Anatomy-Guided Image Deformation for Precise Multi-Scan Alignment
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Solution Overview
Problem
Current medical imaging workflows face challenges in accurately correlating information from multiple image sets due to differences in patient position, anatomical motion, and the lack of effective validation methods, leading to errors and inefficiencies in treatment planning processes.
Innovation Solution
A system and method for medical imaging that utilizes a common coordinate system, auto-registration algorithms, and anatomical structure grouping to generate deformation maps, allowing for precise alignment and transformation of image sets, incorporating artificial intelligence for predictive dose distribution and real-time adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deformable registration tools are used to adjust for changes in position, then flexibility in handling anatomical variations is improved, but accuracy deteriorates in areas of dramatic motion
Solution Approach 1:
The patent segments the anatomical structures into multiple groups (first group with easily visualized boundaries, second group with difficult boundaries) and applies different registration strategies to each group. This segmentation allows the system to handle dramatic motion accurately by treating different anatomical regions with appropriate methods.
Solution Approach 2:
The patent introduces an intermediary process that uses the first image set with easily visualized boundaries as a reference framework to guide the registration of the second image set. This intermediary reference system mediates between the two image sets, improving accuracy in areas of dramatic motion.
2Productivity
If simulation CT is used as the target for deformation, then workflow efficiency is improved, but reliability deteriorates due to propagation of errors
Solution Approach 1:
The patent implements a feedback mechanism where the registration process uses multiple image sets including the simulation CT, but validates results against additional reference data. This feedback loop allows the system to maintain workflow efficiency while correcting errors that would otherwise be propagated from the simulation CT.
Solution Approach 2:
The patent performs preliminary registration and validation steps before final deformation application. By pre-processing and validating the simulation CT against other image sets beforehand, the system maintains workflow efficiency while preventing error propagation to the final results.
3Extent of automation
If algorithm-based pattern matching is used for structure identification, then automation is improved, but measurement precision deteriorates for ambiguous structures
Solution Approach 1:
The patent segments anatomical structures into groups based on their visualizability and boundary definition characteristics. This segmentation enables the system to apply automated pattern matching to well-defined structures while using manual or hybrid methods for ambiguous structures, maintaining both automation where possible and precision where needed.
Solution Approach 2:
The patent applies different identification methods to different anatomical structures based on their local characteristics. Structures with clear boundaries receive automated pattern matching treatment, while structures with ambiguous boundaries receive manual or hybrid treatment, optimizing both automation and precision locally.
4Ease of operation
If uniform margins of uncertainty are applied to all structures, then ease of operation is improved, but measurement precision deteriorates due to ignoring asymmetry and variance
Solution Approach 1:
The patent applies different uncertainty margins to different anatomical structures based on their specific characteristics, visualizability, and motion characteristics. This local quality approach replaces uniform margins with structure-specific margins, improving measurement precision while maintaining ease of operation through automated classification.
Solution Approach 2:
The patent changes the uncertainty parameter from a uniform value to structure-specific values based on anatomical characteristics. By adjusting the uncertainty margin parameter according to each structure's properties, the system achieves both precision and operational simplicity.
Data Source
AI summary
Disclosed herein are systems and methods for medical imaging using anatomy guided image animation workflow. The system and method comprise applying auto-contouring to a medical image; assigning a deformation model to each structure; using a registration algorithm to perform registration; generating a common coordinate system; generating a first displacement map, where the first displacement map shows the displacement of structures from the target image set to the source; generating a first deformation map using the registration algorithm; applying the first deformation map to the source image set to align the geometry of the source to a target position; and generating a deformed, partially synthetic source image.