Objective Idiopathic rolandic epilepsy syndrome （IRES） is the most common epilepsy syndrome in childhood， and its lesion site remains undetermined. This article aims to investigate the source of epileptiform discharges in IRES using magnetoencephalography （MEG）.Methods A total of 70 patients with IRES were enrolled in this prospective MEG-based study， among whom there were 53 children with benign epilepsy of childhood with centrotemporal spikes （BECTS）， 12 children with atypical benign partial epilepsy （ABPE）， 3 children with Landau-Kleffner syndrome （LKS）， and 2 children with epileptic encephalopathy with continuous spike-and-waves during slow-wave sleep （CSWS）. Epileptiform discharges were collected independently from each patient 10 times， and an MEG source analysis was performed. Standardized low-resolution brain electromagnetic tomography was used to perform source localization of the distributed source model. The spike source density was quantified into amplitude， and source location was determined according to the Desikan-Killiany atlas. The association between the distribution of spike source in brain and clinical manifestations was analyzed.Results In IRES， there were significant differences in the source locations of epilepsy discharge between BECTS， ABPE， LKS， and CSWS. The current source density of CSWS was stronger in the frontal lobe， the temporal lobe， and the anterior cingulate gyrus， while that of ABPE was stronger in the frontal lobe， and that of BECTS and LKS were stronger in the temporal lobe. The more severe phenotype of epilepsy， such as generalized tonic-clonic seizure， was associated with a stronger current source density in the brain， which was consistent with electroencephalography manifestations.Conclusion This study identifies different sources of epileptiform discharges in IRES. The density distribution of these spike sources may help to explain the discharge， cognitive， and neuropsychological characteristics in different subtypes of IRES.
Magnetoencephalography

Emergence agitation （EA） is a common complication after general anesthesia， especially in children and adolescents. Remifentanil， as a short-acting μ-receptor agonist， has become an important drug for the prevention and treatment of EA due to its rapid recovery and low risk of respiratory depression. This article reviews the mechanism of action and clinical research progress of remifentanil in the prevention and treatment of EA. Its mechanism of action involves the inhibition of pain signals mediated by traditional μ-receptor activation and potential new mechanism based on neural-endocrine-immune network， including regulation of microglial inflammatory pathways， and the modulation of cytokines and chemokines，etc. Clinical studies have shown that remifentanil can significantly shorten the recovery time， reduce the incidence of EA， and further optimize the analgesic effect and recovery quality by combining with other drugs （such as local anesthetics， sedatives， and opioid drugs）. Future research should further explore the mechanism of action of remifentanil， optimize clinical treatment strategies， and conduct large- scale clinical trials to standardize the drug use plan， while paying attention to its long-term effects and the development of multimodal treatment plans to promote the further development of EA prevention and treatment plans.

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 We propose a dual-domain cone beam computed tomography(CBCT)reconstruction framework DualCBR-Net based on improved differentiable domain transform for cone-angle artifact correction.Methods The proposed CBCT dual-domain reconstruction framework DualCBR-Net consists of 3 individual modules:projection preprocessing,differentiable domain transform,and image post-processing.The projection preprocessing module first extends the original projection data in the row direction to ensure full coverage of the scanned object by X-ray.The differentiable domain transform introduces the FDK reconstruction and forward projection operators to complete the forward and gradient backpropagation processes,where the geometric parameters correspond to the extended data dimension to provide crucial prior information in the forward pass of the network and ensure the accuracy in the gradient backpropagation,thus enabling precise learning of cone-beam region data.The image post-processing module further fine-tunes the domain-transformed image to remove residual artifacts and noises.Results The results of validation experiments conducted on Mayo's public chest dataset showed that the proposed DualCBR-Net framework was superior to other comparison methods in terms of artifact removal and structural detail preservation.Compared with the latest methods,the DualCBR-Net framework improved the PSNR and SSIM by 0.6479 and 0.0074,respectively.Conclusion The proposed DualCBR-Net framework for cone-angle artifact correction allows effective joint training of the CBCT dual-domain network and is especially effective for large cone-angle region.

Objective We propose a motion artifact correction algorithm(DMBL)for reducing motion artifacts in reconstructed dental cone-beam computed tomography(CBCT)images based on deep blur learning.Methods A blur encoder was used to extract motion-related degradation features to model the degradation process caused by motion,and the obtained motion degradation features were imported in the artifact correction module for artifact removal.The artifact correction module adopts a joint learning framework for image blur removal and image blur simulation for treatment of spatially varying and random motion patterns.Comparative experiments were conducted to verify the effectiveness of the proposed method using both simulated motion data sets and clinical data sets.Results The experimental results with the simulated dataset showed that compared with the existing methods,the PSNR of the proposed method increased by 2.88%,the SSIM increased by 0.89%,and the RMSE decreased by 10.58%.The results with the clinical dataset showed that the proposed method achieved the highest expert level with a subjective image quality score of 4.417(in a 5-point scale),significantly higher than those of the comparison methods.Conclusion The proposed DMBL algorithm with a deep blur joint learning network structure can effectively reduce motion artifacts in dental CBCT images and achieve high-quality image restoration.

Objective To propose a dual-domain CBCT reconstruction framework(DualSFR-Net)based on generative projection interpolation to reduce artifacts in sparse-view cone beam computed tomography(CBCT)reconstruction.Methods The proposed method DualSFR-Net consists of a generative projection interpolation module,a domain transformation module,and an image restoration module.The generative projection interpolation module includes a sparse projection interpolation network(SPINet)based on a generative adversarial network and a full-view projection restoration network(FPRNet).SPINet performs projection interpolation to synthesize full-view projection data from the sparse-view projection data,while FPRNet further restores the synthesized full-view projection data.The domain transformation module introduces the FDK reconstruction and forward projection operators to complete the forward and gradient backpropagation processes.The image restoration module includes an image restoration network FIRNet that fine-tunes the domain-transformed images to eliminate residual artifacts and noise.Results Validation experiments conducted on a dental CT dataset demonstrated that DualSFR-Net was capable to reconstruct high-quality CBCT images under sparse-view sampling protocols.Quantitatively,compared to the current best methods,the DualSFR-Net method improved the PSNR by 0.6615 and 0.7658 and increased the SSIM by 0.0053 and 0.0134 under 2-fold and 4-fold sparse protocols,respectively.Conclusion The proposed generative projection interpolation-based dual-domain CBCT sparse-view reconstruction method can effectively reduce stripe artifacts to improve image quality and enables efficient joint training for dual-domain imaging networks in sparse-view CBCT reconstruction.

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 We propose a dual-domain cone beam computed tomography(CBCT)reconstruction framework DualCBR-Net based on improved differentiable domain transform for cone-angle artifact correction.Methods The proposed CBCT dual-domain reconstruction framework DualCBR-Net consists of 3 individual modules:projection preprocessing,differentiable domain transform,and image post-processing.The projection preprocessing module first extends the original projection data in the row direction to ensure full coverage of the scanned object by X-ray.The differentiable domain transform introduces the FDK reconstruction and forward projection operators to complete the forward and gradient backpropagation processes,where the geometric parameters correspond to the extended data dimension to provide crucial prior information in the forward pass of the network and ensure the accuracy in the gradient backpropagation,thus enabling precise learning of cone-beam region data.The image post-processing module further fine-tunes the domain-transformed image to remove residual artifacts and noises.Results The results of validation experiments conducted on Mayo's public chest dataset showed that the proposed DualCBR-Net framework was superior to other comparison methods in terms of artifact removal and structural detail preservation.Compared with the latest methods,the DualCBR-Net framework improved the PSNR and SSIM by 0.6479 and 0.0074,respectively.Conclusion The proposed DualCBR-Net framework for cone-angle artifact correction allows effective joint training of the CBCT dual-domain network and is especially effective for large cone-angle region.

Objective We propose a motion artifact correction algorithm(DMBL)for reducing motion artifacts in reconstructed dental cone-beam computed tomography(CBCT)images based on deep blur learning.Methods A blur encoder was used to extract motion-related degradation features to model the degradation process caused by motion,and the obtained motion degradation features were imported in the artifact correction module for artifact removal.The artifact correction module adopts a joint learning framework for image blur removal and image blur simulation for treatment of spatially varying and random motion patterns.Comparative experiments were conducted to verify the effectiveness of the proposed method using both simulated motion data sets and clinical data sets.Results The experimental results with the simulated dataset showed that compared with the existing methods,the PSNR of the proposed method increased by 2.88%,the SSIM increased by 0.89%,and the RMSE decreased by 10.58%.The results with the clinical dataset showed that the proposed method achieved the highest expert level with a subjective image quality score of 4.417(in a 5-point scale),significantly higher than those of the comparison methods.Conclusion The proposed DMBL algorithm with a deep blur joint learning network structure can effectively reduce motion artifacts in dental CBCT images and achieve high-quality image restoration.

Objective To propose a dual-domain CBCT reconstruction framework(DualSFR-Net)based on generative projection interpolation to reduce artifacts in sparse-view cone beam computed tomography(CBCT)reconstruction.Methods The proposed method DualSFR-Net consists of a generative projection interpolation module,a domain transformation module,and an image restoration module.The generative projection interpolation module includes a sparse projection interpolation network(SPINet)based on a generative adversarial network and a full-view projection restoration network(FPRNet).SPINet performs projection interpolation to synthesize full-view projection data from the sparse-view projection data,while FPRNet further restores the synthesized full-view projection data.The domain transformation module introduces the FDK reconstruction and forward projection operators to complete the forward and gradient backpropagation processes.The image restoration module includes an image restoration network FIRNet that fine-tunes the domain-transformed images to eliminate residual artifacts and noise.Results Validation experiments conducted on a dental CT dataset demonstrated that DualSFR-Net was capable to reconstruct high-quality CBCT images under sparse-view sampling protocols.Quantitatively,compared to the current best methods,the DualSFR-Net method improved the PSNR by 0.6615 and 0.7658 and increased the SSIM by 0.0053 and 0.0134 under 2-fold and 4-fold sparse protocols,respectively.Conclusion The proposed generative projection interpolation-based dual-domain CBCT sparse-view reconstruction method can effectively reduce stripe artifacts to improve image quality and enables efficient joint training for dual-domain imaging networks in sparse-view CBCT reconstruction.