OBJECTIVES: We propose a segmented backprojection tensor degradation feature encoding (SBP-MAC) model for motion artifact correction in dental cone beam computed tomography (CBCT) to improve the quality of the reconstructed images. METHODS: The proposed motion artifact correction model consists of a generator and a degradation encoder. The segmented limited-angle reconstructed sub-images are stacked into the tensors and used as the model input. A degradation encoder is used to extract spatially varying motion information in the tensor, and the generator's skip connection features are adaptively modulated to guide the model for correcting artifacts caused by different motion waveforms. The artifact consistency loss function was designed to simplify the learning task of the generator. RESULTS: The proposed model could effectively remove motion artifacts and improve the quality of the reconstructed images. For simulated data, the proposed model increased the peak signal-to-noise ratio by 8.28%, increased the structural similarity index measurement by 2.29%, and decreased the root mean square error by 23.84%. For real clinical data, the proposed model achieved the highest expert score of 4.4221 (against a 5-point scale), which was significantly higher than those of all the other comparison methods. CONCLUSIONS The SBP-MAC model can effectively extract spatially varying motion information in the tensors and achieve adaptive artifact correction from the tensor domain to the image domain to improve the quality of reconstructed dental CBCT images.
Cone-Beam Computed Tomography/methods* ; Artifacts ; Humans ; Motion ; Image Processing, Computer-Assisted/methods* ; Signal-To-Noise Ratio ; Algorithms

OBJECTIVES: We propose a low-dose CT image restoration method based on central guidance and alternating optimization (FedGP). METHODS: The FedGP framework revolutionizes the traditional federated learning model by adopting a structure without a fixed central server, where each institution alternatively serves as the central server. This method uses an institution-modulated CT image restoration network as the core of client-side local training. Through a federated learning approach of central guidance and alternating optimization, the central server leverages local labeled data to guide client-side network training to enhance the generalization capability of the CT imaging model across multiple institutions. RESULTS: In the low-dose and sparse-view CT image restoration tasks, the FedGP method showed significant advantages in both visual and quantitative evaluation and achieved the highest PSNR (40.25 and 38.84), the highest SSIM (0.95 and 0.92), and the lowest RMSE (2.39 and 2.56). Ablation study of FedGP demonstrated that compared with FedGP(w/o GP) without central guidance, the FedGP method better adapted to data heterogeneity across institutions, thus ensuring robustness and generalization capability of the model in different imaging conditions. CONCLUSIONS FedGP provides a more flexible FL framework to solve the problem of CT imaging heterogeneity and well adapts to multi-institutional data characteristics to improve generalization ability of the model under diverse imaging geometric configurations.
Tomography, X-Ray Computed/methods* ; Humans ; Radiation Dosage ; Image Processing, Computer-Assisted/methods* ; Algorithms

Objective To propose a low-dose CT reconstruction algorithm across different scanners based on federated feature learning(FedCT)to improve the generalization of deep learning models for multiple CT scanners and protect data privacy.Methods In the proposed FedCT framework,each client is assigned an inverse Radon transform-based reconstruction model to serve as a local network model that participates in federated learning.A projection-domain specific learning strategy is adopted to preserve the geometry specificity in the local projection domain.Federated feature learning is introduced in the model,which utilizes conditional parameters to mark the local data and feed the conditional parameters into the network for encoding to enhance the generalization of the model in the image domain.Results In the cross-client,multi-scanner,and multi-protocol low-dose CT reconstruction experiments,FedCT achieved the highest PSNR(+2.8048,+2.7301,and +2.7263 compared to the second best federated learning method),the highest SSIM(+0.0009,+0.0165,and +0.0131 in the same comparison),and the lowest RMSE(-0.6687,-1.5956,and-0.9962).In the ablation experiment,compared with the general federated learning strategy,the model with projection-specific learning strategy showed an average improvement by 1.18 on Q1 of the PSNR and an average decrease by 1.36 on Q3 of the RMSE on the test set.The introduction of federated feature learning in FedCT further improved the Q1 of the PSNR on the test set by 3.56 and reduced the Q3 of the RMSE by 1.80.Conclusion FedCT provides an effective solution for collaborative construction of CT reconstruction models,which can enhance model generalization and further improve the reconstruction performance on global data while protecting data privacy.

Objective To propose a CT truncated data reconstruction model(DDTrans)based on projection and image dual-domain Transformer coupled feature learning for reducing truncation artifacts and image structure distortion caused by insufficient field of view(FOV)in CT scanning.Methods Transformer was adopted to build projection domain and image domain restoration models,and the long-range dependency modeling capability of the Transformer attention module was used to capture global structural features to restore the projection data information and enhance the reconstructed images.We constructed a differentiable Radon back-projection operator layer between the projection domain and image domain networks to enable end-to-end training of DDTrans.Projection consistency loss was introduced to constrain the image forward-projection results to further improve the accuracy of image reconstruction.Results The experimental results with Mayo simulation data showed that for both partial truncation and interior scanning data,the proposed DDTrans method showed better performance than the comparison algorithms in removing truncation artifacts at the edges and restoring the external information of the FOV.Conclusion The DDTrans method can effectively remove CT truncation artifacts to ensure accurate reconstruction of the data within the FOV and achieve approximate reconstruction of data outside the FOV.

Objective:To investigate the association between gestational blood pressure and neurodevelopment in 2-year-old children.Methods:Based on the"Wuhan Healthy Baby Birth Cohort", 3 754 mother-infant pairs were enrolled in this study. Based on multiple blood pressure measurements during pregnancy, the mean, cumulative, and variability of blood pressure throughout the entire pregnancy and each trimester were calculated. Blood pressure variability was evaluated using standard deviation (SD), coefficient of variability (CV), and variability independent of mean (VIM). Follow-up testing of neurodevelopment in infants and young children at the age of two was conducted to obtain the Mental Development Index (MDI) and the Psychomotor Development Index (PDI). The multivariate linear regression and generalized estimation equation were used to analyze the association between gestational blood pressure data and neurodevelopmental index.Results:The age of 3 754 pregnant women was (29.1±3.6) years, with a pre-pregnancy BMI of (20.9±2.7) kg/m2 and a gestational age of (39.3±1.2) weeks. The birth weight of 3 754 children was (3 330.9±397.7) grams, and the birth length was (50.3±1.6) centimeters. The results of the multivariate linear regression analysis showed that after adjusting for relevant confounding factors, the mean blood pressure, cumulative blood pressure, standard deviation of blood pressure, coefficient of variation of blood pressure, independent blood pressure variability of systolic blood pressure, diastolic blood pressure, and pulse pressure throughout pregnancy were negatively associated with the MDI and PDI scores of 2-year-old children. The analysis results of the generalized estimation equation showed that after adjusting for relevant confounding factors, the average systolic blood pressure in the first, second, and third trimesters was negatively associated with MDI/PDI. The negative association between cumulative blood pressure and MDI/PDI was only found in the first trimester. The negative association between blood pressure variation during pregnancy and MDI/PDI was mainly concentrated in the second and third trimesters.Conclusion:There is a negative association between gestational blood pressure and the neurodevelopmental index of 2-year-old children.

Objective To propose a low-dose CT reconstruction algorithm across different scanners based on federated feature learning(FedCT)to improve the generalization of deep learning models for multiple CT scanners and protect data privacy.Methods In the proposed FedCT framework,each client is assigned an inverse Radon transform-based reconstruction model to serve as a local network model that participates in federated learning.A projection-domain specific learning strategy is adopted to preserve the geometry specificity in the local projection domain.Federated feature learning is introduced in the model,which utilizes conditional parameters to mark the local data and feed the conditional parameters into the network for encoding to enhance the generalization of the model in the image domain.Results In the cross-client,multi-scanner,and multi-protocol low-dose CT reconstruction experiments,FedCT achieved the highest PSNR(+2.8048,+2.7301,and +2.7263 compared to the second best federated learning method),the highest SSIM(+0.0009,+0.0165,and +0.0131 in the same comparison),and the lowest RMSE(-0.6687,-1.5956,and-0.9962).In the ablation experiment,compared with the general federated learning strategy,the model with projection-specific learning strategy showed an average improvement by 1.18 on Q1 of the PSNR and an average decrease by 1.36 on Q3 of the RMSE on the test set.The introduction of federated feature learning in FedCT further improved the Q1 of the PSNR on the test set by 3.56 and reduced the Q3 of the RMSE by 1.80.Conclusion FedCT provides an effective solution for collaborative construction of CT reconstruction models,which can enhance model generalization and further improve the reconstruction performance on global data while protecting data privacy.

Objective To propose a CT truncated data reconstruction model(DDTrans)based on projection and image dual-domain Transformer coupled feature learning for reducing truncation artifacts and image structure distortion caused by insufficient field of view(FOV)in CT scanning.Methods Transformer was adopted to build projection domain and image domain restoration models,and the long-range dependency modeling capability of the Transformer attention module was used to capture global structural features to restore the projection data information and enhance the reconstructed images.We constructed a differentiable Radon back-projection operator layer between the projection domain and image domain networks to enable end-to-end training of DDTrans.Projection consistency loss was introduced to constrain the image forward-projection results to further improve the accuracy of image reconstruction.Results The experimental results with Mayo simulation data showed that for both partial truncation and interior scanning data,the proposed DDTrans method showed better performance than the comparison algorithms in removing truncation artifacts at the edges and restoring the external information of the FOV.Conclusion The DDTrans method can effectively remove CT truncation artifacts to ensure accurate reconstruction of the data within the FOV and achieve approximate reconstruction of data outside the FOV.

Objective:To investigate the association between gestational blood pressure and neurodevelopment in 2-year-old children.Methods:Based on the"Wuhan Healthy Baby Birth Cohort", 3 754 mother-infant pairs were enrolled in this study. Based on multiple blood pressure measurements during pregnancy, the mean, cumulative, and variability of blood pressure throughout the entire pregnancy and each trimester were calculated. Blood pressure variability was evaluated using standard deviation (SD), coefficient of variability (CV), and variability independent of mean (VIM). Follow-up testing of neurodevelopment in infants and young children at the age of two was conducted to obtain the Mental Development Index (MDI) and the Psychomotor Development Index (PDI). The multivariate linear regression and generalized estimation equation were used to analyze the association between gestational blood pressure data and neurodevelopmental index.Results:The age of 3 754 pregnant women was (29.1±3.6) years, with a pre-pregnancy BMI of (20.9±2.7) kg/m2 and a gestational age of (39.3±1.2) weeks. The birth weight of 3 754 children was (3 330.9±397.7) grams, and the birth length was (50.3±1.6) centimeters. The results of the multivariate linear regression analysis showed that after adjusting for relevant confounding factors, the mean blood pressure, cumulative blood pressure, standard deviation of blood pressure, coefficient of variation of blood pressure, independent blood pressure variability of systolic blood pressure, diastolic blood pressure, and pulse pressure throughout pregnancy were negatively associated with the MDI and PDI scores of 2-year-old children. The analysis results of the generalized estimation equation showed that after adjusting for relevant confounding factors, the average systolic blood pressure in the first, second, and third trimesters was negatively associated with MDI/PDI. The negative association between cumulative blood pressure and MDI/PDI was only found in the first trimester. The negative association between blood pressure variation during pregnancy and MDI/PDI was mainly concentrated in the second and third trimesters.Conclusion:There is a negative association between gestational blood pressure and the neurodevelopmental index of 2-year-old children.

OBJECTIVE: To propose a semi-supervised material quantitative intelligent imaging algorithm based on prior information perception learning (SLMD-Net) to improve the quality and precision of spectral CT imaging. METHODS: The algorithm includes a supervised and a self- supervised submodule. In the supervised submodule, the mapping relationship between low and high signal-to-noise ratio (SNR) data was constructed through mean square error loss function learning based on a small labeled dataset. In the self- supervised sub-module, an image recovery model was utilized to construct the loss function incorporating the prior information from a large unlabeled low SNR basic material image dataset, and the total variation (TV) model was used to to characterize the prior information of the images. The two submodules were combined to form the SLMD-Net method, and pre-clinical simulation data were used to validate the feasibility and effectiveness of the algorithm. RESULTS: Compared with the traditional model-driven quantitative imaging methods (FBP-DI, PWLS-PCG, and E3DTV), data-driven supervised-learning-based quantitative imaging methods (SUMD-Net and BFCNN), a material quantitative imaging method based on unsupervised learning (UNTV-Net) and semi-supervised learning-based cycle consistent generative adversarial network (Semi-CycleGAN), the proposed SLMD-Net method had better performance in both visual and quantitative assessments. For quantitative imaging of water and bone materials, the SLMD-Net method had the highest PSNR index (31.82 and 29.06), the highest FSIM index (0.95 and 0.90), and the lowest RMSE index (0.03 and 0.02), respectively) and achieved significantly higher image quality scores than the other 7 material decomposition methods (P < 0.05). The material quantitative imaging performance of SLMD-Net was close to that of the supervised network SUMD-Net trained with labeled data with a doubled size. CONCLUSIONS A small labeled dataset and a large unlabeled low SNR material image dataset can be fully used to suppress noise amplification and artifacts in basic material decomposition in spectral CT and reduce the dependence on labeled data-driven network, which considers more realistic scenario in clinics.
Tomography, X-Ray Computed/methods* ; Image Processing, Computer-Assisted/methods* ; Algorithms ; Signal-To-Noise Ratio ; Perception

OBJECTIVE: To propose a tissue- aware contrast enhancement network (T- ACEnet) for CT image enhancement and validate its accuracy in CT image organ segmentation tasks. METHODS: The original CT images were mapped to generate low dynamic grayscale images with lung and soft tissue window contrasts, and the supervised sub-network learned to recognize the optimal window width and level setting of the lung and abdominal soft tissues via the lung mask. The self-supervised sub-network then used the extreme value suppression loss function to preserve more organ edge structure information. The images generated by the T-ACEnet were fed into the segmentation network to segment multiple abdominal organs. RESULTS: The images obtained by T-ACEnet were capable of providing more window setting information in a single image, which allowed the physicians to conduct preliminary screening of the lesions. Compared with the suboptimal methods, T-ACE images achieved improvements by 0.51, 0.26, 0.10, and 14.14 in SSIM, QABF, VIFF, and PSNR metrics, respectively, with a reduced MSE by an order of magnitude. When T-ACE images were used as input for segmentation networks, the organ segmentation accuracy could be effectively improved without changing the model as compared with the original CT images. All the 5 segmentation quantitative indices were improved, with the maximum improvement of 4.16%. CONCLUSION The T-ACEnet can perceptually improve the contrast of organ tissues and provide more comprehensive and continuous diagnostic information, and the T-ACE images generated using this method can significantly improve the performance of organ segmentation tasks.
Learning ; Image Enhancement ; Tomography, X-Ray Computed