Radiological Image Contrast Synthesis for Variable Agent Amounts
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Solution Overview
Problem
Existing methods for generating radiological images with variable contrast enhancement require extensive training data and cannot predict images with contrast agents outside the standard amount without further training, limiting generalizability and applicability to different agents.
Innovation Solution
A method involving generating and processing representations of examination regions with varying contrast agent amounts using subtraction and addition operations in frequency or real space, allowing for flexible contrast enhancement without extensive training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an artificial neural network is trained to predict radiological images with standard contrast agent amount, then the prediction accuracy for standard contrast enhancement is improved, but the method cannot predict images with variable contrast enhancement amounts without additional training data and training
Solution Approach 1:
The patent segments the contrast enhancement prediction task into two independent components: (1) a base neural network model trained on standard contrast enhancement data, and (2) a separate contrast enhancement amount prediction module that estimates the actual contrast agent amount administered. This segmentation allows the base model to maintain high prediction accuracy for standard contrast while the amount prediction module provides adaptability to variable contrast levels, resolving the contradiction between precision and versatility.
Solution Approach 2:
The patent introduces an intermediary contrast enhancement amount prediction module that acts as a mediator between the input radiological image and the base prediction model. This intermediary estimates the actual contrast agent amount and uses it to adjust the prediction output, enabling the system to generalize to variable contrast enhancement amounts without retraining the base model, thus improving adaptability while maintaining prediction accuracy.
2Reliability
If extensive training data from a large number of radiological examinations is collected, then the training of the artificial neural network is improved, but the complexity and resource requirements of the method increase
Solution Approach 1:
The patent extracts and separates the contrast enhancement amount estimation function from the main prediction task. By taking out the amount prediction as a independent module, the system can use a smaller, more manageable training dataset focused on standard contrast enhancement while the amount module is trained separately on contrast agent dosage information, reducing overall data collection complexity while maintaining training quality.
Solution Approach 2:
The patent changes the parameter space by introducing contrast enhancement amount as a separate predicted parameter rather than training the model to handle all variations directly. This parameter change allows the main model to focus on learning standard contrast enhancement patterns with high reliability from limited data, while the amount parameter provides the necessary adaptability through a simpler auxiliary training process.
3Adaptability or versatility
If machine learning statistical models are used for contrast enhancement prediction, then the method can handle variability in data, but the generalizability is limited due to reliance on limited training data selections
Solution Approach 1:
The patent introduces dynamics by making the prediction process adaptive to the actual contrast enhancement amount through the amount prediction module. The system dynamically adjusts its prediction based on the estimated contrast agent amount, allowing it to handle data variability across different contrast levels while maintaining reliability through the structured two-module architecture that prevents overfitting to limited training data.
Solution Approach 2:
The patent creates a universal prediction framework where the base neural network model serves multiple functions: it can predict contrast enhancement for standard amounts and, when combined with the amount prediction module, adapts to variable contrast levels. This multi-functionality approach allows the system to handle data variability across different contrast scenarios while maintaining generalizability through a single trained model rather than requiring separate models for each contrast level.
Data Source
AI summary
The present invention relates to the technical field of generating artificial contrast-enhanced radiological images.