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Zhang J, Gong W, Ye L, Wang F, Shangguan Z, Cheng Y. A Review of deep learning methods for denoising of medical low-dose CT images. Comput Biol Med 2024; 171:108112. [PMID: 38387380 DOI: 10.1016/j.compbiomed.2024.108112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 01/18/2024] [Accepted: 02/04/2024] [Indexed: 02/24/2024]
Abstract
To prevent patients from being exposed to excess of radiation in CT imaging, the most common solution is to decrease the radiation dose by reducing the X-ray, and thus the quality of the resulting low-dose CT images (LDCT) is degraded, as evidenced by more noise and streaking artifacts. Therefore, it is important to maintain high quality CT image while effectively reducing radiation dose. In recent years, with the rapid development of deep learning technology, deep learning-based LDCT denoising methods have become quite popular because of their data-driven and high-performance features to achieve excellent denoising results. However, to our knowledge, no relevant article has so far comprehensively introduced and reviewed advanced deep learning denoising methods such as Transformer structures in LDCT denoising tasks. Therefore, based on the literatures related to LDCT image denoising published from year 2016-2023, and in particular from 2020 to 2023, this study presents a systematic survey of current situation, and challenges and future research directions in LDCT image denoising field. Four types of denoising networks are classified according to the network structure: CNN-based, Encoder-Decoder-based, GAN-based, and Transformer-based denoising networks, and each type of denoising network is described and summarized from the perspectives of structural features and denoising performances. Representative deep-learning denoising methods for LDCT are experimentally compared and analyzed. The study results show that CNN-based denoising methods capture image details efficiently through multi-level convolution operation, demonstrating superior denoising effects and adaptivity. Encoder-decoder networks with MSE loss, achieve outstanding results in objective metrics. GANs based methods, employing innovative generators and discriminators, obtain denoised images that exhibit perceptually a closeness to NDCT. Transformer-based methods have potential for improving denoising performances due to their powerful capability in capturing global information. Challenges and opportunities for deep learning based LDCT denoising are analyzed, and future directions are also presented.
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Affiliation(s)
- Ju Zhang
- College of Information Science and Technology, Hangzhou Normal University, Hangzhou, China.
| | - Weiwei Gong
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China.
| | - Lieli Ye
- College of Information Science and Technology, Hangzhou Normal University, Hangzhou, China.
| | - Fanghong Wang
- Zhijiang College, Zhejiang University of Technology, Shaoxing, China.
| | - Zhibo Shangguan
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China.
| | - Yun Cheng
- Department of Medical Imaging, Zhejiang Hospital, Hangzhou, China.
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Li Q, Li R, Li S, Wang T, Cheng Y, Zhang S, Wu W, Zhao J, Qiang Y, Wang L. Unpaired low-dose computed tomography image denoising using a progressive cyclical convolutional neural network. Med Phys 2024; 51:1289-1312. [PMID: 36841936 DOI: 10.1002/mp.16331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 02/15/2023] [Accepted: 02/18/2023] [Indexed: 02/27/2023] Open
Abstract
BACKGROUND Reducing the radiation dose from computed tomography (CT) can significantly reduce the radiation risk to patients. However, low-dose CT (LDCT) suffers from severe and complex noise interference that affects subsequent diagnosis and analysis. Recently, deep learning-based methods have shown superior performance in LDCT image-denoising tasks. However, most methods require many normal-dose and low-dose CT image pairs, which are difficult to obtain in clinical applications. Unsupervised methods, on the other hand, are more general. PURPOSE Deep learning methods based on GAN networks have been widely used for unsupervised LDCT denoising, but the additional memory requirements of the model also hinder its further clinical application. To this end, we propose a simpler multi-stage denoising framework trained using unpaired data, the progressive cyclical convolutional neural network (PCCNN), which can remove the noise from CT images in latent space. METHODS Our proposed PCCNN introduces a noise transfer model that transfers noise from LDCT to normal-dose CT (NDCT), denoised CT images generated from unpaired CT images, and noisy CT images. The denoising framework also contains a progressive module that effectively removes noise through multi-stage wavelet transforms without sacrificing high-frequency components such as edges and details. RESULTS Compared with seven LDCT denoising algorithms, we perform a quantitative and qualitative evaluation of the experimental results and perform ablation experiments on each network module and loss function. On the AAPM dataset, compared with the contrasted unsupervised methods, our denoising framework has excellent denoising performance increasing the peak signal-to-noise ratio (PSNR) from 29.622 to 30.671, and the structural similarity index (SSIM) was increased from 0.8544 to 0.9199. The PCCNN denoising results were relatively optimal and statistically significant. In the qualitative result comparison, PCCNN without introducing additional blurring and artifacts, the resulting image has higher resolution and complete detail preservation, and the overall structural texture of the image is closer to NDCT. In visual assessments, PCCNN achieves a relatively balanced result in noise suppression, contrast retention, and lesion discrimination. CONCLUSIONS Extensive experimental validation shows that our scheme achieves reconstruction results comparable to supervised learning methods and has performed well in image quality and medical diagnostic acceptability.
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Affiliation(s)
- Qing 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
| | - Saize Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Tao Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yubin Cheng
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Shuming Zhang
- 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
| | - Juanjuan Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
- School of Information Engineering, Jinzhong College of Information, Jinzhong, China
| | - Yan Qiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Long Wang
- School of Information Engineering, Jinzhong College of Information, Jinzhong, China
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Sadia RT, Chen J, Zhang J. CT image denoising methods for image quality improvement and radiation dose reduction. J Appl Clin Med Phys 2024; 25:e14270. [PMID: 38240466 PMCID: PMC10860577 DOI: 10.1002/acm2.14270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 12/15/2023] [Accepted: 12/28/2023] [Indexed: 02/13/2024] Open
Abstract
With the ever-increasing use of computed tomography (CT), concerns about its radiation dose have become a significant public issue. To address the need for radiation dose reduction, CT denoising methods have been widely investigated and applied in low-dose CT images. Numerous noise reduction algorithms have emerged, such as iterative reconstruction and most recently, deep learning (DL)-based approaches. Given the rapid advancements in Artificial Intelligence techniques, we recognize the need for a comprehensive review that emphasizes the most recently developed methods. Hence, we have performed a thorough analysis of existing literature to provide such a review. Beyond directly comparing the performance, we focus on pivotal aspects, including model training, validation, testing, generalizability, vulnerability, and evaluation methods. This review is expected to raise awareness of the various facets involved in CT image denoising and the specific challenges in developing DL-based models.
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Affiliation(s)
- Rabeya Tus Sadia
- Department of Computer ScienceUniversity of KentuckyLexingtonKentuckyUSA
| | - Jin Chen
- Department of Medicine‐NephrologyUniversity of Alabama at BirminghamBirminghamAlabamaUSA
| | - Jie Zhang
- Department of RadiologyUniversity of KentuckyLexingtonKentuckyUSA
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Zhang F, Wang L, Zhao J, Zhang X. Medical applications of generative adversarial network: a visualization analysis. Acta Radiol 2023; 64:2757-2767. [PMID: 37603577 DOI: 10.1177/02841851231189035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/23/2023]
Abstract
BACKGROUND Deep learning (DL) is one of the latest approaches to artificial intelligence. As an unsupervised DL method, a generative adversarial network (GAN) can be used to synthesize new data. PURPOSE To explore GAN applications in medicine and point out the significance of its existence for clinical medical research, as well as to provide a visual bibliometric analysis of GAN applications in the medical field in combination with the scientometric software Citespace and statistical analysis methods. MATERIAL AND METHODS PubMed, MEDLINE, Web of Science, and Google Scholar were searched to identify studies of GAN in medical applications between 2017 and 2022. This study was performed and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Citespace was used to analyze the number of publications, authors, institutions, and keywords of articles related to GAN in medical applications. RESULTS The applications of GAN in medicine are not limited to medical image processing, but will also penetrate wider and more complex fields, or may be applied to clinical medicine. Eligibility criteria were the full texts of peer-reviewed journals reporting the application of GANs in medicine. Research selections included material published in English between 1 January 2017 and 1 December 2022. CONCLUSION GAN has been fully applied to the medical field and will be more deeply and widely used in clinical medicine, especially in the field of privacy protection and medical diagnosis. However, clinical applications of GAN require consideration of ethical and legal issues. GAN-based applications should be well validated by expert radiologists.
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Affiliation(s)
- Fan Zhang
- Radiology department, Huaihe Hospital of Henan University, Kaifeng, PR China
- Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, PR China
| | - Luyao Wang
- School of Computer and Information Engineering, Henan University, Kaifeng, PR China
| | - Jiayin Zhao
- School of Software, Henan University, Kaifeng, PR China
| | - Xinhong Zhang
- School of Software, Henan University, Kaifeng, PR China
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Cao Q, Mao Y, Qin L, Quan G, Yan F, Yang W. Improving image quality and lung nodule detection for low-dose chest CT by using generative adversarial network reconstruction. Br J Radiol 2022; 95:20210125. [PMID: 35994298 PMCID: PMC9815729 DOI: 10.1259/bjr.20210125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Revised: 04/01/2022] [Accepted: 07/21/2022] [Indexed: 01/13/2023] Open
Abstract
OBJECTIVES To investigate the improvement of two denoising models with different learning targets (Dir and Res) of generative adversarial network (GAN) on image quality and lung nodule detectability in chest low-dose CT (LDCT). METHODS In training phase, by using LDCT images simulated from standard dose CT (SDCT) of 200 participants, Dir model was trained targeting SDCT images, while Res model targeting the residual between SDCT and LDCT images. In testing phase, a phantom and 95 chest LDCT, exclusively with training data, were included for evaluation of imaging quality and pulmonary nodules detectability. RESULTS For phantom images, structural similarity, peak signal-to-noise ratio of both Res and Dir models were higher than that of LDCT. Standard deviation of Res model was the lowest. For patient images, image noise and quality of both two models, were better than that of LDCT. Artifacts of Res model was less than that of LDCT. The diagnostic sensitivity of lung nodule by two readers for LDCT, Res and Dir model, were 72/77%, 79/83% and 72/79% respectively. CONCLUSION Two GAN denoising models, including Res and Dir trained with different targets, could effectively reduce image noise of chest LDCT. The image quality evaluation scoring and nodule detectability of Res denoising model was better than that of Dir denoising model and that of hybrid IR images. ADVANCES IN KNOWLEDGE The GAN-trained model, which learned the residual between SDCT and LDCT images, reduced image noise and increased the lung nodule detectability by radiologists on chest LDCT. This demonstrates the potential for clinical benefit.
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Affiliation(s)
- Qiqi Cao
- Department of Radiology, Ruijin Hospital affiliated to School of Medicine, Shanghai Jiao Tong University, Shanghai Jiao Tong, China
| | - Yifu Mao
- Department of CT reconstruction physics algorithm, Shanghai United Imaging Healthcare Co., Ltd, Shanghai, China
| | - Le Qin
- Department of Radiology, Ruijin Hospital affiliated to School of Medicine, Shanghai Jiao Tong University, Shanghai Jiao Tong, China
| | - Guotao Quan
- Department of CT reconstruction physics algorithm, Shanghai United Imaging Healthcare Co., Ltd, Shanghai, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital affiliated to School of Medicine, Shanghai Jiao Tong University, Shanghai Jiao Tong, China
| | - Wenjie Yang
- Department of Radiology, Ruijin Hospital affiliated to School of Medicine, Shanghai Jiao Tong University, Shanghai Jiao Tong, China
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Zhao J, Hou X, Pan M, Zhang H. Attention-based generative adversarial network in medical imaging: A narrative review. Comput Biol Med 2022; 149:105948. [PMID: 35994931 DOI: 10.1016/j.compbiomed.2022.105948] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 07/24/2022] [Accepted: 08/06/2022] [Indexed: 11/18/2022]
Abstract
As a popular probabilistic generative model, generative adversarial network (GAN) has been successfully used not only in natural image processing, but also in medical image analysis and computer-aided diagnosis. Despite the various advantages, the applications of GAN in medical image analysis face new challenges. The introduction of attention mechanisms, which resemble the human visual system that focuses on the task-related local image area for certain information extraction, has drawn increasing interest. Recently proposed transformer-based architectures that leverage self-attention mechanism encode long-range dependencies and learn representations that are highly expressive. This motivates us to summarize the applications of using transformer-based GAN for medical image analysis. We reviewed recent advances in techniques combining various attention modules with different adversarial training schemes, and their applications in medical segmentation, synthesis and detection. Several recent studies have shown that attention modules can be effectively incorporated into a GAN model in detecting lesion areas and extracting diagnosis-related feature information precisely, thus providing a useful tool for medical image processing and diagnosis. This review indicates that research on the medical imaging analysis of GAN and attention mechanisms is still at an early stage despite the great potential. We highlight the attention-based generative adversarial network is an efficient and promising computational model advancing future research and applications in medical image analysis.
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Affiliation(s)
- Jing Zhao
- School of Engineering Medicine, Beihang University, Beijing, 100191, China; School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Xiaoyuan Hou
- School of Engineering Medicine, Beihang University, Beijing, 100191, China; School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Meiqing Pan
- School of Engineering Medicine, Beihang University, Beijing, 100191, China; School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Hui Zhang
- School of Engineering Medicine, Beihang University, Beijing, 100191, China; Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing, 100191, China; Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People's Republic of China, Beijing, 100191, China.
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Mazandarani FN, Marcos L, Babyn P, Alirezaie J. Gradient-based Optimization Algorithm for Hybrid Loss Function in Low-dose CT Denoising. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3834-3838. [PMID: 36085771 DOI: 10.1109/embc48229.2022.9871380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Deep learning techniques have emerged in de-noising low-dose computed tomography (CT) images to avoid the potential health risks of high ionizing radiation dose on patients. Although these post-processing methods display high quality denoised images, the denoising performance still has the potential to improve. The primary purpose of this work was to determine and analyze the most effective and efficient hybrid loss function in deep learning (DL)-based denoising network. Objective functions in deep learning algorithms are the main keys for optimizing the parameters of a network and can affect the quality of the denoised image significantly. Hence, this work examined the various combinations of the most common objective functions in CT denoising networks, namely L1 loss, per-pixel loss, perceptual loss, and structural dissimilarity loss. Further, a hyperparameter learning algorithm was also introduced to find the best scalable factors of the loss functions in each hybrid loss function combination. For simplic-ity, RED-CNN was used in this study to easily demonstrate the performance of the losses during the denoising process. Based on this experiment, the balance between these loss function via the gradient-based optimization algorithm could help in the generalizability prediction of designing future CT denoising networks.
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