Parkinson's disease auxiliary diagnosis system based on multi-modal magnetic resonance brain image

Abstract

The invention relates to a Parkinson's disease auxiliary diagnosis system based on a multi-modal magnetic resonance brain image. The auxiliary diagnosis system comprises an input module, a feature screening module, a feature selection module and a diagnosis module. The input module comprises inputs of a T1WI image, a DTI image and a QSM image. The feature screening module is used for carrying outimage preprocessing on the three groups of images, extracting feature data of a region of interest, connecting the feature data of different modes in series to form a series feature matrix X, and connecting MOCA and sample labels in series to form a corresponding matrix Y. The feature selection module is used for extracting features with high representativeness, and the diagnosis module is used for carrying out learning, data regression and classification on the features and finally obtaining a diagnosis result, so that more accurate auxiliary diagnosis is provided for doctors.

Description

technical field [0001] The invention belongs to the technical field of computer analysis of medical images, and relates to an auxiliary diagnosis system for Parkinson's disease based on multimodal magnetic resonance brain images. Background technique [0002] Parkinson's disease (PD), also known as paralysis agitans, is the second most common degenerative disease of the central nervous system, characterized by degeneration or loss of dopaminergic neurons in the substantia nigra compacta, resulting in motor dysfunction. Currently, the diagnosis of Parkinson's disease mainly relies on clinical symptoms, which largely depends on the experience of clinicians. Therefore, an effective early diagnosis method is particularly necessary. [0003] In order to better diagnose early PD, magnetic resonance imaging (Magnetic Resonance Imaging, MRI) is commonly used to quantify the loss of neurons in different regions of the brain, so as to detect PD. MRI has the advantages of high spatia...

AI Technical Summary

Problems solved by technology

[0006] (1) Auxiliary diagnostic system has a single task: most of the auxiliary diagnostic systems only have a classification model, which simply classifies the test model and cannot perform multiple tasks at the same time. For example, it cannot predict clinical data while classifying diseases. , thus wasting a lot of relevant information

[0007] (2) The features are single and not rich: most auxiliary diagnostic systems are based on a single modality, so the extracted data features are also relatively simple. Earlier researchers used transcranial ultrasound images, MRI images and PET images to extract features. However, the registration feature fusion of images of these three modalities is complex and time-consuming.

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