ABSTRACT
Objective:To analyze the respiratory muscle functioning of stroke survivors and explore factors influencing it so as to provide references for clinical rehabilitation intervention.Methods:A total of 139 stroke survivors were randomly divided into a respiratory muscle dysfunction group and a control group based on the actual strength of their inspiratory muscles divided by the predicted strength. Beyond typical clinical data, information was collected about the subjects′ exercise habits. Balance ability was evaluated using the Fugl-Meyer assessment scale (FMA) and the simplified Berg Balance Scale (BBS). Multivariate logistic regression was used to analyze the factors influencing respiratory muscle dysfunction.Results:Among the 139 patients, 81 (58.27%) had respiratory muscle dysfunction. Univariate analysis showed that patients with stroke in the brainstem and dysphagia and those with poor FMA and BBS scores were at significantly greater risk of respiratory muscle dysfunction. Logistic regression analysis showed that dysphagia, FMA and BBS scores were factors independently predicting respiratory muscle dysfunction among stroke survivors, with dysphagia as a risk factor, and high FMA and BBS scores as protective factors.Conclusion:Some stroke survivors may have respiratory muscle dysfunction, and dysphagia is a risk factor, while the high FMA and BBS scores are protective.
ABSTRACT
As there are not many research reports on segmentation and quantitative analysis of soft tissues in lumbar medical images, this paper presents an algorithm for segmenting and quantitatively analyzing discs in lumbar Magnetic Resonance Imaging (MRI). Vertebrae are first segmented using improved Independent component analysis based active appearance model (ICA-AAM), and lumbar curve is obtained with Minimum Description Length (MDL); based on these results, fast and unsupervised Markov Random Field (MRF) disc segmentation combining disc imaging features and intensity profile is further achieved; finally, disc herniation is quantitatively evaluated. The experiment proves that the proposed algorithm is fast and effective, thus providing doctors with aid in diagnosing and curing lumbar disc herniation.