Co-registration Error Estimation via Iterative Spread
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Solution Overview
Problem
Current methods for estimating co-registration errors between medical images do not provide real-time accuracy, making it difficult to assess the reliability of specific co-registration transformations for treatment planning, especially in intensity-based methods.
Innovation Solution
Performing additional iterative co-registrations with varied initial parameters, such as candidate transformations, sample points, and regions of interest, to estimate the spread in co-registration results, which serves as a measure of error, using metrics like mutual information to assess the confidence in image registration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional iterative co-registrations are performed with varied initial parameters to estimate error, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent performs preliminary error estimation by conducting multiple iterative co-registrations with varied initial parameters before final treatment planning. This preliminary action identifies potential registration errors early, allowing operators to reassess or repeat co-registration if errors are detected, thereby improving measurement precision without requiring complex post-processing corrections
Solution Approach 2:
The patent employs a practical compromise by performing a limited number of additional iterative co-registrations (excessive enough to provide statistically meaningful error estimation, but not so many as to be excessively time-consuming). This partial action provides sufficient error estimation accuracy for clinical decision-making while maintaining reasonable workflow efficiency
2Reliability
If multiple additional co-registrations are performed to estimate error, then reliability is improved, but productivity decreases
Solution Approach 1:
The patent implements a feedback mechanism where the error estimation results from multiple co-registrations are fed back to the operator. If the estimated error exceeds acceptable thresholds, the system prompts the operator to repeat the co-registration process or adjust parameters. This feedback loop ensures reliable co-registration without requiring all cases to undergo maximum numbers of iterations, thus balancing reliability with productivity
Solution Approach 2:
Error estimation is performed as a preliminary step before final treatment planning proceeds. By quickly assessing co-registration reliability through multiple iterations and only requiring repeat procedures when errors are detected, the system maintains high reliability while minimizing the impact on overall treatment planning throughput
Data Source
AI summary
The co-registration error can be estimated by performing a number of additional iterative co-registrations, each iteration having a starting point dictated by the found co-registration transformation, and using a set of initial parameters different to that of the co-registration being tested. The spread in the resulting co-registrations can then be used as the estimate of the co-registration error. The variations in the set of initial parameters can include (i) the candidate transformation which the iteration uses as its starting point, adopting starting points that are offset from the co-registration being tested, (ii) the sample points used for the mutual information metric (or whichever metric is used to optimise the transformation), and (iii) the region-of-interest that is selected. Ideally, all three are varied to some extent within the plurality of additional co-registrations that are performed, and an average value is taken.