Anatomical Image Marker Alignment Across Modalities
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Solution Overview
Problem
Existing methods struggle to accurately position corresponding markers in multiple medical images due to challenges such as pathologies, different acquisition modalities, and noise levels, leading to time-consuming and error-prone tasks in aligning anatomical structures.
Innovation Solution
A method and apparatus that positions a first marker over a feature in a first image and translates the image under this marker to align with a second marker in a second image, using techniques like interpolation and automatic image registration to achieve high precision alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If markers are positioned manually in multiple medical images, then the process allows flexibility in handling different pathologies and acquisition modalities, but the positioning accuracy and consistency across images deteriorates due to human error and cognitive limitations
Solution Approach 1:
The patent introduces an intermediary computational system that acts as a mediator between the multiple medical images and the marker positioning process. This system automatically identifies corresponding anatomical features across images and positions markers with high precision, eliminating the need for manual cognitive processing while maintaining adaptability to different pathologies and modalities through algorithmic flexibility.
Solution Approach 2:
The patent replaces the manual mechanical process of marker positioning with an automated computational system. Instead of relying on human operators to visually align and mark corresponding features across images, the system uses image processing algorithms and coordinate transformations to automatically position markers with sub-voxel precision, thereby eliminating human error while preserving adaptability to various medical imaging scenarios.
2Measurement precision
If existing annotation tools allow markers to be placed in-between voxels, then the positioning flexibility improves, but the complexity and time required for accurate multi-dimensional alignment increases significantly
Solution Approach 1:
The patent implements a self-service mechanism where the computational system automatically performs the complex multi-dimensional alignment task without requiring user intervention. The system independently calculates the optimal marker positions by analyzing image data, performing coordinate transformations, and adjusting for partial volume effects, thereby maintaining high positioning flexibility while eliminating the complexity and time burden from the user's workflow.
Solution Approach 2:
The patent performs preliminary computational actions to pre-calculate and store transformation parameters, feature correspondences, and alignment relationships between multiple images. By preparing these computational foundations in advance, the system enables rapid and accurate marker positioning without requiring users to perform complex manual adjustments, thus maintaining flexibility while reducing operational complexity.
3Adaptability or versatility
If manual marker positioning is performed across multiple images, then the process can handle various clinical scenarios, but the time consumption and error rate increase due to the difficulty of combining information from different images in the user's mind
Solution Approach 1:
The patent replaces the manual cognitive process of combining information from multiple images with an automated computational system. The system efficiently processes and integrates data from different images, pathologies, and acquisition modalities using algorithms that can handle various clinical scenarios, thereby maintaining adaptability while dramatically reducing the time required for marker positioning and eliminating human error.
Solution Approach 2:
The patent dynamically adjusts computational parameters such as transformation matrices, interpolation methods, and feature matching thresholds based on the specific clinical scenario, pathology type, and acquisition modality. This parameter adaptation enables the system to maintain high versatility across different clinical situations while performing rapid automated marker positioning, thus reducing time loss without sacrificing adaptability.
Data Source
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AI summary
There is provided a method and apparatus for positioning markers in images of an anatomical structure. A first marker is positioned over a feature of an anatomical structure in a first image and a second marker is positioned over the same feature of the anatomical structure in a second image (202). The first image is translated under the first marker to adjust the position of the first marker with respect to the feature of the anatomical structure in the first image to correspond to the position of the second marker with respect to the feature of the anatomical structure in the second image (204).