Multi-Modality Brain Disease Diagnosis via Feature Vector Integration
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Solution Overview
Problem
Current medical image analysis for brain disease diagnosis is time-consuming, requires extensive expertise, and often results in misdiagnosis due to limitations in accurately distinguishing between disease-related and normal/growth-related changes in images from multiple imaging modalities like MRI, fMRI, and PET-CT.
Innovation Solution
A multi-modality medical image analysis method and apparatus that acquires images from different modalities, selects pre-trained analysis models, converts output values into feature vectors, and inputs them into pre-trained diagnosis models to predict brain disease progression, thereby enhancing diagnosis accuracy and reducing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a specialist manually analyzes multiple medical images to diagnose brain diseases, then diagnosis accuracy can be maintained through expert knowledge, but diagnosis time and training costs increase significantly
Solution Approach 1:
The patent segments the complex diagnostic task into multiple independent analysis models, each specialized for a specific imaging modality (MRI, CT, PET, fMRI). Each model processes one type of image independently, extracting modality-specific features without requiring a specialist to manually integrate multiple image types. This segmentation enables parallel processing of different modalities, significantly reducing diagnosis time while maintaining accuracy through specialized analysis for each modality.
Solution Approach 2:
The patent transforms medical images into standardized feature vectors by changing the representation parameters from raw pixel data to extracted features. Multiple analysis models process different modalities and output feature vectors that are then integrated by a diagnosis model. This parameter transformation enables efficient computation and reduces diagnosis time while maintaining diagnostic accuracy through standardized feature representation.
2Measurement precision
If structural analysis is performed on brain regions from CT or MRI images, then anatomical changes can be detected, but it becomes difficult to distinguish disease-related changes from normal growth or genetic variations in normal or mild cases
Solution Approach 1:
The patent merges multiple imaging modalities (MRI, CT, PET, fMRI) into a multi-modality analysis framework. By combining structural information from MRI and CT with functional information from fMRI and metabolic information from PET, the system can distinguish disease-related changes from normal variations more reliably. The integration of multiple modalities provides complementary information that enhances diagnostic reliability, especially for normal or mild cases where single-modality analysis is insufficient.
Solution Approach 2:
The diagnosis model serves multiple functions by processing feature vectors from different analysis models corresponding to different imaging modalities. This universal model can handle various disease types and stages by integrating information from multiple sources, enabling reliable detection of subtle disease-related changes while accounting for normal growth and genetic variations through multi-modal comparison.
3Measurement precision
If fMRI is used to detect BOLD signals and analyze brain networks, then functional activation can be detected, but noise in activated regions leads to false positives and reduces diagnostic accuracy
Solution Approach 1:
The patent introduces an intermediary analysis model for fMRI that processes BOLD signals before they reach the final diagnosis model. This intermediary model extracts meaningful features from noisy fMRI data and transforms them into standardized feature vectors, reducing false positives. The intermediary processing step acts as a filter that separates true functional activation from noise, improving diagnostic reliability while preserving functional activation detection capability.
4Measurement precision
If multiple types of medical images are acquired and analyzed through multiple analysis models, then diagnosis accuracy is enhanced, but system complexity increases
Solution Approach 1:
The patent standardizes the output of multiple analysis models by transforming diverse modality-specific results into a unified feature vector representation. This parameter transformation simplifies the system architecture by creating a common interface between different analysis models and the diagnosis model, reducing system complexity while maintaining the ability to process multiple imaging modalities for enhanced diagnostic accuracy.
Data Source
AI summary
Provided is a multi-modality medical image analysis method and apparatus for brain disease diagnosis. The method includes the steps of: acquiring medical images with different modalities for the same patient; selecting at least some of pre-trained analysis models corresponding to the modalities of the medical images; inputting the medical images correspondingly to the analysis models selected with respect to the modalities of the medical image to produce output values related to a plurality of factors used for reading at least one brain disease; converting the output values to produce a plurality of feature vectors corresponding to the output values; and inputting the plurality of feature vectors to at least one diagnosis model pre-trained to read the brain disease to thus predict a degree of brain disease progression.