algorithm for image reconstruction from sparse view data in CT.

Yunxin Yu1, Chenyun Fang1, Yanjun Zhang1

  • 1School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, People's Republic of China.

Physics in Medicine and Biology
|October 17, 2025
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Summary
This summary is machine-generated.

A new springback total variation (STV) algorithm enhances sparse-view computed tomography (CT) image reconstruction. STV improves detail recovery from low-dose CT scans, outperforming traditional methods.

Keywords:
computed tomographyfully linearized ADMMimage reconstructionspringback penaltytotal variation

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Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Image Reconstruction

Background:

  • Reducing radiation dose in computed tomography (CT) is crucial.
  • Traditional total variation (TV) algorithms struggle with sparse-view data, causing over-smoothing.
  • Existing methods like Total p-Variation (TpV) and WDAI-TV have limitations in detail preservation.

Purpose of the Study:

  • To introduce a novel springback total variation (STV) algorithm for sparse-view CT image reconstruction.
  • To address the over-smoothing issue inherent in traditional TV algorithms with sparse data.
  • To improve image quality and detail recovery in low-dose CT applications.

Main Methods:

  • Developed a new STV algorithm using a springback penalty to better approximate the ℓ0 norm and enhance sparsity.
  • Employed the difference of convex algorithm (DCA) and fully linearized alternating direction method of multipliers (FL-ADMM) for efficient model solving.
  • Optimized internal iterations and avoided line search steps for accelerated reconstruction.

Main Results:

  • The STV algorithm demonstrated superior reconstruction quality compared to standard TV, TpV, and WDAI-TV.
  • Experiments on mathematical phantoms and clinical CT images validated the effectiveness of STV.
  • The proposed method showed improved detail recovery and sparsity representation.

Conclusions:

  • The STV algorithm offers a more robust and effective solution for sparse-view CT image reconstruction.
  • It significantly improves detail recovery while maintaining computational efficiency.
  • STV provides a valuable advancement for low-dose CT imaging.