ArticleBMC bioinformatics2018
GPU-accelerated iterative reconstruction for limited-data tomography in CBCT systems.
Article in BMC bioinformatics, 2018. 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.
- Halve the dose while maintaining image quality in paediatric Cone Beam CT.Scientific reports · 2019Trial
- Non-coplanar CBCT image reconstruction using a generative adversarial network for non-coplanar radiotherapy.Journal of applied clinical medical physics · 2024Article
- Directly Filtering the Sparse-View CT Images by BM3D.SL clinical medicine : research · 2022Article
- Toward on-the-fly trajectory optimization for C-arm CBCT under strong kinematic constraints.PloS one · 2021Article
- Sparse-view tomography via displacement function interpolation.Visual computing for industry, biomedicine, and art · 2019Article
- Enabling tomography with low-cost C-arm systems.PloS one · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStandard cone-beam computed tomography (CBCT) involves the acquisition of at least 360 projections rotating through 360 degrees. Nevertheless, there are cases in which only a few projections can be taken in a limited angular span, such as during surgery, where rotation of the source-detector pair is limited to less than 180 degrees. Reconstruction of limited data with the conventional method proposed by Feldkamp, Davis and Kress (FDK) results in severe artifacts. Iterative methods may compensate for the lack of data by including additional prior information, although they imply a high computational burden and memory consumption.
resultsWe present an accelerated implementation of an iterative method for CBCT following the Split Bregman formulation, which reduces computational time through GPU-accelerated kernels. The implementation enables the reconstruction of large volumes (>1024
conclusionEfficient implementation using parallel processing and large-memory management strategies together with GPU kernels enables the use of advanced reconstruction approaches which are needed in limited-data scenarios. Our GPU implementation showed a significant time reduction (up to 48 ×) compared to a CPU-only implementation, resulting in a total reconstruction time from several hours to few minutes.
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