Computed Tomography(CT)images have been extensively employed in disease diagnosis and treatment,causing a huge concern over the dose of radiation to which patients are exposed.Increasing the radiation dose to get a be...Computed Tomography(CT)images have been extensively employed in disease diagnosis and treatment,causing a huge concern over the dose of radiation to which patients are exposed.Increasing the radiation dose to get a better image may lead to the development of genetic disorders and cancer in the patients;on the other hand,decreasing it by using a Low-Dose CT(LDCT)image may cause more noise and increased artifacts,which can compromise the diagnosis.So,image reconstruction from LDCT image data is necessary to improve radiologists’judgment and confidence.This study proposed three novel models for denoising LDCT images based on Wasserstein Generative Adversarial Network(WGAN).They were incorporated with different loss functions,including Visual Geometry Group(VGG),Structural Similarity Loss(SSIM),and Structurally Sensitive Loss(SSL),to reduce noise and preserve important information on LDCT images and investigate the effect of different types of loss functions.Furthermore,experiments have been conducted on the Graphical Processing Unit(GPU)and Central Processing Unit(CPU)to compare the performance of the proposed models.The results demonstrated that images from the proposed WGAN-SSIM,WGAN-VGG-SSIM,and WGAN-VGG-SSL were denoised better than those from state-of-the-art models(WGAN,WGAN-VGG,and SMGAN)and converged to a stable equilibrium compared with WGAN and WGAN-VGG.The proposed models are effective in reducing noise,suppressing artifacts,and maintaining informative structure and texture details,especially WGAN-VGG-SSL which achieved a high peak-signalto-noise ratio(PNSR)on both GPU(26.1336)and CPU(25.8270).The average accuracy of WGAN-VGG-SSL outperformed that of the state-ofthe-art methods by 1 percent.Experiments prove that theWGAN-VGG-SSL is more stable than the other models on both GPU and CPU.展开更多
Because of the growing concern over the radiation dose delivered to patients, X-ray cone-beam CT(CBCT) imaging of low dose is of great interest. It is difficult for traditional reconstruction methods such as Feldkamp ...Because of the growing concern over the radiation dose delivered to patients, X-ray cone-beam CT(CBCT) imaging of low dose is of great interest. It is difficult for traditional reconstruction methods such as Feldkamp to reduce noise and keep resolution at low doses. A typical method to solve this problem is using optimizationbased methods with careful modeling of physics and additional constraints. However, it is computationally expensive and very time-consuming to reach an optimal solution. Recently, some pioneering work applying deep neural networks had some success in characterizing and removing artifacts from a low-dose data set. In this study,we incorporate imaging physics for a cone-beam CT into a residual convolutional neural network and propose a new end-to-end deep learning-based method for slice-wise reconstruction. By transferring 3D projection to a 2D problem with a noise reduction property, we can not only obtain reconstructions of high image quality, but also lower the computational complexity. The proposed network is composed of three serially connected sub-networks: a cone-to-fan transformation sub-network, a 2D analytical inversion sub-network, and an image refinement sub-network. This provides a comprehensive solution for end-to-end reconstruction for CBCT. The advantages of our method are that the network can simplify a 3D reconstruction problem to a 2D slice-wise reconstruction problem and can complete reconstruction in an end-to-end manner with the system matrix integrated into the network design. Furthermore, reconstruction can be less computationally expensive and easily parallelizable compared with iterative reconstruction methods.展开更多
The influence of low tube voltage in dual source CT（DSCT） coronary artery imaging on image quality and radiation dose and its application value in clinical practice were investigated. Totally, 300 cases of chest pai...The influence of low tube voltage in dual source CT（DSCT） coronary artery imaging on image quality and radiation dose and its application value in clinical practice were investigated. Totally, 300 cases of chest pain with low body mass index（BMI 〈18.5 kg/m2） subjected to DSCT coronary artery imaging were prospectively enrolled. The heart rate in all patients were greater than 65/min. The retrospective ECG gated scanning mode and simple random sampling method were used to assign the patients into groups A, B and C（n=100 each）. The patients in groups A, B and C experienced 120-, 100-, and 80-kV tube voltage imaging respectively, and the image quality was evaluated. The CT volume dose index（CTDIvol） and dose length product（DLP） were recorded, and the effective dose（ED） was calculated in each group. The image quality scores and radiation doses in groups were compared, and the influence of tube voltage on image quality and radiation dose was analyzed. The results showed that the excellent rate of image quality in groups A, B and C was 95.69%, 94.72% and 96.33% respectively with the difference being not statistically significant among the three groups（P〉0.05）. The CTDIvol values in groups A, B and C were 51.35±12.21, 21.28±7.13 and 6.34±3.34 mGy, respectively, with the difference being statistically significant（P〈0.05）. The ED values in groups A, B and C were 9.27±1.63, 4.56±2.29 and 2.29±1.69 mSv, respectively, with the difference being statistically significant（P〈0.05）. It was suggested that for the patients with low BMI, the application of DSCT coronary artery imaging with low tube voltage can obtain satisfactory image quality, and simultaneously, significantly reduce the radiation dose.展开更多
文摘Computed Tomography(CT)images have been extensively employed in disease diagnosis and treatment,causing a huge concern over the dose of radiation to which patients are exposed.Increasing the radiation dose to get a better image may lead to the development of genetic disorders and cancer in the patients;on the other hand,decreasing it by using a Low-Dose CT(LDCT)image may cause more noise and increased artifacts,which can compromise the diagnosis.So,image reconstruction from LDCT image data is necessary to improve radiologists’judgment and confidence.This study proposed three novel models for denoising LDCT images based on Wasserstein Generative Adversarial Network(WGAN).They were incorporated with different loss functions,including Visual Geometry Group(VGG),Structural Similarity Loss(SSIM),and Structurally Sensitive Loss(SSL),to reduce noise and preserve important information on LDCT images and investigate the effect of different types of loss functions.Furthermore,experiments have been conducted on the Graphical Processing Unit(GPU)and Central Processing Unit(CPU)to compare the performance of the proposed models.The results demonstrated that images from the proposed WGAN-SSIM,WGAN-VGG-SSIM,and WGAN-VGG-SSL were denoised better than those from state-of-the-art models(WGAN,WGAN-VGG,and SMGAN)and converged to a stable equilibrium compared with WGAN and WGAN-VGG.The proposed models are effective in reducing noise,suppressing artifacts,and maintaining informative structure and texture details,especially WGAN-VGG-SSL which achieved a high peak-signalto-noise ratio(PNSR)on both GPU(26.1336)and CPU(25.8270).The average accuracy of WGAN-VGG-SSL outperformed that of the state-ofthe-art methods by 1 percent.Experiments prove that theWGAN-VGG-SSL is more stable than the other models on both GPU and CPU.
基金supported by the National Natural Science Foundation of China(Nos.61771279,11435007)the National Key Research and Development Program of China(No.2016YFF0101304)
文摘Because of the growing concern over the radiation dose delivered to patients, X-ray cone-beam CT(CBCT) imaging of low dose is of great interest. It is difficult for traditional reconstruction methods such as Feldkamp to reduce noise and keep resolution at low doses. A typical method to solve this problem is using optimizationbased methods with careful modeling of physics and additional constraints. However, it is computationally expensive and very time-consuming to reach an optimal solution. Recently, some pioneering work applying deep neural networks had some success in characterizing and removing artifacts from a low-dose data set. In this study,we incorporate imaging physics for a cone-beam CT into a residual convolutional neural network and propose a new end-to-end deep learning-based method for slice-wise reconstruction. By transferring 3D projection to a 2D problem with a noise reduction property, we can not only obtain reconstructions of high image quality, but also lower the computational complexity. The proposed network is composed of three serially connected sub-networks: a cone-to-fan transformation sub-network, a 2D analytical inversion sub-network, and an image refinement sub-network. This provides a comprehensive solution for end-to-end reconstruction for CBCT. The advantages of our method are that the network can simplify a 3D reconstruction problem to a 2D slice-wise reconstruction problem and can complete reconstruction in an end-to-end manner with the system matrix integrated into the network design. Furthermore, reconstruction can be less computationally expensive and easily parallelizable compared with iterative reconstruction methods.
基金supported by the Natural Science Foundation of Hubei Province,China(No.2012FKB02443)
文摘The influence of low tube voltage in dual source CT（DSCT） coronary artery imaging on image quality and radiation dose and its application value in clinical practice were investigated. Totally, 300 cases of chest pain with low body mass index（BMI 〈18.5 kg/m2） subjected to DSCT coronary artery imaging were prospectively enrolled. The heart rate in all patients were greater than 65/min. The retrospective ECG gated scanning mode and simple random sampling method were used to assign the patients into groups A, B and C（n=100 each）. The patients in groups A, B and C experienced 120-, 100-, and 80-kV tube voltage imaging respectively, and the image quality was evaluated. The CT volume dose index（CTDIvol） and dose length product（DLP） were recorded, and the effective dose（ED） was calculated in each group. The image quality scores and radiation doses in groups were compared, and the influence of tube voltage on image quality and radiation dose was analyzed. The results showed that the excellent rate of image quality in groups A, B and C was 95.69%, 94.72% and 96.33% respectively with the difference being not statistically significant among the three groups（P〉0.05）. The CTDIvol values in groups A, B and C were 51.35±12.21, 21.28±7.13 and 6.34±3.34 mGy, respectively, with the difference being statistically significant（P〈0.05）. The ED values in groups A, B and C were 9.27±1.63, 4.56±2.29 and 2.29±1.69 mSv, respectively, with the difference being statistically significant（P〈0.05）. It was suggested that for the patients with low BMI, the application of DSCT coronary artery imaging with low tube voltage can obtain satisfactory image quality, and simultaneously, significantly reduce the radiation dose.
