ArticleScientific reports2020
A residual dense network assisted sparse view reconstruction for breast computed tomography.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Development and performance evaluation of an upright dedicated cone-beam breast CT system.Medical physics · 2026Article
- Soft-tissue lesion and microcalcification detectability in cone-beam breast CT: cascaded system analysis.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Simultaneous reduction of radiation dose and scatter-to-primary ratio using a truncated detector and advanced algorithms for dedicated cone-beam breast CT.Biomedical physics & engineering express · 2025Article
- Understanding the risk of ionizing radiation in breast imaging: Concepts and quantities, clinical importance, and future directions.European journal of radiology · 2024Review
- Maximizing microcalcification detectability in low-dose dedicated cone-beam breast CT: parallel cascades-based theoretical analysis.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Dedicated Cone-Beam Breast CT: Reproducibility of Volumetric Glandular Fraction with Advanced Image Reconstruction Methods.Tomography (Ann Arbor, Mich.) · 2023Article
- Dedicated cone-beam breast CT: Data acquisition strategies based on projection angle-dependent normalized glandular dose coefficients.Medical physics · 2023Article
- Artificial Intelligence in Breast X-Ray Imaging.Seminars in ultrasound, CT, and MR · 2023Review
- Cone-beam breast CT using an offset detector: effect of detector offset and image reconstruction algorithm.Physics in medicine and biology · 2022Article
- Dedicated breast CT: state of the art-Part I. Historical evolution and technical aspects.European radiology · 2022Review
- Breast Cancer Screening: Opportunities and Challenges with Fully 3D Tomographic X-Ray Imaging.Bridge (Washington, D.C. : 1969) · 2022Article
- Radiation dosimetry of a clinical prototype dedicated cone-beam breast CT system with offset detector.Medical physics · 2021Article
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Authors and funding
5 authors.
Funding
Abstract
To develop and investigate a deep learning approach that uses sparse-view acquisition in dedicated breast computed tomography for radiation dose reduction, we propose a framework that combines 3D sparse-view cone-beam acquisition with a multi-slice residual dense network (MS-RDN) reconstruction. Projection datasets (300 views, full-scan) from 34 women were reconstructed using the FDK algorithm and served as reference. Sparse-view (100 views, full-scan) projection data were reconstructed using the FDK algorithm. The proposed MS-RDN uses the sparse-view and reference FDK reconstructions as input and label, respectively. Our MS-RDN evaluated with respect to fully sampled FDK reference yields superior performance, quantitatively and visually, compared to conventional compressed sensing methods and state-of-the-art deep learning based methods. The proposed deep learning driven framework can potentially enable low dose breast CT imaging.
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