Anatomy-Based Medical Image Registration With Anatomical Constraints
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Solution Overview
Problem
Current deformable-image registration methods in medical imaging face challenges due to high computational overhead and non-uniqueness of deformable transforms, often resulting in low-quality registrations with artifacts and physically unlikely distortions.
Innovation Solution
A three-machine-learning system approach is employed, where MLS_1 generates initial deformable transforms, MLS_2 identifies anatomical features, and MLS_3 updates the transforms with anatomical constraints, iteratively improving the registration until a threshold quality is met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deformable transforms are used for medical image registration, then registration flexibility and accuracy are improved, but computational overhead increases significantly
Solution Approach 1:
The patent segments the deformable transform into anatomical region-specific transforms. Instead of applying a single deformable transform across the entire image, the system divides the image into multiple anatomical regions (e.g., brain, liver, lungs) and applies separate transforms to each region. This segmentation reduces the overall computational complexity by limiting the scope of deformation calculations to only the regions where they are actually needed, while maintaining the flexibility and accuracy benefits of deformable registration.
Solution Approach 2:
The patent applies local quality by using different transform characteristics for different anatomical regions. Each anatomical region is assigned a transform with parameters optimized for its specific deformation characteristics. For example, brain tissue may use one set of deformation parameters while lung tissue uses another, allowing each region to be registered with optimal local accuracy without the computational burden of a global deformable transform.
2Measurement precision
If deformable transforms are used for medical image registration, then anatomical feature alignment is improved, but non-uniqueness of transforms leads to low-quality registrations
Solution Approach 1:
The patent segments the registration problem into anatomical regions, which helps disambiguate the non-uniqueness issue. By restricting deformable transforms to specific anatomical regions with known landmark correspondences, the system narrows down the possible transform solutions. This segmentation provides geometric constraints that eliminate ambiguous transform solutions, ensuring consistent and reliable registration results across different anatomical structures.
Solution Approach 2:
The patent introduces anatomical landmarks and region-based geometric constraints as intermediary elements between the images and the transform. These intermediaries serve as reference points that mediate the registration process, providing stable geometric constraints that resolve the non-uniqueness problem. The landmarks act as mediators that guide the deformable transform toward physically plausible solutions, ensuring consistent and reliable feature alignment.
3Adaptability or versatility
If deformable transforms are used for medical image registration, then image flexibility is improved, but artifacts and physically unlikely distortions increase
Solution Approach 1:
The patent applies local quality by confining deformable transforms to specific anatomical regions with known geometric constraints. Each region is transformed independently with parameters optimized for its anatomical characteristics, preventing physically unlikely distortions from propagating across the entire image. This localized approach maintains image flexibility where needed while preventing artifacts in regions where anatomical constraints prohibit certain deformations.
Solution Approach 2:
The patent introduces anatomical landmarks and region-based geometric constraints as intermediary elements that mediate between the flexible deformable transform and the physical plausibility requirement. These intermediaries act as guards against generating artifacts by enforcing anatomically consistent deformation patterns. The landmarks serve as mediators that ensure transforms remain physically unlikely only when necessary, preventing spurious distortions while maintaining the flexibility needed for accurate registration.
Data Source
AI summary
The current document is directed to methods and systems that carry out deformable-image registration on two or more medical images using machine-learning and anatomical constraints. In one implementation, a first machine-learning system is used to generate a set of anatomical features within a first medical image. A second machine-learning system is used to add anatomical constraints for a selected anatomical feature to an initial map that is then updated, by a third machine-learning system, to generate a current map or transform that can be used to register a second medical image to the first medical image. The current map or transform is iteratively updated, using additional anatomical features, until improvement in the current map or transform falls below a threshold value.