Multi-layer Image Registration via Tissue-Specific Weight Matrices
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Solution Overview
Problem
Existing image registration techniques fail to properly align medical images captured at different energy/radiation levels and exposure times due to contradictory tissue movements, resulting in significant artefacts and distortions.
Innovation Solution
A system that uses a machine learning model to generate multiple registration fields and weight matrices, allowing each pixel in a movable image to have multiple shift vectors that account for the movements of different tissue types, thereby facilitating accurate alignment with a fixed image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If different energy/radiation levels and exposure times are used to capture images of different tissue types, then the images better depict different tissue types, but the images cannot be properly registered/aligned due to contradictory tissue movements
Solution Approach 1:
The patent divides the image registration problem into multiple independent layers, each corresponding to a specific tissue type. Instead of attempting to register all tissues simultaneously with a single transformation, the system segments the anatomical structure into distinct tissue layers (e.g., bone, soft tissue, lung) and generates separate registration fields for each layer. This allows each tissue type to be registered independently according to its own motion characteristics, resolving the contradiction between depicting different tissues and achieving accurate alignment.
Solution Approach 2:
The patent applies different registration transformations to different spatial regions of the image based on tissue type. Each tissue layer receives a customized registration field that accounts for its specific motion patterns. For example, bone tissue may require rigid transformation while soft tissue requires deformable transformation. This local customization of registration quality allows accurate alignment of each tissue type without compromising the depiction accuracy of other tissues.
2Device complexity
If single-layer registration is used to align images, then the registration process is simple, but it cannot account for contradictory movements of different tissue types resulting in artefacts and distortions
Solution Approach 1:
The patent segments the single registration process into multiple independent registration layers, each handling a specific tissue type. This segmentation eliminates the artefacts and distortions caused by single-layer registration that cannot accommodate contradictory tissue movements. Each layer generates its own registration field, allowing independent optimization for that tissue type's motion characteristics.
Solution Approach 2:
The patent combines multiple registration fields into a composite registration solution. Instead of using a single homogeneous transformation, the system creates a composite registration model that integrates multiple tissue-specific registration fields. This composite approach maintains low complexity while eliminating artefacts, as each tissue layer is registered according to its own optimal transformation.
Data Source
AI summary
Systems/techniques that facilitate multi-layer image registration are provided. In various embodiments, a system can access a first image and a second image. In various aspects, the system can generate, via execution of a machine learning model on the first image and the second image, a plurality of registration fields and a plurality of weight matrices that respectively correspond to the plurality of registration fields. In various instances, the system can register the first image with the second image based on the plurality of registration fields and the plurality of weight matrices.