Multi-modality Image Analysis Model for Medical Imaging Fusion
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Solution Overview
Problem
Current image processing methods are inefficient when only one modal image information or more than two types of modal image information are available, as they cannot perform dual-modal processing, leading to a need for redeveloping new image processing methods.
Innovation Solution
An image processing method and device that obtain a first quantity of to-be-analyzed images, perform fusion and enhancement processing using an image analysis model trained on a second quantity of sample images, to produce a first target image that enhances the display of the analysis object's distribution area.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a dual-modal scheme is used to diagnose hepatic tumor, then the diagnosis accuracy is improved, but the processing complexity increases
Solution Approach 1:
The image analysis model is designed to handle multiple modalities (CT, MRI, PET) and varying quantities of images (1-3 images per patient) through a unified processing framework. The model can process single-modality images when dual-modal data is unavailable, making the system universally applicable across different imaging scenarios without requiring separate processing pipelines for each modality combination.
Solution Approach 2:
The system dynamically adapts to different input scenarios by automatically determining the appropriate processing path based on the number and type of images received. When only one image is available, the model performs single-modality analysis; when two or three images are available, it performs multi-modality fusion analysis. This dynamic adjustment eliminates the need for complex manual configuration and maintains optimal processing efficiency across varying conditions.
2Adaptability or versatility
If a new image processing method is developed for single-modality or multi-modality cases, then the method becomes more versatile, but the development efficiency decreases
Solution Approach 1:
A single image analysis model is designed to handle multiple modalities (CT, MRI, PET) and varying quantities of images (1-3 images per patient) through a unified processing framework. The model can process single-modality images when dual-modal data is unavailable, making the system universally applicable across different imaging scenarios without requiring separate processing pipelines for each modality combination.
Solution Approach 2:
The model is pre-trained on a comprehensive dataset containing multiple modalities and scenarios during the development phase. This preliminary training enables the model to automatically adapt to different input conditions during actual use without requiring retraining or complex configuration. The pre-trained model learns to identify and process different modality combinations, eliminating the need for developers to create separate methods for each scenario.
3Loss of information
If dual-modal CT images with contrast agent in hepatic vein and hepatic artery are collected, then the information completeness is improved, but the time consumption increases
Solution Approach 1:
The system dynamically adapts to different input scenarios by automatically determining the appropriate processing path based on the number and type of images received. When only one image is available, the model performs single-modality analysis; when two or three images are available, it performs multi-modality fusion analysis. This dynamic adjustment eliminates the need for complex manual configuration and maintains optimal processing efficiency across varying conditions.
Solution Approach 2:
The image analysis model acts as an intermediary that processes and fuses information from different modalities and time points. When multiple images are provided, the model integrates information from different phases (arterial, venous, delayed) to produce a comprehensive analysis. This intermediary processing enables the system to make the most of available data without requiring all possible modalities, thus reducing time consumption while maintaining diagnostic accuracy.
Data Source
AI summary
An image processing method includes obtaining a first quantity of to-be-analyzed images and performing fusion and enhancement processing on the first quantity of to-be-analyzed images through an image analysis model to obtain a first target image. Each to-be-analyzed image corresponds to a different target modality of a target imaging object. The first target image is used to enhance display of a distribution area of an analysis object of the first quantity of to-be-analyzed images. The analysis object belongs to the imaging object. The image analysis model is obtained by training a second quantity of sample images corresponding to different sample modalities. The first quantity is less than or equal to the second quantity. The target modality belongs to the sample modalities.