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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Dual-domain sparse-view CT reconstruction with Transformers.

Changrong Shi1, Yongshun Xiao1, Zhiqiang Chen1

  • 1Department of Engineering Physics, Tsinghua University, Beijing, 100084, China; Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, China.

Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)
|July 19, 2022
PubMed
Summary
This summary is machine-generated.

A new CT Transformer (CTTR) algorithm effectively reduces artifacts and preserves details in sparse-view CT imaging. CTTR outperforms CNN-based methods, offering improved image quality with fewer radiation doses.

Keywords:
Dual-domainReconstructionSparse-view computed tomographyTransformers

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Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Radiology

Background:

  • Computed Tomography (CT) is vital in medicine, but dose reduction via sparse-view acquisition introduces artifacts.
  • Conventional Filtered Back Projection (FBP) struggles with sparse-view artifacts, while iterative methods are slow and can cause blockiness.

Purpose of the Study:

  • To introduce CT Transformer (CTTR), a novel dual-domain algorithm for sparse-view CT image reconstruction.
  • To address limitations of existing methods by leveraging sinogram information for enhanced image quality.

Main Methods:

  • CTTR processes sinograms as sequences, integrating their characteristics to improve reconstructed images.
  • Comparative evaluation involved qualitative assessment of artifact reduction and detail preservation against TVM-POCS and FBPConvNet.
  • Quantitative analysis used RMSE, PSNR, and SSIM metrics.

Main Results:

  • CTTR demonstrated superior artifact reduction and detail preservation across various projection views and noise levels.
  • On the Lung Image Database Consortium dataset, CTTR significantly improved PSNR by 0.76dB compared to FBPConvNet with 30 projections.
  • Results indicate robust performance in diverse and challenging sparse-view scenarios.

Conclusions:

  • CTTR surpasses CNN-based methods in sparse-view CT, offering better visual and quantitative outcomes.
  • The study presents CTTR as a promising new approach for applying Transformer models in CT image processing.