Branchless distance-driven and hybrid projectors in iterative cone beam CT

PurposeIterative model-based image reconstruction algorithms in cone beam computed tomography (CBCT) require repetitive forward and backward projection operations. We compare the quality of the branchless distance-driven (BDD) projector in iterative CBCT reconstruction with ray- and voxel-based methods in both regular and low-dose examinations, and introduce a hybrid approach that aims at faster computation by using the BDD as the backprojector only. We also demonstrate the potential of the BDD in FDK reconstructions.ApproachTwo measured and one simulated datasets are used. Contrast-to-noise ratio (CNR) and modulation transfer function values are computed for one measured dataset. The structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) are computed with the simulated data.ResultsBased on our results, BDD has reduced noise and better CNR compared to the other methods, with the quality dependent on the scanner geometry. The CNR is improved by 21% with the BDD and 4% with the hybrid method. BDD improves SSIM values by approximately 3.3% in the lowest dose case and 1% in the highest dose case, while for PSNR the values are 5% - 10% better. For the hybrid method, the SSIM improvements range from 0.8% - 2.2%, and the PSNR from 3.7% - 6.6 %. The hybrid method with the BDD as a backprojector can be computationally twice faster with similar image quality.ConclusionsThe hybrid projector is a good choice as a compromise between image quality and computation time. Furthermore, BDD and the hybrid projector are better choices in low-dose CBCT reconstructions.

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Publication Details

Journal
Journal of X-Ray Science and Technology
Published
2026-04-13
DOI
https://doi.org/10.1177/08953996261433875
Citations
1
Primary Topic
Medical Imaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
22.02

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Branchless distance-driven and hybrid projectors in iterative cone beam CT

Ville Kolehmainen, Ari Hietanen, Marko Vauhkonen, Nargiza Djurabekova et al.
1 citations
Journal of X-Ray Science and Technology
Medical Imaging Techniques and Applications
22.02
article

Branchless distance-driven and hybrid projectors in iterative cone beam CT

Ville Kolehmainen, Ari Hietanen, Marko Vauhkonen, Nargiza Djurabekova, Kati Niinimäki, Ville-Veikko Wettenhovi
article en
1 citations

Abstract

PurposeIterative model-based image reconstruction algorithms in cone beam computed tomography (CBCT) require repetitive forward and backward projection operations. We compare the quality of the branchless distance-driven (BDD) projector in iterative CBCT reconstruction with ray- and voxel-based methods in both regular and low-dose examinations, and introduce a hybrid approach that aims at faster computation by using the BDD as the backprojector only. We also demonstrate the potential of the BDD in FDK reconstructions.ApproachTwo measured and one simulated datasets are used. Contrast-to-noise ratio (CNR) and modulation transfer function values are computed for one measured dataset. The structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) are computed with the simulated data.ResultsBased on our results, BDD has reduced noise and better CNR compared to the other methods, with the quality dependent on the scanner geometry. The CNR is improved by 21% with the BDD and 4% with the hybrid method. BDD improves SSIM values by approximately 3.3% in the lowest dose case and 1% in the highest dose case, while for PSNR the values are 5% - 10% better. For the hybrid method, the SSIM improvements range from 0.8% - 2.2%, and the PSNR from 3.7% - 6.6 %. The hybrid method with the BDD as a backprojector can be computationally twice faster with similar image quality.ConclusionsThe hybrid projector is a good choice as a compromise between image quality and computation time. Furthermore, BDD and the hybrid projector are better choices in low-dose CBCT reconstructions.

Journal of X-Ray Science and Technology
University of Eastern Finland (FI), Planmeca (Finland) (FI), Finland University (FI)
Jenny ja Antti Wihurin Rahasto, Luonnontieteiden ja Tekniikan Tutkimuksen Toimikunta
Openalex Percentile: Top 1%
Medical Imaging Techniques and Applications
22.02
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