ArticlePloS one2016
Accelerating an Ordered-Subset Low-Dose X-Ray Cone Beam Computed Tomography Image Reconstruction with a Power Factor and Total Variation Minimization.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Block-Iterative Reconstruction from Dynamically Selected Sparse Projection Views Using Extended Power-Divergence Measure.Entropy (Basel, Switzerland) · 2022Article
- Noise-Robust Image Reconstruction Based on Minimizing Extended Class of Power-Divergence Measures.Entropy (Basel, Switzerland) · 2021Article
- Statistical Iterative CBCT Reconstruction Based on Neural Network.IEEE transactions on medical imaging · 2018Article
- Low-Dose CBCT Reconstruction Using Hessian Schatten Penalties.IEEE transactions on medical imaging · 2017Article
- Primal-dual approach to optical tomography with discretized path integral with efficient formulations.Journal of medical imaging (Bellingham, Wash.) · 2017Article
- Combining Acceleration Techniques for Low-Dose X-Ray Cone Beam Computed Tomography Image Reconstruction.BioMed research international · 2017Article
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Authors and funding
2 authors.
Funding
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Abstract
In recent years, there has been increased interest in low-dose X-ray cone beam computed tomography (CBCT) in many fields, including dentistry, guided radiotherapy and small animal imaging. Despite reducing the radiation dose, low-dose CBCT has not gained widespread acceptance in routine clinical practice. In addition to performing more evaluation studies, developing a fast and high-quality reconstruction algorithm is required. In this work, we propose an iterative reconstruction method that accelerates ordered-subsets (OS) reconstruction using a power factor. Furthermore, we combine it with the total-variation (TV) minimization method. Both simulation and phantom studies were conducted to evaluate the performance of the proposed method. Results show that the proposed method can accelerate conventional OS methods, greatly increase the convergence speed in early iterations. Moreover, applying the TV minimization to the power acceleration scheme can further improve the image quality while preserving the fast convergence rate.
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Registered trials
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