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Cha BK, Lee KH, Lee Y, Kim K. Optimization Method to Predict Optimal Noise Reduction Parameters for the Non-Local Means Algorithm Based on the Scintillator Thickness in Radiography. SENSORS (BASEL, SWITZERLAND) 2023; 23:9803. [PMID: 38139649 PMCID: PMC10747373 DOI: 10.3390/s23249803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 12/09/2023] [Accepted: 12/12/2023] [Indexed: 12/24/2023]
Abstract
The resulting image obtained from an X-ray imaging system depends significantly on the characteristics of the detector. In particular, when an X-ray image is acquired by thinning the detector, a relatively large amount of noise inevitably occurs. In addition, when a thick detector is used to reduce noise in X-ray images, blurring increases and the ability to distinguish target areas deteriorates. In this study, we aimed to derive the optimal X-ray image quality by deriving the optimal noise reduction parameters based on the non-local means (NLM) algorithm. The detectors used were of two thicknesses (96 and 140 μm), and images were acquired based on the IEC 62220-1-1:2015 RQA-5 protocol. The optimal parameters were derived by calculating the edge preservation index and signal-to-noise ratio according to the sigma value of the NLM algorithm. As a result, a sigma value of the optimized NLM algorithm (0.01) was derived, and this algorithm was applied to a relatively thin X-ray detector system to obtain appropriate noise level and spatial resolution data. The no-reference-based blind/referenceless image spatial quality evaluator value, which analyzes the overall image quality, was best when using the proposed method. In conclusion, we propose an optimized NLM algorithm based on a new method that can overcome the noise amplification problem in thin X-ray detector systems and is expected to be applied in various photon imaging fields in the future.
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Affiliation(s)
- Bo Kyung Cha
- Precision Medical Device Research Center, Korea Electrotechnology Research Institute (KERI), 111 Hanggaul-ro, Sangnok-gu, Ansan-si 15588, Republic of Korea; (B.K.C.); (K.-H.L.)
| | - Kyeong-Hee Lee
- Precision Medical Device Research Center, Korea Electrotechnology Research Institute (KERI), 111 Hanggaul-ro, Sangnok-gu, Ansan-si 15588, Republic of Korea; (B.K.C.); (K.-H.L.)
| | - Youngjin Lee
- Department of Radiological Science, Gachon University, 191 Hambangmoe-ro, Yeonsu-gu, Incheon 21936, Republic of Korea
| | - Kyuseok Kim
- Department of Biomedical Engineering, Eulji University, 553 Sanseong-daero, Sujeong-gu, Seongnam-si 13135, Republic of Korea
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2
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Ren J, Liang N, Yu X, Wang Y, Cai A, Li L, Yan B. Projection domain processing for low-dose CT reconstruction based on subspace identification. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:63-84. [PMID: 36314189 DOI: 10.3233/xst-221262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
PURPOSE Low-dose computed tomography (LDCT) has promising potential for dose reduction in medical applications, while suffering from low image quality caused by noise. Therefore, it is in urgent need for developing new algorithms to obtain high-quality images for LDCT. METHODS This study tries to exploit the sparse and low-rank properties of images and proposes a new algorithm based on subspace identification. The collection of transmission data is sparsely represented by singular value decomposition and the eigen-images are then denoised by block-matching frames. Then, the projection is regularized by the correlation information under the frame of prior image compressed sensing (PICCS). With the application of a typical analytical algorithm on the processed projection, the target images are obtained. Both numerical simulations and real data verifications are carried out to test the proposed algorithm. The numerical simulations data is obtained based on real clinical scanning three-dimensional data and the real data is obtained by scanning experimental head phantom. RESULTS In simulation experiment, using new algorithm boots the means of PSNR and SSIM by 1 dB and 0.05, respectively, compared with BM3D under the Gaussian noise with variance 0.04. Meanwhile, on the real data, the proposed algorithm exhibits superiority over compared algorithms in terms of noise suppression, detail preservation and computational overhead. The means of PSNR and SSIM are improved by 1.84 dB and 0.1, respectively, compared with BM3D under the Gaussian noise with variance 0.04. CONCLUSION This study demonstrates the feasibility and advantages of a new algorithm based on subspace identification for LDCT. It exploits the similarity among three-dimensional data to improve the image quality in a concise way and shows a promising potential on future clinical diagnosis.
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Affiliation(s)
- Junru Ren
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou, Henan, China
| | - Ningning Liang
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou, Henan, China
| | - Xiaohuan Yu
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou, Henan, China
| | - Yizhong Wang
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou, Henan, China
| | - Ailong Cai
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou, Henan, China
| | - Lei Li
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou, Henan, China
| | - Bin Yan
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou, Henan, China
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3
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Retraction notice. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:1163. [PMID: 37599555 DOI: 10.3233/xst-190469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/22/2023]
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4
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Fu M, Duan Y, Cheng Z, Qin W, Wang Y, Liang D, Hu Z. Total-body low-dose CT image denoising using a prior knowledge transfer technique with a contrastive regularization mechanism. Med Phys 2022; 50:2971-2984. [PMID: 36542423 DOI: 10.1002/mp.16163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 11/02/2022] [Accepted: 12/05/2022] [Indexed: 12/24/2022] Open
Abstract
PURPOSE Reducing the radiation exposure experienced by patients in total-body computed tomography (CT) imaging has attracted extensive attention in the medical imaging community. A low radiation dose may result in increased noise and artifacts that greatly affect the subsequent clinical diagnosis. To obtain high-quality total-body low-dose CT (LDCT) images, previous deep learning-based research works developed various network architectures. However, most of these methods only employ normal-dose CT (NDCT) images as ground truths to guide the training process of the constructed denoising network. As a result of this simple restriction, the reconstructed images tend to lose favorable image details and easily generate oversmoothed textures. This study explores how to better utilize the information contained in the feature spaces of NDCT images to guide the LDCT image reconstruction process and achieve high-quality results. METHODS We propose a novel intratask knowledge transfer (KT) method that leverages the knowledge distilled from NDCT images as an auxiliary component of the LDCT image reconstruction process. Our proposed architecture is named the teacher-student consistency network (TSC-Net), which consists of teacher and student networks with identical architectures. By employing the designed KT loss, the student network is encouraged to emulate the teacher network in the representation space and gain robust prior content. In addition, to further exploit the information contained in CT scans, a contrastive regularization mechanism (CRM) built upon contrastive learning is introduced. The CRM aims to minimize and maximize the L2 distances from the predicted CT images to the NDCT samples and to the LDCT samples in the latent space, respectively. Moreover, based on attention and the deformable convolution approach, we design a dynamic enhancement module (DEM) to improve the network capability to transform input information flows. RESULTS By conducting ablation studies, we prove the effectiveness of the proposed KT loss, CRM, and DEM. Extensive experimental results demonstrate that the TSC-Net outperforms the state-of-the-art methods in both quantitative and qualitative evaluations. Additionally, the excellent results obtained for clinical readings also prove that our proposed method can reconstruct high-quality CT images for clinical applications. CONCLUSIONS Based on the experimental results and clinical readings, the TSC-Net has better performance than other approaches. In our future work, we may explore the reconstruction of LDCT images by fusing the positron emission tomography (PET) and CT modalities to further improve the visual quality of the reconstructed CT images.
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Affiliation(s)
- Minghan Fu
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Chinese Academy of Sciences Key Laboratory of Health Informatics, Shenzhen, China
| | - Yanhua Duan
- Department of PET/CT, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Zhaoping Cheng
- Department of PET/CT, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Wenjian Qin
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Chinese Academy of Sciences Key Laboratory of Health Informatics, Shenzhen, China
| | - Ying Wang
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Dong Liang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Chinese Academy of Sciences Key Laboratory of Health Informatics, Shenzhen, China
| | - Zhanli Hu
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Chinese Academy of Sciences Key Laboratory of Health Informatics, Shenzhen, China
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5
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Zeng D, Zeng C, Zeng Z, Li S, Deng Z, Chen S, Bian Z, Ma J. Basis and current state of computed tomography perfusion imaging: a review. Phys Med Biol 2022; 67. [PMID: 35926503 DOI: 10.1088/1361-6560/ac8717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 08/04/2022] [Indexed: 12/30/2022]
Abstract
Computed tomography perfusion (CTP) is a functional imaging that allows for providing capillary-level hemodynamics information of the desired tissue in clinics. In this paper, we aim to offer insight into CTP imaging which covers the basics and current state of CTP imaging, then summarize the technical applications in the CTP imaging as well as the future technological potential. At first, we focus on the fundamentals of CTP imaging including systematically summarized CTP image acquisition and hemodynamic parameter map estimation techniques. A short assessment is presented to outline the clinical applications with CTP imaging, and then a review of radiation dose effect of the CTP imaging on the different applications is presented. We present a categorized methodology review on known and potential solvable challenges of radiation dose reduction in CTP imaging. To evaluate the quality of CTP images, we list various standardized performance metrics. Moreover, we present a review on the determination of infarct and penumbra. Finally, we reveal the popularity and future trend of CTP imaging.
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Affiliation(s)
- Dong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Cuidie Zeng
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Zhixiong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Sui Li
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Zhen Deng
- Department of Neurology, Nanfang Hospital, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Sijin Chen
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Zhaoying Bian
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
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6
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Li Q, Li S, Li R, Wu W, Dong Y, Zhao J, Qiang Y, Aftab R. Low-dose computed tomography image reconstruction via a multistage convolutional neural network with autoencoder perceptual loss network. Quant Imaging Med Surg 2022; 12:1929-1957. [PMID: 35284282 PMCID: PMC8899925 DOI: 10.21037/qims-21-465] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 12/01/2021] [Indexed: 07/05/2024]
Abstract
BACKGROUND Computed tomography (CT) is widely used in medical diagnoses due to its ability to non-invasively detect the internal structures of the human body. However, CT scans with normal radiation doses can cause irreversible damage to patients. The radiation exposure is reduced with low-dose CT (LDCT), although considerable speckle noise and streak artifacts in CT images and even structural deformation may result, significantly undermining its diagnostic capability. METHODS This paper proposes a multistage network framework which gradually divides the entire process into 2-staged sub-networks to complete the task of image reconstruction. Specifically, a dilated residual convolutional neural network (DRCNN) was used to denoise the LDCT image. Then, the learned context information was combined with the channel attention subnet, which retains local information, to preserve the structural details and features of the image and textural information. To obtain recognizable characteristic details, we introduced a novel self-calibration module (SCM) between the 2 stages to reweight the local features, which realizes the complementation of information at different stages while refining feature information. In addition, we also designed an autoencoder neural network, using a self-supervised learning scheme to train a perceptual loss neural network specifically for CT images. RESULTS We evaluated the diagnostic quality of the results and performed ablation experiments on the loss function and network structure modules to verify each module's effectiveness in the network. Our proposed network architecture obtained high peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and visual information fidelity (VIF) values in terms of quantitative evaluation. In the analysis of qualitative results, our network structure maintained a better balance between eliminating image noise and preserving image details. Experimental results showed that our proposed network structure obtained better metrics and visual evaluation. CONCLUSIONS This study proposed a new LDCT image reconstruction method by combining autoencoder perceptual loss networks with multistage convolutional neural networks (MSCNN). Experimental results showed that the newly proposed method has performance than other methods.
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Affiliation(s)
- Qing Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Saize Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Runrui Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Wei Wu
- Department of Clinical Laboratory, Affiliated People’s Hospital of Shanxi Medical University, Shanxi Provincial People’s Hospital, Taiyuan, China
| | - Yunyun Dong
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Juanjuan Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yan Qiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Rukhma Aftab
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
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7
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Hegazy MAA, Cho MH, Lee SY. Image denoising by transfer learning of generative adversarial network for dental CT. Biomed Phys Eng Express 2020; 6:055024. [DOI: 10.1088/2057-1976/abb068] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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8
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Sinogram restoration based on shape property in computed tomography. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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9
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Shi M, Myronakis M, Hu YH, Jacobson M, Lehmann M, Fueglistaller R, Huber P, Baturin P, Wang A, Ferguson D, Harris T, Morf D, Berbeco R. A novel method for fast image simulation of flat panel detectors. ACTA ACUST UNITED AC 2019; 64:095019. [DOI: 10.1088/1361-6560/ab12aa] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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10
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Hu Z, Jiang C, Sun F, Zhang Q, Ge Y, Yang Y, Liu X, Zheng H, Liang D. Artifact correction in low‐dose dental
CT
imaging using Wasserstein generative adversarial networks. Med Phys 2019; 46:1686-1696. [DOI: 10.1002/mp.13415] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Revised: 01/16/2019] [Accepted: 01/22/2019] [Indexed: 11/08/2022] Open
Affiliation(s)
- Zhanli Hu
- Lauterbur Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China
| | - Changhui Jiang
- Lauterbur Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China
- Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen 518055 China
| | - Fengyi Sun
- Lauterbur Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China
| | - Qiyang Zhang
- Lauterbur Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China
| | - Yongshuai Ge
- Lauterbur Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China
| | - Yongfeng Yang
- Lauterbur Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China
| | - Xin Liu
- Lauterbur Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China
| | - Hairong Zheng
- Lauterbur Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China
| | - Dong Liang
- Lauterbur Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China
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11
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Deng X, Zhao Y, Li H. Projection data smoothing through noise-level weighted total variation regularization for low-dose computed tomography. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2019; 27:537-557. [PMID: 31282470 DOI: 10.3233/xst-180469] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Reducing radiation dose while maintaining the quality of the reconstructed images is a major challenge in the computed tomography (CT) community. In light of the non-stationary Gaussian noise distribution, we developed a model that incorporates a noise-level weighted total variation (NWTV) regularization term for denoising the projection data. Contrary to the well-known edge-weighted total variation method, which aims for better edge preserving, the proposed NWTV tries to adapt the regularization with the spatially varying noise levels. Experiments on simulated data as well as the real imaging data suggest that the proposed NWTV regularization could achieve quite competitive results. For sinograms with sharp edges, the NWTV could do a better job at balancing noise reduction and edge preserving, such that noise is removed in a more uniform manner. Another conclusion from our experiments is that the well-recognized stair-casing artifacts of TV regularization play little role in the reconstructed images when the NWTV method is applied to low-dose CT imaging data.
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Affiliation(s)
- Xiaojuan Deng
- School of Mathematical Sciences, Capital Normal University, Beijing, China
- Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, China
| | - Yunsong Zhao
- School of Mathematical Sciences, Capital Normal University, Beijing, China
- Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, China
| | - Hongwei Li
- School of Mathematical Sciences, Capital Normal University, Beijing, China
- Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, China
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12
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Bian Z, Zeng D, Zhang Z, Gong C, Tian X, Yan G, Huang J, Guo H, Chen B, Zhang J, Feng Q, Chen W, Ma J. Low-dose dynamic myocardial perfusion CT imaging using a motion adaptive sparsity prior. Med Phys 2018; 44:e188-e201. [PMID: 28901610 DOI: 10.1002/mp.12285] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2016] [Revised: 02/20/2017] [Accepted: 04/09/2017] [Indexed: 11/10/2022] Open
Abstract
PURPOSE Dynamic myocardial perfusion computed tomography (DM-PCT) imaging offers benefits over quantitative assessment of myocardial blood flow (MBF) for diagnosis and risk stratification of coronary artery disease. However, one major drawback of DM-PCT imaging is that a high radiation level is imparted by repeated scanning. To address this issue, in this work, we developed a statistical iterative reconstruction algorithm based on the penalized weighted least-squares (PWLS) scheme by incorporating a motion adaptive sparsity prior (MASP) model to achieve high-quality DM-PCT imaging with low tube current dynamic data acquisition. For simplicity, we refer to the proposed algorithm as "PWLS-MASP''. METHODS The MASP models both the spatial and temporal structured sparsity of DM-PCT sequence images with the assumption that the differences between adjacent frames after motion correction are sparse in the gradient image domain. To validate and evaluate the effectiveness of the present PWLS-MASP algorithm thoroughly, a modified XCAT phantom and preclinical porcine DM-PCT dataset were used in the study. RESULTS The present PWLS-MASP algorithm can obtain high-quality DM-PCT images in both phantom and porcine cases, and outperforms the existing filtered back-projection algorithm and PWLS-based algorithms with total variation regularization (PWLS-TV) and robust principal component analysis regularization (PWLS-RPCA) in terms of noise reduction, streak artifacts mitigation, and time density curve estimation. Moreover, the PWLS-MASP algorithm can yield more accurate diagnostic hemodynamic parametric maps than the PWLS-TV and PWLS-RPCA algorithms. CONCLUSIONS The study indicates that there is a substantial advantage in using the present PWLS-MASP algorithm for low-dose DM-PCT, and potentially in other dynamic tomography areas.
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Affiliation(s)
- Zhaoying Bian
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Dong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Zhang Zhang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Changfei Gong
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Xiumei Tian
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Gang Yan
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Jing Huang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Hong Guo
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Bo Chen
- College of Mathematics and Statistics, Shenzhen University, Shenzhen, 518060, China
| | - Jing Zhang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Qianjin Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Wufan Chen
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China
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13
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Niu S, Yu G, Ma J, Wang J. Nonlocal low-rank and sparse matrix decomposition for spectral CT reconstruction. INVERSE PROBLEMS 2018; 34:024003. [PMID: 30294061 PMCID: PMC6168215 DOI: 10.1088/1361-6420/aa942c] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Spectral computed tomography (CT) has been a promising technique in research and clinic because of its ability to produce improved energy resolution images with narrow energy bins. However, the narrow energy bin image is often affected by serious quantum noise because of the limited number of photons used in the corresponding energy bin. To address this problem, we present an iterative reconstruction method for spectral CT using nonlocal low-rank and sparse matrix decomposition (NLSMD), which exploits the self-similarity of patches that are collected in multi-energy images. Specifically, each set of patches can be decomposed into a low-rank component and a sparse component, and the low-rank component represents the stationary background over different energy bins, while the sparse component represents the rest of different spectral features in individual energy bins. Subsequently, an effective alternating optimization algorithm was developed to minimize the associated objective function. To validate and evaluate the NLSMD method, qualitative and quantitative studies were conducted by using simulated and real spectral CT data. Experimental results show that the NLSMD method improves spectral CT images in terms of noise reduction, artifacts suppression and resolution preservation.
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Affiliation(s)
- Shanzhou Niu
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75235, USA
- School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China
| | - Gaohang Yu
- School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Jing Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75235, USA
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14
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Du W, Chen H, Wu Z, Sun H, Liao P, Zhang Y. Stacked competitive networks for noise reduction in low-dose CT. PLoS One 2017; 12:e0190069. [PMID: 29267360 PMCID: PMC5739486 DOI: 10.1371/journal.pone.0190069] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Accepted: 12/07/2017] [Indexed: 02/05/2023] Open
Abstract
Since absorption of X-ray radiation has the possibility of inducing cancerous, genetic and other diseases to patients, researches usually attempt to reduce the radiation dose. However, reduction of the radiation dose associated with CT scans will unavoidably increase the severity of noise and artifacts, which can seriously affect diagnostic confidence. Due to the outstanding performance of deep neural networks in image processing, in this paper, we proposed a Stacked Competitive Network (SCN) approach to noise reduction, which stacks several successive Competitive Blocks (CB). The carefully handcrafted design of the competitive blocks was inspired by the idea of multi-scale processing and improvement the network's capacity. Qualitative and quantitative evaluations demonstrate the competitive performance of the proposed method in noise suppression, structural preservation, and lesion detection.
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Affiliation(s)
- Wenchao Du
- School of Computer Science, Sichuan University, Chengdu, China
- National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu, China
| | - Hu Chen
- School of Computer Science, Sichuan University, Chengdu, China
- National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu, China
| | - Zhihong Wu
- School of Computer Science, Sichuan University, Chengdu, China
- National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu, China
| | - Huaiqiang Sun
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Peixi Liao
- Department of Scientific Research and Education, The Sixth People’s Hospital of Chengdu, Chengdu, China
| | - Yi Zhang
- School of Computer Science, Sichuan University, Chengdu, China
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[Total generalized variation minimization based on projection data for low?dose CT reconstruction]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2017. [PMID: 29292249 PMCID: PMC6744009 DOI: 10.3969/j.issn.1673-4254.2017.12.04] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
OBJECTIVE To obtain high?quality low?dose CT images using total generalized variation regularization based on the projection data for low?dose CT reconstruction. METHODS The projection data of the CT images were transformed from Poisson distribution to Gaussian distribution using the linear Anscombe transform. The transformed data were then restored by an efficient total generalized variation minimization algorithm. Reconstruction was finally achieved by inverse Anscombe transform and filtered back projection (FBP) method. RESULTS The image quality of low?dose CT was greatly improved by the proposed algorithm in both Clock and Shepp?Logan phantoms. The signal?to?noise ratios (SNRs) of the Clock and Shepp-Logan images reconstructed by FBP algorithm were 17.752 dB and 19.379 dB, which were increased by the proposed algorithm to 24.0352 and 23.4181 dB, respectively. The NMSE of the Clock and Shepp?Logan images reconstructed by FBP algorithm was 0.86% and 0.58%, which was reduced by the proposed algorithm to 0.2% and 0.23%, respectively. CONCLUSION The proposed method can effectively suppress noise and strip artifacts in low?dose CT images when piecewise constant assumption is not possible.
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牛 善, 吴 恒, 喻 泽, 郑 子, 喻 高. [Total generalized variation minimization based on projection data for low?dose CT reconstruction]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2017; 37:1585-1591. [PMID: 29292249 PMCID: PMC6744009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Indexed: 07/30/2024]
Abstract
OBJECTIVE To obtain high?quality low?dose CT images using total generalized variation regularization based on the projection data for low?dose CT reconstruction. METHODS The projection data of the CT images were transformed from Poisson distribution to Gaussian distribution using the linear Anscombe transform. The transformed data were then restored by an efficient total generalized variation minimization algorithm. Reconstruction was finally achieved by inverse Anscombe transform and filtered back projection (FBP) method. RESULTS The image quality of low?dose CT was greatly improved by the proposed algorithm in both Clock and Shepp?Logan phantoms. The signal?to?noise ratios (SNRs) of the Clock and Shepp-Logan images reconstructed by FBP algorithm were 17.752 dB and 19.379 dB, which were increased by the proposed algorithm to 24.0352 and 23.4181 dB, respectively. The NMSE of the Clock and Shepp?Logan images reconstructed by FBP algorithm was 0.86% and 0.58%, which was reduced by the proposed algorithm to 0.2% and 0.23%, respectively. CONCLUSION The proposed method can effectively suppress noise and strip artifacts in low?dose CT images when piecewise constant assumption is not possible.
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Affiliation(s)
- 善洲 牛
- />赣南师范大学数学与计算机科学学院,江西 赣州 341000School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China
| | - 恒 吴
- />赣南师范大学数学与计算机科学学院,江西 赣州 341000School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China
| | - 泽峰 喻
- />赣南师范大学数学与计算机科学学院,江西 赣州 341000School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China
| | - 子君 郑
- />赣南师范大学数学与计算机科学学院,江西 赣州 341000School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China
| | - 高航 喻
- />赣南师范大学数学与计算机科学学院,江西 赣州 341000School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China
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Boudjelal A, Messali Z, Elmoataz A, Attallah B. Improved Simultaneous Algebraic Reconstruction Technique Algorithm for Positron-Emission Tomography Image Reconstruction via Minimizing the Fast Total Variation. J Med Imaging Radiat Sci 2017; 48:385-393. [PMID: 31047474 DOI: 10.1016/j.jmir.2017.09.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Revised: 09/05/2017] [Accepted: 09/15/2017] [Indexed: 12/15/2022]
Abstract
CONTEXT There has been considerable progress in the instrumentation for data measurement and computer methods for generating images of measured PET data. These computer methods have been developed to solve the inverse problem, also known as the "image reconstruction from projections" problem. AIM In this paper, we propose a modified Simultaneous Algebraic Reconstruction Technique (SART) algorithm to improve the quality of image reconstruction by incorporating total variation (TV) minimization into the iterative SART algorithm. METHODOLOGY The SART updates the estimated image by forward projecting the initial image onto the sinogram space. Then, the difference between the estimated sinogram and the given sinogram is back-projected onto the image domain. This difference is then subtracted from the initial image to obtain a corrected image. Fast total variation (FTV) minimization is applied to the image obtained in the SART step. The second step is the result obtained from the previous FTV update. The SART and the FTV minimization steps run iteratively in an alternating manner. Fifty iterations were applied to the SART algorithm used in each of the regularization-based methods. In addition to the conventional SART algorithm, spatial smoothing was used to enhance the quality of the image. All images were sized at 128 × 128 pixels. RESULTS The proposed algorithm successfully accomplished edge preservation. A detailed scrutiny revealed that the reconstruction algorithms differed; for example, the SART and the proposed FTV-SART algorithm effectively preserved the hot lesion edges, whereas artifacts and deviations were more likely to occur in the ART algorithm than in the other algorithms. CONCLUSIONS Compared to the standard SART, the proposed algorithm is more robust in removing background noise while preserving edges to suppress the existent image artifacts. The quality measurements and visual inspections show a significant improvement in image quality compared to the conventional SART and Algebraic Reconstruction Technique (ART) algorithms.
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Affiliation(s)
- Abdelwahhab Boudjelal
- Electronics Department, University of Mohammed Boudiaf-M'sila, M'sila, Algeria; Image Team, GREYC Laboratory, University of Caen Normandy, Caen Cedex, France.
| | - Zoubeida Messali
- Electronics Department, University of Mohamed El Bachir El Ibrahimi-Bordj Bou Arréridj, Bordj Bou Arréridj, Algeria
| | - Abderrahim Elmoataz
- Image Team, GREYC Laboratory, University of Caen Normandy, Caen Cedex, France
| | - Bilal Attallah
- Electronics Department, University of Mohammed Boudiaf-M'sila, M'sila, Algeria; Image Team, GREYC Laboratory, University of Caen Normandy, Caen Cedex, France
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19
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[Sinogram restoration for low-dose cerebral perfusion CT images]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2017; 37. [PMID: 28446398 PMCID: PMC6744093 DOI: 10.3969/j.issn.1673-4254.2017.04.08] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
In clinical cerebral perfusion CT examination, repeated scanning the region of interest in the cine mode increases the radiation dose of the patients, while decreasing the radiation dose by lowering the scanning current results in poor image quality and affects the clinical diagnosis. We propose a penalized weighted least-square (PWLS) method for recovering the projection data to improve the quality of low-dose cerebral perfusion CT imaged. This method incorporates the statistical distribution characteristics of brain perfusion CT projection data and uses the statistical properties of the projection data for modeling. The PWLS method was used to recover the data, and the Gauss-Seidel (GS) method was employed for iterative solving. Adaptive weighting is introduced between the original projection data and the projection data after PWLS restoration. The experimental results on the clinical data demonstrated that the PWLS-based sinogram restoration method improved noise reduction and artifact suppression as compared with the conventional noise reduction methods, and better retained the edges and details to generate better cerebral perfusion maps.
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Niu S, Huang J, Bian Z, Zeng D, Chen W, Yu G, Liang Z, Ma J. Iterative reconstruction for sparse-view x-ray CT using alpha-divergence constrained total generalized variation minimization. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2017; 25:XST16239. [PMID: 28387700 DOI: 10.3233/xst-16239] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
BCKGROUND Accurate statistical model of the measured projection data is essential for computed tomography (CT) image reconstruction. The transmission data can be described by a compound Poisson distribution upon an electronic noise background. However, such a statistical distribution is numerically intractable for image reconstruction. OBJECTIVE Although the sinogram data is easily manipulated, it lacks a statistical description for image reconstruction. To address this problem, we present an alpha-divergence constrained total generalized variation (AD-TGV) method for sparse-view x-ray CT image reconstruction. METHODS The AD-TGV method is formulated as an optimization problem, which balances the alpha-divergence (AD) fidelity and total generalized variation (TGV) regularization in one framework. The alpha-divergence is used to measure the discrepancy between the measured and estimated projection data. The TGV regularization can effectively eliminate the staircase and patchy artifacts which is often observed in total variation (TV) regularization. A modified proximal forward-backward splitting algorithm was proposed to minimize the associated objective function. RESULTS Qualitative and quantitative evaluations were carried out on both phantom and patient data. Compared with the original TV-based method, the evaluations clearly demonstrate that the AD-TGV method achieves higher accuracy and lower noise, while preserving structural details. CONCLUSIONS The experimental results show that the presented AD-TGV method can achieve more gains over the AD-TV method in preserving structural details and suppressing image noise and undesired patchy artifacts. The authors can draw the conclusion that the presented AD-TGV method is potential for radiation dose reduction by lowering the milliampere-seconds (mAs) and/or reducing the number of projection views.
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Affiliation(s)
- Shanzhou Niu
- School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, China
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Jing Huang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Zhaoying Bian
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Dong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Wufan Chen
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Gaohang Yu
- School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, China
| | - Zhengrong Liang
- Department of Radiology, State University of New York, Stony Brook, NY, USA
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
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Yenumula L, Acharya RV, Kumar U, Palani Selvam T, Roy K, Patankar VH, Kar S. Ring artifact correction in gamma-ray process tomography imaging. Appl Radiat Isot 2017; 124:75-82. [PMID: 28343080 DOI: 10.1016/j.apradiso.2017.03.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2016] [Revised: 02/06/2017] [Accepted: 03/16/2017] [Indexed: 11/20/2022]
Abstract
Ring artifacts have been studied for X-ray based Computed Tomography (CT) systems but not on γ-ray based in-situ applications. This paper discusses application of recently proposed automatic ring artifact reduction method (Yoon et al., 2016) on previously obtained experimental projection data from a γ-ray based Industrial Process Tomography (IPT) system for a prototype catalytic column. Studies include qualitative and quantitative evaluation of the method. It is observed that ring artifacts are suppressed without loss of significant information in γ-ray PT images.
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Affiliation(s)
- Lakshminarayana Yenumula
- Industrial Tomography and Instrumentation Section, Isotope Production and Applications Division, Bhabha Atomic Research Centre (BARC), Trombay, Mumbai - 400085, India.
| | - Rajesh V Acharya
- Industrial Tomography and Instrumentation Section, Isotope Production and Applications Division, Bhabha Atomic Research Centre (BARC), Trombay, Mumbai - 400085, India
| | - Umesh Kumar
- Industrial Tomography and Instrumentation Section, Isotope Production and Applications Division, Bhabha Atomic Research Centre (BARC), Trombay, Mumbai - 400085, India
| | - T Palani Selvam
- Radiological Physics & Advisory Division, Bhabha Atomic Research Centre (BARC), Trombay, Mumbai - 400085, India
| | - Kallol Roy
- Bharatiya Nabhikiya Vidyut Nigam Limited, BHAVINI Project Station Building, Kancheepuram, Kalpakkam 603 102, Tamil Nadu, India
| | - V H Patankar
- Electronics Division, Bhabha Atomic Research Centre (BARC), Trombay, Mumbai 400085, India
| | - Soumitra Kar
- Electronics & Instrumentation Systems Division, Bhabha Atomic Research Centre (BARC), Trombay, Mumbai 400085, India
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Zeng D, Zhang X, Bian Z, Huang J, Zhang H, Lu L, Lyu W, Zhang J, Feng Q, Chen W, Ma J. Cerebral perfusion computed tomography deconvolution via structure tensor total variation regularization. Med Phys 2017; 43:2091. [PMID: 27147322 DOI: 10.1118/1.4944866] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Cerebral perfusion computed tomography (PCT) imaging as an accurate and fast acute ischemic stroke examination has been widely used in clinic. Meanwhile, a major drawback of PCT imaging is the high radiation dose due to its dynamic scan protocol. The purpose of this work is to develop a robust perfusion deconvolution approach via structure tensor total variation (STV) regularization (PD-STV) for estimating an accurate residue function in PCT imaging with the low-milliampere-seconds (low-mAs) data acquisition. METHODS Besides modeling the spatio-temporal structure information of PCT data, the STV regularization of the present PD-STV approach can utilize the higher order derivatives of the residue function to enhance denoising performance. To minimize the objective function, the authors propose an effective iterative algorithm with a shrinkage/thresholding scheme. A simulation study on a digital brain perfusion phantom and a clinical study on an old infarction patient were conducted to validate and evaluate the performance of the present PD-STV approach. RESULTS In the digital phantom study, visual inspection and quantitative metrics (i.e., the normalized mean square error, the peak signal-to-noise ratio, and the universal quality index) assessments demonstrated that the PD-STV approach outperformed other existing approaches in terms of the performance of noise-induced artifacts reduction and accurate perfusion hemodynamic maps (PHM) estimation. In the patient data study, the present PD-STV approach could yield accurate PHM estimation with several noticeable gains over other existing approaches in terms of visual inspection and correlation analysis. CONCLUSIONS This study demonstrated the feasibility and efficacy of the present PD-STV approach in utilizing STV regularization to improve the accuracy of residue function estimation of cerebral PCT imaging in the case of low-mAs.
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Affiliation(s)
- Dong Zeng
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Xinyu Zhang
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Zhaoying Bian
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Jing Huang
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Hua Zhang
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Lijun Lu
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Wenbing Lyu
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Jing Zhang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Qianjin Feng
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Wufan Chen
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Jianhua Ma
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China
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Gong C, Han C, Gan G, Deng Z, Zhou Y, Yi J, Zheng X, Xie C, Jin X. Low-dose dynamic myocardial perfusion CT image reconstruction using pre-contrast normal-dose CT scan induced structure tensor total variation regularization. Phys Med Biol 2017; 62:2612-2635. [PMID: 28140366 DOI: 10.1088/1361-6560/aa5d40] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Dynamic myocardial perfusion CT (DMP-CT) imaging provides quantitative functional information for diagnosis and risk stratification of coronary artery disease by calculating myocardial perfusion hemodynamic parameter (MPHP) maps. However, the level of radiation delivered by dynamic sequential scan protocol can be potentially high. The purpose of this work is to develop a pre-contrast normal-dose scan induced structure tensor total variation regularization based on the penalized weighted least-squares (PWLS) criteria to improve the image quality of DMP-CT with a low-mAs CT acquisition. For simplicity, the present approach was termed as 'PWLS-ndiSTV'. Specifically, the ndiSTV regularization takes into account the spatial-temporal structure information of DMP-CT data and further exploits the higher order derivatives of the objective images to enhance denoising performance. Subsequently, an effective optimization algorithm based on the split-Bregman approach was adopted to minimize the associative objective function. Evaluations with modified dynamic XCAT phantom and preclinical porcine datasets have demonstrated that the proposed PWLS-ndiSTV approach can achieve promising gains over other existing approaches in terms of noise-induced artifacts mitigation, edge details preservation, and accurate MPHP maps calculation.
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Affiliation(s)
- Changfei Gong
- Department of Radiotherapy and Chemotherapy, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People's Republic of China
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Tian X, Zeng D, Zhang S, Huang J, Zhang H, He J, Lu L, Xi W, Ma J, Bian Z. Robust low-dose dynamic cerebral perfusion CT image restoration via coupled dictionary learning scheme. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2016; 24:837-853. [PMID: 27612048 DOI: 10.3233/xst-160593] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Dynamic cerebral perfusion x-ray computed tomography (PCT) imaging has been advocated to quantitatively and qualitatively assess hemodynamic parameters in the diagnosis of acute stroke or chronic cerebrovascular diseases. However, the associated radiation dose is a significant concern to patients due to its dynamic scan protocol. To address this issue, in this paper we propose an image restoration method by utilizing coupled dictionary learning (CDL) scheme to yield clinically acceptable PCT images with low-dose data acquisition. Specifically, in the present CDL scheme, the 2D background information from the average of the baseline time frames of low-dose unenhanced CT images and the 3D enhancement information from normal-dose sequential cerebral PCT images are exploited to train the dictionary atoms respectively. After getting the two trained dictionaries, we couple them to represent the desired PCT images as spatio-temporal prior in objective function construction. Finally, the low-dose dynamic cerebral PCT images are restored by using a general DL image processing. To get a robust solution, the objective function is solved by using a modified dictionary learning based image restoration algorithm. The experimental results on clinical data show that the present method can yield more accurate kinetic enhanced details and diagnostic hemodynamic parameter maps than the state-of-the-art methods.
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Affiliation(s)
- Xiumei Tian
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
| | - Dong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
| | - Shanli Zhang
- The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, Guangdong, China
| | - Jing Huang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
| | - Hua Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
| | - Ji He
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
| | - Lijun Lu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
| | - Weiwen Xi
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
| | - Zhaoying Bian
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
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Yan B, Zhang W, Li L, Zhang H, Wang L. Quantitative study on exact reconstruction sampling condition by verifying solution uniqueness in limited-view CT. Phys Med 2016; 32:1321-1330. [DOI: 10.1016/j.ejmp.2016.07.094] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2016] [Revised: 07/17/2016] [Accepted: 07/19/2016] [Indexed: 10/21/2022] Open
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Karimi D, Ward RK. Patch-based models and algorithms for image processing: a review of the basic principles and methods, and their application in computed tomography. Int J Comput Assist Radiol Surg 2016; 11:1765-77. [PMID: 27287761 DOI: 10.1007/s11548-016-1434-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2016] [Accepted: 05/27/2016] [Indexed: 11/28/2022]
Abstract
PURPOSE Image models are central to all image processing tasks. The great advancements in digital image processing would not have been made possible without powerful models which, themselves, have evolved over time. In the past decade, "patch-based" models have emerged as one of the most effective models for natural images. Patch-based methods have outperformed other competing methods in many image processing tasks. These developments have come at a time when greater availability of powerful computational resources and growing concerns over the health risks of the ionizing radiation encourage research on image processing algorithms for computed tomography (CT). The goal of this paper is to explain the principles of patch-based methods and to review some of their recent applications in CT. METHODS We first review the central concepts in patch-based image processing and explain some of the state-of-the-art algorithms, with a focus on aspects that are more relevant to CT. Then, we review some of the recent application of patch-based methods in CT. RESULTS Patch-based methods have already transformed the field of image processing, leading to state-of-the-art results in many applications. More recently, several studies have proposed patch-based algorithms for various image processing tasks in CT, from denoising and restoration to iterative reconstruction. Although these studies have reported good results, the true potential of patch-based methods for CT has not been yet appreciated. CONCLUSIONS Patch-based methods can play a central role in image reconstruction and processing for CT. They have the potential to lead to substantial improvements in the current state of the art.
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Affiliation(s)
| | - Rabab K Ward
- , 2366 Main Mall, Vancouver, BC, V6T 1Z4, Canada
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Niu S, Zhang S, Huang J, Bian Z, Chen W, Yu G, Liang Z, Ma J. Low-dose cerebral perfusion computed tomography image restoration via low-rank and total variation regularizations. Neurocomputing 2016; 197:143-160. [PMID: 27440948 DOI: 10.1016/j.neucom.2016.01.090] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Cerebral perfusion x-ray computed tomography (PCT) is an important functional imaging modality for evaluating cerebrovascular diseases and has been widely used in clinics over the past decades. However, due to the protocol of PCT imaging with repeated dynamic sequential scans, the associative radiation dose unavoidably increases as compared with that used in conventional CT examinations. Minimizing the radiation exposure in PCT examination is a major task in the CT field. In this paper, considering the rich similarity redundancy information among enhanced sequential PCT images, we propose a low-dose PCT image restoration model by incorporating the low-rank and sparse matrix characteristic of sequential PCT images. Specifically, the sequential PCT images were first stacked into a matrix (i.e., low-rank matrix), and then a non-convex spectral norm/regularization and a spatio-temporal total variation norm/regularization were then built on the low-rank matrix to describe the low rank and sparsity of the sequential PCT images, respectively. Subsequently, an improved split Bregman method was adopted to minimize the associative objective function with a reasonable convergence rate. Both qualitative and quantitative studies were conducted using a digital phantom and clinical cerebral PCT datasets to evaluate the present method. Experimental results show that the presented method can achieve images with several noticeable advantages over the existing methods in terms of noise reduction and universal quality index. More importantly, the present method can produce more accurate kinetic enhanced details and diagnostic hemodynamic parameter maps.
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Affiliation(s)
- Shanzhou Niu
- School of Mathematics and Computer Sciences, Gannan Normal University, Ganzhou 341000, China ; School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Shanli Zhang
- The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou 510405, China
| | - Jing Huang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China ; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Zhaoying Bian
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China ; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Wufan Chen
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China ; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Gaohang Yu
- School of Mathematics and Computer Sciences, Gannan Normal University, Ganzhou 341000, China
| | - Zhengrong Liang
- Department of Radiology, State University of New York, Stony Brook, NY 11794, USA
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China ; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China
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Mokri SS, Saripan MI, Abd Rahni AA, Nordin AJ, Hashim S, Marhaban MH. PET Image Reconstruction Incorporating 3D Mean-Median Sinogram Filtering. IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2016; 63:157-169. [DOI: 10.1109/tns.2015.2513484] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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Zeng D, Huang J, Zhang H, Bian Z, Niu S, Zhang Z, Feng Q, Chen W, Ma J. Spectral CT Image Restoration via an Average Image-Induced Nonlocal Means Filter. IEEE Trans Biomed Eng 2015; 63:1044-1057. [PMID: 26353358 DOI: 10.1109/tbme.2015.2476371] [Citation(s) in RCA: 56] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
GOAL Spectral computed tomography (SCT) images reconstructed by an analytical approach often suffer from a poor signal-to-noise ratio and strong streak artifacts when sufficient photon counts are not available in SCT imaging. In reducing noise-induced artifacts in SCT images, in this study, we propose an average image-induced nonlocal means (aviNLM) filter for each energy-specific image restoration. Methods: The present aviNLM algorithm exploits redundant information in the whole energy domain. Specifically, the proposed aviNLM algorithm yields the restored results by performing a nonlocal weighted average operation on the noisy energy-specific images with the nonlocal weight matrix between the target and prior images, in which the prior image is generated from all of the images reconstructed in each energy bin. Results: Qualitative and quantitative studies are conducted to evaluate the aviNLM filter by using the data of digital phantom, physical phantom, and clinical patient data acquired from the energy-resolved and -integrated detectors, respectively. Experimental results show that the present aviNLM filter can achieve promising results for SCT image restoration in terms of noise-induced artifact suppression, cross profile, and contrast-to-noise ratio and material decomposition assessment. Conclusion and Significance: The present aviNLM algorithm has useful potential for radiation dose reduction by lowering the mAs in SCT imaging, and it may be useful for some other clinical applications, such as in myocardial perfusion imaging and radiotherapy.
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Gao Y, Bian Z, Huang J, Zhang Y, Niu S, Feng Q, Chen W, Liang Z, Ma J. Low-dose X-ray computed tomography image reconstruction with a combined low-mAs and sparse-view protocol. OPTICS EXPRESS 2014; 22:15190-210. [PMID: 24977611 PMCID: PMC4083059 DOI: 10.1364/oe.22.015190] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2014] [Revised: 05/31/2014] [Accepted: 06/03/2014] [Indexed: 05/26/2023]
Abstract
To realize low-dose imaging in X-ray computed tomography (CT) examination, lowering milliampere-seconds (low-mAs) or reducing the required number of projection views (sparse-view) per rotation around the body has been widely studied as an easy and effective approach. In this study, we are focusing on low-dose CT image reconstruction from the sinograms acquired with a combined low-mAs and sparse-view protocol and propose a two-step image reconstruction strategy. Specifically, to suppress significant statistical noise in the noisy and insufficient sinograms, an adaptive sinogram restoration (ASR) method is first proposed with consideration of the statistical property of sinogram data, and then to further acquire a high-quality image, a total variation based projection onto convex sets (TV-POCS) method is adopted with a slight modification. For simplicity, the present reconstruction strategy was termed as "ASR-TV-POCS." To evaluate the present ASR-TV-POCS method, both qualitative and quantitative studies were performed on a physical phantom. Experimental results have demonstrated that the present ASR-TV-POCS method can achieve promising gains over other existing methods in terms of the noise reduction, contrast-to-noise ratio, and edge detail preservation.
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Affiliation(s)
- Yang Gao
- School of Biomedical Engineering, Southern Medical University, Guangdong, Guangzhou 510515,
China
| | - Zhaoying Bian
- School of Biomedical Engineering, Southern Medical University, Guangdong, Guangzhou 510515,
China
| | - Jing Huang
- School of Biomedical Engineering, Southern Medical University, Guangdong, Guangzhou 510515,
China
| | - Yunwan Zhang
- School of Biomedical Engineering, Southern Medical University, Guangdong, Guangzhou 510515,
China
| | - Shanzhou Niu
- School of Biomedical Engineering, Southern Medical University, Guangdong, Guangzhou 510515,
China
| | - Qianjin Feng
- School of Biomedical Engineering, Southern Medical University, Guangdong, Guangzhou 510515,
China
| | - Wufan Chen
- School of Biomedical Engineering, Southern Medical University, Guangdong, Guangzhou 510515,
China
| | - Zhengrong Liang
- Departments of Radiology, Computer Science and Biomedical Engineering, Stony Brook University, Stony Brook, NY 1179, USA
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangdong, Guangzhou 510515,
China
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Niu S, Gao Y, Bian Z, Huang J, Chen W, Yu G, Liang Z, Ma J. Sparse-view x-ray CT reconstruction via total generalized variation regularization. Phys Med Biol 2014; 59:2997-3017. [PMID: 24842150 DOI: 10.1088/0031-9155/59/12/2997] [Citation(s) in RCA: 115] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Sparse-view CT reconstruction algorithms via total variation (TV) optimize the data iteratively on the basis of a noise- and artifact-reducing model, resulting in significant radiation dose reduction while maintaining image quality. However, the piecewise constant assumption of TV minimization often leads to the appearance of noticeable patchy artifacts in reconstructed images. To obviate this drawback, we present a penalized weighted least-squares (PWLS) scheme to retain the image quality by incorporating the new concept of total generalized variation (TGV) regularization. We refer to the proposed scheme as 'PWLS-TGV' for simplicity. Specifically, TGV regularization utilizes higher order derivatives of the objective image, and the weighted least-squares term considers data-dependent variance estimation, which fully contribute to improving the image quality with sparse-view projection measurement. Subsequently, an alternating optimization algorithm was adopted to minimize the associative objective function. To evaluate the PWLS-TGV method, both qualitative and quantitative studies were conducted by using digital and physical phantoms. Experimental results show that the present PWLS-TGV method can achieve images with several noticeable gains over the original TV-based method in terms of accuracy and resolution properties.
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Affiliation(s)
- Shanzhou Niu
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China
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Huang J, Zhang Y, Ma J, Zeng D, Bian Z, Niu S, Feng Q, Liang Z, Chen W. Iterative image reconstruction for sparse-view CT using normal-dose image induced total variation prior. PLoS One 2013; 8:e79709. [PMID: 24260288 PMCID: PMC3832537 DOI: 10.1371/journal.pone.0079709] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2013] [Accepted: 09/24/2013] [Indexed: 11/19/2022] Open
Abstract
X-ray computed tomography (CT) iterative image reconstruction from sparse-view projection data has been an important research topic for radiation reduction in clinic. In this paper, to relieve the requirement of misalignment reduction operation of the prior image constrained compressed sensing (PICCS) approach introduced by Chen et al, we present an iterative image reconstruction approach for sparse-view CT using a normal-dose image induced total variation (ndiTV) prior. The associative objective function of the present approach is constructed under the penalized weighed least-square (PWLS) criteria, which contains two terms, i.e., the weighted least-square (WLS) fidelity and the ndiTV prior, and is referred to as "PWLS-ndiTV". Specifically, the WLS fidelity term is built based on an accurate relationship between the variance and mean of projection data in the presence of electronic background noise. The ndiTV prior term is designed to reduce the influence of the misalignment between the desired- and prior- image by using a normal-dose image induced non-local means (ndiNLM) filter. Subsequently, a modified steepest descent algorithm is adopted to minimize the associative objective function. Experimental results on two different digital phantoms and an anthropomorphic torso phantom show that the present PWLS-ndiTV approach for sparse-view CT image reconstruction can achieve noticeable gains over the existing similar approaches in terms of noise reduction, resolution-noise tradeoff, and low-contrast object detection.
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Affiliation(s)
- Jing Huang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Yunwan Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Dong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Zhaoying Bian
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Shanzhou Niu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Qianjin Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Zhengrong Liang
- Department of Radiology, State University of New York, Stony Brook, New York, United States of America
| | - Wufan Chen
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
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