Objective To observe the value of 2D SECara-Net and 3D U2-Net models constructed based on 2D maximal intensity projection(MIP)and 3D time-of-flight MR angiography(3D TOF-MRA)images,respectively,also of their combination for MRA detecting unruptured saccular intracranial aneurysms(USIA).Methods Totally 973 patients with single USIA and 300 subjects who underwent healthy physical examination were retrospectively collected and divided into training set(n=923,containing 723 cases of USIA and 200 healthy subjects)and test set(n=350,containing 250 cases of USIA and 100 healthy subjects)at the ratio of 7:3.Pre-processed 3D TOF-MRA and the obtained 2D-MIP images in training set were imported into 3D U2-Net and 2D SECara-Net models for training and adjusting parameters,respectively.The efficiency of 2 models and their combination for detecting USIA were evaluated.Results The sensitivity,specificity and accuracy of 2D SECara-Net model for detecting USIA in test set was 78.80％(197/250),95.00％(95/100)and 83.43％(292/350),of 3D U2-Net model was 82.80％(207/250),86.00％(86/100)and 83.71％(293/350),respectively.The specificity of 2D SECara-Net model was higher than that of 3D U2-Net model(P=0.030),while no significant difference of sensitivity nor accuracy was found between 2 models(both P＞0.05).The specificity of the combination of the 2 models was 99.00％(99/100),higher than that of 3D U2-Net model(P＜0.05),and the sensitivity and accuracy of the combination was 91.20％(228/250)and 93.43％(327/350),respectivelty,both higher than those of 2 single models(all P＜0.05).Conclusion 2D SECara-Net and 3D U2-Net models had similar,sensitivity and accuracy for MRA detecting USIA.Combination of them could improve the detecting efficacy.

Objective To observe the value of 2D SECara-Net and 3D U2-Net models constructed based on 2D maximal intensity projection(MIP)and 3D time-of-flight MR angiography(3D TOF-MRA)images,respectively,also of their combination for MRA detecting unruptured saccular intracranial aneurysms(USIA).Methods Totally 973 patients with single USIA and 300 subjects who underwent healthy physical examination were retrospectively collected and divided into training set(n=923,containing 723 cases of USIA and 200 healthy subjects)and test set(n=350,containing 250 cases of USIA and 100 healthy subjects)at the ratio of 7:3.Pre-processed 3D TOF-MRA and the obtained 2D-MIP images in training set were imported into 3D U2-Net and 2D SECara-Net models for training and adjusting parameters,respectively.The efficiency of 2 models and their combination for detecting USIA were evaluated.Results The sensitivity,specificity and accuracy of 2D SECara-Net model for detecting USIA in test set was 78.80％(197/250),95.00％(95/100)and 83.43％(292/350),of 3D U2-Net model was 82.80％(207/250),86.00％(86/100)and 83.71％(293/350),respectively.The specificity of 2D SECara-Net model was higher than that of 3D U2-Net model(P=0.030),while no significant difference of sensitivity nor accuracy was found between 2 models(both P＞0.05).The specificity of the combination of the 2 models was 99.00％(99/100),higher than that of 3D U2-Net model(P＜0.05),and the sensitivity and accuracy of the combination was 91.20％(228/250)and 93.43％(327/350),respectivelty,both higher than those of 2 single models(all P＜0.05).Conclusion 2D SECara-Net and 3D U2-Net models had similar,sensitivity and accuracy for MRA detecting USIA.Combination of them could improve the detecting efficacy.

Objective To investigate the temporal properties of dynamic functional connectivity of brain networks and the variability of network topology in patients with end-stage renal disease(ESRD).Methods Data of 30 ESRD patients(ESRD group)and 33 healthy subjects(control group)were retrospectively analyzed.Based on cranial resting-state functional MRI(rs-fMRI),dynamic functional connectivity(dFC)and graph theory analysis were employed,and the abnormalities in network topology and dFC in ESRD patients were assessed through comparison of groups.Pearson correlation analysis was used to observe the correlation between abnormal dFC indicators and clinical variables.Results Compared with control group,temporal scores and the mean residence time in ESRD group were significantly higher under state Ⅱ but significantly lower under state Ⅲ(both P＜0.05).The abnormal functional connectivity in ESRD patients under states Ⅱ and Ⅲ distributed mainly within and between default mode network,sensorimotor network,subcortical nuclei,execution and attention network,visual network and cerebellum networks.Network density and bilateral superior temporal gyrus nodal degrees in ESRD group were all significantly lower than those in control group(all P＜0.05).No significant correlation was found between the abnormal parameters of functional connectivity and graph theory attributes in ESRD group and clinical indicators under states Ⅱ nor Ⅲ(all P＞0.05).Conclusion ESRD patients had abnormal temporal attributes and network topology of brain dynamic networks related to cognitive impairments.