ArticleVisual computing for industry, biomedicine, and art2019
Sparse-view tomography via displacement function interpolation.
Article in Visual computing for industry, biomedicine, and art, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Development of an optically emulated computed tomography scanner for college education.Visual computing for industry, biomedicine, and art · 2026Article
- Image Reconstruction with Maclaurin Series Expansion.International journal of biomedical research & practice · 2025Article
- Extreme Few-View Tomography without Training Data.Biomedical journal of scientific & technical research · 2024Article
- Sinogram Interpolation Inspired by Single-Image Super Resolution.Journal of biotechnology and its applications · 2023Article
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1 author.
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Abstract
Sparse-view tomography has many applications such as in low-dose computed tomography (CT). Using under-sampled data, a perfect image is not expected. The goal of this paper is to obtain a tomographic image that is better than the naïve filtered backprojection (FBP) reconstruction that uses linear interpolation to complete the measurements. This paper proposes a method to estimate the un-measured projections by displacement function interpolation. Displacement function estimation is a non-linear procedure and the linear interpolation is performed on the displacement function (instead of, on the sinogram itself). As a result, the estimated measurements are not the linear transformation of the measured data. The proposed method is compared with the linear interpolation methods, and the proposed method shows superior performance.
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Registered trials
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