Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Regarding the challenge of early identification of critically ill patients with acute exacerbation of chronic obstructive pulmonary disease(AECOPD),a radiomics-clinical fusion model is proposed based on non-contrast CT images of accessory respiratory muscles to predict life-threatening conditions.A retrospective study is conducted involving 233 AECOPD patients(153 non-life-threatening and 80 life-threatening cases).Patients are divided into a training set(n=186)and a test set(n=47)at a 4:1 ratio.A total of 1 874 radiomic features are extracted from the erector spinae and pectoralis muscle regions delineated by radiologists on non-contrast CT images,and the features selection is performed using maximum relevance minimum redundancy and least absolute shrinkage and selection operator(LASSO)algorithms.Meanwhile,clinical data are analyzed with t-test and LASSO for variable screening.The selected features are input into C-support vector classification,Logistic regression,random forest,adaptive boosting(AdaBoost),and extreme gradient boosting(XGBoost)to construct radiomics model,clinical model,and fusion model.Predictive performance and clinical practicality are evaluated in the test set using receiver operating characteristic curve,area under the curve(AUC),and decision curve analysis.The radiomics-clinical fusion model built with XGBoost outperformed standalone radiomics and clinical models,achieving an AUC of 0.902(95％CI 0.846,0.994),with accuracy,sensitivity,specificity,and precision of 0.837,0.933,0.786,and 0.7,respectively.Results demonstrate that the fusion model based on the non-contrast CT radiomics of accessory respiratory muscles and clinical data exhibits promising diagnostic performance,highlighting its potential clinical significance for stratified management and preemptive critical care intervention in AECOPD patients.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

A novel approach named hierarchical vascular morphology awareness and noisy label refine for hepatic vessel segmentation(HN-Seg)is proposed to achieve precise vessel segmentation while reducing dependency on high-quality labels.HN-Seg comprises of(1)hierarchical vascular morphology aware network which employs a multi-scale local morphology attention mechanism and a global morphology preservation loss function to ensure the integrity of overall vascular morphology,and(2)self-distillation noisy label refine module which leverages the uncertainty in model outputs to optimize noisy labels through uncertainty optimization and consistency regularization,thereby maximizing the knowledge extracted from images during training and refining noisy labels.Experimental results on the hepatic vessel dataset demonstrate that HN-Seg achieves superior segmentation performance,outperforming 6 methods(UNet,UNet++,UNETR,SwinUNetR,FRUNet,and MTCL).HN-Seg attains DSC and clDice scores of 0.727 and 0.773,showing improvements of 9.6%and 21.5%over the baseline method UNETR.

Skull segmentation in magnetic resonance image(MRI)provides realistic skull models for MEG and EEG positive problems.An MRI skull segmentation method based on bi-stream pyramid decoder and deep supervision(BiNETR)is proposed to solve the problem of difficult segmentation due to the blurred and complex structure of MRI skull imaging.The method uses a bi-stream pyramid decoder as the main decoder in the network structure of encoding-decoding,including serial dual decoders for edge information oriented and precise feature merging.Specifically,edge information oriented pyramid decoder effectively enhances the edge information based on feature sharpening to improve the edge segmentation accuracy,and the precise feature merging pyramid decoder further refines and reuses the edge-enhanced features to promote the fusion of deep and shallow features.In addition,deep supervised computation of intermediate feature loss is introduced to implant the gradient into the deep network for enhancing network training.The segmentation algorithm is validated on the skull dataset,achieving a Dice similarity coefficient of 0.880±0.039 and an average symmetric surface distance of(0.931±0.286)mm,outperforming other state-of-the-art methods.The experimental results demonstrate the effectiveness and accuracy of the proposed algorithm in MRI skull segmentation.

A self-supervised super-resolution reconstruction method based on scale adaptive and coordinate encoding is proposed to realize super-resolution reconstruction of anisotropic brain magnetic resonance images with different slice thicknesses even in the absence of paired isotropic brain magnetic resonance images.Firstly,an image encoding module that integrates super-resolution scale information is used to learn the specific features of images with different slice thicknesses.Subsequently,a coordinate encoding module is employed to facilitate the deep fusion of coordinate information and image features.Finally,an overall loss function comprising reconstruction loss and brain tissue edge perception loss is adopted to optimize the recovery of edge high-frequency information,while global residual learning is introduced to enhance network training.Experimental results on the HCP-1200 and OASIS-1 datasets demonstrate that the proposed method outperforms other self-supervised super-resolution reconstruction methods.

Regarding the challenge of early identification of critically ill patients with acute exacerbation of chronic obstructive pulmonary disease(AECOPD),a radiomics-clinical fusion model is proposed based on non-contrast CT images of accessory respiratory muscles to predict life-threatening conditions.A retrospective study is conducted involving 233 AECOPD patients(153 non-life-threatening and 80 life-threatening cases).Patients are divided into a training set(n=186)and a test set(n=47)at a 4:1 ratio.A total of 1 874 radiomic features are extracted from the erector spinae and pectoralis muscle regions delineated by radiologists on non-contrast CT images,and the features selection is performed using maximum relevance minimum redundancy and least absolute shrinkage and selection operator(LASSO)algorithms.Meanwhile,clinical data are analyzed with t-test and LASSO for variable screening.The selected features are input into C-support vector classification,Logistic regression,random forest,adaptive boosting(AdaBoost),and extreme gradient boosting(XGBoost)to construct radiomics model,clinical model,and fusion model.Predictive performance and clinical practicality are evaluated in the test set using receiver operating characteristic curve,area under the curve(AUC),and decision curve analysis.The radiomics-clinical fusion model built with XGBoost outperformed standalone radiomics and clinical models,achieving an AUC of 0.902(95％CI 0.846,0.994),with accuracy,sensitivity,specificity,and precision of 0.837,0.933,0.786,and 0.7,respectively.Results demonstrate that the fusion model based on the non-contrast CT radiomics of accessory respiratory muscles and clinical data exhibits promising diagnostic performance,highlighting its potential clinical significance for stratified management and preemptive critical care intervention in AECOPD patients.