Image Registration Transformation Selection via Local Maxima Ranking
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Solution Overview
Problem
Current image registration processes in minimally-invasive procedures often result in a single registration result corresponding to the largest maximum, which may not be clinically-optimal, and are hindered by multiple local maxima in similarity functions, requiring time-consuming manual alignment by physicians.
Innovation Solution
A system that computes a similarity function to rank multiple registration transformations based on local maxima, allowing physicians to select the clinically-optimal transformation from a displayed list, minimizing manual alignment by using image dataset acquisition devices, a processor, and a user-interface to present and rank registration transformations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single registration result corresponding to the largest maximum is displayed, then the registration process is simplified and fast, but the clinical optimality of the registration may be compromised due to multiple local maxima in the similarity function
Solution Approach 1:
The patent segments the single registration result into multiple candidate transformations, each corresponding to a different local maximum in the similarity function. By dividing the registration output into multiple discrete options rather than presenting a single result, the system allows physicians to evaluate and select the clinically optimal transformation while maintaining computational efficiency.
Solution Approach 2:
The system changes the parameter of the registration output from a single transformation to multiple transformations with different transformation parameters. Each candidate transformation represents a different local maximum with distinct parameter sets, allowing the physician to select the optimal parameters for their specific clinical situation.
2Reliability
If multiple registration transformations are computed and displayed for physician selection, then clinical optimality is improved, but the complexity of the registration system increases
Solution Approach 1:
The system performs preliminary computation of multiple candidate transformations before the physician needs to make a selection. By pre-computing and ranking the transformations based on the similarity function, the system prepares multiple options in advance, reducing the complexity of the decision-making process during the actual procedure while maintaining high clinical optimality.
Solution Approach 2:
The system provides feedback to the physician by displaying multiple candidate transformations with their corresponding similarity scores or rankings. This feedback mechanism allows the physician to make an informed selection based on objective metrics, reducing the cognitive load and simplifying the user interface despite the underlying computational complexity.
3Extent of automation
If the largest maximum of the similarity function is selected automatically, then the registration process is automated and fast, but manual alignment may still be required if the result is not clinically acceptable
Solution Approach 1:
The system transitions from a static single-result automation to a dynamic multi-option presentation. By providing a ranked list of candidate transformations, the system adapts to the physician's needs - if the top candidate is acceptable, selection is rapid; if not, the physician can efficiently evaluate alternatives without starting manual alignment from scratch.
Solution Approach 2:
The system prepares multiple candidate transformations in advance as a cushion against the possibility that the largest maximum may not be clinically optimal. This preliminary preparation ensures that if the automatic selection is unsatisfactory, the physician immediately has alternative options ready, preventing time loss that would otherwise be spent on manual realignment.
Data Source
AI summary
System and method for enabling intra-operative selection of an image registration transformation for use in displaying a first image dataset and a second image dataset in correspondence with one another. Image dataset acquisition devices (12, 14) obtain the first and second image datasets. A similarity function indicative of a likelihood that the first and second image datasets are in correspondence with one another is computed by a processor (16) and then a ranking of each of a plurality of local maxima of the similarity function is determined. Registration transformations derived from a plurality of the local maxima are displayed on a display (18), and using a user-interface (22), a physician can select each registration transformation to ascertain visually whether it is the clinically-optimal registration transformation for subsequent use.