Synthetic Contrast Radiology Using Frequency-Domain Gain Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for generating radiological images with variable contrast enhancement require additional training data and are prone to errors, limiting their generalizability and applicability across different contrast agents and doses, which can lead to artifacts and diagnostic uncertainties.
Innovation Solution
A method involving two models that process frequency-space representations of radiological images with varying contrast agent amounts, using a frequency-dependent weighting function and gain factor to generate synthetic images with controlled contrast enhancement, reducing deviations through model parameter adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an artificial neural network is trained to predict radiological images after administration of a standard amount of contrast agent, then the prediction accuracy for standard contrast enhancement is improved, but the method cannot predict images for other contrast agent amounts without additional training data and further training
Solution Approach 1:
The patent applies parameter changes by modifying the contrast enhancement level through a gain factor applied to the contrast component in the frequency domain. Instead of training separate neural networks for different contrast amounts, the system changes the parameter (gain factor) to generate predictions for various contrast agent dosages from a single trained model, thereby achieving versatility without additional training data.
Solution Approach 2:
The patent achieves universality by designing a prediction system that can handle multiple contrast enhancement levels using a single neural network. By decomposing the image into contrast and non-contrast components in the frequency domain and applying a可调 gain factor, the system becomes multi-functional, capable of predicting images for any contrast agent amount within a reasonable range without requiring separate models for each scenario.
2Extent of automation
If machine learning methods are used to generate artificial contrast-enhanced images, then the generation process can be automated, but the statistical models are limited in generalizability due to restricted training data sets
Solution Approach 1:
The patent introduces an intermediary approach by using frequency domain transformation as a bridge between the input images and the prediction process. By converting images to the frequency domain, separating contrast components, and applying a gain factor in this intermediate representation space, the system achieves better generalizability while maintaining automation. This intermediary step allows the model to understand contrast enhancement as a separable, scalable operation rather than learning from limited examples.
3Productivity
If a trained machine learning model generates artificial medical images, then productivity is improved, but errors and artifacts may occur that lead to diagnostic uncertainties
Solution Approach 1:
The patent implements feedback by comparing the generated contrast-enhanced images with actual contrast-enhanced reference images and calculating a quantitative difference metric. This feedback mechanism allows for validation and adjustment of the prediction process, ensuring that generated images remain diagnostically accurate. The system can identify and correct artifacts or errors by measuring deviations from ground truth data, thereby maintaining reliability while preserving productivity.
Data Source
Figure 1
Figure 2~3
Figure 4~6
AI summary
The present disclosure deals with the technical field of generating artificial contrast-enhanced radiological images.