Computed Tomography Sinogram Inpainting With Compound Prior Modelling Both Sinogram and Image Sparsity

Zhang, Hanming; Li, Lei; Wang, Linyuan; Sun, Yanmin; Yan, Bin; Cai, Ailong; Hu, Guoen

doi:10.1109/tns.2016.2577045

Cited by 16 publications

(13 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…wherein J(·) represents data fidelity, ψ(·) is a functional regularization operator, S represents the sparse transforming coefficient set, and λ is a regularization parameter to optimize the computing. Due to the sparse view conditions for the low-dosed reconstruction, the regularized algorithms have trade-offs between image quality, computational complexity, and diagnostic ability, as mentioned in the literature reviews and studies [23][24][25][26][27][28]. However, excessively generating noise and significantly reducing the diagnostic values of the reconstructed images, sparsity, and low-dose exposure per projection are highly desirable in the recent studies.…”

Section: Equipment Configuration Of the Duplicated Simulationmentioning

confidence: 99%

“…Another study by Zhu et al (2013) applied compressed sensing (CS) algorithms when violating the sampling theory in the discrete wavelet or discrete gradient transforms by using the total-variation-based (TV-) CS algorithm to suppress the streaking artifacts significantly without any image quality compromises [24]. Zhang et al (2016) used the sinogram-based inpainting technique in metal artifact reducing (MAR) [25]. In a different study Zhang et al (2015) [26] used the combination of compressed sensing and dictionary learning to regularize parameter determination via sparse constraint of the TV minimization to reduce the computational cost.…”

Section: Equipment Configuration Of the Duplicated Simulationmentioning

confidence: 99%

See 1 more Smart Citation

Generative Noise Reduction in Dental Cone-Beam CT by a Selective Anatomy Analytic Iteration Reconstruction Algorithm

Dao-Ngoc

2019

Electronics

View full text Add to dashboard Cite

Dental cone-beam computed tomography (CBCT) is a powerful tool in clinical treatment planning, especially in a digital dentistry platform. Currently, the "as low as diagnostically acceptable" (ALADA) principle and diagnostic ability are a trade-off in most of the 3D integrated applications, especially in the low radio-opaque densified tissue structure. The CBCT benefits in comprehensive diagnosis and its treatment prognosis for post-operation predictability are clinically known in modern dentistry. In this paper, we propose a new algorithm called the selective anatomy analytic iteration reconstruction (SA2IR) algorithm for the sparse-projection set. The algorithm was simulated on a phantom structure analogous to a patient's head for geometric similarity. The proposed algorithm is projection-based. Interpolated set enrichment and trio-subset enhancement were used to reduce the generative noise and maintain the scan's clinical diagnostic ability. The results show that proposed method was highly applicable in medico-dental imaging diagnostics fusion for the computer-aided treatment planning, because it had significant generative noise reduction and lowered computational cost when compared to the other common contemporary algorithms for sparse projection, which generate a low-dosed CBCT reconstruction.

show abstract

Section: Equipment Configuration Of the Duplicated Simulationmentioning

confidence: 99%

Section: Equipment Configuration Of the Duplicated Simulationmentioning

confidence: 99%

Generative Noise Reduction in Dental Cone-Beam CT by a Selective Anatomy Analytic Iteration Reconstruction Algorithm

Dao-Ngoc

2019

Electronics

View full text Add to dashboard Cite

show abstract

“…Among the state-of-the-art low-dose CT techniques, many traditional methods have been proposed to improve the quality of low-dose CT images, including (a) sinogram domain filtration methods (3)(4)(5)(6); (b) iterative reconstruction methods, such as the total variation (TV) method and its variants (7)(8)(9)(10); dictionary learning (DL) (11)(12)(13)(14); and (c) image postprocessing methods, such as the nonlocal-mean (NLM) method (15) and the block-matching 3D (BM3D) algorithm (16). However, these methods are not ideal as they were all developed primarily for image denoising.…”

Section: Introductionmentioning

confidence: 99%

The synthesis of high-energy CT images from low-energy CT images using an improved cycle generative adversarial network

Zhou¹,

Liu²,

Wang³

et al. 2022

Quant Imaging Med Surg

View full text Add to dashboard Cite

Background: The dose of radiation a patient receives when undergoing dual-energy computed tomography (CT) is of significant concern to the medical community, and balancing the tradeoffs between the level of radiation used and the quality of CT images is challenging. This paper proposes a method of synthesizing high-energy CT (HECT) images from low-energy CT (LECT) images using a neural network that achieves an alternative to HECT scanning by employing an LECT scan, which greatly reduces the radiation dose a patient receives.Methods: In the training phase, the proposed structure cyclically generates HECT and LECT images to improve the accuracy of extracting edge and texture features. Specifically, we combine multiple connection methods with channel attention (CA) and pixel attention (PA) mechanisms to improve the network's mapping ability of image features. In the prediction phase, we use a model consisting of only the network component that synthesizes HECT images from LECT images.Results: Our proposed method was conducted on clinical hip CT image data sets from Guizhou Provincial People's Hospital. In a comparison with other available methods [a generative adversarial network (GAN), a residual encoder-to-decoder network with a visual geometry group (VGG) pretrained model (RED-VGG), a Wasserstein GAN (WGAN), and CycleGAN] in terms of metrics of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), normalized mean square error (NMSE), and a visual effect evaluation, the proposed method was found to perform better on each of these evaluation criteria. Compared with the results produced by CycleGAN, the proposed method improved the PSNR by 2.44%, the SSIM by 1.71%, and the NMSE by 15.2%. Furthermore, the differences in the statistical indicators are statistically significant, proving the strength of the proposed method. Conclusions:The proposed method synthesizes high-energy CT images from low-energy CT images, which significantly reduces both the cost of treatment and the radiation dose received by patients. Based on both image quality score metrics and visual effects comparisons, the results of the proposed method are superior to those obtained by other methods. 2 Zhou et al. Synthesis of high-energy CT images

show abstract

“…These efforts can be divided into two categories. First is to compensate for the missing sinogram by extrapolation [29]- [31], and the other is to combine additional a priori information in the reconstruction process [32], [33]. A priori information about the unknown object, such as surface information, density range or the previous image, is helpful for artifact suppression and edge retention.…”

Section: Introductionmentioning

confidence: 99%

Feasibility Study of Limited-Angle Reconstruction for in Vivo Optical Projection Tomography Based on Novel Sample Fixation

Wang

Chen

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Optical projection tomography (OPT) is a novel three-dimensional imaging technique, which provides an approach to recreating the three-dimensional images of biological specimens ranging from millimeters to centimeters. In the current OPT setup, the specimen was immersed in the index-matching fluid. In this case, the specimen could not survive or the survival time was too short for longitudinal imaging. In this paper, we first designed a new type of sample fixation method for in vivo OPT imaging. The specimen was embedded into a transparent gel in a petri dish, and the dish was affixed to the rotational stage of our homemade OPT system. As the specimen does not need to be immersed in the index-matching fluid, this method can reduce the damage to the specimen and it is more conducive to longitudinal observation for in vivo OPT. However, this fixation method induces a problem of insufficient measurements. The angles parallel to or nearly parallel to the surface of the dish cannot be acquired. To address this problem, we, then, used a limited-angle reconstruction framework for the novel sample fixation-based OPT, which combines the algebraic reconstruction technique (ART) algorithm with prior information to solve the inverse reconstruction problem. The feasibility and effectiveness of the proposed sample fixation method and the limited-angle reconstruction framework were verified by the simulations and experiments. INDEX TERMS Optical projection tomography, in vivo imaging, limited-angle reconstruction, sample fixation. XUELI CHEN received the B.S. degree in biomedical engineering and the Ph.D. degree in pattern recognition and intelligent system from Xidian University, Xi'an, China, in 2007 and 2012, respectively, where he is currently a Professor with the School of Life Science and Technology. From 2015 to 2017, he was a Postdoctoral Researcher with Purdue University. His research interest includes multi-scale and multi-modality biophotonics imaging and its applications. He has authored or coauthored more than 60 peer-reviewed journal papers on these topics. He is currently a member of OSA and SPIE. SHOUPING ZHU received the B.S. degree in biomedical engineering and the M.S. degree in signal and information processing from Xidian University, Xi'an, China, in 2004 and 2007, respectively, and the Ph.D. degree in pattern recognition and intelligent systems from the Chinese Academy of Sciences, Beijing, China, in 2010. He is currently a Professor with the School of Life Science and Technology, Xidian University. His research interests include multi-modality medical imaging systems, micro-CT imaging, PET imaging, optical imaging, and tomography reconstruction with GPU.

show abstract

Computed Tomography Sinogram Inpainting With Compound Prior Modelling Both Sinogram and Image Sparsity

Cited by 16 publications

References 47 publications

Generative Noise Reduction in Dental Cone-Beam CT by a Selective Anatomy Analytic Iteration Reconstruction Algorithm

Generative Noise Reduction in Dental Cone-Beam CT by a Selective Anatomy Analytic Iteration Reconstruction Algorithm

The synthesis of high-energy CT images from low-energy CT images using an improved cycle generative adversarial network

Feasibility Study of Limited-Angle Reconstruction for in Vivo Optical Projection Tomography Based on Novel Sample Fixation

Contact Info

Product

Resources

About