Criteria for Emergency Brain MRI During Stroke-Alert

Study on the treatment of dysphagia after stroke with electromyographic biofeedback intensive training

BACKGROUND

Dysphagia, or swallowing disorder, is a common complication following stroke, significantly impacting patients' quality of life. Electromyographic biofeedback (EMGBF) therapy has emerged as a potential rehabilitation technique to improve swallowing function, but its efficacy in comparison with conventional treatments remains to be further explored.

AIM

To investigate the effects of different treatment intensities of EMGBF on swallowing function and motor speed after stroke.

METHODS

The participants were divided into three groups, all of which received routine neurological drug therapy and motor function rehabilitation training. On the basis of routine swallowing disorder training, the EMGBF group received additional EMGBF training, while the enhanced EMGBF group received two additional training sessions. Four weeks before and after treatment, the degree of swallowing disorder was evaluated using the degree of swallowing disorder score (VGF) and the Rosenbek penetration-aspiration scale (PAS).

RESULTS

Initially, there was no significant difference in VGF and PAS scores among the groups (P > 0.05). After four weeks, all groups showed significant improvement in both VGF scores and PAS scores. Furthermore, the standardized swallowing assessment and videofluoroscopic dysphagia scale scores also improved significantly post-treatment, indicating enhanced swallowing function and motor function of the hyoid-bone laryngeal complex, particularly in the intensive EMGBF group.

CONCLUSION

EMGBF training is more effective than traditional swallowing training in improving swallowing function and the movement rate of the hyoid laryngeal complex in patients with post-stroke dysphagia.

Traumatic Brain Magnetic Resonance Imaging Feature Extraction Based on Variable Model Algorithm in Stroke Examination

The purpose of this study was to explore the diagnostic value of different sequence scanning of nonparametric variable model-based cranial magnetic resonance imaging (MRI) for ischemic stroke. A histogram analysis-based nonparametric variable model was proposed first, which was compared with the parametric deformation (PD) model and geometric deformation (GD) model. Then, 116 patients with acute ischemic stroke were selected as the research subjects. Routine MRI (T2WI, T1WI, FLAIR, DWI, SWI, and 3D TOF MRA) and MR SCALE-PWI were performed. The results showed that the nonparametric variable model algorithm was relatively complete in the actual segmentation results of MRI images, and the display clarity of lesions was better than PD and GD algorithms. The diagnostic sensitivity, specificity, and overall performance of the variable model algorithm were significantly higher than those of the other two algorithms (P < 0.05). According to ROC curve analysis, the AUC areas of DWI, SWI, 3D TOF MRA, and MR SCALE-PWI for the diagnosis of ischemic penumbra were 0.793, 0.825, 0.871, and 0.933, respectively. In summary, the segmentation results of MRI images by the nonparametric variable model based on histogram analysis were relatively complete, and the clarity of lesions was better than that of the traditional model. MRI images can effectively identify the occurrence of ischemic stroke. Moreover, MR SCALE-PWI had a good early identification effect on ischemic penumbra, which can reduce unnecessary treatment for patients.