Image Reconstruction Output Validation via Forward Projection Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Medical images reconstructed using AI techniques are not used for diagnosis due to the unknown algorithmic nature, necessitating time-consuming and unpredictable FDA validation.
Innovation Solution
A framework for output validation of image reconstruction algorithms that includes analytical forward projection and a likeness discriminator to validate the reconstructed images, potentially reducing the need for extensive FDA validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI techniques are used for image reconstruction, then radiation dose can be reduced and reconstruction speed improved, but the algorithmic nature becomes unknown and reliability for diagnosis deteriorates
Solution Approach 1:
The patent implements a feedback mechanism by performing forward projection on the reconstructed image and comparing the result with the original input data. The likeness discriminator provides feedback on whether the reconstructed image is consistent with the measured data, thereby validating the AI reconstruction process and improving diagnostic reliability while maintaining fast reconstruction speeds
Solution Approach 2:
The patent introduces an intermediary validation framework that acts as a mediator between the AI reconstruction algorithm and clinical diagnosis. This framework includes forward projection and likeness discrimination steps that verify the reconstructed images without requiring extensive FDA validation, thus enabling both fast reconstruction and reliable diagnosis
2Object-affected harmful factors
If AI techniques are used for image reconstruction, then radiation dose can be reduced to unprecedented levels, but the unknown algorithmic nature prevents use for diagnosis
Solution Approach 1:
The validation framework uses feedback from forward projection to ensure that AI-reconstructed images from low-dose scans remain consistent with the original measured data. This feedback mechanism guarantees diagnostic reliability even when radiation dose is reduced to unprecedented levels
Solution Approach 2:
The system performs self-validation through the likeness discriminator, which automatically checks whether the reconstructed image is consistent with the input data without requiring external validation. This self-service approach enables low-dose imaging with maintained diagnostic reliability
3Reliability
If extensive FDA validation is performed on AI reconstruction algorithms, then reliability for diagnosis is improved, but time and complexity of the validation process increases
Solution Approach 1:
The patent extracts the essential validation functionality into a separate, efficient framework that performs only the necessary forward projection and likeness discrimination steps. This extracted validation approach achieves required reliability without the time and complexity of extensive FDA validation processes
Solution Approach 2:
The validation framework changes the validation parameters from extensive FDA-style validation to a more efficient likeness discrimination metric. This parameter change maintains diagnostic reliability while significantly reducing validation time and complexity
Data Source
AI summary
A framework for output validation of an image reconstruction algorithm. The framework receives original input data and reconstructed image data generated by the image reconstruction algorithm based on the original input data. Analytical forward projection is performed on the reconstructed image data to generate an algorithmic version of the original input data. The original input data and the algorithmic version of the original input data are applied as input to a likeness discriminator to generate a validation value that validates the image reconstruction algorithm.