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Zhong H, Huang Q, Zheng X, Wang Y, Qian Y, Chen X, Wang J, Duan S. Generation of virtual monoenergetic images at 40 keV of the upper abdomen and image quality evaluation based on generative adversarial networks. BMC Med Imaging 2024; 24:151. [PMID: 38890572 PMCID: PMC11184875 DOI: 10.1186/s12880-024-01331-3] [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: 04/11/2024] [Accepted: 06/11/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Abdominal CT scans are vital for diagnosing abdominal diseases but have limitations in tissue analysis and soft tissue detection. Dual-energy CT (DECT) can improve these issues by offering low keV virtual monoenergetic images (VMI), enhancing lesion detection and tissue characterization. However, its cost limits widespread use. PURPOSE To develop a model that converts conventional images (CI) into generative virtual monoenergetic images at 40 keV (Gen-VMI40keV) of the upper abdomen CT scan. METHODS Totally 444 patients who underwent upper abdominal spectral contrast-enhanced CT were enrolled and assigned to the training and validation datasets (7:3). Then, 40-keV portal-vein virtual monoenergetic (VMI40keV) and CI, generated from spectral CT scans, served as target and source images. These images were employed to build and train a CI-VMI40keV model. Indexes such as Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity (SSIM) were utilized to determine the best generator mode. An additional 198 cases were divided into three test groups, including Group 1 (58 cases with visible abnormalities), Group 2 (40 cases with hepatocellular carcinoma [HCC]) and Group 3 (100 cases from a publicly available HCC dataset). Both subjective and objective evaluations were performed. Comparisons, correlation analyses and Bland-Altman plot analyses were performed. RESULTS The 192nd iteration produced the best generator mode (lower MAE and highest PSNR and SSIM). In the Test groups (1 and 2), both VMI40keV and Gen-VMI40keV significantly improved CT values, as well as SNR and CNR, for all organs compared to CI. Significant positive correlations for objective indexes were found between Gen-VMI40keV and VMI40keV in various organs and lesions. Bland-Altman analysis showed that the differences between both imaging types mostly fell within the 95% confidence interval. Pearson's and Spearman's correlation coefficients for objective scores between Gen-VMI40keV and VMI40keV in Groups 1 and 2 ranged from 0.645 to 0.980. In Group 3, Gen-VMI40keV yielded significantly higher CT values for HCC (220.5HU vs. 109.1HU) and liver (220.0HU vs. 112.8HU) compared to CI (p < 0.01). The CNR for HCC/liver was also significantly higher in Gen-VMI40keV (2.0 vs. 1.2) than in CI (p < 0.01). Additionally, Gen-VMI40keV was subjectively evaluated to have a higher image quality compared to CI. CONCLUSION CI-VMI40keV model can generate Gen-VMI40keV from conventional CT scan, closely resembling VMI40keV.
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Affiliation(s)
- Hua Zhong
- Department of Radiology, ZhongShan Hospital of Xiamen University, School of Medicine, Xiamen University, Hubinnan Road, Siming District, Xiamen, Fujian, 361004, China.
| | - Qianwen Huang
- Department of Radiology, ZhongShan Hospital of Xiamen University, School of Medicine, Xiamen University, Hubinnan Road, Siming District, Xiamen, Fujian, 361004, China
| | - Xiaoli Zheng
- Department of Radiology, ZhongShan Hospital of Xiamen University, School of Medicine, Xiamen University, Hubinnan Road, Siming District, Xiamen, Fujian, 361004, China
| | - Yong Wang
- Department of Radiology, ZhongShan Hospital of Xiamen University, School of Medicine, Xiamen University, Hubinnan Road, Siming District, Xiamen, Fujian, 361004, China
| | - Yanan Qian
- Department of Radiology, ZhongShan Hospital of Xiamen University, School of Medicine, Xiamen University, Hubinnan Road, Siming District, Xiamen, Fujian, 361004, China
| | - Xingbiao Chen
- Clinical Science, Philips Healthcare, Shanghai, China
| | - Jinan Wang
- Department of Radiology, ZhongShan Hospital of Xiamen University, School of Medicine, Xiamen University, Hubinnan Road, Siming District, Xiamen, Fujian, 361004, China
| | - Shaoyin Duan
- Department of Radiology, ZhongShan Hospital of Xiamen University, School of Medicine, Xiamen University, Hubinnan Road, Siming District, Xiamen, Fujian, 361004, China
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Peng J, Chang CW, Xie H, Qiu RLJ, Roper J, Wang T, Ghavidel B, Tang X, Yang X. Image-domain material decomposition for dual-energy CT using unsupervised learning with data-fidelity loss. Med Phys 2024. [PMID: 38865687 DOI: 10.1002/mp.17255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 05/27/2024] [Accepted: 05/28/2024] [Indexed: 06/14/2024] Open
Abstract
BACKGROUND Dual-energy computed tomography (DECT) and material decomposition play vital roles in quantitative medical imaging. However, the decomposition process may suffer from significant noise amplification, leading to severely degraded image signal-to-noise ratios (SNRs). While existing iterative algorithms perform noise suppression using different image priors, these heuristic image priors cannot accurately represent the features of the target image manifold. Although deep learning-based decomposition methods have been reported, these methods are in the supervised-learning framework requiring paired data for training, which is not readily available in clinical settings. PURPOSE This work aims to develop an unsupervised-learning framework with data-measurement consistency for image-domain material decomposition in DECT. METHODS The proposed framework combines iterative decomposition and deep learning-based image prior in a generative adversarial network (GAN) architecture. In the generator module, a data-fidelity loss is introduced to enforce the measurement consistency in material decomposition. In the discriminator module, the discriminator is trained to differentiate the low-noise material-specific images from the high-noise images. In this scheme, paired images of DECT and ground-truth material-specific images are not required for the model training. Once trained, the generator can perform image-domain material decomposition with noise suppression in a single step. RESULTS In the simulation studies of head and lung digital phantoms, the proposed method reduced the standard deviation (SD) in decomposed images by 97% and 91% from the values in direct inversion results. It also generated decomposed images with structural similarity index measures (SSIMs) greater than 0.95 against the ground truth. In the clinical head and lung patient studies, the proposed method suppressed the SD by 95% and 93% compared to the decomposed images of matrix inversion. CONCLUSIONS Since the invention of DECT, noise amplification during material decomposition has been one of the biggest challenges, impeding its quantitative use in clinical practice. The proposed method performs accurate material decomposition with efficient noise suppression. Furthermore, the proposed method is within an unsupervised-learning framework, which does not require paired data for model training and resolves the issue of lack of ground-truth data in clinical scenarios.
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Affiliation(s)
- Junbo Peng
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Chih-Wei Chang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Huiqiao Xie
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Richard L J Qiu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Justin Roper
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Tonghe Wang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Beth Ghavidel
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Xiangyang Tang
- Department of Radiology and Imaging Sciences and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
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Shi Z, Kong F, Cheng M, Cao H, Ouyang S, Cao Q. Multi-energy CT material decomposition using graph model improved CNN. Med Biol Eng Comput 2024; 62:1213-1228. [PMID: 38159238 DOI: 10.1007/s11517-023-02986-w] [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: 04/12/2023] [Accepted: 11/30/2023] [Indexed: 01/03/2024]
Abstract
In spectral CT imaging, the coefficient image of the basis material obtained by the material decomposition technique can estimate the tissue composition, and its accuracy directly affects the disease diagnosis. Although the precision of material decomposition is increased by employing convolutional neural networks (CNN), extracting the non-local features from the CT image is restricted using the traditional CNN convolution operator. A graph model built by multi-scale non-local self-similar patterns is introduced into multi-material decomposition (MMD). We proposed a novel MMD method based on graph edge-conditioned convolution U-net (GECCU-net) to enhance material image quality. The GECCU-net focuses on developing a multi-scale encoder. At the network coding stage, three paths are applied to capture comprehensive image features. The local and non-local feature aggregation (LNFA) blocks are designed to integrate the local and non-local features from different paths. The graph edge-conditioned convolution based on non-Euclidean space excavates the non-local features. A hybrid loss function is defined to accommodate multi-scale input images and avoid over-smoothing of results. The proposed network is compared quantitatively with base CNN models on the simulated and real datasets. The material images generated by GECCU-net have less noise and artifacts while retaining more information on tissue. The Structural SIMilarity (SSIM) of the obtained abdomen and chest water maps reaches 0.9976 and 0.9990, respectively, and the RMSE reduces to 0.1218 and 0.4903 g/cm3. The proposed method can improve MMD performance and has potential applications.
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Affiliation(s)
- Zaifeng Shi
- School of Microelectronics, Tianjin University, Tianjin, 300072, China.
- Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology, Tianjin, China.
| | - Fanning Kong
- School of Microelectronics, Tianjin University, Tianjin, 300072, China
| | - Ming Cheng
- School of Microelectronics, Tianjin University, Tianjin, 300072, China
| | - Huaisheng Cao
- School of Microelectronics, Tianjin University, Tianjin, 300072, China
| | - Shunxin Ouyang
- School of Microelectronics, Tianjin University, Tianjin, 300072, China
| | - Qingjie Cao
- School of Mathematical Sciences, Tianjin Normal University, Tianjin, 300387, China
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Bousse A, Kandarpa VSS, Rit S, Perelli A, Li M, Wang G, Zhou J, Wang G. Systematic Review on Learning-based Spectral CT. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2024; 8:113-137. [PMID: 38476981 PMCID: PMC10927029 DOI: 10.1109/trpms.2023.3314131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
Abstract
Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: dual-energy computed tomography (DECT) and photon-counting computed tomography (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT.
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Affiliation(s)
- Alexandre Bousse
- LaTIM, Inserm UMR 1101, Université de Bretagne Occidentale, 29238 Brest, France
| | | | - Simon Rit
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Étienne, CNRS, Inserm, CREATIS UMR 5220, U1294, F-69373, Lyon, France
| | - Alessandro Perelli
- Department of Biomedical Engineering, School of Science and Engineering, University of Dundee, DD1 4HN, UK
| | - Mengzhou Li
- Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, New York, USA
| | - Guobao Wang
- Department of Radiology, University of California Davis Health, Sacramento, USA
| | - Jian Zhou
- CTIQ, Canon Medical Research USA, Inc., Vernon Hills, 60061, USA
| | - Ge Wang
- Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, New York, USA
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Ren J, Zhang W, Wang Y, Liang N, Wang L, Cai A, Wang S, Zheng Z, Li L, Yan B. A dual-energy CT reconstruction method based on anchor network from dual quarter scans. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024; 32:229-252. [PMID: 38306088 DOI: 10.3233/xst-230245] [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: 02/03/2024]
Abstract
Compared with conventional single-energy computed tomography (CT), dual-energy CT (DECT) provides better material differentiation but most DECT imaging systems require dual full-angle projection data at different X-ray spectra. Relaxing the requirement of data acquisition is an attractive research to promote the applications of DECT in wide range areas and reduce the radiation dose as low as reasonably achievable. In this work, we design a novel DECT imaging scheme with dual quarter scans and propose an efficient method to reconstruct the desired DECT images from the dual limited-angle projection data. We first study the characteristics of limited-angle artifacts under dual quarter scans scheme, and find that the negative and positive artifacts of DECT images are complementarily distributed in image domain because the corresponding X-rays of high- and low-energy scans are symmetric. Inspired by this finding, a fusion CT image is generated by integrating the limited-angle DECT images of dual quarter scans. This strategy enhances the true image information and suppresses the limited-angle artifacts, thereby restoring the image edges and inner structures. Utilizing the capability of neural network in the modeling of nonlinear problem, a novel Anchor network with single-entry double-out architecture is designed in this work to yield the desired DECT images from the generated fusion CT image. Experimental results on the simulated and real data verify the effectiveness of the proposed method. This work enables DECT on imaging configurations with half-scan and largely reduces scanning angles and radiation doses.
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Affiliation(s)
- Junru Ren
- Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, P.R. China
| | - Wenkun Zhang
- Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, P.R. China
| | - YiZhong Wang
- Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, P.R. China
| | - Ningning Liang
- Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, P.R. China
| | - Linyuan Wang
- Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, P.R. China
| | - Ailong Cai
- Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, P.R. China
| | - Shaoyu Wang
- Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, P.R. China
| | - Zhizhong Zheng
- Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, P.R. China
| | - Lei Li
- Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, P.R. China
| | - Bin Yan
- Key Laboratory of Imaging and Intelligent Processing of Henan Province, PLA Strategic Support Force Information Engineering University, Zhengzhou, P.R. China
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Shi Z, Wang N, Kong F, Cao H, Cao Q. A semi-supervised learning method of latent features based on convolutional neural networks for CT metal artifact reduction. Med Phys 2022; 49:3845-3859. [PMID: 35322430 DOI: 10.1002/mp.15633] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 02/15/2022] [Accepted: 03/15/2022] [Indexed: 11/09/2022] Open
Abstract
PURPOSE X-ray computed tomography (CT) has become a convenient and efficient clinical medical technique. However, in the presence of metal implants, CT images may be corrupted by metal artifacts. The metal artifact reduction (MAR) methods based on deep learning are mostly supervised methods trained with labeled synthetic-artifact CT images. However, this causes the neural network to be biased toward learning specific synthetic-artifact patterns and leads to a poor generalization for unlabeled real-artifact CT images. In this study, a semi-supervised learning method of latent features based on convolutional neural networks (SLF-CNN) is developed to remove metal artifacts while ensuring a good generalization ability for real-artifact CT images. METHODS The proposed semi-supervised method extracts CT image features in alternate iterations of a synthetic-artifact learning stage and a real-artifact learning stage. In the synthetic-artifact learning stage, SLF-CNN is fed with paired synthetic-artifact CT images and is constrained using mean-squared-error (MSE) loss and Perceptual loss in a supervised learning fashion. In the real-artifact learning stage, the network weight is updated by minimizing the error between the pseudo-ground truths and the predicted latent features. The feature level pseudo-ground truths are obtained by modeling latent features using the Gaussian process. The overall framework of SLF-CNN adopts an encoder-decoder structure. The encoder is composed of artifact information collection groups to map the input artifact-affected synthetic-artifact CT images and real-artifact CT images into latent features. The decoder is composed of stacked ResNeXt blocks and is responsible for decoding latent features with high-level semantic information to reconstruct artifact-free CT images. The performance of the proposed method is evaluated through contrast experiments and ablation experiments. RESULTS The contrast experimental results indicate that the artifact-free CT images obtained by SLF-CNN have good metrics values, which are close to or better than those of typical supervised MAR methods. The metal artifacts in artifact-affected CT images are eliminated and the tissue structure details are preserved using SLF-CNN. In the ablation experiment shows that adding real-artifact CT images greatly improves the generalization ability of the network. CONCLUSIONS The proposed semi-supervised learning method of latent features for MAR effectively suppresses metal artifacts and improves the generalization ability of the network. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Zaifeng Shi
- School of Microelectronics, Tianjin University, Tianjin, 300072, China.,Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology, Tianjin, 300072, China
| | - Ning Wang
- School of Microelectronics, Tianjin University, Tianjin, 300072, China
| | - Fanning Kong
- School of Microelectronics, Tianjin University, Tianjin, 300072, China
| | - Huaisheng Cao
- School of Microelectronics, Tianjin University, Tianjin, 300072, China
| | - Qingjie Cao
- School of Mathematical Sciences, Tianjin Normal University, Tianjin, 300387, China
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