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Background <p>Cone-beam computed tomography (CBCT) is a widely used imaging technique. In practical applications, reducing projection views can decrease radiation exposure and accelerate scanning speed, with potential benefits for stationary CT systems. However, ultra-sparse-view acquisition (e.g., ≤ 30 views) introduces severe streak artifacts that degrade image quality. This poses a critical challenge because it is difficult to differentiate artifacts from real structures.</p> Methods <p>We propose a sliding volume-based streak artifact reduction network (S-STAR Net) to remove artifacts while preserving structural details. Our method introduces three key technical innovations: (1) A sliding sampling sub-volume approach to process 3D sub-volumes, fully leveraging spatial context. (2) A difference enhancement (DE) loss to help separate artifacts from real structures. (3) Novel network includes volume-attention aided residual (VAR) blocks and Fourier transform convolution (FTC) blocks for multi-domain feature learning.</p> Results <p>Evaluated under 30 projection views, the method was tested on two datasets: a walnut dataset and the CQ500 head CT dataset. Quantitative metrics (PSNR/SSIM) and qualitative assessments (multi-planar visualization, residual error maps) demonstrate that S-STAR Net achieves superior performance in both artifacts suppression and detail preservation compared to existing approaches.</p> Conclusions <p>The proposed method effectively addresses streak artifacts and recovers subtle structures in ultra-sparse-view CBCT reconstruction. Its robustness suggests broad applicability for medical image denoising, artifact reduction, and 3D image enhancement tasks.</p>

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Sliding volume-based streak artifact reduction network (S-STAR Net) for ultra-sparse-view computed tomography

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月1日

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  • Shiang Zhang,
  • Yibo Hu,
  • Ziheng Deng,
  • Yujie Wang,
  • Jun Zhao,
  • Jianqi Sun

摘要

Background

Cone-beam computed tomography (CBCT) is a widely used imaging technique. In practical applications, reducing projection views can decrease radiation exposure and accelerate scanning speed, with potential benefits for stationary CT systems. However, ultra-sparse-view acquisition (e.g., ≤ 30 views) introduces severe streak artifacts that degrade image quality. This poses a critical challenge because it is difficult to differentiate artifacts from real structures.

Methods

We propose a sliding volume-based streak artifact reduction network (S-STAR Net) to remove artifacts while preserving structural details. Our method introduces three key technical innovations: (1) A sliding sampling sub-volume approach to process 3D sub-volumes, fully leveraging spatial context. (2) A difference enhancement (DE) loss to help separate artifacts from real structures. (3) Novel network includes volume-attention aided residual (VAR) blocks and Fourier transform convolution (FTC) blocks for multi-domain feature learning.

Results

Evaluated under 30 projection views, the method was tested on two datasets: a walnut dataset and the CQ500 head CT dataset. Quantitative metrics (PSNR/SSIM) and qualitative assessments (multi-planar visualization, residual error maps) demonstrate that S-STAR Net achieves superior performance in both artifacts suppression and detail preservation compared to existing approaches.

Conclusions

The proposed method effectively addresses streak artifacts and recovers subtle structures in ultra-sparse-view CBCT reconstruction. Its robustness suggests broad applicability for medical image denoising, artifact reduction, and 3D image enhancement tasks.