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TD-STrans: Tri-domain sparse-view CT reconstruction based on sparse transformer.

Yu Li1, Xueqin Sun1, Sukai Wang2

  • 1Department of Information and Communication Engineering, North University of China, Taiyuan 030051, China; The State Key Lab for Electronic Testing Technology, North University of China, Taiyuan 030051, China.

Computer Methods and Programs in Biomedicine
|December 29, 2024
PubMed
Summary
This summary is machine-generated.

A new Tri-Domain Sparse Transformer (TD-STrans) model enhances sparse-view computed tomography (CT) by integrating frequency domain information. This method effectively reduces over-smoothing and preserves image details in low-view CT scans.

Keywords:
Deep learningMulti-domain joint loss functionSparse transformerSparse-view computed tomography (CT)

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Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Artificial Intelligence in Radiology

Background:

  • Sparse-view computed tomography (CT) accelerates scanning and lowers radiation dose.
  • Severe under-sampling in sparse-view CT leads to over-smoothing and loss of high-frequency details in deep learning reconstructions.

Purpose of the Study:

  • To introduce a novel sparse-view CT reconstruction model, the Tri-Domain Sparse Transformer (TD-STrans).
  • To address over-smoothing and detail loss in low-view CT by incorporating frequency domain information.

Main Methods:

  • TD-STrans utilizes three modules: projection recovery, Fourier domain filling for high-frequency details, and image refinement.
  • A multi-domain joint loss function optimizes reconstruction quality across projection, image, and frequency domains.

Main Results:

  • TD-STrans effectively removes artifacts and suppresses over-smoothing.
  • The model demonstrates superior preservation of structural fidelity in both simulated (lymph node) and real (walnut) datasets.

Conclusions:

  • The TD-STrans model, leveraging sparse transformers across multiple domains, successfully mitigates over-smoothing and detail loss in sparse-view CT.
  • This offers a promising new approach for ultra-sparse-view CT imaging reconstruction.