Longitudinal Image Ensemble Synchronization Without Reference Bias
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Solution Overview
Problem
Conventional image registration techniques face inconsistencies when synchronizing longitudinal data sets, particularly in medical applications, due to biases from reference images, leading to inaccurate temporal trends and interpretations.
Innovation Solution
A method and system for synchronizing longitudinal data sets by determining a second reference image and ensemble registration estimate using optimization techniques, incorporating transformations to align image ensembles in a common coordinate space, thereby generating a synchronized image ensemble that mitigates reference bias and ensures consistent temporal analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image registration techniques are used to align images, then image alignment is achieved, but reference image bias introduces inaccuracies in temporal trend analysis
Solution Approach 1:
The patent extracts and removes the reference image bias from the registration process by using group-wise registration that does not depend on a single reference image. Instead, it uses all images in the ensemble to compute transformations, thereby eliminating the harmful reference bias while maintaining alignment capability.
Solution Approach 2:
The patent merges all images in the longitudinal data set into a single group-wise registration process. By combining all images and simultaneously determining transformations for each image relative to the common coordinate space, it eliminates reference bias while achieving consistent temporal alignment.
2Adaptability or versatility
If group-wise registration is performed on batches of images, then temporal trends can be analyzed, but inconsistencies arise when new batches are added
Solution Approach 1:
The patent implements a dynamic registration approach where the common coordinate space and transformations are continuously updated as new image batches are added. The system adapts to new data while maintaining consistency with previous registrations, allowing longitudinal analysis to evolve with new batches without introducing inconsistencies.
Solution Approach 2:
The patent uses feedback from new image batches to update and refine the registration transformations. When new batches are added, the system uses the new data to adjust and improve the transformations, ensuring that temporal trends remain consistent and accurate as the data set grows over time.
3Ease of manufacture
If reference-based registration is used, then image alignment is simplified, but temporal variations and diagnostic accuracy are compromised
Solution Approach 1:
The patent extracts the reference image dependency from the registration process, transforming it from a reference-based approach to a group-wise approach. This removes the simplification benefit of reference-based methods while achieving superior temporal accuracy through simultaneous registration of all images.
Solution Approach 2:
The patent changes the fundamental parameters of the registration process by shifting from reference-based transformations to group-wise transformations. Instead of aligning all images to a single reference, it determines transformations for all images simultaneously relative to a common coordinate space, improving temporal accuracy while maintaining computational feasibility.
Data Source
AI summary
A method for synchronization of a longitudinal data set from a subject includes receiving a first ensemble registration estimate having a first reference image corresponding to a first image ensemble and receiving a second image ensemble different from the first image ensemble. The method includes determining a second reference image based on the second image ensemble and the first reference image. Further, the method includes determining a second ensemble registration estimate based on the first ensemble registration estimate, the second reference image, the first image ensemble and the second image ensemble using an optimization technique. The method further includes generating a synchronized image ensemble corresponding to the first image ensemble and the second image ensemble based on the second ensemble registration estimate. The method also includes determining a medical condition of the subject by a medical practitioner based on the synchronized image ensemble.