In this paper, a low-dose CT denoising method based on L1/L2regularization method of Markov chain Monte Carlo is studied. Firstly, the mathematical model and regularization method of low-dose CT denoising are summariz...In this paper, a low-dose CT denoising method based on L1/L2regularization method of Markov chain Monte Carlo is studied. Firstly, the mathematical model and regularization method of low-dose CT denoising are summarized, and then the theoretical basis of MCMC method and its application in image denoising are introduced. We evaluated the performance of various regularization strategies by comparing the denoising effects of L1, L2, and L1/L2regularization terms in MCMC sampling at Gaussian noise levels. The experimental results show that L1/L2regularization has the best performance in balancing noise removal and image detail retention, significantly superior to single L1and L2regularization, which proves its effectiveness for low-dose CT denoising.展开更多
In order to solve the problems of artifacts and noise in low-dose computed tomography(CT)images in clinical medical diagnosis,an improved image denoising algorithm under the architecture of generative adversarial netw...In order to solve the problems of artifacts and noise in low-dose computed tomography(CT)images in clinical medical diagnosis,an improved image denoising algorithm under the architecture of generative adversarial network(GAN)was proposed.First,a noise model based on style GAN2 was constructed to estimate the real noise distribution,and the noise information similar to the real noise distribution was generated as the experimental noise data set.Then,a network model with encoder-decoder architecture as the core based on GAN idea was constructed,and the network model was trained with the generated noise data set until it reached the optimal value.Finally,the noise and artifacts in low-dose CT images could be removed by inputting low-dose CT images into the denoising network.The experimental results showed that the constructed network model based on GAN architecture improved the utilization rate of noise feature information and the stability of network training,removed image noise and artifacts,and reconstructed image with rich texture and realistic visual effect.展开更多
Background: Mortality outcomes in trials of low-dose computed tomography(CT) screening for lung cancer are inconsistent. This study aimed to evaluate whether CT screening in urban areas of China could reduce lung canc...Background: Mortality outcomes in trials of low-dose computed tomography(CT) screening for lung cancer are inconsistent. This study aimed to evaluate whether CT screening in urban areas of China could reduce lung cancer mortality and to investigate the factors that associate with the screening effect.Methods: A decision tree model with three scenarios(low-dose CT screening, chest X-ray screening, and no screening) was developed to compare screening results in a simulated Chinese urban cohort(100,000 smokers aged45-80 years). Data of participant characteristics were obtained from national registries and epidemiological surveys for estimating lung cancer prevalence. The selection of other tree variables such as sensitivities and specificities of low-dose CT and chest X-ray screening were based on literature research. Differences in lung cancer mortality(primary outcome), false diagnoses, and deaths due to false diagnosis were calculated. Sensitivity analyses were performed to identify the factors that associate with the screening results and to ascertain worst and optimal screening effects considering possible ranges of the variables.Results: Among the 100,000 subjects, there were 448,541, and 591 lung cancer deaths in the low-dose CT, chest X-ray, and no screening scenarios, respectively(17.2% reduction in low-dose CT screening over chest X-ray screening and 24.2% over no screening). The costs of the two screening scenarios were 9387 and 2497 false diagnoses and 7and 2 deaths due to false diagnosis among the 100,000 persons, respectively. The factors that most influenced death reduction with low-dose CT screening over no screening were lung cancer prevalence in the screened cohort, lowdose CT sensitivity, and proportion of early-stage cancers among low-dose CT detected lung cancers. Considering all possibilities, reduction in deaths(relative numbers) with low-dose CT screening in the worst and optimal cases were16(5.4%) and 288(40.2%) over no screening, respectively.Conclusions: In terms of mortality outcomes, our findings favor conducting low-dose CT screening in urban China.However, approaches to reducing false diagnoses and optimizing important screening conditions such as enrollment criteria for screening are highly needed.展开更多
Objective: To assess the value of helical CT in the di- agnosis of liver diseases. Methods: 59 patients with different liver diseases were examined by two-phase or multi-phase dynamic helical CT. Results: Small hepato...Objective: To assess the value of helical CT in the di- agnosis of liver diseases. Methods: 59 patients with different liver diseases were examined by two-phase or multi-phase dynamic helical CT. Results: Small hepatocellular carcinoma showed a higher density in the arterial phase, and a lower den- sity in the portal vein phase. Large hepatic carcino- ma showed a mixed pattern of higher-density in the arterial phase, and a lower density in the portal vein phase. Metastasis carcinoma showed an 'oxeye sign' in the portal vein phase. Hemangioma was not obvi- ously enhanced in the early arterial phase, marginal- ly enhanced in the arterial phase, and equally-densed in the balanced phase. Conclusion: Two-phase helical CT is of value in im- proving the detection rate of or determining the fea- tures of hepatic diseases by two-phase helical dyna- mic scan (2.0-3.0 ml/s speed, and delay time 25- 30 s and 70-85 s).展开更多
In order to improve the quality of low-dose computational tomography (CT)images, the paper proposes an improved image denoising approach based on WGAN-gpwith Wasserstein distance. For improving the training and the co...In order to improve the quality of low-dose computational tomography (CT)images, the paper proposes an improved image denoising approach based on WGAN-gpwith Wasserstein distance. For improving the training and the convergence efficiency, thegiven method introduces the gradient penalty term to WGAN network. The novelperceptual loss is introduced to make the texture information of the low-dose imagessensitive to the diagnostician eye. The experimental results show that compared with thestate-of-art methods, the time complexity is reduced, and the visual quality of low-doseCT images is significantly improved.展开更多
In this paper,we propose Hformer,a novel supervised learning model for low-dose computer tomography(LDCT)denoising.Hformer combines the strengths of convolutional neural networks for local feature extraction and trans...In this paper,we propose Hformer,a novel supervised learning model for low-dose computer tomography(LDCT)denoising.Hformer combines the strengths of convolutional neural networks for local feature extraction and transformer models for global feature capture.The performance of Hformer was verified and evaluated based on the AAPM-Mayo Clinic LDCT Grand Challenge Dataset.Compared with the former representative state-of-the-art(SOTA)model designs under different architectures,Hformer achieved optimal metrics without requiring a large number of learning parameters,with metrics of33.4405 PSNR,8.6956 RMSE,and 0.9163 SSIM.The experiments demonstrated designed Hformer is a SOTA model for noise suppression,structure preservation,and lesion detection.展开更多
Cerebral perfusion computed tomography(PCT)is an important imaging modality for evaluating cerebrovascular diseases and stroke symptoms.With widespread public concern about the potential cancer risks and health hazard...Cerebral perfusion computed tomography(PCT)is an important imaging modality for evaluating cerebrovascular diseases and stroke symptoms.With widespread public concern about the potential cancer risks and health hazards associated with cumulative radiation exposure in PCT imaging,considerable research has been conducted to reduce the radiation dose in X-ray-based brain perfusion imaging.Reducing the dose of X-rays causes severe noise and artifacts in PCT images.To solve this problem,we propose a deep learning method called NCS-Unet.The exceptional characteristics of non-subsampled contourlet transform(NSCT)and the Sobel filter are introduced into NCS-Unet.NSCT decomposes the convolved features into high-and low-frequency components.The decomposed high-frequency component retains image edges,contrast imaging traces,and noise,whereas the low-frequency component retains the main image information.The Sobel filter extracts the contours of the original image and the imaging traces caused by the contrast agent decay.The extracted information is added to NCS-Unet to improve its performance in noise reduction and artifact removal.Qualitative and quantitative analyses demonstrated that the proposed NCS-Unet can improve the quality of low-dose cone-beam CT perfusion reconstruction images and the accuracy of perfusion parameter calculations.展开更多
In helical cone-beam computed tomography(CT), Feldkamp-Davis-Kress(FDK) based image reconstruction algorithms are by far the most popular. However, artifacts are commonly met in the presence of lateral projection trun...In helical cone-beam computed tomography(CT), Feldkamp-Davis-Kress(FDK) based image reconstruction algorithms are by far the most popular. However, artifacts are commonly met in the presence of lateral projection truncation. The reason is that the ramp filter is global. To restrain the truncation artifacts, an approximate reconstruction formula is proposed based on the Derivative-Hilbert-Backprojection(DHB) framework. In the method, the first order derivative filter is followed by the Hilbert transform. Since the filtered projection values are almost zero by the first order derivative filter, the following Hilbert transform has little influence on the projection values, even though the projections are laterally truncated. The proposed method has two main advantages. First, it has comparable computational efficiency and image quality as well as the conventional helical FDK algorithm for non-truncated projections. The second advantage is that images can be reconstructed with acceptable quality and much lower computational cost in comparison to the Laplace operator based algorithm in cases with truncated projections. To point out the advantages of our method, simulations on the computer and real data experiments on our laboratory industrial cone-beam CT are conducted. The simulated and experimental results demonstrate that the method is feasible for image reconstruction in the case of projection truncation.展开更多
To explore the value of helical CT volume rendering technique (VRT) in post-operative evaluation of screw fixation of axis fractures.Methods There were 21 cases of screw fixation of axis fractures between February 200...To explore the value of helical CT volume rendering technique (VRT) in post-operative evaluation of screw fixation of axis fractures.Methods There were 21 cases of screw fixation of axis fractures between February 2002 and May 2004 in the study including six cases with fractures on axis body,five on odontoid process and 10 on axis body and odontoid process.All cases received X-ray plain film,helical CT scanning,multi-planar reformatting(MPR) and VRT.Results Screw fixation through axis body and massa lateralis atlantis was performed in 10 cases and that through axis body and odontoid process in 11.VRT could clearly display full aperture of screw orbit,location of screw and angle of fixation and hence was superior to X-ray plain film and MPR.Multi-angle VRT displayed asymmetrical space of odontoid process and massa lateralis atlantis in four cases and medial deviation of 2～5 mm of half screw in screw fixation through axis body and massa lateralis atlantis in six.Conclusion VRT can eliminate false shadow of fixation screw,clearly display full aperture of screw orbit and hence supply improtant imaging evidence for post-operative evaluation of screw fixation of axis fractures.7 refs,1 fig,1 tab.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
文摘In this paper, a low-dose CT denoising method based on L1/L2regularization method of Markov chain Monte Carlo is studied. Firstly, the mathematical model and regularization method of low-dose CT denoising are summarized, and then the theoretical basis of MCMC method and its application in image denoising are introduced. We evaluated the performance of various regularization strategies by comparing the denoising effects of L1, L2, and L1/L2regularization terms in MCMC sampling at Gaussian noise levels. The experimental results show that L1/L2regularization has the best performance in balancing noise removal and image detail retention, significantly superior to single L1and L2regularization, which proves its effectiveness for low-dose CT denoising.
基金supported by National Natural Science Foundation of China(No.11802272)China Postdoctoral Science Foundation(No.2019M651085)。
文摘In order to solve the problems of artifacts and noise in low-dose computed tomography(CT)images in clinical medical diagnosis,an improved image denoising algorithm under the architecture of generative adversarial network(GAN)was proposed.First,a noise model based on style GAN2 was constructed to estimate the real noise distribution,and the noise information similar to the real noise distribution was generated as the experimental noise data set.Then,a network model with encoder-decoder architecture as the core based on GAN idea was constructed,and the network model was trained with the generated noise data set until it reached the optimal value.Finally,the noise and artifacts in low-dose CT images could be removed by inputting low-dose CT images into the denoising network.The experimental results showed that the constructed network model based on GAN architecture improved the utilization rate of noise feature information and the stability of network training,removed image noise and artifacts,and reconstructed image with rich texture and realistic visual effect.
基金supported by Peking Union Medical College Youth Fund and the Fundamental Research Funds for the Central Universities(No.2017310049)
文摘Background: Mortality outcomes in trials of low-dose computed tomography(CT) screening for lung cancer are inconsistent. This study aimed to evaluate whether CT screening in urban areas of China could reduce lung cancer mortality and to investigate the factors that associate with the screening effect.Methods: A decision tree model with three scenarios(low-dose CT screening, chest X-ray screening, and no screening) was developed to compare screening results in a simulated Chinese urban cohort(100,000 smokers aged45-80 years). Data of participant characteristics were obtained from national registries and epidemiological surveys for estimating lung cancer prevalence. The selection of other tree variables such as sensitivities and specificities of low-dose CT and chest X-ray screening were based on literature research. Differences in lung cancer mortality(primary outcome), false diagnoses, and deaths due to false diagnosis were calculated. Sensitivity analyses were performed to identify the factors that associate with the screening results and to ascertain worst and optimal screening effects considering possible ranges of the variables.Results: Among the 100,000 subjects, there were 448,541, and 591 lung cancer deaths in the low-dose CT, chest X-ray, and no screening scenarios, respectively(17.2% reduction in low-dose CT screening over chest X-ray screening and 24.2% over no screening). The costs of the two screening scenarios were 9387 and 2497 false diagnoses and 7and 2 deaths due to false diagnosis among the 100,000 persons, respectively. The factors that most influenced death reduction with low-dose CT screening over no screening were lung cancer prevalence in the screened cohort, lowdose CT sensitivity, and proportion of early-stage cancers among low-dose CT detected lung cancers. Considering all possibilities, reduction in deaths(relative numbers) with low-dose CT screening in the worst and optimal cases were16(5.4%) and 288(40.2%) over no screening, respectively.Conclusions: In terms of mortality outcomes, our findings favor conducting low-dose CT screening in urban China.However, approaches to reducing false diagnoses and optimizing important screening conditions such as enrollment criteria for screening are highly needed.
文摘Objective: To assess the value of helical CT in the di- agnosis of liver diseases. Methods: 59 patients with different liver diseases were examined by two-phase or multi-phase dynamic helical CT. Results: Small hepatocellular carcinoma showed a higher density in the arterial phase, and a lower den- sity in the portal vein phase. Large hepatic carcino- ma showed a mixed pattern of higher-density in the arterial phase, and a lower density in the portal vein phase. Metastasis carcinoma showed an 'oxeye sign' in the portal vein phase. Hemangioma was not obvi- ously enhanced in the early arterial phase, marginal- ly enhanced in the arterial phase, and equally-densed in the balanced phase. Conclusion: Two-phase helical CT is of value in im- proving the detection rate of or determining the fea- tures of hepatic diseases by two-phase helical dyna- mic scan (2.0-3.0 ml/s speed, and delay time 25- 30 s and 70-85 s).
基金supported by National Natural Science Foundation ofChina (61672279)Project of “Six Talents Peak” in Jiangsu (2012-WLW-023)OpenFoundation of State Key Laboratory of Hydrology-Water Resources and HydraulicEngineering, Nanjing Hydraulic Research Institute, China (2016491411).
文摘In order to improve the quality of low-dose computational tomography (CT)images, the paper proposes an improved image denoising approach based on WGAN-gpwith Wasserstein distance. For improving the training and the convergence efficiency, thegiven method introduces the gradient penalty term to WGAN network. The novelperceptual loss is introduced to make the texture information of the low-dose imagessensitive to the diagnostician eye. The experimental results show that compared with thestate-of-art methods, the time complexity is reduced, and the visual quality of low-doseCT images is significantly improved.
基金supported by the National Natural Science Foundation of China(Nos.11975292,12222512)the CAS"Light of West Chin"Program+1 种基金the CAS Pioneer Hundred Talent Programthe Guangdong Major Project of Basic and Applied Basic Research(No.2020B0301030008)。
文摘In this paper,we propose Hformer,a novel supervised learning model for low-dose computer tomography(LDCT)denoising.Hformer combines the strengths of convolutional neural networks for local feature extraction and transformer models for global feature capture.The performance of Hformer was verified and evaluated based on the AAPM-Mayo Clinic LDCT Grand Challenge Dataset.Compared with the former representative state-of-the-art(SOTA)model designs under different architectures,Hformer achieved optimal metrics without requiring a large number of learning parameters,with metrics of33.4405 PSNR,8.6956 RMSE,and 0.9163 SSIM.The experiments demonstrated designed Hformer is a SOTA model for noise suppression,structure preservation,and lesion detection.
基金supported in part by Science and Technology Program of Guangdong (No. 2018B030333001)the State’s Key Project of Research and Development Plan (Nos. 2017YFC0109202,2017YFA0104302 and 2017YFC0107900)the National Natural Science Foundation (Nos. 81530060 and 61871117)
文摘Cerebral perfusion computed tomography(PCT)is an important imaging modality for evaluating cerebrovascular diseases and stroke symptoms.With widespread public concern about the potential cancer risks and health hazards associated with cumulative radiation exposure in PCT imaging,considerable research has been conducted to reduce the radiation dose in X-ray-based brain perfusion imaging.Reducing the dose of X-rays causes severe noise and artifacts in PCT images.To solve this problem,we propose a deep learning method called NCS-Unet.The exceptional characteristics of non-subsampled contourlet transform(NSCT)and the Sobel filter are introduced into NCS-Unet.NSCT decomposes the convolved features into high-and low-frequency components.The decomposed high-frequency component retains image edges,contrast imaging traces,and noise,whereas the low-frequency component retains the main image information.The Sobel filter extracts the contours of the original image and the imaging traces caused by the contrast agent decay.The extracted information is added to NCS-Unet to improve its performance in noise reduction and artifact removal.Qualitative and quantitative analyses demonstrated that the proposed NCS-Unet can improve the quality of low-dose cone-beam CT perfusion reconstruction images and the accuracy of perfusion parameter calculations.
基金Supported by the National High Technology Research and Development Program of China(No.2012AA011603)National Nature Science Foundation of China(No.61372172)
文摘In helical cone-beam computed tomography(CT), Feldkamp-Davis-Kress(FDK) based image reconstruction algorithms are by far the most popular. However, artifacts are commonly met in the presence of lateral projection truncation. The reason is that the ramp filter is global. To restrain the truncation artifacts, an approximate reconstruction formula is proposed based on the Derivative-Hilbert-Backprojection(DHB) framework. In the method, the first order derivative filter is followed by the Hilbert transform. Since the filtered projection values are almost zero by the first order derivative filter, the following Hilbert transform has little influence on the projection values, even though the projections are laterally truncated. The proposed method has two main advantages. First, it has comparable computational efficiency and image quality as well as the conventional helical FDK algorithm for non-truncated projections. The second advantage is that images can be reconstructed with acceptable quality and much lower computational cost in comparison to the Laplace operator based algorithm in cases with truncated projections. To point out the advantages of our method, simulations on the computer and real data experiments on our laboratory industrial cone-beam CT are conducted. The simulated and experimental results demonstrate that the method is feasible for image reconstruction in the case of projection truncation.
文摘To explore the value of helical CT volume rendering technique (VRT) in post-operative evaluation of screw fixation of axis fractures.Methods There were 21 cases of screw fixation of axis fractures between February 2002 and May 2004 in the study including six cases with fractures on axis body,five on odontoid process and 10 on axis body and odontoid process.All cases received X-ray plain film,helical CT scanning,multi-planar reformatting(MPR) and VRT.Results Screw fixation through axis body and massa lateralis atlantis was performed in 10 cases and that through axis body and odontoid process in 11.VRT could clearly display full aperture of screw orbit,location of screw and angle of fixation and hence was superior to X-ray plain film and MPR.Multi-angle VRT displayed asymmetrical space of odontoid process and massa lateralis atlantis in four cases and medial deviation of 2～5 mm of half screw in screw fixation through axis body and massa lateralis atlantis in six.Conclusion VRT can eliminate false shadow of fixation screw,clearly display full aperture of screw orbit and hence supply improtant imaging evidence for post-operative evaluation of screw fixation of axis fractures.7 refs,1 fig,1 tab.
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.
