Application of Modal Brain Imaging Data Fusion Techniques in the Diagnosis of Neurological Disorders

Abstract

Due to the high incidence and complexity of neurological diseases in clinical medicine, they have a greater impact on the diagnosis and treatment of doctors. This paper firstly analyzes the concept of modal brain imaging data fusion and modal imaging available for fusion, and obtains the types of modal imaging available for fusion. Second, the patient's case information is collected, and the data processing technology is carried out on the collected information to ensure the completeness and clarity of the data. Finally, the feature extraction of the processed data is carried out to obtain the fusion features of the data, and the features are fused together to complete the fusion of modal imaging data. The performance and effect of the modal brain imaging data fusion technique is analyzed from three perspectives: the LGI analysis of patients with neurological disorders, the global network analysis, and the power spectrum analysis of the time series.The LGI values of 3.5 and 3.7 at pain values of 1 and 6 indicate that the intensity of the patient's pain is related to the negative emotions. It was concluded that there is a negative pattern between pain and LGI values and that the characteristic path lengths of patients with neurological disorders and control patients are lower at around 1.05. It proves that the fusion effect of modal imaging is better, which facilitates the diagnosis and treatment of doctors, comprehensively understands the status of patients' diseases, and improves the cure rate of neurological diseases.