Objective To explore the value of static and dynamic MRI before and after operation of pelvic organ prolapse (POP). Methods 29 patients with POP (POP group)and 12 normal women (control group)underwent static and dynamic MRI.The morphologic changes of pelvic floor were observed on MR images.The measurements of bladder,uterus,Douglas pouch to pubococcygeal line (B-PCL,U-PCL,D-PCL),the puborectal hiatus line (H-line),muscular pelvic floor descent (M-line),the levator hiatus size (LHS),the levator plate angle (LPA),the iliococcygeus angle (ICA)and the urethral inclination angle (UA)were recorded on dynamic MR images.Results 19 cystoceles,28 uterine prolapses,4 rectoceles and 14 hernias of Douglas pouch were detected with MRI.29 cases of pelvic floor relaxation,27 cases of levator ani muscle defect and 24 cases of pubocervical fascial defect were found.The values of B-PCL,U-PCL, D-PCL,H-line,M-line,LHS,LPA,ICA and UA of POP group were larger than control group (P<0.01).The positions of pelvic organ returned to normal in 9 cases of 21 postoperative cases,while 12 cases remained prolapses.There was no displacement of mesh in 8 cases of mesh implant.The values of B-PCL,U-PCL,D-PCL,UA after operation were smaller than those before operation (P<0.05).Conclusion Static and dynamic MRI can evaluate morphological and functional changes of pelvic floor before and after operation of POP comprehensively,and may reveal those invisible pelvic floor dysfunction and postoperative remnant defects.

Objective To noninvasively assess the cerebral ischemic status using the velocity profile of the carotid and vertebral arteries measured by Doppler ultrasound and a neural network model.Methods Imaging data were collected from patients who underwent computed tomography perfusion(CTP)and Doppler ultrasound.Hemodynamic parameters were extracted from the ultrasound images.These parameters were used to train a fully connected neural network model.The model was validated using the CTP results.Results Sixty-two eligible patients were included;44 were randomly selected as the training dataset and 18 were designated for validation.In the training set,the area under the curve(AUC)of the receiver operating characteristic,sensitivity,specificity,and accuracy were 0.95,0.833,0.923,and 0.886,respectively.In the test set,the AUC,sensitivity,specificity,and accuracy were 0.860,0.714,1.000,and 0.889,respectively.Conclusions The model based on Doppler ultrasound and neural network was clinically verified and had good accuracy for assessing cerebral ischemia,showing its clinical potential for the early screening of cerebral ischemia.