Discovery Of Imaging Indicators Of Alzheimer’s Disease Based On ICC

Alzheimer’s disease is now the third major disease.At present,the cause is unknown.Because of the slow onset of AD,it is not obvious in the early stage.Once obvious symptoms appear,most cases have already entered the advanced stage and cannot get timely and effective treatment.Therefore,how to diagnose alzheimer’s disease earlier has become the focus of extensive research and attention.Some studies have shown that although the diagnosis of AD based on magnetic resonance imaging has achieved certain results,it mainly relies on structural characteristics.Once the structure changes,the patient enters an advanced,irreversible stage.In clinical practice,professional doctors use MMSE to diagnose alzheimer’s disease.Although this method is effective,it is not accurate.If functional abnormalities can also be used as a basis for early diagnosis of alzheimer’s disease,early detection and early intervention can be achieved.Therefore,the key to early diagnosis of AD is to seek stable and reliable indicators on MRI in patients with AD.The bottleneck of the current study is the inability to find stable and reliable indicators in MRI imaging.Therefore,this study applied the structure and function of MRI imaging to the diagnosis and classification of alzheimer’s disease.A method of finding stable and reliable index in MRI imaging is presented.Taking 105 cases of data in ADNI2 database as experimental subjects,the validity of the index was obtained through the accurate acquisition of hippocampal volume and the spontaneous brain activity of patients,and the retest reliability analysis.The main research contents and achievements are as follows:(1)the hippocampal volume and interested area were obtained based on MRI imaging.In the structural image,due to the significant individual differences in hippocampus,it is not easy to segment,and manual segmentation requires a large amount of time and energy.In this study,semi-automatic segmentation was adopted,and the non-brain tissue was firstly eliminated through Free Surfer,and then standardized and normalized.Finally,the volume of the hippocampus is obtained by running the command.In the functional image,image preprocessing is first carried out,then itk-snap is used to remove the skull from the image data,and the non-conforming image is manually corrected under the guidance of a professional doctor,which is used after the correction is approved by the doctor.Finally,the image was registered to the MNI152 template by FSL to extract the region of interest(ROI).(2)related indicators of hippocampal structure and function were obtained based on MRI.Based on the data processed in step(2),the relevant data of hippocampal volume were sorted out,and the relevant indicators of spontaneous brain functional activity were calculated according to the extracted ROI(ALFF\Re Ho).Statistical analysis was carried out on the experimental results to study the relationship between hippocampal volume,spontaneous brain functional activity index and AD.The results showed that,compared with NC,AD,LMCI and EMCI had significantly decreased hippocampal volume,which provided a valuable basis for early diagnosis of AD.(3)retest reliability analysis of acquired indexes.Intra-class correlation(ICC)can quantitatively characterize the consistency between two or more measurements and is often used to reflect the reliability of two or more measurements.Therefore,in this study,re-test reliability analysis was used to analyze the obtained indexes and explore their stability and reliability.In summary,compared with the retest reliability of structure and function,the retest reliability of structure is obviously higher than that of spontaneous brain activity.(4)the stability index is used as a classification feature for classification research To check whether the obtained indicators can be used as a classification.Based on the obtained stability index and MMSE score,this study based on experience-driven screening can be used for early diagnosis of AD.The results show that the classification results are optimal when all the indexes are taken as characteristics.The results of the above studies consistently show that the indicators such as Volume ALFF Re Ho are a stable and reliable set of indicators and an effective characteristic indicator of alzheimer’s disease.Compared with many studies in recent years,the indicators obtained in this study can be used as diagnostic basis.