The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Low Dose CT Image Reconstruction Based on Structure Tensor Total Variation Using Accelerated Fast Iterative Shrinkage Thresholding Algorithm
Open accessAbstract
Low dose computed tomography (CT) has drawn much attention in the medical imaging field because of its ability to reduce the radiation dose. Recently, statistical iterative reconstruction (SIR) with total variation (TV) penalty has been developed to low dose CT image reconstruction. Nevertheless, the TV penalty has the drawback of creating blocky effects in the reconstructed images. To overcome the limitations of TV, in this paper we firstly introduce the structure tensor total variation (STV1) penalty into SIR framework for low dose CT image reconstruction. Then, an accelerated fast iterative shrinkage thresholding algorithm (AFISTA) is developed to minimize the objective function. The proposed AFISTA reconstruction algorithm was evaluated using numerical simulated low dose projection based on two CT images and realistic low dose projection data of a sheep lung CT perfusion. The experimental results demonstrated that our proposed STV1-based algorithm outperform FBP and TV-based algorithm in terms of removing noise and restraining blocky effects.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
A low-dose scanning technique generates Gaussian noise on diagnostic images, which degrades their quality.
“In order to reduce the radiation dose that patients are exposed to, a low-dose scanning technique has been investigated [13]; however, it was found that it generates Gaussian noise on the diagnostic images, which degrades their quality.”
From A Novel Singular Value Decomposition-Based Denoising Method in 4-Dimensional Computed Tomography of the Brain in Stroke Patients with Statistical Evaluation (2020) · cites this paper as [13]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
Donoho, D. L. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.871582 · Cited by 23,994
Donoho, D. L. (2004) · Cited by 16,703
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
Beck, A., & Teboulle, M. (2009) · SIAM Journal on Imaging Sciences · doi:10.1137/080716542 · Cited by 11,952
Estimated Risks of Radiation-Induced Fatal Cancer from Pediatric CT
Brenner, D. J., Elliston, C. D., Hall, E. J., & Berdon, W. E. (2001) · American Journal of Roentgenology · doi:10.2214/ajr.176.2.1760289 · Cited by 3,222
Sidky, E. Y., & Pan, X. (2008) · Physics in Medicine and Biology · doi:10.1088/0031-9155/53/17/021 · Cited by 1,995
Projected Cancer Risks From Computed Tomographic Scans Performed in the United States in 2007
de González, A. B. (2009) · Archives of Internal Medicine · doi:10.1001/archinternmed.2009.440 · Cited by 1,880
Cancer risks from diagnostic radiology
Hall, E. J., & Brenner, D. J. (2008) · British Journal of Radiology · doi:10.1259/bjr/01948454 · Cited by 916
Statistical image reconstruction for polyenergetic X-ray computed tomography
Elbakri, I. A., & Fessler, J. A. (2002) · IEEE Transactions on Medical Imaging · doi:10.1109/42.993128 · Cited by 693
Low-Dose X-ray CT Reconstruction via Dictionary Learning
Xu, Q., Yu, H., Mou, X., Lei Zhang, A., Hsieh, J., & Wang, G. (2012) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2012.2195669 · Cited by 690
Self-citationOrdered subsets algorithms for transmission tomography
Erdogan, H., & Fessler, J. A. (1999) · Physics in Medicine and Biology · doi:10.1088/0031-9155/44/11/311 · Cited by 559
Low-dose CT of the lungs: preliminary observations.
Naidich, D. P., Marshall, C. H., Gribbin, C. E., Arams, R. S., & McCauley, D. I. (1990) · Radiology · doi:10.1148/radiology.175.3.2343122 · Cited by 340
Tang, J., Nett, B. E., & Chen, G. (2009) · Physics in Medicine and Biology · doi:10.1088/0031-9155/54/19/008 · Cited by 282
A Splitting-Based Iterative Algorithm for Accelerated Statistical X-Ray CT Reconstruction
Ramani, S., & Fessler, J. A. (2011) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2175233 · Cited by 257
Compressed sensing based cone‐beam computed tomography reconstruction with a first‐order methoda)
Choi, K., Wang, J., Zhu, L., Suh, T. S., Boyd, S., & Xing, L. (2010) · Medical Physics · doi:10.1118/1.3481510 · Cited by 255
Combining Ordered Subsets and Momentum for Accelerated X-Ray CT Image Reconstruction
Kim, D., Ramani, S., & Fessler, J. A. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2350962 · Cited by 140
Structure Tensor Total Variation
Lefkimmiatis, S., Roussos, A., Maragos, P. A., & Unser, M. A. (2015) · SIAM Journal on Imaging Sciences · doi:10.1137/14098154x · Cited by 136
JointL1and total variation regularization for fluorescence molecular tomography
Dutta, J., Ahn, S., Li, C., Cherry, S. R., & Leahy, R. M. (2012) · Physics in Medicine and Biology · doi:10.1088/0031-9155/57/6/1459 · Cited by 131
Liu, J., Ma, J., Zhang, Y., Chen, Y., Yang, J., Shu, H., Luo, L., Coatrieux, G., Yang, W., Feng, Q., et al. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2739841 · Cited by 107
Self-citationStatistical iterative reconstruction using adaptive fractional order regularization
Zhang, Y., Wang, Y., Zhang, W., Lin, F., Pu, Y., & Zhou, J. (2016) · Biomedical Optics Express · doi:10.1364/boe.7.001015 · Cited by 87
Zeng, D., Gao, Y., Huang, J., Bian, Z., Zhang, H., Lu, L., & Ma, J. (2016) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2016.07.002 · Cited by 47
Xu, Q., Yang, D., Tan, J., Sawatzky, A., & Anastasio, M. A. (2016) · Medical Physics · doi:10.1118/1.4942812 · Cited by 46
Iterative CBCT reconstruction using Hessian penalty
Sun, T., Sun, N., Wang, J., & Tan, S. (2015) · Physics in Medicine and Biology · doi:10.1088/0031-9155/60/5/1965 · Cited by 43
Low-dose CT statistical iterative reconstruction via modified MRF regularization
Shangguan, H., Zhang, Q., Liu, Y., Cui, X., Bai, Y., & Gui, Z. (2015) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2015.10.004 · Cited by 36
Low‐dose CT reconstruction using spatially encoded nonlocal penalty
Kim, K., Fakhri, G. E., & Li, Q. (2017) · Medical Physics · doi:10.1002/mp.12523 · Cited by 36
Accelerated statistical reconstruction for C‐arm cone‐beam CT using Nesterov's method
Wang, A., Stayman, J. W., Otake, Y., Vogt, S. K., Kleinszig, G., & Siewerdsen, J. H. (2015) · Medical Physics · doi:10.1118/1.4914378 · Cited by 35
Zeng, D., Zhang, X., Bian, Z., Huang, J., Zhang, H., Lu, L., Lyu, W., Zhang, J., Feng, Q., Chen, W., et al. (2016) · Medical Physics · doi:10.1118/1.4944866 · Cited by 31
Gong, C., Han, C., Gan, G., Deng, Z., Zhou, Y., Yi, J., Zheng, X., Xie, C., & Jin, X. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa5d40 · Cited by 27
Low-Dose CBCT Reconstruction Using Hessian Schatten Penalties
Liu, L., Li, X., Xiang, K., Wang, J., & Tan, S. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2766185 · Cited by 25
Yang, W., Hong, J.-Y., Kim, J.-Y., Paik, S.-H., Lee, S. H., Park, J.-S., Lee, G., Kim, B. M., & Jung, Y.-J. (2020) · Sensors · doi:10.3390/s20113063 · Cited by 17
Gamma regularization based reconstruction for low dose CT
Zhang, J., Chen, Y., Hu, Y., Luo, L., Shu, H., Li, B., Liu, J., & Coatrieux, J.-L. (2015) · Physics in Medicine and Biology · doi:10.1088/0031-9155/60/17/6901 · Cited by 12
Self-citationStructure-adaptive CBCT reconstruction using weighted total variation and Hessian penalties
Shi, Q., Sun, N., Sun, T., Wang, J., & Tan, S. (2016) · Biomedical Optics Express · doi:10.1364/boe.7.003299 · Cited by 8
Block‐matching sparsity regularization‐based image reconstruction for low‐dose computed tomography
Cai, A., Li, L., Zheng, Z., Wang, L., & Yan, B. (2018) · Medical Physics · doi:10.1002/mp.12911 · Cited by 8
Flagged first, then by citations
Cited by
Sorted by citations
Yang, W., Hong, J.-Y., Kim, J.-Y., Paik, S.-H., Lee, S. H., Park, J.-S., Lee, G., Kim, B. M., & Jung, Y.-J. (2020) · Sensors · doi:10.3390/s20113063 · Cited by 17
Lee, H., Sung, J., Choi, Y., Kim, J. W., & Lee, I. J. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.751057 · Cited by 6
Zhang, H., Zheng, H., Deng, R., Luo, K., & Duan, S. (2022) · Contrast Media & Molecular Imaging · doi:10.1155/2022/1199841 · Cited by 6
Liu, Y., Gu, M., Liu, L., Cui, L., & Xing, A. (2022) · Contrast Media & Molecular Imaging · doi:10.1155/2022/2622316 · Cited by 1
Weighted structure tensor total variation for image denoising
Sheng, X., Yang, L., & Chang, J. (2024) · Journal of Electronic Imaging · doi:10.1117/1.jei.33.1.013049 · Cited by 1
Brain Perfusion Analysis Method using Computed Tomography Images
Khudiakova, E., Gavrilov, A. V., & Krylov, A. S. (2024) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-x-2-w1-2024-9-2024
A Survey on Low-Dose CT Image Denoising Algorithms in IoT
Wang, M. (2025) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-90084-6_26
Li, X., Wang, K., Chang, Y., Wu, Y., & Liu, J. (2025) · Photonics · doi:10.3390/photonics12050492
Reconstructing CT Scan Images with SADNN: A Promising Approach for Medical Imaging
Dayanika, J., & Balakrishna, S. (2025) · IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) · doi:10.1109/icmlant68509.2025.11394734
Metadata
- Authors
- Wu, J., Wang, X., Mou, X., Chen, Y., & Liu, S.
- Affiliations
- Xi'an University of Technology (CN)
- Xi'an Jiaotong University (CN)
- Southeast University (CN)
- Air Force Engineering University (CN)
- Corresponding author
- Wu, Junfeng
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2020-03-16
- Volume
- 20
- Issue
- 6
- Pages
- 1647
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC7146515
- OpenAlex
- W3012217191
- Cited by
- 9
- References
- 33
- Citations by year
- 2025: 3 · 2024: 2 · 2022: 2 · 2021: 1 · 2020: 1
- FWCI
- 0.39
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Natural Science Foundation of China (61976176, 61772416)
- Key Laboratory Project of the Education Department of Shaanxi Province of China (17JS098)
- Shaanxi Province Technology Innovation Guiding Fund Project (2018XNGG-G-02)
- Key Laboratory of Computer Network and Information Integration, Southeast University and Ministry of Education of China (K93-9-2017-04)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiation Dose and Imaging
- Keywords
- low-dose CT, CT image reconstruction, total variation penalty, statistical iterative reconstruction, blocking artifacts, noise reduction, CT perfusion, sheep lung
- MeSH terms
- Algorithms, Animals, Brain, Humans, Lung, Radiation Dosage, Radiographic Image Interpretation, Computer-Assisted, Sheep, Tomography, X-Ray Computed, Phantoms, Imaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
