Evidence map›Paper›PMID 40890634›Full record

ArticleBMC medical imaging2025

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

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

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Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Shiang ZhangMed-X Research Institute and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Yibo HuMed-X Research Institute and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Ziheng DengMed-X Research Institute and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Yujie WangSchool of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai, 200240, China.
Jun ZhaoMed-X Research Institute and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Jianqi SunMed-X Research Institute and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China. milesun@sjtu.edu.cn.

Funding

National Key Research and Development Program Grant 2023YFC2411401National Natural Science Foundation of China Grant 62471297Shanghai Jiao Tong University Medical Engineering Cross Research Funds Grant YG2021ZD05Shanghai Zhangjiang National Independent Innovation Demonstration Zone Special Development Fund Major Project Grant ZJ2021-ZD-007
6 · The paper itself

Abstract

backgroundCone-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.

methodsWe 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.

resultsEvaluated 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.

conclusionsThe 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.

Indexed as

ArtifactsCone-Beam Computed TomographyImage Processing, Computer-AssistedAlgorithmsFourier AnalysisHumansImaging, Three-DimensionalPhantoms, ImagingArtifact reductionCone-beam CTFourier transform convolutionUltra-sparse-viewVolume-attention

Identifiers

PMID40890634
PMCPMC12403527

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.