To solve the problem that metal artifacts severely damage the clarity of the organization structure in computed tomography（CT） images, a sinogram fusion-based metal artifact correction method is proposed. First, the...To solve the problem that metal artifacts severely damage the clarity of the organization structure in computed tomography（CT） images, a sinogram fusion-based metal artifact correction method is proposed. First, the metal image is segmented from the original CT image by the pre-set threshold. The original CT image and metal image are forward projected into the original projection sinogram and metal projection sinogram, respectively. The interpolation-based correction method and mean filter are used to correct the original CT image and preserve the edge of the corrected CT image, respectively. The filtered CT image is forward projected into the filtered image sinogram. According to the position of the metal sinogram in the original sinogram and filtered image sinogram, the corresponding sinograms PM^D （ in the original sinogram） and PM^C （ in the filtered image sinogram）can be acquired from the original sinogram and filtered image sinogram, respectively. Then, PM^D and PM^C are fused into the fused metal sinogram PM^F according to a certain proportion.The final sinogram can be acquired by fusing PM^F , PM^D and the original sinogram P^O. Finally, the final sinogram is reconstructed into the corrected CT image and metal information is compensated into the corrected CT image.Experiments on clinical images demonstrate that the proposed method can effectively reduce metal artifacts. A comparison with classical metal artifacts correction methods shows that the proposed metal artifacts correction method performs better in metal artifacts suppression and tissue feature preservation.展开更多
The widespread use of computed tomography(CT)in clinical practice has made the public focus on the cumulative radiation dose delivered to patients.Low-dose CT(LDCT)reduces the X-ray radiation dose,yet compromises qual...The widespread use of computed tomography(CT)in clinical practice has made the public focus on the cumulative radiation dose delivered to patients.Low-dose CT(LDCT)reduces the X-ray radiation dose,yet compromises quality and decreases diagnostic performance.Researchers have made great efforts to develop various algorithms for LDCT and introduced deep-learning techniques,which have achieved impressive results.However,most of these methods are directly performed on reconstructed LDCT images,in which some subtle structures and details are readily lost during the reconstruction procedure,and convolutional neural network(CNN)-based methods for raw LDCT projection data are rarely reported.To address this problem,we adopted an attention residual dense CNN,referred to as AttRDN,for LDCT sinogram denoising.First,it was aided by the attention mechanism,in which the advantages of both feature fusion and global residual learning were used to extract noise from the contaminated LDCT sinograms.Then,the denoised sinogram was restored by subtracting the noise obtained from the input noisy sinogram.Finally,the CT image was reconstructed using filtered back-projection.The experimental results qualitatively and quantitatively demonstrate that the proposed AttRDN can achieve a better performance than state-of-the-art methods.Importantly,it can prevent the loss of detailed information and has the potential for clinical application.展开更多
In sparse-angle X-ray tomography reconstruction, where only a small number of projection images are taken around the object, appropriate sinogram interpolation has a significant impact on image quality. A novel sinogr...In sparse-angle X-ray tomography reconstruction, where only a small number of projection images are taken around the object, appropriate sinogram interpolation has a significant impact on image quality. A novel sinogram interpolation method is introduced for extreme sparse tomographic reconstruction where only nine measured projection images are available. The sinogram is interpolated by solving characteristics of the so-called warps, which can be considered as approximation sine waves in a limited region. The numerical evidence suggests that this approach gives superior results over standard interpolation methods when the tomographic data are extremely sparse and noisy.展开更多
Computer aided detection(CADe)of pulmonary nodules plays an important role in assisting radiologists’diagnosis and alleviating interpretation burden for lung cancer.Current CADe systems,aiming at simulating radiologi...Computer aided detection(CADe)of pulmonary nodules plays an important role in assisting radiologists’diagnosis and alleviating interpretation burden for lung cancer.Current CADe systems,aiming at simulating radiologists’examination procedure,are built upon computer tomography(CT)images with feature extraction for detection and diagnosis.Human visual perception in CT image is reconstructed from sinogram,which is the original raw data acquired from CT scanner.In this work,different from the conventional image based CADe system,we propose a novel sinogram based CADe system in which the full projection information is used to explore additional effective features of nodules in the sinogram domain.Facing the challenges of limited research in this concept and unknown effective features in the sinogram domain,we design a new CADe system that utilizes the self-learning power of the convolutional neural network to learn and extract effective features from sinogram.The proposed system was validated on 208 patient cases from the publicly available online Lung Image Database Consortium database,with each case having at least one juxtapleural nodule annotation.Experimental results demonstrated that our proposed method obtained a value of 0.91 of the area under the curve(AUC)of receiver operating characteristic based on sinogram alone,comparing to 0.89 based on CT image alone.Moreover,a combination of sinogram and CT image could further improve the value of AUC to 0.92.This study indicates that pulmonary nodule detection in the sinogram domain is feasible with deep learning.展开更多
X-ray computed tomography （CT） is one of widely used diagnostic tools for medical and dental tomographic imaging of the human body. However, the standard filtered back- projection reconstruction method requires the ...X-ray computed tomography （CT） is one of widely used diagnostic tools for medical and dental tomographic imaging of the human body. However, the standard filtered back- projection reconstruction method requires the complete knowledge of the projection data. In the case of limited data, the inverse problem of CT becomes more ill-posed, which makes the reconstructed image deteriorated by the artifacts. In this paper, we consider two dimensional CT reconstruction using the projections truncated along the spatial direc- tion in the Radon domain. Over the decades, the numerous results including the sparsity model based approach has enabled the reconstruction of the image inside the region of interest （ROI） from the limited knowledge of the data. However, unlike these existing methods, we try to reconstruct the entire CT image from the limited knowledge of the sinogram via the tight frame regularization and the simultaneous sinogram extrapolation. Our proposed model shows more promising numerical simulation results compared with the existing sparsity model based approach.展开更多
基金Open Research Fund of the Key Laboratory of Computer Netw ork and Information Integration of Ministry of Education of Southeast University(No.K93-9-2014-10C)the Scientific Research Foundation of Education Department of Anhui Province(No.KJ2014A186,SK2015A433)the National Basic Research Program of China(973 Program)(No.2010CB732503)
文摘To solve the problem that metal artifacts severely damage the clarity of the organization structure in computed tomography（CT） images, a sinogram fusion-based metal artifact correction method is proposed. First, the metal image is segmented from the original CT image by the pre-set threshold. The original CT image and metal image are forward projected into the original projection sinogram and metal projection sinogram, respectively. The interpolation-based correction method and mean filter are used to correct the original CT image and preserve the edge of the corrected CT image, respectively. The filtered CT image is forward projected into the filtered image sinogram. According to the position of the metal sinogram in the original sinogram and filtered image sinogram, the corresponding sinograms PM^D （ in the original sinogram） and PM^C （ in the filtered image sinogram）can be acquired from the original sinogram and filtered image sinogram, respectively. Then, PM^D and PM^C are fused into the fused metal sinogram PM^F according to a certain proportion.The final sinogram can be acquired by fusing PM^F , PM^D and the original sinogram P^O. Finally, the final sinogram is reconstructed into the corrected CT image and metal information is compensated into the corrected CT image.Experiments on clinical images demonstrate that the proposed method can effectively reduce metal artifacts. A comparison with classical metal artifacts correction methods shows that the proposed metal artifacts correction method performs better in metal artifacts suppression and tissue feature preservation.
基金This work was supported in part by the National Key R&D Program of China(Nos.2016YFC0104609 and 2019YFC0605203)The Fundamental Research Funds for the Central Universities(Nos.2019CDYGYB019 and 2020CDJ-LHZZ-075)。
文摘The widespread use of computed tomography(CT)in clinical practice has made the public focus on the cumulative radiation dose delivered to patients.Low-dose CT(LDCT)reduces the X-ray radiation dose,yet compromises quality and decreases diagnostic performance.Researchers have made great efforts to develop various algorithms for LDCT and introduced deep-learning techniques,which have achieved impressive results.However,most of these methods are directly performed on reconstructed LDCT images,in which some subtle structures and details are readily lost during the reconstruction procedure,and convolutional neural network(CNN)-based methods for raw LDCT projection data are rarely reported.To address this problem,we adopted an attention residual dense CNN,referred to as AttRDN,for LDCT sinogram denoising.First,it was aided by the attention mechanism,in which the advantages of both feature fusion and global residual learning were used to extract noise from the contaminated LDCT sinograms.Then,the denoised sinogram was restored by subtracting the noise obtained from the input noisy sinogram.Finally,the CT image was reconstructed using filtered back-projection.The experimental results qualitatively and quantitatively demonstrate that the proposed AttRDN can achieve a better performance than state-of-the-art methods.Importantly,it can prevent the loss of detailed information and has the potential for clinical application.
文摘In sparse-angle X-ray tomography reconstruction, where only a small number of projection images are taken around the object, appropriate sinogram interpolation has a significant impact on image quality. A novel sinogram interpolation method is introduced for extreme sparse tomographic reconstruction where only nine measured projection images are available. The sinogram is interpolated by solving characteristics of the so-called warps, which can be considered as approximation sine waves in a limited region. The numerical evidence suggests that this approach gives superior results over standard interpolation methods when the tomographic data are extremely sparse and noisy.
基金This work was partially supported by the NIH/NCI grant#CA206171 of the National Cancer Institute and the PSC-CUNY award 62310–0050.
文摘Computer aided detection(CADe)of pulmonary nodules plays an important role in assisting radiologists’diagnosis and alleviating interpretation burden for lung cancer.Current CADe systems,aiming at simulating radiologists’examination procedure,are built upon computer tomography(CT)images with feature extraction for detection and diagnosis.Human visual perception in CT image is reconstructed from sinogram,which is the original raw data acquired from CT scanner.In this work,different from the conventional image based CADe system,we propose a novel sinogram based CADe system in which the full projection information is used to explore additional effective features of nodules in the sinogram domain.Facing the challenges of limited research in this concept and unknown effective features in the sinogram domain,we design a new CADe system that utilizes the self-learning power of the convolutional neural network to learn and extract effective features from sinogram.The proposed system was validated on 208 patient cases from the publicly available online Lung Image Database Consortium database,with each case having at least one juxtapleural nodule annotation.Experimental results demonstrated that our proposed method obtained a value of 0.91 of the area under the curve(AUC)of receiver operating characteristic based on sinogram alone,comparing to 0.89 based on CT image alone.Moreover,a combination of sinogram and CT image could further improve the value of AUC to 0.92.This study indicates that pulmonary nodule detection in the sinogram domain is feasible with deep learning.
文摘X-ray computed tomography （CT） is one of widely used diagnostic tools for medical and dental tomographic imaging of the human body. However, the standard filtered back- projection reconstruction method requires the complete knowledge of the projection data. In the case of limited data, the inverse problem of CT becomes more ill-posed, which makes the reconstructed image deteriorated by the artifacts. In this paper, we consider two dimensional CT reconstruction using the projections truncated along the spatial direc- tion in the Radon domain. Over the decades, the numerous results including the sparsity model based approach has enabled the reconstruction of the image inside the region of interest （ROI） from the limited knowledge of the data. However, unlike these existing methods, we try to reconstruct the entire CT image from the limited knowledge of the sinogram via the tight frame regularization and the simultaneous sinogram extrapolation. Our proposed model shows more promising numerical simulation results compared with the existing sparsity model based approach.
