The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Noise correlation in CBCT projection data and its application for noise reduction in low‐dose CBCT
Abstract
Purpose: To study the noise correlation properties of cone‐beam CT (CBCT) projection data and to incorporate the noise correlation information to a statistics‐based projection restoration algorithm for noise reduction in low‐dose CBCT. Methods: In this study, the authors systematically investigated the noise correlation properties among detector bins of CBCT projection data by analyzing repeated projection measurements. The measurements were performed on a TrueBeam onboard CBCT imaging system with a 4030CB flat panel detector. An anthropomorphic male pelvis phantom was used to acquire 500 repeated projection data at six different dose levels from 0.1 to 1.6 mAs per projection at three fixed angles. To minimize the influence of the lag effect, lag correction was performed on the consecutively acquired projection data. The noise correlation coefficient between detector bin pairs was calculated from the corrected projection data. The noise correlation among CBCT projection data was then incorporated into the covariance matrix of the penalized weighted least‐squares (PWLS) criterion for noise reduction of low‐dose CBCT. Results: The analyses of the repeated measurements show that noise correlation coefficients are nonzero between the nearest neighboring bins of CBCT projection data. The average noise correlation coefficients for the first‐ and second‐order neighbors are 0.20 and 0.06, respectively. The noise correlation coefficients are independent of the dose level. Reconstruction of the pelvis phantom shows that the PWLS criterion with consideration of noise correlation (PWLS‐Cor) results in a lower noise level as compared to the PWLS criterion without considering the noise correlation (PWLS‐Dia) at the matched resolution. At the 2.0 mm resolution level in the axial‐plane noise resolution tradeoff analysis, the noise level of the PWLS‐Cor reconstruction is 6.3% lower than that of the PWLS‐Dia reconstruction. Conclusions: Noise is correlated among nearest neighboring detector bins of CBCT projection data. An accurate noise model of CBCT projection data can improve the performance of the statistics‐based projection restoration algorithm for low‐dose CBCT.
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“Electronic noise is signal independent and can be modeled as additive Gaussian noise with correlation along rows due to sequential readout [15].”
From DeepDRR – A Catalyst for Machine Learning in Fluoroscopy-Guided Procedures (2018) · cites this paper as [15]
In the citing paper
“Noise in X-ray images is a composite of uncorrelated quantum noise due to photon statistics that becomes correlated due to pixel crosstalk, and correlated readout noise [15].”
From DeepDRR – A Catalyst for Machine Learning in Fluoroscopy-Guided Procedures (2018) · cites this paper as [15]
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Cited by
DeepDRR – A Catalyst for Machine Learning in Fluoroscopy-Guided Procedures
Unberath, M., Zaech, J.-N., Lee, S. C., Bier, B., Fotouhi, J., Armand, M., & Navab, N. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00937-3_12 · Cited by 145
“Since real flat panel detectors suffer from pixel crosstalk, we correlate the quantum noise of neighboring pixels by convolving the noise signal with a blurring kernel [15].”
BackgroundMethodInfluentialIterative CT Reconstruction Using Shearlet-Based Regularization
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Metadata
- Authors
- Zhang, H., Ouyang, L., Ma, J., Huang, J., Chen, W., & Wang, J.
- Affiliations
- Southern Medical University (CN)
- The University of Texas Southwestern Medical Center (US)
- Corresponding author
- Ma, Jianhua
- Wang, Jing
- Venue
- Medical Physics
- Publisher
- Wiley
- Published
- 2014-02-19
- Volume
- 41
- Issue
- 3
- Pages
- 031906-031906
- ISSN
- 0094-2405 (ISSN-L)
- 2473-4209
- OpenAlex
- W2014598003
- Cited by
- 47
- References
- 32
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 4 · 2023: 3 · 2022: 2 · 2021: 1 · 2020: 4 · 2019: 6 · 2018: 8 · 2017: 3 · 2016: 4 · 2015: 5 · 2014: 2
- FWCI
- 3.65
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- onlinelibrary.wiley.com/termsAndConditions#vor (vor)
- doi.wiley.com/10.1002/tdm_license_1.1 (tdm, 559 d)
- Locations
- Medical Physics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- NNSFC (81371544, 81000613, 81101046)
- National Natural Science Foundation of China (81371544, 81000613, 81101046)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiation Dose and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Physical Sciences › Engineering › Biomedical Engineering › Advanced X-ray and CT Imaging
- Keywords
- cone-beam computed tomography, noise correlation, low-dose CT, noise reduction, penalized weighted least squares, covariance matrix, noise models, pelvis phantom
- MeSH terms
- Algorithms, Anthropometry, Humans, Image Processing, Computer-Assisted, Male, Radiation Dosage, Radiographic Image Enhancement, Radiographic Image Interpretation, Computer-Assisted, Reproducibility of Results, Models, Statistical, Poisson Distribution, Least-Squares Analysis, Phantoms, Imaging, Cone-Beam Computed Tomography
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
