Research On Parkinson Quantitative Analysis Method And Software Platform Based On Multi - Modal Brain Image

Parkinson’s disease(PD)is the fourth common neurodegenerative disease among the elderly,with the advent of an aging society,Parkinson’s prevalence is on the rise.PD is a chronic progressive disease that will accompany the patient’s lifetime and will easily progress to dementia,early diagnosis of PD helps to timely intervention,is of great significance.Medical imaging and computer technology play an important role in assisting physicians in Parkinson’s diagnosis.Existing research shows that the Parkinson patient’s caudate shows atrophic lesion in brain PET imaging under the AV-133 tracer compare to pre-illness,and the proportion of the caudate lesion can provide a basis for early diagnosis.However,the problem encounter in the current research is that the caudate volume before the lesion can’t be estimated base on brain PET imaging,thus,the required proportions can’t be calculated accurately according to the patient’s caudate brain PET imaging under the AV-133 tracer.In response to this problem,consider that the morphology of the patient’s caudate doesn’t change in the early stage of the PD,the patient’s brain MR imaging results show that the caudate is consistent with its premorbid caudate.Thus,in this paper,the proportion of caudate lesion of PD patients is calculated based on the multimodal brain images such as MRI,PET and CT to achieve quantitative analysis of PD.In order to set different modes of brain images under the unified format,the brain MR,PET,and CT original images need to be pre-processed,including redirection,resampling and noise reduction.At the same time,in order to make different modes of brain images in the same space coordinates,brain MR and CT images are registered based on mutual information,and the registration accuracy is improved by pre-splitting the skull in brain CT images during registration.The brain MR images after registration and brain PET images after preconditioning are respectively segmented to get the complete caudate and nonlesion caudate,then calculate their respective volume,on this basis to calculate the proportion of caudate lesions of PD patients.In order to facilitate doctors to directly obtain the structure information of the caudate as well as the data information such as caudate volume and lesion ratio,the three-dimensional reconstruction of the caudate and skull based on the MC(Marching Cubes)algorithm is performed and the reconstruction result and the data result are displayed in the same interface.Finally,the decentralized functional modules are integrated to build a PD quantitative analysis software platform,which provides a new tool for assisting physicians in diagnosing and analyzing PD.